An underground pipe gallery intelligent security control method and system

Abstract

The application provides an underground pipe gallery intelligent security control method and system, relates to the underground pipe gallery security technical field, and solves the technical problems of serious signal attenuation, power supply difficulty, communication susceptible to electromagnetic interference and high video analysis false alarm rate in the prior art. The method comprises the following steps: arranging piezoelectric units on the surface of the pipe gallery structure along the length direction to form an acoustic topological waveguide; arranging passive sensing nodes on the waveguide, which are driven by structural acoustic wave energy excited by events; performing multi-band decomposition on the structural acoustic wave to generate a frequency fingerprint, and modulating an event type and a node identity code onto the structural acoustic wave to form a modulated acoustic wave; and converging nodes to demodulate and fuse positioning, and outputting a hierarchical early warning signal. The application is used in the underground pipe gallery security monitoring and early warning process.

Description

Technical Field

[0001] This invention belongs to the field of intelligent security and communication technology, specifically an intelligent security control method and system for underground utility tunnels. Background Technology

[0002] Underground utility tunnels, as public tunnels for the centralized laying of urban underground pipelines, have enclosed, humid, long-distance, multi-compartment environments and contain hazardous sources such as high-voltage cables and gas pipelines, placing extremely high demands on the reliability of their security systems. Currently, underground utility tunnel security systems mainly rely on active monitoring methods such as distributed fiber optic sensing, wireless sensor networks, and video surveillance.

[0003] While distributed fiber optic sensing can achieve long-distance distributed measurement, it requires high-power laser sources and precision demodulation equipment, resulting in high system costs. The fiber itself is easily damaged in the complex environment of the utility tunnel due to construction, rodent damage, or fire. Furthermore, once the fiber breaks, the monitoring function of the entire link will be lost. The problems of signal attenuation and decreased positioning accuracy are prominent during long-distance transmission.

[0004] While wireless sensor networks have addressed the issue of flexible deployment to some extent, wireless signal propagation within underground utility tunnels is affected by factors such as metal pipes, cable trays, and bends, resulting in numerous communication blind spots and severe signal attenuation. Furthermore, each sensor node relies on batteries or external power, and the humid, high-temperature, and corrosive environment within the tunnels significantly shortens battery life, leading to frequent node power outages and failures, and a heavy maintenance workload. In addition, areas with strong electromagnetic interference (such as high-voltage cable ducts) can severely impact wireless communication quality, causing data loss or bit errors.

[0005] While video surveillance is intuitive, image quality deteriorates significantly in harsh environments such as low light, condensation, fog, and dust in utility tunnels. Intrusion detection and anomaly recognition algorithms that rely on image analysis have a high false alarm rate, requiring manual review and consuming a lot of manpower. At the same time, the large amount of video data places high demands on transmission bandwidth and storage resources, making it difficult to achieve real-time and reliable transmission in long-distance utility tunnels. Summary of the Invention

[0006] This application provides an intelligent security control method and system for underground utility tunnels, which solves the technical problems in the prior art such as severe signal attenuation in underground utility tunnel security, difficulty in powering equipment, easy interruption of communication due to electromagnetic interference, and high false alarm rate in video analysis.

[0007] To achieve the above objectives, this application adopts the following technical solution: Firstly, a method for intelligent security control of underground utility tunnels is provided, including: Voltage elements are arranged along the length of the tube gallery structure surface to obtain an acoustic topological waveguide. Multiple passive sensing nodes are spaced out on the acoustic topology waveguide, and each node is driven by the energy of structural acoustic waves excited by an event. The structure acoustic wave is decomposed into multiple frequency bands to generate a frequency fingerprint reflecting the event type. The event type and node identity are encoded and modulated onto the structure acoustic wave using an impedance modulation circuit to form a modulated acoustic wave. The modulated sound wave propagates along the acoustic topology waveguide to the convergence node. The convergence node demodulates the signal to obtain the event code, and combines the node identity code and the time difference of arrival of the sound wave to identify and locate the event, and outputs a graded early warning signal containing the event type, confidence level and event location.

[0008] Based on the above technical solution, in the intelligent security control method for underground utility tunnels provided in this application, piezoelectric units are arranged along the surface of the tunnel structure to form an acoustic topological waveguide. The boundary states protected by the topology are used to achieve low-loss, anti-scattering transmission of structural acoustic waves, solving the problems of severe signal attenuation and communication blind spots in long-distance transmission in underground utility tunnels. By deploying passive sensing nodes driven by the energy of structural acoustic waves excited by events on the acoustic topological waveguide, dependence on external power supply is avoided, improving the system's survivability and reliability in power outages or harsh environments. Furthermore, by performing multi-band decomposition of the structural acoustic waves to generate signals reflecting events... The system uses frequency fingerprints for event types and modulates event types and node identity codes onto structural acoustic waves to form modulated acoustic waves, achieving integrated sensing and communication. This eliminates the need for traditional video analysis and radio electromagnetic wave communication methods, avoiding false alarms and communication interruptions caused by factors such as low light, condensation, fog, and electromagnetic interference. By demodulating event codes at aggregation nodes and combining node identity codes and acoustic wave arrival time differences, the system identifies and locates events, outputting hierarchical early warning signals that include event type, confidence level, and event location. This results in highly accurate, reliable, and resilient intelligent security early warning systems for underground utility tunnels.

[0009] Furthermore, the specific construction process of the acoustic topological waveguide includes: To obtain the bending propagation characteristics of the utility tunnel structure, a mass-spring equivalent model is established. The wall of the utility tunnel is discretized into multiple mass blocks along the length direction, and adjacent mass blocks are connected by equivalent springs. Adjusting the coupling stiffness between adjacent piezoelectric units to increase the intracellular coupling strength Less than the inter-cell coupling strength The topological boundary states are obtained; wherein, piezoelectric elements are arranged on the wall of the pipe rack or the surface of the metal cable tray at a period of 'a' along the length of the pipe rack, and each cell contains two piezoelectric plates with an intracellular spacing of 1. The intercellular spacing is ,and < By changing and Ratio control and The relative size; Based on the one-dimensional SSH model, the dispersion relation of the topological boundary states is calculated, the topological waveguide group velocity is selected using the dispersion relation, and the frequency band located within the bulk bandgap is taken as the operating frequency band; the bulk bandgap is the band gap between the bulk energy bands of the acoustic topological waveguide, which is generated by a phononic crystal structure formed by periodically arranged piezoelectric units; Within the operating frequency band, structural acoustic waves are excited, causing the acoustic waves to propagate along the waveguide, thus obtaining an acoustic topological waveguide.

[0010] Among them, the SSH model stands for Su-Schrieffer-Heeger model, which is also known as the Su-Schrieffer-Heeger model in Chinese.

[0011] Furthermore, the excitation method of the structural acoustic wave includes: Direct excitation method of event source: When an event occurs in the utility tunnel, the event source directly acts on the utility tunnel structure to generate vibration and produce structural sound waves; The activation method of active interrogation by the aggregation node: When it is necessary to inspect or confirm the node status, the aggregation node actively transmits an interrogation pulse. The interrogation pulse is a broadband acoustic pulse or a swept-frequency acoustic pulse. The spectrum of the interrogation pulse covers the inherent resonant frequency of the piezoelectric resonator in each passive sensing node.

[0012] Furthermore, the structural acoustic wave is an elastic wave excited by an event in the pipe gallery structure, including at least one of bending waves, longitudinal waves, and transverse waves; when an event occurs in the pipe gallery, the event source acts on the pipe gallery structure to generate vibration, and the vibration propagates along the pipe wall or metal structure in the form of elastic waves to form the structural acoustic wave.

[0013] Furthermore, the passive sensing node is a node integrating a piezoelectric microbeam array, an energy harvesting and power management circuit, an impedance modulation circuit, and an identity resonant unit. It is used to sense structural acoustic waves and generate frequency fingerprints, as well as to encode and modulate the event type and node identity onto the continuing propagation of structural acoustic waves. The piezoelectric microbeam array is composed of multiple piezoelectric cantilever beams with different resonant frequencies. Each beam is manufactured using MEMS technology and has a piezoelectric thin film deposited on its surface. The energy harvesting and power management circuit rectifies, boosts, and stores the alternating current generated by the acoustic waves exciting the piezoelectric microbeam array in a supercapacitor to power the node circuit.

[0014] MEMS stands for Micro-Electro-Mechanical Systems.

[0015] Furthermore, the step of performing multi-band decomposition on the structural acoustic waves to generate a frequency fingerprint reflecting the event type specifically includes: When the sound wave propagates to the node, different frequency components excite piezoelectric microbeams with different resonant frequencies to resonate, thereby increasing the output voltage amplitude of the corresponding piezoelectric microbeam; Collect all voltages output from the piezoelectric microbeam array to construct a frequency fingerprint vector. Where N is the number of piezoelectric microbeams, ∈V, representing the output voltage of the i-th piezoelectric microbeam; The node matches the frequency fingerprint vector with a preset event type template, determines the event type code using a lookup table method or a classification model trained by a machine learning algorithm, and generates a confidence score. The event type template is obtained through offline calibration and includes, but is not limited to, standard frequency fingerprints for personnel intrusion, mechanical cutting, mechanical drilling, cable overheating, and gas leakage. The event type code is used to identify the event type.

[0016] Furthermore, the step of using an impedance modulation circuit to modulate the event type and node identity code onto the structured acoustic wave to form a modulated acoustic wave specifically includes: Generate a binary encoding sequence based on the event type code and node ID; Amplitude keying is used to control the variable capacitor in the impedance modulation circuit to switch between two capacitance values, thereby changing the acoustic impedance at the node and modulating the amplitude of the structural acoustic wave passing through the node; wherein, the capacitance value of the variable capacitor is controlled by the voltage signal corresponding to the frequency fingerprint, and the relationship between the capacitance change and the acoustic impedance change is linear. The modulated sound wave, carrying the binary encoded sequence, continues to propagate along the acoustic topology guide wave, forming the modulated sound wave.

[0017] Furthermore, the event encoding obtained by the demodulation at the aggregation node specifically includes: The aggregation node performs bandpass filtering on the modulated acoustic wave to extract the signal in the frequency band where the topological boundary state is located, thus obtaining the modulated signal; Envelope detection is performed on the modulated signal to recover the event type code, node ID, and confidence level, thereby obtaining the demodulated data frame; The demodulated data frame is synchronized and cyclic redundancy checked according to a preset frame structure to obtain an event code; the frame structure includes a synchronization header, a node ID field, an event type code field, a confidence field, and a check field.

[0018] Furthermore, the method of combining node identity encoding and sound wave arrival time difference to identify and locate events specifically includes: Bayesian inference is used to fuse event types and confidence levels sent by multiple nodes to calculate the posterior probability of each event. ,in, For the i-th event, This represents the observation at the j-th node; The maximum posterior probability of each event is determined based on the posterior probability. When the maximum posterior probability exceeds a preset threshold, the event is confirmed to have occurred, and the corresponding event type is output. Obtain the timestamps of the structural acoustic waves received by each node, and calculate the time difference of arrival between any two nodes. ; Based on the known topological waveguide group velocity Establish a system of equations relating the arrival time difference to the event location: ;in, For the location of the event, , These are the coordinate positions of the i-th and j-th nodes along the length of the pipe gallery, respectively, and the coordinate system is a one-dimensional coordinate system with the starting point of the pipe gallery as the zero point; the topological waveguide group velocity represents the propagation speed of sound waves in the topological boundary state, which is obtained by differentiating the dispersion relation; The system of equations is solved using the least squares method or the Chan algorithm to obtain an estimated event location. The estimated event locations from multiple calculations are then filtered to output the final event location.

[0019] Furthermore, before or after the aggregation node receives the modulated acoustic wave, the method further includes a passive node identification step: The convergence node periodically transmits interrogation pulses into the acoustic topology waveguide; the spectrum of the interrogation pulses covers the inherent resonant frequency of the piezoelectric resonator in each passive sensing node. When the interrogation sound wave pulse passes through the passive sensing node, the piezoelectric resonator absorbs the energy of the frequency component in the interrogation sound wave pulse that corresponds to the inherent resonant frequency, forming a depression in the spectrum of the transmitted sound wave. The aggregation node receives the transmitted sound wave, analyzes the spectrum of the transmitted sound wave, detects the center frequency of the depression, and identifies the node identity based on the mapping relationship between frequency and node ID. This is used to confirm the working status and location of the node and to provide node identity information for subsequent fusion identification.

[0020] Furthermore, the method also includes energy management and gain compensation steps: The convergence node emits low-frequency pumping acoustic waves at a set period to charge the passive sensing nodes along the line; the nodes harvest energy during the arrival of the pumping waves and store it in energy storage capacitors; a gain unit is set in the acoustic topology waveguide, which adaptively adjusts the amplification factor according to the acoustic wave intensity to replenish the energy of the propagating acoustic waves; based on the current attenuation coefficient of the waveguide... Dynamically adjust the gain factor g to make g ≥ This is to compensate for propagation loss and achieve stable long-distance transmission.

[0021] Preferably, the output graded early warning signal specifically includes: Based on the maximum posterior probability, the warning is divided into three levels: When the maximum posterior probability is between the first threshold and the second threshold, the warning signal is marked as the first level, indicating that the event dynamics need to be continuously monitored, the sampling frequency is automatically increased, the event location is continuously monitored, and the event development trend is recorded. When the maximum a posteriori probability is between the second and third thresholds, the warning signal is marked as the second level, indicating that remote verification and confirmation are required. The operation and maintenance personnel are notified to retrieve sensor data and historical records near the event location to confirm whether the event has actually occurred remotely and prepare for emergency response. When the maximum posterior probability exceeds the third threshold, the warning signal is marked as the third level, indicating that an alarm needs to be triggered immediately and an emergency response needs to be activated. This triggers the security subsystem and pushes the event type, confidence level, and event location to the monitoring center. The first threshold, the second threshold, and the third threshold are calibrated based on historical false alarm rates and false negative rates, and can be adaptively adjusted during operation.

[0022] Secondly, this application provides an intelligent security control system for underground utility tunnels, including: an acoustic topology waveguide construction module, a passive sensing node module, and a convergence node module; The acoustic topology waveguide construction module is used to arrange piezoelectric units along the length direction on the surface of the pipe gallery structure to obtain an acoustic topology waveguide. The passive sensing node modules are spaced apart on the acoustic topology waveguide and are used to drive the structure acoustic wave energy excited by the event to perform multi-band decomposition on the structure acoustic wave, generate a frequency fingerprint reflecting the event type, and use an impedance modulation circuit to modulate the event type and node identity code onto the structure acoustic wave to form a modulated acoustic wave. The aggregation node module is used to receive the modulated sound wave, demodulate it to obtain the event code, and combine the node identity code and the sound wave arrival time difference to identify and locate the event, and output a graded early warning signal containing the event type, confidence level and event location.

[0023] Compared with the prior art, the beneficial effects of this application are: This application constructs an acoustic topological waveguide along the surface of the utility tunnel structure, utilizing topologically protected boundary states to transmit structural acoustic waves. This suppresses backscattering and defect scattering, enabling acoustic waves to cross defect areas such as bends, welds, and bolts while maintaining high energy transmittance. This achieves long-distance, low-loss structural acoustic wave transmission, reducing missed detections caused by signal attenuation and communication blind spots in traditional underground utility tunnel security systems. Simultaneously, the passive sensing nodes are driven by the energy of structural acoustic waves excited by events, requiring no external power supply. Energy harvesting and gain compensation mechanisms enable node charging and propagation loss compensation, overcoming the difficulties of power supply in underground utility tunnels, power outage failures, and short battery life. This enhances the system's survivability and operational reliability in harsh environments.

[0024] This application utilizes a piezoelectric microbeam array to perform multi-band decomposition of structural acoustic waves, generating frequency fingerprints reflecting event types. Combined with template matching or machine learning classification models, it identifies event types such as personnel intrusion, mechanical cutting, mechanical drilling, cable overheating, and gas leaks. This avoids the problems of traditional video analysis being susceptible to interference from environmental factors such as low light, condensation, and fog, thus reducing the false alarm rate. Simultaneously, by using an impedance modulation circuit to modulate event type codes and node IDs onto the structural acoustic waves, it achieves integrated sensing and communication. Nodes do not need to actively emit electromagnetic waves, resulting in low power consumption and immunity to electromagnetic interference, improving the system's concealment and anti-interference capabilities. Furthermore, the aggregation node, by demodulating event codes and fusing Bayesian inference and time-of-arrival positioning from multiple nodes, can accurately output event type, confidence level, and event location. Combined with passive identification and hierarchical early warning strategies, it improves early warning accuracy and positioning precision while providing maintenance personnel with tiered and actionable handling guidelines, enhancing the overall intelligence level and emergency response efficiency of underground utility tunnel security. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A system architecture diagram of an intelligent security control system for underground utility tunnels provided in this application embodiment; Figure 2 A flowchart illustrating an intelligent security control method for underground utility tunnels provided in this application embodiment; Figure 3 A flowchart illustrating another intelligent security control method for underground utility tunnels provided in this application embodiment; Figure 4A flowchart illustrating another intelligent security control method for underground utility tunnels provided in this application embodiment; Figure 5 A flowchart illustrating another intelligent security control method for underground utility tunnels provided in this application embodiment. Detailed Implementation

[0027] In the description of this application, unless otherwise stated, " / " means "or," for example, A / B can mean A or B. The "and / or" in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, "at least one" means one or more, and "multiple" means two or more. The terms "first," "second," etc., do not limit the quantity or order of execution, and "first," "second," etc., do not necessarily imply differences.

[0028] It should be noted that, in this application, the terms "exemplary" or "for example" are used to indicate that something is being described as an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in this application should not be construed as being more preferred or advantageous than other embodiments or design solutions. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0029] The intelligent security control method for underground utility tunnels provided in this application embodiment can be applied to, for example... Figure 1 In the intelligent security control system for underground utility tunnels shown, such as Figure 1 As shown, the system includes: an acoustic topology waveguide construction module, a passive sensing node module, and a convergence node module; Among them, the acoustic topology waveguide construction module is used to arrange piezoelectric units along the length direction on the surface of the pipe gallery structure to obtain the acoustic topology waveguide; Passive sensing node modules are spaced out on the acoustic topology waveguide and are used to drive the structure acoustic wave energy excited by the event to perform multi-band decomposition on the structure acoustic wave, generate frequency fingerprints that reflect the event type, and use impedance modulation circuit to modulate the event type and node identity code onto the structure acoustic wave to form a modulated acoustic wave. The aggregation node module is used to receive modulated sound waves, demodulate them to obtain event codes, and combine the node identity code and the time difference of arrival of the sound waves to identify and locate the events, and output a graded early warning signal containing the event type, confidence level and event location.

[0030] To address the technical problems in existing underground utility tunnel security systems, such as severe signal attenuation due to long-distance transmission, difficulty in powering active nodes and easy failure upon power outage, electromagnetic interference and blind spots affecting wireless communication, and high false alarm rates in low-light and humid environments, this application provides an intelligent security control method for underground utility tunnels. The method includes: Piezoelectric elements are arranged along the length of the tube gallery structure to obtain an acoustic topological waveguide. Multiple passive sensing nodes are spaced out on an acoustic topological waveguide, and each node is driven by the energy of structural acoustic waves excited by an event. The structure acoustic wave is decomposed into multiple frequency bands to generate a frequency fingerprint that reflects the event type. The event type and node identity are encoded and modulated onto the structure acoustic wave using an impedance modulation circuit to form a modulated acoustic wave. The modulated sound wave propagates along the acoustic topology waveguide to the convergence node. The convergence node demodulates the signal to obtain the event code, and combines the node identity code and the time difference of arrival of the sound wave to identify and locate the event, and outputs a graded early warning signal containing the event type, confidence level and event location.

[0031] Based on this, this application achieves low-loss structural acoustic wave transmission by constructing acoustic topology waveguides and integrates sensing and communication through passive sensing nodes. It requires no external power supply and is not affected by electromagnetic interference, effectively improving the resilience, early warning accuracy and environmental adaptability of the underground utility tunnel security system.

[0032] like Figure 2 As shown in the embodiment of this application, an intelligent security control method for underground utility tunnels includes: S1. Piezoelectric elements are arranged along the length of the pipe gallery structure to obtain an acoustic topological waveguide.

[0033] Acoustic topological waveguides are artificial periodic waveguide structures with topologically protected boundary states. A waveguide is a structure capable of guiding elastic waves or acoustic waves along a specific path. In a utility tunnel environment, the tunnel walls and metal pipes themselves can serve as the propagation medium for elastic waves. However, in ordinary structures, elastic waves experience strong scattering and reflection when encountering defects such as bends, welds, and bolts, leading to signal attenuation and dead zones. By arranging piezoelectric elements along the length of the tunnel structure surface and constructing a periodic or quasi-periodic array with topologically nontrivial properties, topologically protected edge states or interface states can be formed within a specific frequency band. This allows structural acoustic waves to propagate directionally along this channel and provides backscatter suppression capabilities against defects, bends, and local disturbances, thereby improving the reliability and range of acoustic wave transmission.

[0034] In some implementations, the piezoelectric elements can be made of piezoelectric ceramic sheets, piezoelectric films, or other materials with piezoelectric effects. These elements can be installed on the wall of the tube gallery or the surface of a metal structure through methods such as bonding, embedding, pre-embedding, or etching. The periodic arrangement of the piezoelectric elements can be adjusted according to the vibration characteristics of the tube gallery structure to form a periodic structure with topologically nontrivial phases.

[0035] For example, piezoelectric units are arranged at a certain period along the length of the tube gallery. Each cell contains two piezoelectric plates. By adjusting the ratio of the intra-cell spacing to the inter-cell spacing, the intra-cell coupling strength is made less than the inter-cell coupling strength, thereby obtaining topologically nontrivial phases and protected boundary states.

[0036] S2. Multiple passive sensing nodes are spaced out on the acoustic topological waveguide, and each node is driven by the structural acoustic wave energy excited by the event.

[0037] Passive sensing nodes are those that do not require external power supply but instead obtain energy from the environment to operate. Structural acoustic waves are elastic waves excited by events within a utility tunnel structure, including at least one of bending waves, longitudinal waves, and transverse waves; they are a guided wave mode propagating in thin-plate structures. When events such as mechanical collisions, illegal intrusions, leaks, vibrations, or excavation damage occur within the utility tunnel, the event source directly acts on the tunnel structure, generating vibrations. These vibrations propagate along the pipe wall or metal structure in the form of elastic waves, forming structural acoustic waves. The nodes then utilize the mechanical energy carried by these structural acoustic waves to convert it into electrical energy, powering the node's sensing and modulation circuits, thus eliminating the need for batteries or external power sources.

[0038] In some implementations, passive sensing nodes can integrate piezoelectric transducers, energy harvesting and management units, signal conditioning units, frequency decomposition units, impedance modulation circuits, and storage units. Passive sensing nodes can be fixed to the acoustic topological waveguide by bonding, welding, bolting, magnetic attraction, co-curing, or pre-embedding. They can also share the same piezoelectric element with the piezoelectric units in the acoustic topological waveguide. Multiple passive sensing nodes can be arranged at equal intervals along the acoustic topological waveguide, or they can be arranged at non-equal intervals depending on the characteristics of the pipe gallery structure, sound wave attenuation characteristics, and the importance of the protected area.

[0039] For example, a passive sensing node is installed every few meters on the acoustic topology waveguide. The node uses a piezoelectric ceramic sheet as an energy conversion element. When the pipe gallery wall is impacted by an external force, the structural acoustic wave generated by the impact propagates to the piezoelectric ceramic sheet of the node. The piezoelectric ceramic sheet outputs an alternating voltage, which is rectified and regulated by an energy storage capacitor to power the frequency decomposition circuit and impedance modulation circuit inside the node. The node can encode and modulate the event information onto the reflected or transmitted structural acoustic wave without an external power supply.

[0040] S3. Perform multi-band decomposition on the structure acoustic wave to generate a frequency fingerprint reflecting the event type, and use an impedance modulation circuit to modulate the event type and node identity onto the structure acoustic wave to form a modulated acoustic wave.

[0041] Multi-band decomposition refers to decomposing the received structured acoustic wave signal into multiple frequency bands or sub-bands in the time domain, frequency domain, or time-frequency domain, and extracting features such as energy distribution, peak frequency, spectral centroid, spectral entropy, wavelet packet coefficients, and Mel-frequency cepstral coefficients for each band. Frequency fingerprinting refers to a set of frequency features or feature vectors obtained from multi-band decomposition that reflect the event type. Different event types, such as mechanical collisions, leaks, cutting, drilling, footsteps, and vehicle crushing, generate structured acoustic waves with different frequency band energy distributions and spectral characteristics; therefore, frequency fingerprinting can be used to distinguish event types. Impedance modulation circuits are circuits that change the electrical impedance coupled to the structured acoustic wave propagation medium to alter the reflection, transmission, or scattering characteristics of the sound wave. Using impedance modulation circuits to modulate the event type and node identity onto the structured acoustic wave involves encoding the locally generated event type code and node identity identifier into a binary sequence, and then modulating the amplitude of the structured acoustic wave passing through the node by changing the acoustic impedance at the node, allowing the modulated sound wave to carry event information and continue propagating. This allows for the integration of sensing and communication without the need for additional electromagnetic wave emission, reducing node power consumption and improving resistance to electromagnetic interference.

[0042] In some implementations, multi-band decomposition can be achieved using methods such as analog filter banks, digital filter banks, fast Fourier transform, short-time Fourier transform, wavelet transform, wavelet packet transform, empirical mode decomposition, variational mode decomposition, or convolutional neural networks. Frequency fingerprints can include band energy ratios, spectral peak positions, spectral kurtosis, wavelet packet node energy, cepstral coefficients, or combinations of these features. Impedance modulation circuits can include variable capacitors, variable resistors, variable inductors, switched capacitor arrays, switched diodes, field-effect transistor switches, microelectromechanical system switches, or piezoelectric shunt circuits. Modulation methods can employ amplitude shift keying, frequency shift keying, phase shift keying, on / off keying, etc. Event type and node identity encoding can be performed after source coding, channel coding, interleaving, or spread spectrum processing before modulation.

[0043] S4. The modulated sound wave propagates along the acoustic topology waveguide to the convergence node. The convergence node demodulates to obtain the event code, and combines the node identity code and the time difference of arrival of the sound wave to identify and locate the event, and outputs a graded early warning signal containing the event type, confidence level and event location.

[0044] In this process, due to the scatter suppression and low-loss characteristics of the topological waveguide, modulated acoustic waves can reliably reach the convergence node across long distances and defective regions. The convergence node then processes the received modulated acoustic waves through filtering and envelope detection to recover information such as the event type code, node identification, and confidence level. Event identification and localization combining node identification codes and acoustic wave arrival time differences involves using the arrival time differences of multiple passive sensing nodes, combined with the propagation speed of acoustic waves in the topological waveguide, to determine the event location; simultaneously, it integrates the event types and confidence levels reported by multiple nodes to improve the accuracy of event identification.

[0045] In some implementations, the convergence node may include a piezoelectric transducer, a signal conditioning unit, an analog-to-digital converter, a processor, a memory, and a communication interface. The convergence node can be deployed at one or both ends of the acoustic topological waveguide, or multiple convergence nodes can be deployed at equal intervals along the acoustic topological waveguide to form a distributed receiving array. The time difference of arrival (TDOA) can be calculated using cross-correlation, generalized cross-correlation, phase difference, time of arrival estimation, or beamforming. Event location can employ time difference of arrival (TDOA) location, time of arrival (TOA) location, angle of arrival (ATO) location, received signal strength location, or a hybrid location method. Confidence can be determined by the matching degree between the frequency fingerprint and the template, the signal-to-noise ratio, the TDOA estimation error, or the classifier output probability. Graded early warning signals can be transmitted to the utility tunnel monitoring platform or actuator via wired communication, fiber optic communication, wireless communication, or visible light communication.

[0046] It should be noted that one or more aggregation nodes can be set along the acoustic topology waveguide, or they can be connected to the utility tunnel monitoring center via wired or wireless means. The event codes obtained by the aggregation node demodulation can contain only event type codes and node identification codes, or they can contain check codes, timestamps, sequence numbers, or other control information. Event identification and location can be completed locally at the aggregation node, or the demodulated data can be uploaded to the remote monitoring center for completion.

[0047] Based on the above technical solutions, this application provides an intelligent security control method for underground utility tunnels. By constructing acoustic topological waveguides on the surface of the tunnel structure, topological protection is used to suppress backscattering and defect scattering during the transmission of structural acoustic waves, thereby improving signal transmission distance and reliability. By deploying passive sensing nodes driven by the energy of structural acoustic waves excited by events, the wiring and maintenance costs associated with active sensors and battery power are avoided, achieving low-power or even self-powered event sensing. Frequency fingerprints are generated through multi-band decomposition, and the event type and node identity are encoded and modulated onto the structural acoustic waves using impedance modulation circuits. Event information can be uploaded to the aggregation node without the nodes actively emitting acoustic waves, reducing node power consumption and electromagnetic interference. By demodulating and encoding the aggregation node and combining it with the time difference of arrival of the acoustic waves for identification and positioning, a graded early warning signal containing event type, confidence level, and event location can be output, improving the accuracy of event identification and positioning. This solves the problems of high power supply wiring costs, poor signal transmission reliability, and insufficient event identification and positioning accuracy in existing underground utility tunnel security monitoring technologies.

[0048] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 3 As shown, the above S1 can be implemented through the following S101, S102 and S103, which are explained in detail below: S101. Obtain the bending wave propagation characteristics of the pipe gallery structure and establish a mass-spring equivalent model.

[0049] Specifically, the structure of a utility tunnel is typically made of reinforced concrete or metal, and its walls can be considered as elastic plates or shells. Through theoretical calculations or experimental measurements, parameters such as the dispersion relation of bending waves on the tunnel walls, material attenuation coefficients, and equivalent bending stiffness can be obtained.

[0050] The wall of the utility tunnel is discretized along its length into multiple mass blocks, each with a mass of [mass value missing]. Adjacent mass blocks are connected by an equivalent spring, and the equivalent spring stiffness is... Corresponding local bending stiffness. Displacement of the nth mass block. The following equations of motion are satisfied: Among them, the mass of the mass block and equivalent spring stiffness It can be determined by the density of the pipe wall material Elastic modulus E, moment of inertia I, and discrete spacing Determine, for example , A is the cross-sectional area.

[0051] This equation is the core mathematical expression of the mass-spring equivalent model, describing the propagation characteristics of bending waves in the pipe wall. It discretizes the continuous pipe wall into a mass-spring chain, enabling a simplified model to accurately characterize the propagation law of bending waves. This facilitates the subsequent construction of a topological periodic structure by adjusting local stiffness. This step fully utilizes the elastic wave conduction capability of the pipe gallery structure itself, avoiding the need for additional cabling or waveguide materials, thus laying the physical foundation for long-distance, low-loss signal transmission.

[0052] It should be noted that structural acoustic waves are elastic waves excited by events within the utility tunnel structure, including at least one of bending waves, longitudinal waves, and transverse waves. When an event occurs within the utility tunnel, the event source acts on the tunnel structure, causing vibrations. These vibrations propagate along the tunnel wall or metal structure in the form of elastic waves, forming structural acoustic waves. The event source refers to the initial excitation source that causes the elastic waves to be generated in the utility tunnel structure. Bending waves are the most prevalent propagation mode on the tunnel wall; their propagation speed is frequency-dependent, and their attenuation coefficient is affected by material friction, ranging from approximately 0.1 to 0.5 dB / m in the 1–10 kHz frequency band within concrete.

[0053] The propagation loss of structural acoustic waves mainly originates from internal material losses. However, the topological protection provided by acoustic topological waveguides suppresses backscattering and defect scattering, enabling structural acoustic waves to traverse defect areas such as bends, welds, and bolts while maintaining high energy transmittance. Specifically, topological boundary states have no backscattering channels, so acoustic waves do not produce reflected echoes when encountering defects, and energy is not lost due to scattering. Therefore, compared to ordinary periodic waveguides, structural acoustic waves in topological waveguides experience less attenuation and travel farther over the same length.

[0054] S102. Determine the periodic arrangement parameters of the piezoelectric unit, adjust the coupling stiffness to make the intra-cell coupling strength less than the inter-cell coupling strength, and obtain the topological boundary state.

[0055] Specifically, piezoelectric elements are arranged along the length of the tube wall at a period of 'a', with each cell containing two piezoelectric plates and an intracellular spacing of 'a'. The intercellular spacing is ,and By changing and The ratio can control the intracellular coupling stiffness. and inter-cell coupling stiffness The relative size. When < hour, < The system is in a topologically nontrivial phase, exhibiting topologically protected boundary states at the endpoints of finite-length waveguides. This one-dimensional dimerization model can be described by the Su-Schrieffer-Heeger model (SSH model for short), whose Hamiltonian can be written as: ; in, and These represent the intracellular and intercellular coupling strengths, respectively. , To generate operators, let represent the generation of a phonon excitation in subcells A and B within the nth cell, respectively. At this time, the system is in a topologically nontrivial phase, with a winding number W=1, and a zero-energy boundary state exists. In an elastic wave system, and Corresponding to and .

[0056] In some implementations, based on the mass-spring equivalent model and the SSH model, the specific construction process of the topological boundary state includes: The first step is to map the discrete mass-spring chain to an SSH dimer model.

[0057] The equivalent spring stiffness established in S101 and These are respectively mapped to the intracellular coupling strength in the SSH model. and intercellular coupling strength For the nth cell, let the displacements of subcells A and B be respectively... and Then the equation of motion for this cell can be written as: ; Where t is a time variable.

[0058] The second step is to solve for the volume band structure under periodic boundary conditions.

[0059] Assume that the vibration displacement of each mass block has a simple harmonic wave form, that is , Substituting these two equations of motion, we obtain the eigenvalue equations: Where q is the wave vector, representing the spatial frequency of the elastic wave in the periodic structure; a is the lattice period, i.e., the total length of a cell; Angular frequency; Let A be the displacement amplitude of subgrid A in the simple harmonic solution. Let be the displacement amplitude of subgrid B in the simple harmonic solution; i is the imaginary unit.

[0060] Solving this eigenvalue equation yields the bulk band dispersion relation: ; when At that time, the two strength bands were at the boundary of the Brillouin Zone. When the band gap opens at a certain point, the band gap width of the energy band is approximately: .

[0061] The third step is to apply open boundary conditions to solve for the boundary states of the finite-length chain.

[0062] For a finite-length chain containing N cells, apply open boundary conditions and solve the eigenvalue problem of the equations of motion. At this point, a pair of frequency-degenerate topological boundary states appear at the center of the bandgap, with frequencies of: The wavefunction exponent of this boundary state is localized at both ends of the chain, where the amplitude of the nth cell satisfies: ;in For local length, Let be the displacement amplitude of the nth cell. At that time, the local length is finite, and the boundary state exists stably.

[0063] The fourth step is to compute topological invariants to verify the topological nontriviality.

[0064] For a one-dimensional SSH model, the number of entanglements W is defined as: ;in , representing a complex function on the Brillouin zone; when When W=1, the system is in a topologically nontrivial phase, confirming that the boundary states are topologically protected; when When W=0, the system is in a topologically trivial phase with no protected boundary states.

[0065] The fifth step is to determine the geometric parameters of the piezoelectric unit for implementation. .

[0066] The added mass and piezoelectric effect of the piezoelectric element alter the local equivalent stiffness, resulting in changes in the intercellular spacing. and intercellular spacing Decide separately and The size. By taking ,make This leads to a topologically nontrivial phase. For example, let's take a period of a = 80 mm. =30mm, =50mm, at this time ,satisfy With a winding number W=1, topologically protected boundary states appear at both ends of the waveguide.

[0067] Through the above process, the specific construction of the topological boundary states in the acoustic topological waveguide can be completed, providing a physical basis for the low-loss transmission of subsequent structural acoustic waves.

[0068] By arranging piezoelectric units in a dimerized manner, ordinary pipe walls are transformed into acoustic topological waveguides with a topologically nontrivial band structure. The topologically nontrivial phase ensures the existence of boundary states, making the sound waves robust to defects such as bends, welds, and bolts during propagation. This suppresses backscattering and defect scattering, solving the signal attenuation and dead zone problems caused by scattering in traditional waveguides. Simultaneously, the piezoelectric units serve as both waveguide building blocks and sensitive elements for subsequent energy harvesting and sensing, achieving structural-functional integration and simplifying system complexity.

[0069] In another implementation, piezoelectric units can be used as waveguide carriers instead of adhesive bonding, utilizing existing metal pipes or cable trays within the utility tunnel. Specifically, piezoelectric rings or patches are periodically installed on the surface of the metal pipes or cable trays, achieving rigid connections through clamps or welding. Due to the low acoustic impedance and minimal attenuation of metal pipes, the transmission distance of the topological waveguide can be further extended, and the node spacing can be increased accordingly. Furthermore, piezoelectric units can be embedded at the joints of prefabricated pipe segments within the utility tunnel, utilizing the structural discontinuities between segments to construct dimerized periods, thereby forming an acoustic topological waveguide without increasing the amount of additional construction work. This method is particularly suitable for newly constructed utility tunnels, allowing for simultaneous completion with structural construction and reducing the difficulty of later modifications.

[0070] S103. Calculate the dispersion relation of the topological boundary states, select the operating frequency band, and complete the construction of the acoustic topological waveguide.

[0071] Specifically, the dispersion relation based on the one-dimensional SSH model is as follows: ; Where q is the wave vector and m is the equivalent mass. At that time, the bulk band gap is located in the frequency range Internal boundary state frequency Located near the center of the band gap, approximately Group velocity It is approximately constant within the boundary state frequency band and has no backscattering channel.

[0072] The operating frequency band is selected as the band with a group velocity variation rate of less than 5% and located within the bulk bandgap. For example, for the above parameters, the bulk bandgap ranges from 8 to 12 kHz, the boundary state center frequency is approximately 10 kHz, and the group velocity is approximately 2000 m / s. Within this operating frequency band, structural acoustic waves are excited. The acoustic waves propagate along the topological boundary states with low loss. Even when encountering defects such as bends or welds, the energy transmittance can still remain above 90%, thus completing the construction of the acoustic topological waveguide.

[0073] By precisely selecting the operating frequency band through dispersion relations, the group velocity of the acoustic wave is ensured to be approximately constant during transmission, avoiding waveform distortion and mode coupling. Simultaneously, the operating frequency band lies within the volume bandgap, allowing only topological boundary states to propagate, effectively suppressing volume mode interference and improving signal purity. This step provides a physical guarantee for the high-reliability sensing and communication of subsequent passive sensing nodes, and is a crucial foundation for the system to achieve high-accuracy early warning.

[0074] In some implementations, the excitation methods for structural acoustic waves include direct excitation by the event source and excitation by active interrogation by the converging node.

[0075] In the direct excitation method, when events such as personnel intrusion, mechanical cutting, mechanical drilling, cable overheating, or gas leakage occur within the utility tunnel, the event source directly acts on the tunnel structure, generating vibrations. These vibrations propagate along the pipe wall or metal structure in the form of elastic waves, forming structural acoustic waves. In this case, no external excitation is required; the structural acoustic waves are the natural elastic waves excited by the event. The nodes passively receive these acoustic waves and complete the sensing and encoding.

[0076] For the active interrogation method of the aggregation node, when it is necessary to inspect or confirm the node status, the aggregation node actively transmits an interrogation pulse. The interrogation pulse is a broadband acoustic pulse or a swept-frequency acoustic pulse, and its spectrum covers the inherent resonant frequency of the piezoelectric resonator in each passive sensing node. In specific operation, the aggregation node excites a broadband acoustic wave in the topological waveguide through a piezoelectric transducer. The pulse width can be 10~100ms, and the repetition period can be 1~10s. The interrogation pulse propagates along the topological waveguide, passing through each passive sensing node in sequence, and is used for node identification or node charging. This method can actively detect the node status and waveguide integrity when no event occurs, providing a basis for system maintenance.

[0077] It should be noted that the direct triggering method of the event source is used for event early warning, while the active querying method of the aggregation node is used for system self-checking and energy management. The two can be performed alternately or simultaneously without interfering with each other.

[0078] Based on the above technical solution, S1 transforms the pipe gallery itself into an acoustic topological waveguide with topologically protected boundary states by arranging piezoelectric units along its length on the surface of the pipe gallery structure. Utilizing the pipe wall structure as the sound wave transmission medium eliminates the need for additional cabling or waveguide materials, reducing system deployment costs and construction complexity. Simultaneously, topological protection suppresses backscattering and defect scattering, enabling sound waves to cross bends, welds, bolts, and other defect areas while maintaining high energy transmittance. This solves the problems of severe signal attenuation and communication blind spots inherent in traditional waveguides in complex pipe gallery environments. Furthermore, the piezoelectric units integrate waveguide construction, sensing, and energy harvesting functions, achieving structural-functional integration. This provides a stable physical transmission channel for the low-power, high-reliability operation of subsequent passive sensing nodes, enhancing the overall security system's resilience and environmental adaptability.

[0079] In one possible implementation of this application embodiment, the above-mentioned S2 can be specifically implemented by the following S201, S202 and S203, which are described in detail below: S201. Determine the spacing of passive sensor nodes based on the acoustic attenuation coefficient and the minimum detectable signal-to-noise ratio.

[0080] Specifically, passive sensing nodes are deployed on the acoustic topology waveguide, and the node spacing d should satisfy the requirement that the signal strength is still higher than the minimum signal-to-noise ratio requirement for the node to be detectable when the acoustic wave of the event propagates to the adjacent node.

[0081] Let the amplitude of the sound wave excited by the event source on the surface of the structure be _____. The material attenuation coefficient of the topological waveguide is The amplitude corresponding to the minimum detectable signal-to-noise ratio of a node is Then the node spacing d must satisfy: ; Therefore, the maximum allowable spacing is: .

[0082] For example, for concrete pipe walls, the material attenuation coefficient of 1~10kHz bending waves Approximately 0.1~0.5 dB / m, take =0.2dB / m, if the event source amplitude The minimum detectable amplitude at the node is 1 m / s². If it is 0.01 m / s², then m. Considering the gain compensation of the topological waveguide and the actual engineering margin, the node spacing can be taken as 30-50m to ensure that adjacent nodes can reliably receive event acoustic waves.

[0083] By theoretically calculating and rationally determining the node spacing, the continuity of event acoustic wave coverage is ensured while avoiding increased costs due to overly dense node deployment. This step directly correlates acoustic wave propagation loss with node sensitivity, providing a quantitative basis for the system's detectability and economy. Simultaneously, it fully utilizes the low-loss characteristics of the topological waveguide, allowing for a larger node spacing than traditional wireless sensor networks while maintaining the same detection capability, thus reducing the number of devices required.

[0084] S202. Design a piezoelectric microbeam array of passive sensing nodes for multi-band sensing and frequency decomposition of structural acoustic waves.

[0085] Specifically, each passive sensing node integrates a piezoelectric microbeam array, which consists of multiple piezoelectric cantilever beams with different resonant frequencies. Each cantilever beam is manufactured using microelectromechanical systems (MEMS) technology. A piezoelectric thin film (such as PZT or AlN) is deposited on the surface of the cantilever beam. When structural acoustic waves propagate to the node, different frequency components excite the microbeams with different resonant frequencies to resonate.

[0086] The nth order resonant frequency of the microbeam Determined by the following formula: ;in For the eigenvalues ​​of the nth mode, for the first mode... L is the beam length; E is the equivalent Young's modulus; I is the moment of inertia of the cross section; Let L be the material density; A be the cross-sectional area of ​​the beam. By designing microbeams of different lengths L, a resonant array covering a frequency range of 1~50kHz can be formed. For example, taking L=2mm, Pa, m 4 , =7500kg / m³, If m², then the first-order resonant frequency is approximately 10kHz.

[0087] When the structural acoustic wave contains a certain frequency component, the amplitude of the microbeam at the corresponding resonant frequency increases significantly, and its output voltage amplitude is significantly higher than that of other microbeams. Finally, the voltage output by each microbeam constitutes a frequency fingerprint vector, used for subsequent event type identification. Simultaneously, the vibration energy of the microbeams can also be harvested to power the nodes.

[0088] Piezoelectric microbeam arrays physically decompose structural acoustic waves by frequency, eliminating the need for complex analog-to-digital converters and spectrum analysis circuits, thus reducing node power consumption and complexity. This design fully utilizes the narrowband selectivity of microbeam resonances, enabling independent energy output in each frequency band to form a frequency fingerprint closely related to event type, providing a foundation for high-accuracy event identification.

[0089] S203. Design an energy harvesting and power management circuit so that the node is driven by the structural acoustic wave energy excited by the event.

[0090] Specifically, the passive sensing node does not rely on an external power source; its operating energy comes from the alternating current generated by the piezoelectric microbeam excited by the acoustic waves of the event. The energy harvesting and power management circuit includes a three-stage structure: a full-bridge rectifier, a charge pump boost, and an energy storage capacitor. When the acoustic waves propagate to the node, the piezoelectric microbeam outputs a voltage under resonant conditions. The voltage is rectified by a full-bridge rectifier to DC, and then boosted to the operating voltage required by the node circuit (e.g., 1.8–3.3V) by a charge pump or DC-DC boost circuit. The voltage is stored in a supercapacitor or ceramic capacitor to power the node circuit. The node is in standby mode when there is no event sound wave. It only starts sensing and encoding when the sound wave energy exceeds a preset wake-up threshold, thereby minimizing power consumption.

[0091] Suppose that the output power of a single microbeam at resonance is The energy required for a node to complete one event recognition and encoding / modulation is Then the energy that the energy storage capacitor needs to store should satisfy: ; in For energy conversion efficiency, Let i be the output power of the i-th microbeam. The duration of the event acoustic wave is denoted as t. For example, if the output power of a single microbeam is 5 μW, the duration of the event acoustic wave is 1 ms, and the energy conversion efficiency is 50%, then a single microbeam can collect approximately 2.5 nJ of energy. If a node integrates 20 microbeams, it can collect approximately 50 nJ, which is sufficient to drive a low-power encoding circuit to complete one encoding transmission (typical energy consumption is approximately 10 nJ to 1 μJ).

[0092] The energy harvesting and power management circuitry achieves complete passivity for the nodes, solving the problems of power supply difficulties, power outage failures, and short battery life in underground utility tunnels. The nodes only activate when event acoustic waves arrive, remaining in standby mode otherwise. Power consumption is extremely low, and the event acoustic waves themselves are both the sensed object and the energy source for the nodes, forming a self-driving mechanism of "event as power." This approach enhances the system's survivability and maintenance-free operation in harsh environments.

[0093] In another implementation, the energy source for passive sensing nodes is not limited to event acoustic waves; it can also incorporate other energy forms within the utility tunnel environment. For example, near tunnel entrances or vents, nodes can integrate photovoltaic thin films to supplement energy using daily sunlight. In areas with temperature differences or airflow, nodes can integrate micro-thermal energy collectors or micro-wind turbines to achieve multi-source energy harvesting. Furthermore, the aggregation node can periodically emit frequency-sweeping pump acoustic waves. These waves, in addition to charging, can carry energy management commands, enabling the node to dynamically adjust its operating mode based on its energy storage status. For instance, it can increase the sampling frequency when energy storage is sufficient and only respond to high-priority events when energy storage is insufficient. This approach further enhances the node's self-sufficiency under long-term unattended conditions.

[0094] Based on the above technical solution, S2 deploys passive sensing nodes at intervals on the acoustic topology waveguide and rationally determines the node spacing according to the acoustic wave attenuation characteristics. This ensures the continuity of event acoustic wave coverage while avoiding overly dense node deployment, reducing system construction and maintenance costs. The piezoelectric microbeam array in the passive sensing nodes utilizes microbeams with different resonant frequencies to perform multi-band physical decomposition of structural acoustic waves. This eliminates the need for complex analog-to-digital conversion and spectrum analysis circuits, achieving low-power, highly integrated frequency fingerprint extraction, laying the foundation for accurate event type identification. The energy harvesting and power management circuit converts the energy of the event acoustic waves into electrical energy required for node operation, completely freeing the nodes from dependence on external power supplies and batteries. They are normally in a zero-power standby state and autonomously wake up when an event occurs, realizing a self-driven working mechanism. This design enables the passive sensing nodes to possess extremely high survivability and maintenance-free operation in long-distance, humid, and enclosed underground utility tunnel environments. It simultaneously enhances the system's reliability and environmental adaptability from both the sensing and power supply layers, providing a stable and reliable distributed sensing front-end for subsequent event encoding modulation and convergence identification.

[0095] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 4 As shown, the above S3 can be implemented through the following S301, S302 and S303, which are explained in detail below: S301. Use piezoelectric microbeam array to perform multi-band physical decomposition of structural acoustic waves and obtain frequency fingerprint vector.

[0096] Specifically, when the structured acoustic wave excited by the event propagates to the passive sensing node, the piezoelectric microbeam array in the node vibrates under the excitation of the acoustic wave. Since different microbeams in the array have different inherent resonant frequencies, the frequency components in the acoustic wave that are the same as or close to the resonant frequency of a certain microbeam will excite that microbeam to resonate, causing its output voltage amplitude to increase significantly.

[0097] Let the natural resonant frequency of the i-th piezoelectric microbeam be... Sound waves at frequencies The acceleration amplitude at that point is Then the output voltage of the microbeam can be approximately expressed as: ; in The quality factor of the microbeam. The transverse piezoelectric constant of the piezoelectric thin film is... Let be the length of the microbeam. From this formula, we can see that the output voltage of the microbeam is positively correlated with the sound wave energy at its resonant frequency. Therefore, the output voltage of all microbeams collectively reflects the energy distribution of the sound wave in different frequency bands.

[0098] By collecting the output voltages of all N microbeams, a frequency fingerprint vector is formed: Where N is the number of piezoelectric microbeams, Let be the output voltage of the i-th microbeam.

[0099] Leveraging the narrowband resonance characteristics of microbeam arrays, frequency band decomposition of sound waves can be directly achieved at the physical layer, eliminating the need for complex digital signal processing such as analog-to-digital conversion and fast Fourier transform. This results in extremely low node power consumption and rapid response. The frequency fingerprint vector is essentially a compressed representation of the spectral characteristics of an event's sound waves. Different events exhibit significant differences in energy distribution across frequency bands due to their varying sound wave generation mechanisms; therefore, this vector provides highly discriminative feature input for subsequent event type identification.

[0100] S302. Identify the event type based on the frequency fingerprint vector and generate the event type code and confidence level.

[0101] Specifically, the node matches the frequency fingerprint vector V with a preset event type template, or uses a lightweight classification model to determine the event type.

[0102] Preset event type templates can be obtained through offline calibration, including standard frequency fingerprint templates for typical events such as personnel intrusion, mechanical cutting, mechanical drilling, early acoustic emission from cable overheating, and gas leaks. , where j represents the j-th event type.

[0103] For template matching, the cosine similarity between the frequency fingerprint vector and each template is first calculated: ;in The similarity score ranges from [0,1]. The event type with the highest similarity is then selected as the recognition result, and this maximum similarity score is the confidence score C. For example, when When, the event type identification is considered reliable; when When C < 0.5, the node is marked as uncertain, but the event type code and confidence level are still sent to the aggregation node for fusion judgment; when C < 0.5, the node does not trigger modulation, but only maintains energy collection.

[0104] For classification models, lightweight support vector machines (SVMs) or decision tree models can be used. For example, after the SVM classifier is trained, its decision function is: ;in For Lagrange multipliers, For training sample labels, is the kernel function, and b is the bias.

[0105] The event type corresponding to the maximum value of the decision function output is the identification result, and the output value is normalized and used as the confidence level. Finally, the identified event types are encoded as event type codes, such as 001 for personnel intrusion, 010 for mechanical cutting, and 100 for cable overheating.

[0106] Instead of the raw waveform data, the node directly outputs the event type code and confidence level, significantly reducing the amount of information that needs to be transmitted. The encoding length is only a few tens of bits, thus reducing the demand for communication bandwidth. At the same time, by setting a confidence level threshold, some noise and uncertain events can be filtered out at the node, reducing the processing burden on the aggregation node and improving the overall system response efficiency.

[0107] S303. Generate a binary encoding sequence and control the impedance modulation circuit to modulate the amplitude of the structural acoustic wave to form a modulated acoustic wave.

[0108] Specifically, the node generates a binary encoded sequence based on the event type code and the node identification ID. This encoded sequence includes a frame header, a node ID field, an event type code field, a confidence level field, and a checksum field. The node uses amplitude keying to control the variable capacitor in the impedance modulation circuit to switch between two capacitance values, thereby changing the acoustic impedance at the node and modulating the amplitude of the structural acoustic waves passing through the node.

[0109] Let the capacitance values ​​of the variable capacitor in the two states be respectively. and The acoustic impedances at the corresponding nodes are respectively and The change in capacitance and the change in acoustic impedance are approximately linearly related, that is: ;in The proportionality constant is the change in capacitance. .

[0110] Changes in the acoustic impedance at the nodes cause changes in the transmission coefficient of the acoustic waves passing through the nodes. Assuming the unmodulated node acoustic impedance is matched to the waveguide impedance, the transmission coefficient is... When the acoustic impedance of the node changes According to acoustic transmission line theory, the sound pressure transmission coefficient can be expressed as a function of the node impedance. Under conditions of small impedance mismatch, the change in transmission coefficient is... With change in acoustic impedance Approximately proportional, that is: ;in The characteristic acoustic impedance of the topological waveguide is given.

[0111] Will Substituting into the above equation, we can obtain the relationship between the change in capacitance and the change in transmission coefficient: ;make ,but This allows for linear control of the sound wave amplitude through capacitor tuning.

[0112] Next, the modulated sound wave, carrying the binary encoded sequence, continues to propagate along the acoustic topological waveguide, forming a modulated sound wave. Due to the scatter suppression and low-loss characteristics of the topological waveguide, the modulated sound wave can reliably reach the convergence node, and the modulation process does not affect the propagation mode of the topological boundary states.

[0113] Passive modulation transmission of event information is achieved through impedance modulation. Nodes do not need to actively emit electromagnetic waves; they can simply load information onto existing structural acoustic waves by changing their own acoustic impedance. This method enables sensing and communication to be completed in the same physical process. The event acoustic wave is both the sensed object and the information carrier, reducing node power consumption and system complexity, while avoiding electromagnetic radiation and improving the system's survivability in environments with strong electromagnetic interference.

[0114] Based on the above technical solution, S3 performs multi-band physical decomposition of structural acoustic waves using a piezoelectric microbeam array, directly obtaining a high-discrimination frequency fingerprint vector reflecting the event type. Event type identification is completed locally at the node, requiring only the transmission of a brief event type code and confidence level, significantly reducing the amount of communication data. Impedance modulation is used to load event information onto the structural acoustic waves, eliminating the need for active transmitting circuits at the node. This allows sensing and communication to share the same acoustic wave carrier, achieving completely passive integrated sensing and communication. This step not only reduces node power consumption and cost but also avoids the problems of electromagnetic interference, signal attenuation, and spectrum resource limitations inherent in traditional wireless communication. Furthermore, the dual mechanism of initial confidence screening at the node end and multi-node fusion at the aggregation end improves event identification accuracy and overall system robustness.

[0115] In one possible implementation of the embodiments of this application, combined with Figure 2 ,like Figure 5 As shown, the above S4 specifically includes the following S401 to S403: S401. The aggregation node receives the modulated acoustic wave, performs bandpass filtering and envelope detection, and recovers the event code.

[0116] Specifically, after the modulated acoustic wave propagates along the acoustic topology waveguide to the sinking node, the sinking node first performs bandpass filtering on the received signal to extract the signal in the frequency band where the topology boundary state is located, and suppresses bulk mode interference and out-of-band noise.

[0117] Let the center frequency of the bandpass filter be the center frequency of the operating frequency band of the topological boundary state. The bandwidth is 2 to 3 times the effective bandwidth of the modulation signal, and the filter transfer function can be expressed as: ;in This represents the filter bandwidth.

[0118] The filtered modulation signal is an amplitude-shifting (APS) signal. The sink node performs envelope detection on it to recover the baseband coded sequence. Envelope detection can be achieved using the Hilbert transform. Let the filtered real signal be x(t), and its analytic signal be: ;in This represents the Hilbert transform, where j represents the imaginary unit. This represents an analytical signal.

[0119] The envelope signal is then: .

[0120] Then, a threshold decision is made on the envelope signal e(t) to recover the binary encoded sequence. The decision threshold can be 0.5 times the mean of the envelope signal.

[0121] Subsequently, the aggregation node performs synchronization and cyclic redundancy check (CRC) on the demodulated data according to the preset frame structure. The frame structure includes a synchronization header, a node ID field, an event type code field, a confidence field, and a check field. The synchronization header can use an 8-bit pseudo-random sequence, and the CRC-8 generator polynomial is [formula missing]. If the verification passes, the event encoding is confirmed to be valid; if the verification fails, the frame is discarded and the system waits for the next reception.

[0122] In this application, bandpass filtering effectively removes interference from non-topological boundary state frequency bands, ensuring a high signal-to-noise ratio for the demodulated signal. Envelope detection achieves amplitude keying signal demodulation with low complexity, eliminating the need for local carrier synchronization and making it suitable for low-power designs of passive nodes. Frame structure and cyclic redundancy check ensure the integrity and reliability of event-coded transmission, providing an accurate data foundation for subsequent fusion identification and localization. The low scattering characteristics of the topological waveguide ensure minimal waveform distortion when the modulated acoustic wave reaches the convergence node, thereby reducing the bit error rate and being crucial for achieving high-reliability transmission in the system.

[0123] In some implementations, before or after the aggregation node receives the modulated acoustic wave, the working status and location of each passive sensor node can be actively inspected when no event occurs, providing node identity information for subsequent multi-node fusion and identification. The specific operation process is as follows: First, the convergence node periodically transmits interrogation pulses into the acoustic topology waveguide. These interrogation pulses can be broadband pulses or sweep pulses, and their spectrum covers the inherent resonant frequency range of the piezoelectric resonators in each passive sensing node.

[0124] Let the spectrum of the query pulse be... Its frequency range covers ,in and These are the minimum and maximum values ​​of the natural resonant frequencies of all node piezoelectric resonators, respectively.

[0125] Secondly, when the acoustic pulse passes through the i-th passive sensing node, the piezoelectric resonator in the node is at its natural resonant frequency. Resonance occurs at the point, absorbing the sound wave pulse and... The corresponding frequency component energy causes the transmitted sound wave to... The amplitude decreases in the vicinity, creating a dip in the transmission spectrum. Let... The quality factor of the piezoelectric resonator at the i-th node is Then the transmission coefficient T(f) is in The vicinity can be approximated as: Where f is the frequency, The absorption coefficient, which is related to the material and structure of the node resonator, ranges from 0 to 1. This formula indicates that the transmission coefficient... The minimum value is reached at the point. This creates a distinct spectral dip, the center frequency of which is the natural resonant frequency of the node. .

[0126] Then, the convergence node receives the transmitted sound waves and analyzes their spectrum. .because Given a known wideband flat spectrum, the location of the depression in the transmission spectrum is primarily determined by T(f). Therefore, the sink node detects... The center frequency of the depression is used to identify the node. Detection methods can include peak search or thresholding: for example, setting an amplitude threshold of 50% of the average value of the spectrum, and determining a depression when the amplitude at a certain frequency is lower than this threshold; or using local minima search to find significant depression points in the spectrum.

[0127] Finally, based on the pre-stored mapping relationship between frequency and node ID, the detected center frequency of the depression is determined. The nodes are identified by matching their frequencies against those in the mapping table. If the frequency of a node disappears or shifts, it indicates that the node may have failed, detached, or been damaged. Through this passive identification process, the aggregation node can confirm the working status and location of each node even when no event occurs, providing reliable node identity information for subsequent event fusion and identification, thus improving the system's maintainability and early warning accuracy.

[0128] It should be noted that this step can be performed periodically to check whether the nodes are online and whether the topological waveguide is intact. If the depression corresponding to a node disappears or the frequency shifts, it indicates that the node may have failed or been damaged.

[0129] S402: Integrate reports from multiple passive sensor nodes and use Bayesian inference for event identification.

[0130] Specifically, the aggregation node receives event codes reported by multiple passive sensor nodes. Each node reports an event type code, node ID, and confidence level. The aggregation node uses Bayesian inference to fuse the reports from multiple nodes and calculates the posterior probability of each event type.

[0131] Let the possible event types be ( The observation of the j-th node is Then the event The posterior probability is: ;in This is the prior probability, which can be obtained from historical event statistics; the default is a uniform distribution. ; Let be the likelihood function, representing the event type as The j-th node outputs the observation. The probability. This likelihood can be obtained through an offline confusion matrix and node confidence mapping. For example, the event type code reported by the j-th node is Confidence level is Then when i=m, ;when hour, .

[0132] It should be noted that the observation This refers to the identification result information reported by the j-th passive sensing node to the aggregation node, specifically including the event type code, confidence level, and node ID.

[0133] When the maximum posterior probability When the threshold is exceeded, the event is confirmed and the corresponding event type is output; otherwise, the event is marked as pending and monitoring continues or remote verification is performed. For example, if event type K=5, and all three nodes report "mechanical cutting" with confidence levels of 0.85, 0.90, and 0.80 respectively, then the posterior probability... The calculated value is approximately 0.97. If the preset threshold is 0.9, then the event is confirmed to have occurred.

[0134] In this application, multi-node fusion effectively reduces the impact of misjudgments by a single node on the overall result, improving the accuracy of event recognition. The Bayesian framework can naturally fuse confidence information and prior knowledge from different nodes, and its low computational complexity makes it suitable for real-time processing of converged nodes. By setting a posterior probability threshold, a balance can be achieved between false alarm rate and false negative rate, providing a reliable quantitative basis for subsequent hierarchical early warning, which is a key step in achieving high-accuracy early warning in the system.

[0135] S403. Utilize the time difference of sound wave arrival to locate events and output graded early warning signals.

[0136] Specifically, the aggregation node uses the node ID and receiving timestamp reported by each passive sensor node to calculate the arrival time difference between any two nodes. .

[0137] Let the event location be The coordinates of the i-th and j-th nodes along the length of the utility tunnel are respectively , The topological waveguide group velocity is Then the arrival time difference satisfies: ; Establish a system of equations for the arrival time difference for all pairwise node combinations, and then use the least squares method or Chan's algorithm to solve for the event location. Taking the least squares method as an example, after linearizing the equation, we obtain the following about... Solve the overdetermined system of equations to minimize the sum of squared residuals. This allows us to obtain the least squares estimate of the event location.

[0138] Finally, the aggregation node categorizes the warning signal into three levels based on the fused maximum a posteriori probability. For example, when the maximum a posteriori probability is between the first threshold of 0.6 and the second threshold of 0.8, it is marked as Level 1, indicating that continuous monitoring of the event dynamics is required, and the system automatically increases the sampling frequency to continuously monitor the event location. When the maximum a posteriori probability is between the second threshold of 0.8 and the third threshold of 0.95, it is marked as Level 2, indicating that remote verification and confirmation are required, and maintenance personnel are notified to retrieve sensor data and historical records near the event location to remotely confirm whether the event has actually occurred. When the maximum a posteriori probability exceeds the third threshold of 0.95, it is marked as Level 3, indicating that an immediate alarm and emergency response are required, triggering audible and visual alarms and emergency broadcasts, and pushing the event type, confidence level, and event location to the monitoring center. The first, second, and third thresholds can be calibrated based on historical false alarm and false negative rates and adaptively adjusted during operation.

[0139] In this application, leveraging the constant velocity and fixed propagation path of a topological waveguide group, time-of-arrival (TOA) positioning eliminates the need for global clock synchronization, requiring only relative timestamps between nodes, thus reducing system complexity. By combining multi-node fusion event recognition results, a tiered early warning signal is output, including event type, confidence level, and event location. This provides maintenance personnel with clear and actionable guidelines, reducing false alarms and improving the intelligence level and emergency response efficiency of the underground utility tunnel security system.

[0140] In another implementation, the tiered early warning signal not only classifies levels based on the maximum a posteriori probability but can also be dynamically adjusted based on the sensitivity of the event's location. For example, for events located in high-risk areas such as gas chambers or high-voltage cable chambers, the warning level can be increased even if the a posteriori probability is slightly below the third threshold; for events located in general drainage chambers, the warning level can be appropriately decreased. Furthermore, the system can automatically adjust the thresholds corresponding to each level based on historical event records and maintenance feedback, achieving adaptive early warning. This approach makes the early warning strategy more aligned with the actual maintenance needs of the utility tunnel, avoiding missed alarms in important areas or frequent false alarms in unimportant areas.

[0141] In some implementations, when the sink node detects that the structural acoustic signal strength in the acoustic topology waveguide is lower than a preset reliable reception threshold, or when the voltage of the energy storage capacitor of the passive sensing node is lower than the minimum operating voltage of the node circuit, energy management and gain compensation are required for the passive sensing node and the acoustic topology waveguide. Specifically, this includes: First, the converging node emits low-frequency pump acoustic waves at a set period. The frequency of these low-frequency pump acoustic waves can be selected from 100 to 500 Hz, and the duration is 1 to 5 seconds. The emission period is adaptively adjusted according to the node's energy storage state and the current attenuation coefficient of the topological waveguide. The pump acoustic waves propagate with low loss along the acoustic topological waveguide, providing mechanical excitation for the piezoelectric microbeams or dedicated piezoelectric energy harvesting units of each passive sensing node along the line.

[0142] Secondly, the passive sensing node harvests energy during the arrival of the pump wave. The piezoelectric microbeams in the node generate an AC voltage under the excitation of the pump acoustic wave, which is then rectified by a full-bridge rectifier and boosted by a charge pump before being stored in a storage capacitor. Let the initial voltage of the node's storage capacitor be... The energy collected during the duration of the pump sound wave is The minimum operating voltage of the node circuit is If the energy storage capacitor has a capacity of C, then the voltage of the energy storage capacitor after charging is... satisfy: ; By appropriately selecting the pump acoustic wave power and duration, it can be ensured that the voltage of the node energy storage capacitor recovers to above the minimum operating voltage, enabling the node to operate normally.

[0143] Secondly, a gain unit is incorporated into the acoustic waveguide topology to replenish the energy of the propagating sound wave. This gain unit consists of a piezoelectric element and a feedback amplifier circuit. When the sound wave passes through the gain unit, the piezoelectric element senses the sound wave and generates a voltage. After amplification, this voltage drives the same piezoelectric element to vibrate in phase with the incident wave, achieving coherent amplification. Let the amplitude gain of the sound wave after passing through a single gain unit be G. The amplification factor of the gain unit needs to be adaptively adjusted according to the current amplitude of the sound wave. When the sound wave amplitude is lower than a preset threshold, the amplification factor increases; conversely, it decreases.

[0144] Finally, based on the current waveguide attenuation coefficient The gain factor g of the gain unit is dynamically adjusted. Assume the material attenuation per unit distance of sound wave propagation along the waveguide is... Then the change of the sound wave amplitude A(x) with the propagation distance x satisfies: .

[0145] when At this time, the sound wave amplitude does not decrease with distance or even increases, thus compensating for propagation loss and achieving stable long-distance transmission. The aggregation node adjusts the amplification factor of each gain unit through feedback control based on the deviation between the real-time monitored sound wave signal strength and the desired signal strength, ensuring that g always satisfies... This ensures the reliable operation of the system throughout the entire utility tunnel.

[0146] Based on the above technical solution, S4 performs bandpass filtering, envelope detection, and cyclic redundancy check on the modulated acoustic wave through the convergence node, accurately recovering the event code from the topological acoustic wave and ensuring the reliability of information transmission. By fusing reports from multiple passive sensor nodes through Bayesian inference, it suppresses single-node misjudgments and improves the accuracy of event type identification. Through time-of-arrival positioning combined with a hierarchical early warning strategy, it achieves accurate estimation of event location and hierarchical early warning output. This step fully utilizes the transmission characteristics and multi-node collaborative capabilities of the acoustic topological waveguide, providing high-precision and high-reliability event identification and positioning capabilities for intelligent security in underground utility tunnels while ensuring a low false alarm rate, enhancing the overall performance and practical value of the system in complex environments.

[0147] The working principle of this application is as follows: First, using a one-dimensional Su-Schrieffer-Heeger model, piezoelectric elements arranged along the length of the pipe gallery structure are constructed as an acoustic topological waveguide with a topologically nontrivial phase. By adjusting the intra- and inter-cell coupling stiffness, elastic waves are allowed to propagate in topologically protected boundary states. These boundary states are robust to structural defects such as bends, welds, and bolts, effectively suppressing backscattering and defect scattering, thus enabling low-loss, long-distance transmission of structural acoustic waves within the pipe gallery. When events such as personnel intrusion, mechanical cutting, cable overheating, or gas leakage occur within the pipe gallery, the event source directly excites the pipe gallery structure to generate elastic waves. These elastic waves, as structural acoustic waves, propagate along the acoustic topological waveguide, serving as both the sensed object and the carrier of information transmission.

[0148] Passive sensing nodes, spaced apart on an acoustic topological waveguide, are driven by the energy of structure-acoustic waves excited by events, requiring no external power supply. The piezoelectric microbeam array within each node has multiple resonant frequencies. When a structure-acoustic wave reaches the node, different frequency components excite corresponding microbeam resonances. The microbeam output voltage constitutes a frequency fingerprint vector, reflecting the spectral characteristics of the event-induced acoustic wave. The node matches the frequency fingerprint with a preset event type template or processes it using a lightweight classification model to obtain an event type code and confidence level. Simultaneously, the electrical energy generated by the vibration of the piezoelectric microbeams is rectified, boosted, and stored in an energy storage capacitor to power the node circuitry. The node generates a binary encoding sequence based on the event type code and node identification. By changing the capacitance value of the variable capacitor at the node through an impedance modulation circuit, the node's acoustic impedance is altered, and the amplitude of the structure-acoustic wave passing through the node is modulated. This loads event information onto the structure-acoustic wave, forming a modulated acoustic wave, thus achieving integrated sensing and communication.

[0149] The modulated acoustic wave propagates at low loss along the acoustic topological waveguide to the convergence node. The convergence node performs bandpass filtering on the received signal to extract the topological boundary state frequency band signal, recovers the event type code, node identification, and confidence level through envelope detection, and obtains a valid event code after synchronization and verification. The convergence node uses Bayesian inference to fuse the event types and confidence levels reported by multiple nodes, calculates the posterior probability of each event, and confirms the event occurrence when the maximum posterior probability exceeds a preset threshold. Simultaneously, based on the arrival time difference of the received acoustic waves at each node and the known group velocity of the topological waveguide, an equation is established to solve for the event location. Finally, based on the maximum posterior probability, the warning is divided into different levels, and a graded warning signal containing the event type, confidence level, and event location is output, thereby achieving high accuracy and high reliability intelligent security for underground utility tunnels.

[0150] Although this application has been described herein in conjunction with various embodiments, those skilled in the art, by reviewing the accompanying drawings, disclosure, and appended claims, will understand and implement other variations of the disclosed embodiments in carrying out the claimed application. In the claims, the word "comprising" does not exclude other components or steps, and "a" or "an" does not exclude multiple instances. A single processor or other unit can implement several functions listed in the claims. While different dependent claims may recite certain measures, this does not mean that these measures cannot be combined to produce good results.

[0151] Although this application has been described in conjunction with specific features and embodiments, it is obvious that various modifications and combinations can be made thereto without departing from the spirit and scope of this application. Accordingly, this specification and drawings are merely illustrative descriptions of the application as defined by the appended claims, and are considered to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from the spirit and scope of this application. Thus, if such modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and variations.

Claims

1. A method for intelligent security control of underground utility tunnels, characterized in that, include: Voltage elements are arranged along the length of the tube gallery structure surface to obtain an acoustic topological waveguide. Multiple passive sensing nodes are spaced out on the acoustic topology waveguide, and each node is driven by the energy of structural acoustic waves excited by an event. The structure acoustic wave is decomposed into multiple frequency bands to generate a frequency fingerprint reflecting the event type. The event type and node identity are encoded and modulated onto the structure acoustic wave using an impedance modulation circuit to form a modulated acoustic wave. The modulated sound wave propagates along the acoustic topology waveguide to the convergence node. The convergence node demodulates the signal to obtain the event code, and combines the node identity code and the time difference of arrival of the sound wave to identify and locate the event, and outputs a graded early warning signal containing the event type, confidence level and event location.

2. The intelligent security control method for underground utility tunnels according to claim 1, characterized in that, The specific construction process of the acoustic topological waveguide includes: To obtain the bending propagation characteristics of the utility tunnel structure, a mass-spring equivalent model is established. The wall of the utility tunnel is discretized into multiple mass blocks along the length direction, and adjacent mass blocks are connected by equivalent springs. Adjusting the coupling stiffness between adjacent piezoelectric units to increase the intracellular coupling strength Less than the inter-cell coupling strength The topological boundary states are obtained; wherein, piezoelectric elements are arranged on the wall of the pipe rack or the surface of the metal cable tray at a period of 'a' along the length of the pipe rack, and each cell contains two piezoelectric plates with an intracellular spacing of 1. The intercellular spacing is ,and < By changing and Ratio control and The relative size; Based on the one-dimensional SSH model, the dispersion relation of the topological boundary states is calculated, the topological waveguide group velocity is selected using the dispersion relation, and the frequency band located within the bulk bandgap is taken as the operating frequency band; the bulk bandgap is the band gap between the bulk energy bands of the acoustic topological waveguide, which is generated by a phononic crystal structure formed by periodically arranged piezoelectric units; Within the operating frequency band, structural acoustic waves are excited, causing the acoustic waves to propagate along the waveguide, thus obtaining an acoustic topological waveguide.

3. The intelligent security control method for underground utility tunnels according to claim 1, characterized in that, The excitation methods of the structural acoustic waves include: Direct excitation method of event source: When an event occurs in the utility tunnel, the event source directly acts on the utility tunnel structure to generate vibration and produce structural sound waves; The activation method of active interrogation by the aggregation node: When it is necessary to inspect or confirm the node status, the aggregation node actively transmits an interrogation pulse. The interrogation pulse is a broadband acoustic pulse or a swept-frequency acoustic pulse. The spectrum of the interrogation pulse covers the inherent resonant frequency of the piezoelectric resonator in each passive sensing node.

4. The intelligent security control method for underground utility tunnels according to claim 1, characterized in that, The structural acoustic wave is an elastic wave excited by an event in the pipe gallery structure, including at least one of bending wave, longitudinal wave and transverse wave; when an event occurs in the pipe gallery, the event source acts on the pipe gallery structure to generate vibration, and the vibration propagates along the pipe wall or metal structure in the form of elastic wave, forming the structural acoustic wave.

5. The intelligent security control method for underground utility tunnels according to claim 1, characterized in that, The passive sensing node is a node integrating a piezoelectric microbeam array, energy harvesting and power management circuit, impedance modulation circuit, and identity resonant unit. It is used to sense structural acoustic waves and generate frequency fingerprints, as well as to encode and modulate the event type and node identity onto the continuing propagation of structural acoustic waves. The piezoelectric microbeam array is composed of multiple piezoelectric cantilever beams with different resonant frequencies. Each beam is manufactured using MEMS technology and has a piezoelectric thin film deposited on its surface. The energy harvesting and power management circuit rectifies, boosts, and stores the AC power generated by the acoustic waves exciting the piezoelectric microbeam array in a supercapacitor to power the node circuit.

6. The intelligent security control method for underground utility tunnels according to claim 1, characterized in that, The step of performing multi-band decomposition on the structural acoustic waves to generate a frequency fingerprint reflecting the event type specifically includes: When the sound wave propagates to the node, different frequency components excite piezoelectric microbeams with different resonant frequencies to resonate, thereby increasing the output voltage amplitude of the corresponding piezoelectric microbeam; Collect all voltages output from the piezoelectric microbeam array to construct a frequency fingerprint vector. Where N is the number of piezoelectric microbeams, ∈V, representing the output voltage of the i-th piezoelectric microbeam; The node matches the frequency fingerprint vector with a preset event type template, determines the event type code using a lookup table method or a classification model trained by a machine learning algorithm, and generates a confidence score. The event type template is obtained through offline calibration and includes standard frequency fingerprints for personnel intrusion, mechanical cutting, mechanical drilling, cable overheating, and gas leakage. The event type code is used to identify the event type.

7. The intelligent security control method for underground utility tunnels according to claim 6, characterized in that, The step of using an impedance modulation circuit to modulate the event type and node identity codes onto the structural acoustic wave to form a modulated acoustic wave specifically includes: Generate a binary encoding sequence based on the event type code and node ID; Amplitude keying is used to control the variable capacitor in the impedance modulation circuit to switch between two capacitance values, thereby changing the acoustic impedance at the node and modulating the amplitude of the structural acoustic wave passing through the node; wherein, the capacitance value of the variable capacitor is controlled by the voltage signal corresponding to the frequency fingerprint, and the relationship between the capacitance change and the acoustic impedance change is linear. The modulated sound wave, carrying the binary encoded sequence, continues to propagate along the acoustic topology guide wave, forming the modulated sound wave.

8. The intelligent security control method for underground utility tunnels according to claim 2, characterized in that, The event encoding obtained by the demodulation at the aggregation node specifically includes: The aggregation node performs bandpass filtering on the modulated acoustic wave to extract the signal in the frequency band where the topological boundary state is located, thus obtaining the modulated signal; Envelope detection is performed on the modulated signal to recover the event type code, node ID, and confidence level, thereby obtaining the demodulated data frame; The demodulated data frame is synchronized and cyclic redundancy checked according to a preset frame structure to obtain an event code; the frame structure includes a synchronization header, a node ID field, an event type code field, a confidence field, and a check field.

9. The intelligent security control method for underground utility tunnels according to claim 1, characterized in that, The method of identifying and locating events by combining node identity codes and sound wave arrival time differences specifically includes: Bayesian inference is used to fuse event types and confidence levels sent by multiple nodes to calculate the posterior probability of each event. ,in, For the i-th event, This represents the observation at the j-th node; The maximum posterior probability of each event is determined based on the posterior probability. When the maximum posterior probability exceeds a preset threshold, the event is confirmed to have occurred, and the corresponding event type is output. Obtain the timestamps of the structural acoustic waves received by each node, and calculate the time difference of arrival between any two nodes. ; Based on the known topological waveguide group velocity Establish a system of equations relating the arrival time difference to the event location: ;in, For the location of the event, , These are the coordinate positions of the i-th and j-th nodes along the length of the pipe gallery, respectively, and the coordinate system is a one-dimensional coordinate system with the starting point of the pipe gallery as the zero point; the topological waveguide group velocity represents the propagation speed of sound waves in the topological boundary state, which is obtained by differentiating the dispersion relation; The system of equations is solved using the least squares method or the Chan algorithm to obtain an estimated event location. The estimated event locations from multiple calculations are then filtered to output the final event location.

10. An intelligent security control system for underground utility tunnels, characterized in that, include: Acoustic topology waveguide construction module, passive sensing node module, and convergence node module; The acoustic topology waveguide construction module is used to arrange piezoelectric units along the length direction on the surface of the pipe gallery structure to obtain an acoustic topology waveguide. The passive sensing node modules are spaced apart on the acoustic topology waveguide and are used to drive the structure acoustic wave energy excited by the event to perform multi-band decomposition on the structure acoustic wave, generate a frequency fingerprint reflecting the event type, and use an impedance modulation circuit to modulate the event type and node identity code onto the structure acoustic wave to form a modulated acoustic wave. The aggregation node module is used to receive the modulated sound wave, demodulate it to obtain the event code, and combine the node identity code and the sound wave arrival time difference to identify and locate the event, and output a graded early warning signal containing the event type, confidence level and event location.

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