A severe environment-resistant bridge very low frequency acoustic wave intelligent monitoring and early warning system and a measuring method

Abstract

This invention discloses an intelligent monitoring and early warning system and measurement method for very low frequency infrasound (VLS) waves in bridges under harsh environments, aiming to solve the problems of poor anti-interference capability, short lifespan, and low accuracy of traditional strain gauge technology. The system includes an acoustic-noise compatible VLS earpiece array and an intelligent early warning processing platform. The earpieces incorporate dual-capacitor range sensing sensor pairs, support three switchable communication modes, are solar-powered, and have a built-in temperature compensation module. The method involves deploying an earpiece array adapted to the bridge structure to collect and preprocess the bridge vibration infrasound signals. After being encrypted and stored via blockchain, the signals are analyzed by an AI intelligent computing center compatible with general computing and supercomputing. This enables accurate detection of five load modes, assessment of structural stress and strain, fatigue damage, and remaining lifespan, and classification into eight safety levels with graded early warnings.

Description

Technical Field

[0001] This invention relates to the field of bridge structural safety monitoring technology, specifically to a bridge very low frequency sound wave intelligent monitoring and early warning system and measurement method that is resistant to strong impact vibration noise and harsh environments with large temperature changes. Background Technology

[0002] Traditional bridge load-bearing structure tensile and compressive strength testing mainly adopts strain gauge sensor detection method. This method consists of passive strain gauge semiconductor sensor, analog-to-digital acquisition circuit, computer and related algorithm software. It indirectly obtains the stress and strain information of the bridge by measuring the change of strain gauge resistance with structural deformation.

[0003] Although this technology has been widely used in many fields after more than half a century of development, it still has the following inherent defects:

[0004] 1. High temperature sensitivity: Semiconductor strain gauge sensors are made of silicon material, which is extremely sensitive to temperature changes and is not suitable for use in outdoor bridge environments with large temperature ranges;

[0005] 2. Short service life: Semiconductor materials have limited mechanical elastic life, and their working life under continuous dynamic conditions does not exceed six months;

[0006] 3. Installation damage: Adhesive is required to firmly attach the strain gauges to the surface of the bridge structure, which will cause some damage to the structure. In addition, the bonding process is complicated and difficult to operate.

[0007] 4. Measurement accuracy is greatly affected by power supply interference: The common-mode voltage of the power supply ripple generated by the switching power supply is difficult to filter out and will be converted from analog to digital along with the strain signal, which will greatly reduce the measurement accuracy and increase the falsehood of the spectrum analysis.

[0008] 5. Limitations of testing: Only sampling inspection is possible, and it is impossible to perform full parameter verification on every sensor product. Furthermore, the sampled products will be discarded.

[0009] Large steel structure bridges are located in harsh geographical and natural environments. Human overloading, seasonal temperature differences, and natural disasters such as typhoons, rainstorms, earthquakes, and landslides can cause the bridges to vibrate in the very low frequency (VLF) sound wave range, which is a major cause of bridge collapse. Traditional strain gauge technology cannot effectively adapt to such harsh environments and is unable to accurately capture VLF sound vibration signals, thus failing to meet the needs of long-term bridge safety monitoring. Summary of the Invention

[0010] The purpose of this invention is to overcome the above-mentioned defects of the prior art and provide a bridge very low frequency sound wave intelligent monitoring and early warning system and measurement method with a large detection range, long life, fast dynamic response, high sensitivity, good stability, simple installation and strong resistance to interference from harsh environments, so as to realize real-time online monitoring and intelligent early warning of vibration stiffness and resonance of the load-bearing structure of large steel bridges.

[0011] To address the aforementioned technical problems, this invention provides the following technical solution: a bridge very low frequency (VLF) infrasound intelligent monitoring and early warning system resistant to harsh environments, comprising a noise- and sound-compatible VLF infrasound interlocking array and an intelligent early warning processing platform. The noise- and sound-compatible VLF infrasound interlocking array consists of multiple solid-state infrasound vector interlocking devices, each with a built-in pair of vector infrasound capacitance range sensing sensors, supporting three switchable communication modes. The intelligent early warning processing platform receives data through corresponding communication links, and after big data storage, blockchain processing, and AI computing, performs security assessment and early warning.

[0012] Furthermore, the vector infrasound capacitance range sensing sensor pair consists of a matched vector infrasound capacitance range sensing sensor and a scalar environmental capacitance range sensing sensor. The sensing ends of the two sensors are connected to form a common sensing end. By utilizing the capacitance changes that are opposite in phase, a common-mode signal is output, which effectively filters out environmental interference.

[0013] Furthermore, each sensor employs a main lobe infrasound sensing capacitor structure, which converts the capacitance change caused by the vibration of the moving plate into a frequency signal, exhibiting extremely high very low frequency signal sensitivity.

[0014] Furthermore, the solid-state infrasound vector earpiece integrates an intelligent microprocessor, a wireless / wired data transceiver processor, and a solar power supply. It has a built-in temperature sensor for temperature compensation, and the solar power system ensures long-term stable operation in the field.

[0015] Furthermore, the hindsight array can be flexibly arranged according to the bridge structure to cover all key stress locations and can collect infrasound signals of five different load modes.

[0016] Furthermore, the AI ​​computing center of the intelligent early warning and processing platform is compatible with general computing and supercomputing functions, enabling digital twin modeling, disaster simulation and prediction, and providing multi-dimensional monitoring results display through the human-machine interface.

[0017] The corresponding monitoring method includes five steps: array deployment, communication mode configuration, signal acquisition and preprocessing, big data intelligent computing analysis, and safety early warning and display. It eliminates environmental interference through differential operation, classifies safety levels based on vibration amplitude, and achieves accurate damage location and graded early warning.

[0018] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages compared with the prior art:

[0019] 1. Wideband, high-sensitivity measurement: Sensing frequency range of 10 for load-bearing structures. -3 Hz~10 3 Hz, vibration amplitude sensing range is 10 -4 mm~20mm, fully covering the vibration response range of safety threats for large, medium and small steel structure bridges;

[0020] 2. Strong resistance to harsh environmental interference: It adopts a dual-sensor common-mode design to effectively filter out environmental interference caused by strong impact vibration noise and large temperature changes, while the built-in temperature sensor performs parameter compensation.

[0021] 3. Long lifespan and maintenance-free: It adopts the non-contact capacitive sensing principle, with no mechanical wear, and its service life is much longer than that of traditional strain gauges. The solar power supply system enables long-term maintenance-free operation in the field.

[0022] 4. Multi-communication mode adaptation: Supports three communication modes: wireless, wired, and digital-analog wired. It can be flexibly selected according to different needs in civilian or confidential fields to ensure the reliability and security of data transmission.

[0023] 5. Intelligent full-dimensional analysis: The AI ​​intelligent computing center can independently analyze five vibration modes, establish a digital twin model, realize the advanced prediction and accurate positioning of bridge structure fatigue, damage and fracture, and provide a scientific basis for bridge safe operation and maintenance.

[0024] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0025] Figure 1 Schematic diagram of the working principle of a multilayer capacitive range sensing acoustic field resonant solid infrasound vector sensor for low-frequency vibration monitoring;

[0026] Figure 2 A schematic diagram of a noise-compatible very low frequency infrasound vector hearing device;

[0027] Figure 3 A structural layout diagram illustrating the working principle of a hearing array sonar system for wireless network communication mode.

[0028] Figure 4 This is a structural layout diagram illustrating the working principle of a wired network / digital-analog wired communication mode earpiece array sonar system.

[0029] Figure 5 Layout diagram for the implementation of a hinge array project for wireless network communication mode in the civilian field;

[0030] Figure 6 Layout diagram for the implementation of wired network / digital-analog wired communication mode earpiece array in confidential and restricted areas;

[0031] Figure 7 Detailed structural diagram of a multilayer capacitive range sensing solid-state infrasound vector sensor;

[0032] Figure 8 This is a block diagram of the internal components of a noise-compatible very low frequency infrasound vector hearing device.

[0033] Figure 9 The upper half of the schematic diagram of the acoustic noise compatibility principle of a hearing aid under harsh conditions of vibration and temperature changes;

[0034] Figure 10 The lower half of the schematic diagram of the acoustic noise compatibility principle of a hearing aid under harsh conditions of vibration and temperature changes;

[0035] Figure 11 This is a diagram illustrating the overall architecture of a wireless network communication mode hinge array system.

[0036] Figure 12 This is a diagram illustrating the overall architecture of a wired network / digital-to-analog wired communication mode earpiece array system.

[0037] Figure 13 A schematic diagram of the deployment of bridge articulator arrays for civilian use.

[0038] Figure 14 A schematic diagram of the deployment of a bridge hinge array in a confidential and restricted area. Detailed Implementation

[0039] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0040] It should be noted that the terms "vertical," "horizontal," "up," "down," "left," "right," and similar expressions used in this article are for illustrative purposes only and do not represent the only possible implementation.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains; the terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to limit the invention; the term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0042] The following is in conjunction with the appendix Figure 1-14 The present invention will be further described in detail with reference to specific embodiments.

[0043] Example 1: Structure and Working Principle of Very Low Frequency Sound Vector Sensor

[0044] like Figure 1 and Figure 7 As shown, the vector infrasound capacitive range sensing sensor CF consists of a main lobe infrasound sensing capacitor C, a harmonic logic shaper LZ, a differential voltage-frequency converter XC, a variable capacitance range resonator GF, a reference tuner JT1, and an acoustic signal acquisition terminal CT2. The main lobe infrasound sensing capacitor C is composed of the stator terminal of capacitor plate CT1 and the mover terminal of capacitor plate CT0.

[0045] The multilayer capacitive range sensing structure used in this embodiment achieves 10 -3 Hz~10 3 Ultra-wide frequency sensing range of Hz and 10 -4 With an ultra-high amplitude sensing accuracy of 20mm to 10mm, it fully covers the vibration response range posing a safety threat to large, medium, and small steel structure bridges (10 for large and medium-sized bridges). -2 Hz~10 2 Hz, small bridges 10 -1 Hz~10 3 Compared to traditional strain gauges that can only detect mid-to-high frequency vibrations, this instrument (Hz) can accurately capture very low frequency infrasound load signals that cause bridge collapses. Furthermore, the dual-sensor design with opposite capacitance changes achieves physical-level suppression of common-mode interference at the signal source, eliminating the need for complex post-processing filtering algorithms. This effectively solves the problem of decreased measurement accuracy caused by power supply ripple and environmental vibrations in traditional technologies, maintaining stable signal output even under strong impact vibrations and large temperature variations.

[0046] During operation, the acoustic acquisition terminal CT2, under the influence of the external sound field, causes the moving element of capacitor plate CT0 to vibrate horizontally. This changes the overlapping area of ​​the stator plate CT1 and the moving element plate CT0, resulting in a capacitance change of C±ΔC in the main lobe infrasound sensing capacitor C. This capacitance change causes a shift of f±Δf in the resonant frequency of the variable capacitance differential resonator GF. After shaping by the harmonic logic shaper LZ and conversion by the differential voltage-frequency converter XC, the signal is sent to the microprocessor AIC for processing, ultimately outputting the PFO frequency modulation signal.

[0047] The scalar environmental capacitance difference sensing sensor CF2 has the same structure as the vector infrasound capacitance difference sensing sensor CF1. The only difference is that the vibration direction of the moving plate is opposite to that of CF1 during installation. Thus, when the common sensing end is subjected to a composite sound field, it outputs a capacitance change signal that is opposite to that of CF1.

[0048] Example 2: Composition and Working Principle of a Noise-Compatible Very Low Frequency Infrasound Vector Hinge

[0049] like Figure 2 and Figure 8 As shown, the solid-state infrasound vector earpiece JSQ consists of six parts: a vector infrasound capacitance difference sensing sensor pair CF, an intelligent microprocessor AIC, a wireless data transceiver processor FMT, a solar power supply TGE, and a wired data transceiver processor YXAIC.

[0050] The vector infrasound capacitive range sensing sensor pair CF consists of a vector infrasound capacitive range sensing sensor CF1 and a scalar environmental capacitive range sensing sensor CF2. Their sensing ends CT1 and CT2 are connected to form a common sensing end CT0. When the composite sound field formed by the bridge vibration infrasound and environmental interference acts on CT0, CF1 outputs a signal of C1±ΔC, and CF2 outputs a signal of C2∓ΔC, providing a basis for subsequent common-mode interference filtering.

[0051] The intelligent microprocessor (AIC) consists of a single intelligent processing chip (WPC), an ambient temperature sensor (HW), digital converters (YC1 and YC2), a peripheral interface processor (SZCT), and a digital bus (DC). YC1 and YC2 receive the output signals from CF1 and CF2 respectively and perform digitization processing. The WPC receives the digitized signal and the signal from the temperature sensor (HW), performs temperature compensation and differential calculations, filters out environmental interference, and then sends the data to the communication module.

[0052] Wireless transceiver processor (FMT): Composed of an FM transceiver (FMT) and a wireless transceiver antenna (TX), it enables bidirectional wireless data transmission, sending pre-processed infrasound data to the intelligent early warning processing platform and receiving control commands from the platform.

[0053] The TGE solar power unit consists of solar silicon photovoltaic panels, energy storage lithium batteries, and a DC voltage regulator. The solar silicon photovoltaic panels convert sunlight into electrical energy and store it in the energy storage lithium batteries. The DC voltage regulator converts the battery voltage into stable DC voltages E1 and E2 required by the various modules of the device, providing continuous power from the energy storage lithium batteries during periods without sunlight.

[0054] The wired data transceiver processor YXAIC consists of a wired communication data transceiver processor SZCT, a dedicated database ZCDA, an internet EINT, a blockchain TZJS, a big data database DCDA, an intelligent computing processor AIPC, and an instruction center DCK, enabling wired data transmission and system control.

[0055] The earpiece supports switching between three communication modes: when the peripheral encoding command selects the wireless network communication mode, the wired communication and digital-to-analog wired communication modes automatically go into sleep mode; when the wired communication mode is selected, the wireless and digital-to-analog wired modes go into sleep mode; when the digital-to-analog wired communication mode is selected, the wireless and wired modes go into sleep mode.

[0056] This embodiment of the solid-state infrasound vector earpiece integrates three switchable communication modes, allowing for flexible selection based on the field environment: wireless network communication mode is suitable for medium-to-long distances and areas with harsh geological conditions, requiring no wiring and facilitating installation; wired network communication mode is suitable for short distances and areas with mild geological conditions, offering stable transmission; and analog-digital wired communication mode is suitable for very short distances and areas with favorable geological conditions, providing strong compatibility. Unselected communication modes automatically enter sleep mode, significantly reducing system power consumption. The built-in ambient temperature sensor compensates for the impact of temperature changes on capacitance parameters in real time, maintaining stable measurement accuracy over a wide temperature range and overcoming the high temperature sensitivity of traditional strain gauges. The solar power unit uses a combination of energy storage lithium batteries and silicon photovoltaic panels, providing continuous power for over 30 days even in continuous cloudy or rainy weather, enabling long-term maintenance-free operation in the field. Employing a non-contact capacitive sensing principle, it eliminates mechanical wear, and compared to the six-month lifespan of traditional strain gauges, this earpiece can operate without failure for over 10 years. In addition, the intelligent microprocessor can achieve precise control of the working mode of the sensor group, support independent calibration of a single sensor and online detection of all parameters, and solve the problems of traditional strain gauges being unable to perform full parameter verification and sample scrapping during sampling inspection. Example 3: Layout and System Architecture of Hinge Array

[0057] like Figure 3 , Figure 4 , Figure 11 and Figure 12 As shown, the noise-compatible VLF infrasound kinetic array consists of n solid-state infrasound vector kinetics, JSQ1, JSQ2, ..., JSQn, which can be arranged in a straight line, L-shape, parallel shape, herringbone shape, or any custom shape according to the bridge structure layout. Each kinetic is deployed at key stress locations on the bridge's load-bearing frame, crossbeams, load-bearing arches, and connecting beams to collect infrasound signals of different load modes in real time.

[0058] The overall system architecture consists of two parts: a clasper array and an intelligent early warning processing platform.

[0059] Wireless network communication mode: Suitable for civilian applications with wireless IoT coverage. The earpiece array communicates with the intelligent early warning processing platform via the wireless microwave relay station WZJZ. The information flow preprocessor ZCFL preprocesses the uplink and downlink data to perform communication mode preprocessing, the big data database DCK stores all raw monitoring data, and the AI ​​computing center AIPC performs data analysis and security assessment.

[0060] The haptic array in this embodiment can be flexibly arranged in any shape, such as a straight line, L-shape, parallel shape, or herringbone shape, comprehensively covering all key stress-bearing components of the bridge, including the load-bearing frame, crossbeams, load-bearing arches, and connecting beams. It collects infrasound signals for five load modes: longitudinal vibration, shear vibration, torsional vibration, flexural vibration, and coiling vibration. Specifically, the torsional vibration array is suitable for monitoring the torsional vibration of the main beams and cables of extra-long span suspension bridges; the flexural vibration array is suitable for monitoring the bending deformation of the main beams; and the longitudinal and shear vibration arrays are suitable for monitoring the support loads of bridge piers and towers, solving the problem that traditional single-point monitoring cannot comprehensively reflect the overall stress state of the bridge. Simultaneously, the array can uniformly switch communication modes according to the application scenario. Wireless mode is used in civilian applications to reduce engineering costs, while wired mode is used in confidential and restricted areas to ensure data security, achieving a single system adaptable to multiple application needs. Each haptic in the array acts as an independent sensing unit, allowing for individual fault diagnosis and replacement without affecting the normal operation of the entire system, significantly improving the system's reliability and maintainability.

[0061] Wired network / digital-analog wired communication mode: suitable for confidential and restricted areas covered by wired networks. The listening device array communicates with the intelligent early warning processing platform through the wired information exchange ZCDA, which provides higher data transmission security.

[0062] Example 4: Functions and Workflow of the Intelligent Early Warning Processing Platform

[0063] like Figure 9 and Figure 10 As shown, the AI ​​Computing Center (AIPC) of the intelligent early warning processing platform is compatible with both general computing and supercomputing functions. Its core functions include:

[0064] Multi-dimensional mechanical property analysis: The collected infrasound data is analyzed for stress-strain resonance characteristics, natural resonance frequency, and external excitation resonance dynamics.

[0065] Structural health assessment: Based on vibration amplitude, the safety status of bridges is divided into eight levels to assess the fatigue level, damage status and remaining life of the structure;

[0066] Digital twins and advanced prediction: Establishing digital twin models of bridge structures to simulate structural responses under different disaster conditions, enabling advanced prediction and location of damage;

[0067] Tiered early warning: When a safety hazard is detected, a corresponding early warning signal is issued according to the hazard level, and detailed information is displayed through the human-machine interface.

[0068] The human-machine interface CZT presents the analysis results in various forms, including numbers, dynamic curves, prediction curves, dynamic digital twins, early warning color marks, and on-site online videos, making it convenient for maintenance personnel to intuitively understand the safety status of the bridge.

[0069] The AI ​​computing center in this embodiment is compatible with both general-purpose and supercomputing functions, capable of simultaneously processing real-time data streams from tens of thousands of chord receivers, supporting exascale floating-point operations per second, and rapidly completing mechanical property analysis of large-scale bridge structures. Based on the collected raw infrasound data, the AI ​​computing center can establish a high-precision digital twin model of the bridge, enabling simulation and early prediction of various disaster scenarios such as typhoons, earthquakes, vehicle overloading, and ship collisions, predicting the development trend of structural damage up to 72 hours in advance. Based on vibration amplitude, the bridge's safety status is divided into eight levels: elastic health, recoverable fatigue, irreversible fatigue, minor cracks, moderate cracks, local fractures, severe fractures, and collapse fractures. Each level corresponds to specific laboratory verification data and response plans, achieving tiered and precise early warning. The human-machine interface provides multi-dimensional display methods, including digital data, dynamic curves, prediction curves, dynamic digital twins, early warning color codes, and on-site online video, allowing maintenance personnel to intuitively grasp the safety status of various parts of the bridge, significantly improving emergency response efficiency. At the same time, blockchain technology is used to store all raw and pre-processed data to ensure that the data is tamper-proof and traceable, providing a reliable legal basis for determining bridge safety responsibility and life assessment.

[0070] Example 5: Detailed Procedure of Intelligent Monitoring Method for Very Low Frequency Infrasound Waves in Bridges Resistant to Harsh Environments

[0071] This embodiment details the specific steps for using the above system for bridge safety monitoring:

[0072] Knocking device array deployment: Based on the bridge's structural type, span, and stress distribution, solid infrasound vector knocking devices are deployed at key load-bearing locations such as piers, towers, main beams, and cables to form a knocking device array adapted to the bridge structure. For example, for extra-long span suspension bridges, torsional and flexural knocking device arrays are deployed on the main beams and cables, while longitudinal and shear knocking device arrays are deployed on the towers and piers.

[0073] Communication mode configuration: For ordinary civilian bridges, select wireless network communication mode to reduce wiring costs; for bridges in special fields such as military and classified information, select wired network communication or digital-analog wired communication mode to ensure data security. Communication mode configuration commands are sent to all earpieces via peripheral encoding instructions; communication modes not selected automatically enter low-power sleep mode.

[0074] Signal Acquisition and Preprocessing: The hindrance's vector infrasound capacitance difference sensing sensor array acquires infrasound signals from bridge vibration and environmental interference signals in real time. The intelligent microprocessor first digitizes both signals, then performs temperature compensation on the parameters based on measurements from the ambient temperature sensor. Finally, differential operations are used to eliminate common-mode environmental interference such as strong impact vibrations and temperature changes, extracting a clean bridge vibration infrasound signal. The preprocessed data is transmitted to the intelligent early warning processing platform via a selected communication link.

[0075] Big Data Intelligent Computing Analysis: The intelligent early warning and processing platform uses blockchain technology to encrypt and store raw and pre-processed data, ensuring data immutability and traceability. The AI ​​intelligent computing center performs multi-dimensional analysis of the data, including the separation and identification of different vibration modes, stress and strain calculation, resonance risk assessment, and fatigue damage accumulation calculation. A digital twin model of the bridge structure is established by combining historical monitoring data to simulate the structural response under different load conditions and predict the development trend of structural damage.

[0076] Safety Early Warning and Display: When the AI ​​intelligent computing center detects that the vibration amplitude of the bridge structure exceeds the corresponding safety level threshold, or predicts that the structure will suffer damage, fracture, or resonance, the system automatically issues an early warning signal of the corresponding level. Maintenance personnel can view real-time monitoring data, analysis results, the location of potential hazards, and development trends through a human-machine interface, allowing them to take timely maintenance measures to ensure the safe operation of the bridge.

[0077] The monitoring method in this embodiment eliminates common-mode environmental interference through differential computation. Even in harsh environments with strong impact vibration and large temperature variations, the measurement signal-to-noise ratio is improved by more than 30dB, effectively solving the problem of data distortion in traditional technologies under harsh conditions. Based on an eight-level safety classification of vibration amplitude, it can accurately identify the entire life cycle state of a bridge structure, from minor fatigue to collapse. Compared to the limitation of traditional technologies that can only detect obvious damage, it enables early detection of potential hazards. The adaptive switching function of the communication mode allows the system to automatically switch to the backup communication mode within 10 seconds when the main communication link fails, ensuring the continuity and integrity of monitoring data transmission. Simultaneously, the AI ​​intelligent computing center can combine the propagation characteristics of different vibration modes to achieve precise location of damage, with a positioning error of no more than 1 meter, providing accurate target guidance for bridge maintenance. This method eliminates the need for destructive operations such as drilling or pasting on the bridge structure, and the installation process does not affect normal bridge traffic, truly achieving non-destructive online monitoring of bridge structures.

[0078] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0079] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Anyone skilled in the art can make various modifications and alterations without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be determined by the claims.

Claims

1. A smart monitoring and early warning system for very low frequency acoustic waves in bridges resistant to harsh environments, characterized in that, This includes a noise- and sound-compatible very low frequency infrasound earpiece array and an intelligent early warning processing platform; The acoustic noise-compatible very low frequency infrasound earpiece array consists of multiple solid infrasound vector earpieces. Each solid infrasound vector earpiece has a built-in pair of vector infrasound capacitance range sensing sensors for collecting very low frequency vibration infrasound load signals and environmental interference signals of the bridge load-bearing structure. The solid-state infrasound vector hearing device supports three switchable communication modes: wireless network communication, wired network communication, and wired digital-to-analog communication. The communication mode that is not selected will automatically enter a sleep state. The intelligent early warning processing platform receives raw data collected by the hindsight array through the corresponding communication link. After big data storage, blockchain signal processing and AI intelligent computing analysis, it realizes the safety status assessment and early warning of the bridge load-bearing structure.

2. The intelligent monitoring and early warning system for very low frequency acoustic waves of bridges resistant to harsh environments according to claim 1, characterized in that, The vector infrasound capacitance difference sensing sensor pair includes a matched vector infrasound capacitance difference sensing sensor and a scalar environmental capacitance difference sensing sensor, with their sensing ends connected as a common sensing end. When the composite sound field formed by the bridge vibration infrasound and environmental interference acts on the common sensing end, the sensing capacitance of the two sensors undergoes opposite phase capacitance changes, and outputs a common differential mode signal for subsequent filtering of environmental interference.

3. The intelligent monitoring and early warning system for very low frequency acoustic waves of bridges resistant to harsh environments according to claim 2, characterized in that, Both the vector infrasound capacitance range sensing sensor and the scalar environmental capacitance range sensing sensor include a main lobe infrasound sensing capacitor, a harmonic logic shaper, a differential capacitance voltage-frequency converter, a variable capacitance range resonator, and a reference tuner. The main lobe infrasound sensing capacitor is composed of a stator plate and a mover plate. Under the action of an external sound field, the mover plate vibrates horizontally, causing a change in the projected overlapping area of ​​the stator and mover plates. This results in a change in the capacitance of the sensing capacitor, C±ΔC, which triggers a shift in the resonant frequency of the variable capacitance range resonator, f±Δf. After harmonic logic shaping, differential capacitance voltage-frequency conversion, and microprocessor processing, a frequency modulation signal is output.

4. The intelligent monitoring and early warning system for very low frequency acoustic waves of bridges resistant to harsh environments according to claim 1, characterized in that, The solid-state infrasound vector earpiece also includes an intelligent microprocessor, a wireless data transceiver processor, a wired data transceiver processor, and a solar power supply. The intelligent microprocessor has a built-in ambient temperature sensor, a dual-channel digital converter, and a single intelligent processing chip. It is used to receive the output signals from the two sensors, compensate for the error of the measurement parameters caused by temperature changes, and control the switching of the working mode of the sensor pair and the three communication modes. The solar power supply consists of a solar silicon photovoltaic panel, an energy storage lithium battery, and a DC voltage regulator, which converts solar or wind energy into electrical energy to provide a stable DC power supply for each module of the earpiece.

5. The intelligent monitoring and early warning system for very low frequency infrasound waves in bridges resistant to harsh environments according to claim 1, characterized in that, The sound-noise compatible very low frequency infrasound kinetic array can be arranged in a straight line, L-shape, parallel shape, herringbone shape, or any custom shape according to the bridge structure layout. Each kinetic in the array is placed at a key stress position on the bridge's load-bearing frame, crossbeam, load-bearing arch frame, and connecting beam to collect infrasound signals of five different load modes: longitudinal vibration, shear vibration, torsional vibration, flexural vibration, and roll vibration.

6. The intelligent monitoring and early warning system for very low frequency infrasound waves in bridges resistant to harsh environments according to claim 1, characterized in that, The intelligent early warning processing platform includes a data transmission relay unit, an information flow preprocessor, a big data database, an AI computing center, and a human-machine interface. The AI ​​computing center is compatible with general computing and supercomputing functions. Based on the collected infrasound data, it performs stress-strain resonance characteristic analysis, natural resonance frequency analysis, fatigue damage assessment, life prediction, and disaster simulation of bridge structures. The monitoring results are displayed through the human-machine interface in the form of numbers, dynamic curves, prediction curves, dynamic digital twins, early warning color marks, and on-site online videos.

7. A method for intelligent monitoring of very low frequency acoustic waves in bridges resistant to harsh environments, characterized in that, The monitoring and early warning system according to any one of claims 1-6 includes the following steps: S1 Hinge Array Deployment: Based on the structural characteristics and stress distribution of the bridge, multiple solid infrasound vector hinges are deployed at key load-bearing locations to form a hinge array of corresponding shape. S2 communication mode configuration: Select one of the following communication modes—wireless network communication, wired network communication, or wired digital-to-analog communication—through peripheral encoding commands according to the application scenario. The other two communication modes will automatically enter sleep mode. S3 Signal Acquisition and Preprocessing: The vector infrasound capacitance difference sensing sensor group synchronously acquires the infrasound signal of bridge vibration and environmental interference signal. After temperature compensation and common-mode interference filtering by the intelligent microprocessor, the preprocessed data is transmitted to the intelligent early warning processing platform through the selected communication link. S4 Big Data Intelligent Computing Analysis: The intelligent early warning processing platform uses blockchain technology to store raw data and multi-level pre-processed data. The AI ​​intelligent computing center performs multi-dimensional mechanical characteristic analysis and digital twin modeling on the data to assess the safety status of the bridge structure. S5 Safety Warning and Display: When fatigue, damage or resonance risks are detected in the bridge structure, the system automatically issues a warning signal of the corresponding level and displays detailed monitoring data, analysis results and the location of potential hazards through the human-machine interface.

8. The intelligent monitoring method for very low frequency acoustic waves of bridges resistant to harsh environments according to claim 7, characterized in that, In step S3, when the composite sound field acts on the common sensing end, the vector infrasound capacitance difference sensing sensor outputs a capacitance change signal of C1±ΔC, and the scalar environmental capacitance difference sensing sensor outputs a capacitance change signal of C2∓ΔC. The intelligent microprocessor eliminates common-mode environmental interference such as strong impact vibration and temperature change through differential operation, and extracts pure infrasound signals of bridge structure vibration.

9. The intelligent monitoring method for very low frequency acoustic waves of bridges resistant to harsh environments according to claim 7, characterized in that, In step S4, the AI ​​intelligent computing center classifies the safety status of the bridge structure into eight levels based on the vibration amplitude: elastic health, recoverable fatigue, irreversible fatigue, minor cracks, moderate cracks, local fractures, severe fractures, and collapse fractures. It then combines the propagation characteristics of different vibration modes to achieve precise location of the damage.

10. The intelligent monitoring method for very low frequency acoustic waves of bridges resistant to harsh environments according to claim 7, characterized in that, In step S2, the civilian sector prioritizes wireless network communication mode to reduce cabling costs, while the confidential and restricted sector prioritizes wired network communication or wired digital-to-analog communication mode to ensure data security. When the main communication link fails, the system automatically switches to the backup communication mode to ensure the continuity of monitoring data transmission.

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