Environment detection analysis method and system based on simulation micro-motion signal excitation generator

Abstract

The invention relates to the technical field of data processing, and discloses an environment detection analysis method and system based on a simulation micro-motion signal excitation generator. The method comprises the following steps: generating a vertical vibration signal through an integrated structure of a constant-resistance vibration exciter and a special coupling base; performing multi-band signal excitation and station arrangement according to the frequency parameter combination; filtering and normalizing the original signal, and calculating a spatial autocorrelation coefficient; a frequency dispersion curve is extracted through an SPAC method; inverting an underground S-wave velocity structure by using a divergence genetic algorithm; and analyzing the speed abnormal region by adopting an edge detection algorithm, and determining the position and state of the box culvert body. According to the invention, high-precision non-destructive detection of the buried power pipeline box culvert body is realized in an urban complex underground environment.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to an environment detection and analysis method and system based on an analog micro-motion signal excitation generator. Background Art

[0002] The current geophysical exploration in the field of engineering survey mainly relies on the principles and methods such as gravity method, seismic method, electrical method, magnetic method and drilling method. Among them, the micro-vibration method, as a branch of the seismic method, is a passive source geophysical detection technology, which analyzes the wave velocity distribution and structural characteristics of the underground medium by collecting weak vibration signals in the natural environment (such as environmental vibration, traffic load, etc.). The most commonly used algorithms of the traditional micro-vibration method in the field of engineering survey are the spatial autocorrelation method (SPAC method) or the extended spatial autocorrelation method (ESPAC method), followed by the frequency-wavenumber method (FK method). These methods can invert the S-wave velocity structure of the underground medium by analyzing the spatial distribution characteristics of surface vibration, and then identify underground anomalies. In the application of non-destructive detection of urban underground facilities, the seismic method has the characteristics of high resolution and wide applicability, but the traditional active and passive source seismic methods are often limited by the urban environment.

[0003] However, the existing technology has obvious defects in the non-destructive detection of urban buried power pipeline box culverts. First, electromagnetic noise and vibration sources (such as subways and vehicles) in the urban underground environment will significantly reduce the detection accuracy, and the traditional micro-motion method lacks an effective anti-interference mechanism. Secondly, the traditional seismic method is constrained by the urban environment. The active source large-scale equipment has sufficient energy but is not applicable, the artificial hammering energy is insufficient, and the high-order surface waves on the hard ground are strong and easy to trigger. The final resolution affects the final resolution. Thirdly, the economy of the existing detection methods is low, and the deployment cost of artificial sources or high-precision sensors is high, which is difficult to meet the needs of large-scale power pipeline surveys. Finally, the traditional source method may have an impact on the surrounding environment and infrastructure, and does not meet the safety and environmental protection requirements of the city. The various problems including but not limited to the above have seriously restricted the application effect of seismic geophysical methods in the detection of urban underground infrastructure. Summary of the invention

[0004] The present application provides an environmental detection and analysis method and system based on an analog micro-motion signal excitation generator, which is used to achieve high-precision non-destructive detection of buried power pipeline box culverts in complex urban underground environments, especially to overcome the problem of detection accuracy deviation caused by multi-source noise interference in urban environments. By designing a dedicated analog micro-motion signal excitation generator device and combining it with a signal processing algorithm, the signal-to-noise ratio is improved, and accurate positioning, morphological description, and status evaluation of underground power facilities in complex urban environments are achieved, while meeting the detection requirements of non-invasiveness, safety, environmental protection, and economic efficiency.

[0005] In the first aspect, the present application provides an environmental detection and analysis method based on an analog micro-motion signal excitation generator, and the environmental detection and analysis method based on the analog micro-motion signal excitation generator includes: generating a vertical vibration signal through a constant resistance exciter and a dedicated coupling base integrated structure to obtain an analog micro-motion signal excitation source; performing multi-band micro-motion signal excitation and station array arrangement according to the frequency parameter combination of the analog micro-motion signal excitation source to obtain original micro-motion signal data; performing bandpass filtering and time domain normalization preprocessing on the original micro-motion signal data, calculating the power spectrum and cross-power spectrum through fast Fourier transform, and obtaining the spatial autocorrelation coefficient; based on the spatial autocorrelation coefficient, extracting the dispersion curve through the SPAC method, optimizing the fitting accuracy by using the weighted least squares method, and obtaining the phase velocity dispersion curve; using the phase velocity dispersion curve, inverting and calculating the initial underground velocity structure model through a divergence genetic algorithm to generate an underground S-wave velocity structure; based on the underground S-wave velocity structure, using an edge detection algorithm to identify and analyze the S-wave velocity abnormal area, and determine the position parameters and structural state of the buried power pipeline box culvert.

[0006] In a second aspect, the present application provides an environment detection and analysis system based on an analog micro-motion signal excitation generator, the environment detection and analysis system based on an analog micro-motion signal excitation generator comprising:

[0007] A generation module is used to generate a vertical vibration signal through a constant resistance vibration exciter and a dedicated coupling base integrated structure to obtain a simulated micro-motion signal excitation source;

[0008] An excitation module, used to perform multi-band micro-motion signal excitation and station array arrangement according to the frequency parameter combination of the simulated micro-motion signal excitation source, and obtain original micro-motion signal data;

[0009] A processing module, used for performing bandpass filtering and time domain normalization preprocessing on the original micro-motion signal data, calculating the power spectrum and the cross-power spectrum by fast Fourier transform, and obtaining the spatial autocorrelation coefficient;

[0010] An extraction module is used to extract the dispersion curve based on the spatial autocorrelation coefficient by SPAC method, and optimize the fitting accuracy by weighted least square method to obtain the phase velocity dispersion curve;

[0011] An inversion module is used to use the phase velocity dispersion curve to perform inversion calculation on the initial underground velocity structure model through a divergence genetic algorithm to generate an underground S-wave velocity structure;

[0012] The identification module is used to identify and analyze the S wave velocity abnormal area based on the underground S wave velocity structure by using an edge detection algorithm to determine the position parameters and structural status of the buried power pipeline box culvert.

[0013] The third aspect of the present invention provides a computer device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the computer device executes the above-mentioned environmental detection and analysis method based on the simulated micro-motion signal excitation generator.

[0014] A fourth aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the above-mentioned environmental detection and analysis method based on a simulated micro-motion signal excitation generator.

[0015] In the technical solution provided in the present application, vertical vibration signals are generated through the integrated structure of a constant resistance vibrator and a special coupling base, thereby achieving lossless transmission and uniform distribution of vibration energy, effectively avoiding secondary coupling interference of traditional seismic sources, and enabling the analog micro-motion signal excitation source to provide a stable and controllable vibration field, overcoming the limitations of hard surfaces on the propagation of traditional artificial seismic source signals in urban environments; multi-band micro-motion signal excitation and station array arrangement are performed according to the frequency parameter combination of the analog micro-motion signal excitation source, and accurate detection of targets at different depths is achieved by targeted excitation of signals in different frequency bands, thereby improving the system's detection depth adaptability and resolution; the original micro-motion signal data is preprocessed by bandpass filtering and time domain normalization, and the power spectrum and cross-power spectrum are calculated by fast Fourier transform, thereby effectively eliminating electromagnetic noise in the urban environment. and non-target vibration sources, thereby improving data quality and signal-to-noise ratio; based on the spatial autocorrelation coefficient, the SPAC method is used to extract dispersion curves, and the weighted least squares method is used to optimize the fitting accuracy, which significantly enhances the stability and reliability of dispersion curves in a multi-source noise environment, providing high-quality basic data for underground structure inversion; using the phase velocity dispersion curve, the divergence genetic algorithm is used to invert the initial underground velocity structure model, avoiding the problem that traditional inversion methods are prone to fall into local optimality, enhancing the ability to recognize complex stratigraphic structures, and making the reconstruction of underground S-wave velocity structure more accurate; based on the underground S-wave velocity structure, the edge detection algorithm is used to identify and analyze the S-wave velocity abnormal area, and the position parameters and structural status of the buried power pipeline box culvert are accurately determined, meeting the accuracy requirements of urban underground facility management. It is particularly worth emphasizing that in this scheme, the application of divergence genetic algorithm fully considers the characteristics of multi-peak optimization problems. By introducing niche technology and fitness sharing mechanism, the algorithm is prevented from falling into a single solution space, effectively overcoming the limitations of traditional optimization algorithms in complex underground structure inversion; at the same time, the edge detection algorithm processes the S-wave velocity anomaly area, making full use of the sensitivity of the Canny detector to weak edges and its ability to accurately describe the real boundary, so that the geometric characteristics and structural status of the buried power pipeline box culvert can be accurately identified in the complex underground environment of the city. The clever combination of these algorithm features makes this scheme show significant technical advantages in the field of urban underground facility detection, and realizes the comprehensive benefits of non-invasive detection, anti-interference optimization, cost reduction, safety and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] One or more embodiments are exemplarily described by pictures in the corresponding drawings, and these exemplified descriptions do not constitute limitations on the embodiments. Elements with the same reference numerals in the drawings represent similar elements, and unless otherwise stated, the figures in the drawings do not constitute proportional limitations.

[0017] Figure 1A schematic diagram of an embodiment of an environment detection and analysis method based on a simulated micro-motion signal excitation generator in an embodiment of the present application;

[0018] Figure 2 This is a schematic diagram of an embodiment of an environment detection and analysis system based on an analog micro-motion signal excitation generator in an embodiment of the present application;

[0019] Figure 3 This is a schematic structural diagram of an integrated excitation source device according to an embodiment of the present application;

[0020] Figure 4 It is a schematic block diagram of the structure of a computer device in an embodiment of the present invention. DETAILED DESCRIPTION

[0021] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0022] Those skilled in the art can understand that the terms "first", "second" and the like in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor represent the necessary logical order between them. It should also be understood that in the embodiments of the present disclosure, "multiple" can refer to two or more, and "at least one" can refer to one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present disclosure, in the absence of explicit limitation or contrary revelation given in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present disclosure is only a description of the association relationship of the associated objects, indicating that there can be three relationships, for example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present disclosure generally indicates that the associated objects before and after are an "or" relationship. It should also be understood that the description of each embodiment in the present disclosure emphasizes the differences between the embodiments, and the same or similar parts can refer to each other. For the sake of brevity, they will not be repeated one by one.

[0023] At the same time, it should be understood that, for ease of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. The techniques, methods and devices known to ordinary technicians in the relevant fields may not be discussed in detail, but where appropriate, the techniques, methods and devices should be considered part of the specification. It should be noted that similar numbers and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the environment detection and analysis method based on the simulated micro-motion signal excitation generator includes:

[0026] Step S101, generating a vertical vibration signal through a constant resistance vibration exciter and a dedicated coupling base integrated structure to obtain a simulated micro-motion signal excitation source;

[0027] Step S102, performing multi-band micro-motion signal excitation and station array arrangement according to the frequency parameter combination of the simulated micro-motion signal excitation source to obtain original micro-motion signal data;

[0028] Step S103, performing bandpass filtering and time domain normalization preprocessing on the original micro-motion signal data, calculating the power spectrum and cross-power spectrum by fast Fourier transform, and obtaining the spatial autocorrelation coefficient;

[0029] Step S104, based on the spatial autocorrelation coefficient, the dispersion curve is extracted by SPAC method, and the fitting accuracy is optimized by weighted least square method to obtain the phase velocity dispersion curve;

[0030] Step S105, using the phase velocity dispersion curve, the initial underground velocity structure model is inverted and calculated by a divergence genetic algorithm to generate an underground S-wave velocity structure;

[0031] Step S106: Based on the underground S-wave velocity structure, an edge detection algorithm is used to identify and analyze the S-wave velocity abnormal area to determine the position parameters and structural status of the buried power pipeline box culvert.

[0032] It is understandable that the execution subject of the present application may be an environment detection and analysis system based on an analog micro-motion signal excitation generator, or a terminal or a server, which is not specifically limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0033] The vertical vibration signal is generated by forming an integrated excitation source device through a constant resistance vibrator and a special coupling base. The original vibration signal is generated by a constant resistance vibrator with a force constant of 13.8N / A. The operating frequency range of the vibrator is 0-2000Hz, and the maximum force exceeds 500N. The vibration energy generated by the upper vibrator is directly transmitted to the conductive base with a wide surface design (a circular base with a diameter of 190mm). The base is tightly combined with the upper vibrator using an integrated process to avoid the secondary coupling interference caused by the traditional screw and nut connection. The wide surface design of the base ensures that single-point signal transmission is avoided during the vibration process, and uniform vertical conduction of the vibration signal is achieved. For example, when the vibrator works at a zero-order resonant frequency of 24Hz, the wide surface distribution of the base can make the vibration signal form an effective detection area on the ground with a coverage range of about 2-3 times the diameter of the base. Multi-band micro-motion signal excitation and station array arrangement are carried out according to the frequency parameter combination of the simulated micro-motion signal excitation source. In this method, the working frequency range of 10-100 Hz is divided into multiple detection frequency bands (usually 5-10), and a suitable amplitude value is set for each frequency band (controlled between ±1mm and ±5mm). High-sensitivity seismic signal receiving stations are arranged in the detection area to form a circular array topology. For a circular area with a single radius, the circular array corresponding to the SPAC method is used (6-8 receiving points are evenly distributed on the circumference), and for a larger area, the multiple circular array corresponding to the ESPAC method is used. The station spacing design follows the principle that the minimum spacing is not less than 1 / 10 of the target burial depth, and each receiving station achieves high-precision time synchronization with the excitation source device. For example, when detecting a power pipeline box culvert with a burial depth of about 2 meters, the station spacing is set between 0.2-4 meters, and the number of receiving points is not less than 6.

[0034] The raw micromotion signal data is preprocessed by bandpass filtering and time domain normalization, and the power spectrum and cross-power spectrum are calculated by fast Fourier transform. The raw data is detected for anomalies, and the data segments with obvious interference are eliminated. Then the effective micromotion signal is divided into several segments with a time length of 10-20 seconds, and adjacent segments are allowed to overlap by 25%-50%. A bandpass filter (usually 1-80Hz) is applied to the data to eliminate high-frequency noise and very low-frequency drift interference, and then time domain normalization is performed to balance the signal intensity changes in different time periods. Fast Fourier transform is performed on each segment of the processed data, and the spatial autocorrelation function of any two stations in the array is calculated, where the Fourier spectrum represents the performance of the micromotion signal in the frequency domain, and the complex conjugate operation helps to extract the correlation characteristics of the signal. Then all spatial autocorrelation functions with the same distance but different directions between the two stations are averaged in azimuth to obtain the spatial autocorrelation coefficient, which reflects the wave propagation characteristics of the underground medium at different frequencies.

[0035] Based on the spatial autocorrelation coefficient, the SPAC method is used to extract the dispersion curve, and the weighted least squares method is used to optimize the fitting accuracy. The spatial autocorrelation coefficients are grouped and sorted according to the array radius and frequency to form a spatial autocorrelation data matrix, and the low-frequency band maximum value (not less than 0.8) area is identified in the matrix to determine the effective frequency range boundary. The variable conversion is performed through the spatial autocorrelation coefficient and the first-order zero-order Bessel function relationship to establish the frequency-volume correspondence relationship. The volume sampling point set is constructed by inversely calculating the volume value of different frequency points, and then the weighted least squares method is used for curve fitting to reduce the influence of local noise interference, generate a frequency-phase velocity fitting curve, extract the phase velocity value corresponding to each frequency, and obtain the phase velocity dispersion curve. Using the phase velocity dispersion curve, the initial underground velocity structure model is inverted and calculated by the divergence genetic algorithm. The hierarchical structure parameters (usually 3-6 layers) are constructed based on the geological background information of the survey area to form the initial underground velocity structure model. The phase velocity dispersion curve is used as the target feature input, and the S-wave velocity (usually between 100-2000m / s) and layer thickness search range are set to establish the inversion parameter space. The theoretical dispersion curve is forward calculated for the model samples in the inversion parameter space to generate the model dispersion feature set, and the fitness value of each sample is quantitatively evaluated by comparing the fit with the measured phase velocity dispersion curve. The divergent genetic algorithm optimizes and iterates the fitness value through selection, crossover, mutation and other operations, selects the optimal parameter combination, constructs the velocity structure profile, and finally generates the underground S-wave velocity structure.

[0036] Based on the underground S-wave velocity structure, the edge detection algorithm is used to identify and analyze the S-wave velocity anomaly area. The underground S-wave velocity structure is numerically filtered to extract the velocity gradient distribution map, and the wave velocity anomaly threshold is calibrated (usually the S-wave velocity of the buried power pipeline box culvert is 30%-60% higher than the surrounding medium), and the S-wave velocity anomaly area is divided. The edge detection algorithm is used to extract the velocity change boundary line to form the contour feature of the box culvert, from which the plane position coordinates and burial depth parameters are quantified to construct the spatial positioning information of the box culvert. By comparing the medium velocity difference ratio with the dispersion characteristic pattern, the position parameters (accuracy ±0.3m), burial depth (accuracy ±0.2m), geometric dimensions (accuracy ±5%) and structural status of the buried power pipeline box culvert are determined.

[0037] In a case study of underground power facility detection in a certain city, this method was used to detect a power pipeline box culvert with a burial depth of about 1.5 meters. The exciter was set to work in the range of 20-60Hz with an amplitude of ±3mm. Eight receiving stations were arranged in the detection area to form a circular array with a radius of 2 meters. After bandpass filtering and FFT processing, the spatial autocorrelation coefficient was obtained. The dispersion curve extracted by the SPAC method showed that the phase velocity had obvious variation characteristics in the range of 25-35Hz. The inverted S-wave velocity structure showed that there was an obvious high-speed anomaly area at the target position (the velocity was about 800m / s, while the surrounding soil was about 500m / s). The edge detection algorithm determined the plane position of the box culvert, the burial depth of 1.52 meters, and the geometric dimensions of about 2.1m×1.3m, verifying the effectiveness of this method in the detection of underground power facilities in cities.

[0038] In the embodiment of the present application, vertical vibration signals are generated by an integrated structure of a constant resistance vibrator and a dedicated coupling base, thereby achieving lossless transmission and uniform distribution of vibration energy, effectively avoiding secondary coupling interference of traditional seismic sources, and enabling the analog micro-motion signal excitation source to provide a stable and controllable vibration field, overcoming the limitations of hard surfaces on the propagation of traditional artificial seismic source signals in urban environments; multi-band micro-motion signal excitation and station array arrangement are performed according to the frequency parameter combination of the analog micro-motion signal excitation source, and accurate detection of targets at different depths is achieved by targeted excitation of signals in different frequency bands, thereby improving the system's detection depth adaptability and resolution; the original micro-motion signal data is band-pass filtered and time-domain normalized pre-processed, and the power spectrum and cross-power spectrum are calculated by fast Fourier transform, which effectively eliminates electromagnetic noise and non- The target vibration source improves the data quality and signal-to-noise ratio; based on the spatial autocorrelation coefficient, the dispersion curve is extracted by the SPAC method, and the weighted least squares method is used to optimize the fitting accuracy, which significantly enhances the stability and reliability of the dispersion curve in a multi-source noise environment, providing high-quality basic data for underground structure inversion; using the phase velocity dispersion curve, the initial underground velocity structure model is inverted and calculated by the divergence genetic algorithm, avoiding the problem that the traditional inversion method is prone to fall into the local optimum, enhancing the ability to recognize complex stratigraphic structures, and making the reconstruction of the underground S-wave velocity structure more accurate; based on the underground S-wave velocity structure, the edge detection algorithm is used to identify and analyze the S-wave velocity abnormal area, and the position parameters and structural status of the buried power pipeline box culvert are accurately determined, meeting the accuracy requirements of urban underground facility management. It is particularly worth emphasizing that in this scheme, the application of divergence genetic algorithm fully considers the characteristics of multi-peak optimization problems. By introducing niche technology and fitness sharing mechanism, the algorithm is prevented from falling into a single solution space, effectively overcoming the limitations of traditional optimization algorithms in complex underground structure inversion; at the same time, the edge detection algorithm processes the S-wave velocity anomaly area, making full use of the sensitivity of the Canny detector to weak edges and its ability to accurately describe the real boundary, so that the geometric characteristics and structural status of the buried power pipeline box culvert can be accurately identified in the complex underground environment of the city. The clever combination of these algorithm features makes this scheme show significant technical advantages in the field of urban underground facility detection, and realizes the comprehensive benefits of non-invasive detection, anti-interference optimization, cost reduction, safety and environmental protection.

[0039] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0040] (1) Generate an original vibration signal according to the working frequency range through a constant resistance vibration exciter with a preset force constant to obtain initial excitation energy;

[0041] (2) The initial excitation energy is transmitted to the wide-surface conductive base to eliminate secondary coupling interference and form a uniform vibration conductive medium;

[0042] (3) applying a vertical constraint force to the conductive base to control the vibration amplitude within a predetermined range and generate a directional vertical vibration transmission channel;

[0043] (4) According to the directional vertical vibration transmission channel, the ratio of the upper end exciter mover mass to the total mass is adjusted to construct a stable excitation waveform;

[0044] (5) Through the direct coupling of the stable excitation waveform and the conductive base, a vertical vibration field with a wide distribution is formed on the ground, generating a simulated micro-motion signal excitation source.

[0045] Specifically, vertical vibration signals are generated through the integrated structure of a constant resistance vibrator and a special coupling base. The constant resistance vibrator with a preset force constant generates an original vibration signal according to the working frequency range to obtain the initial excitation energy. The constant resistance vibrator is a special vibration generating device whose force output is linearly proportional to the current input. The force constant of the vibrator is set to 13.8N / A, that is, 13.8 Newtons of force are generated per ampere of current. The working frequency range is 0-2000Hz, the maximum amplitude can reach ±5mm, and the zero-order resonance frequency is 24±2Hz. By controlling the input current size and frequency, the vibrator generates vibration signals of different intensities and frequencies to form the initial excitation energy for underground detection. Transmitting the initial excitation energy to the conductive base with a wide surface design is a key step to ensure signal quality. The conductive base adopts a special design, a circular structure with a diameter of 190mm, a center diameter of 164mm, and a fixing hole on the edge. It is connected to the upper end vibrator in an integrated manner, completely avoiding the interference that may be introduced by secondary coupling connection methods such as traditional screws and nuts. When the excitation energy is transferred from the vibrator to the base, the integrated structure ensures the integrity of the energy transfer, eliminates the secondary vibration and signal distortion that may be generated at the connection point, and forms a uniform vibration transmission medium. This design enables the vibration signal to be transmitted losslessly in the original waveform, providing a pure source signal for subsequent detection. Applying vertical constraint force to the conductive base is an important measure to control the directionality of vibration. By precisely designing the weight distribution and geometric shape of the base, the base mainly produces vertical vibration under the action of the vibrator, while suppressing the horizontal vibration component. The vibration amplitude is strictly controlled within a predetermined range, usually between ±1mm and ±5mm. This control is achieved by adjusting the input current to ensure that the vibration energy is sufficient to penetrate the surface while not being too strong to cause nonlinear effects. The controlled vertical vibration forms a directional vertical vibration transmission channel, so that the vibration energy is mainly transmitted into the ground in the vertical direction, improving energy utilization efficiency and signal directivity.

[0046] According to the directional vertical vibration transmission channel, the ratio of the upper end exciter mover mass to the total mass is further adjusted to construct a stable excitation waveform. The mover mass refers to the mass of the part of the exciter that actually moves and generates vibration, which is 34.8kg, while the total mass of the entire exciter is 37.6kg. The ratio of the two is about 0.925, which has an important influence on the vibration stability. By precisely controlling the mover motion parameters, including acceleration, velocity and displacement, a stable vibration waveform is generated during operation. The adjustment of the mover mass takes into account the ground coupling characteristics, and fine adjustments can be made for different surface types (such as hard pavement, soft soil surface, etc.) to ensure the best vibration transmission efficiency. The direct coupling of the stable excitation waveform with the conductive base is the last link to form an effective detection source. A good contact interface is formed between the base and the ground, and the vibration energy is transmitted to the underground through this interface. Because the base adopts a wide surface design, it avoids the energy concentration and local deformation that may be caused by single-point contact, so that the vibration energy is evenly distributed over a larger area. This distribution characteristic forms a vertical vibration field with a wide distribution, expands the effective detection range, and reduces the impact of near-surface inhomogeneities on vibration propagation, ultimately producing a simulated micro-motion signal excitation source suitable for underground detection.

[0047] For example, when detecting the box culvert of an underground power pipeline in a city, the working state of the exciter is set as follows: select an operating frequency of 30Hz, control the input current within 20A, generate a vibration force of about 270N, and control the amplitude within the range of ±3mm. A thin rubber pad can be laid between the base and the ground to improve the contact conditions and reduce high-frequency energy loss. The exciter works continuously for about 60 seconds, generating stable simulated micro-motion signals, which propagate downward through the ground and are recorded by the receiving station after interacting with the box culvert of the underground power pipeline. Compared with the traditional hammer source, this simulated micro-motion signal excitation source provides more stable and controllable vibration energy, a wider frequency range, and more uniform energy distribution, which is particularly suitable for non-destructive detection of buried facilities in complex urban environments. The entire excitation process does not require damage to the ground, is easy to operate, and can generate high-quality detection signal sources, providing a reliable raw data basis for subsequent multi-band micro-motion signal processing and analysis.

[0048] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0049] (1) According to the working frequency range of the simulated micro-motion signal excitation source, multiple detection frequency bands are divided to generate a frequency parameter scanning sequence;

[0050] (2) According to the characteristics of each frequency band in the frequency parameter scanning sequence, the amplitude of the excitation signal is dynamically adjusted to form a frequency band excitation scheme;

[0051] (3) Based on the frequency band excitation scheme, high-sensitivity seismic signal receiving stations are arranged in the detection area to construct a ring array topology structure;

[0052] (4) Setting a synchronization time reference for each receiving station in the ring array topology and establishing a signal acquisition time window;

[0053] (5) Through the signal acquisition time window, the excitation signals of each frequency band are recorded with high precision, and the environmental background noise is collected as a comparison benchmark;

[0054] (6) Integrate the high-precision records with the comparison benchmark to generate raw micro-motion signal data including time stamps.

[0055] Specifically, after forming the simulated micro-motion signal excitation source, the environmental detection and analysis method based on the simulated micro-motion signal excitation generator needs to perform multi-band micro-motion signal excitation and station array arrangement to obtain effective original micro-motion signal data. According to the working frequency range of the simulated micro-motion signal excitation source, multiple detection frequency bands are divided to generate a frequency parameter scanning sequence. The specific operation is to divide the full-band working range of the exciter 0-2000Hz into different frequency bands according to the characteristics of the detection target. For the detection of buried power pipeline box culverts, the 10-100Hz range is usually divided into 5-10 frequency bands, and each frequency band is 5-10Hz apart. The frequency division needs to consider the size and burial depth of the target body, and is determined according to the relationship between the wavelength and the target size. For example, for the power box culvert with a burial depth of 1-3 meters, the 15-60Hz frequency band is mainly selected for fine division, forming a continuous frequency parameter scanning sequence such as 15-20Hz, 20-25Hz, 25-30Hz, etc., and each sequence corresponds to a set of excitation parameters.

[0056] According to the characteristics of each frequency band in the frequency parameter scanning sequence, the amplitude of the excitation signal is dynamically adjusted to form a frequency band excitation scheme. The low-frequency band (such as 15-30Hz) has strong signal penetration but slow attenuation, and requires a larger amplitude (±3mm to ±5mm) to provide sufficient energy; while the high-frequency band (such as 40-60Hz) has weak penetration but high resolution, and is suitable for a smaller amplitude (±1mm to ±3mm) to avoid the impact of excessive vibration on the environment. The excitation duration of each frequency band is set to 30-60 seconds to ensure signal stability. By controlling the input current and waveform of the exciter, the actual output characteristics of each frequency band are accurately adjusted, and finally a complete set of frequency band excitation schemes is formed, including the frequency range, amplitude, duration and excitation sequence of each frequency band.

[0057] Based on the frequency band excitation scheme, high-sensitivity seismic signal receiving stations are arranged in the detection area to construct a ring array topology. The receiving station is the core equipment for collecting seismic wave signals. It usually uses a high-sensitivity three-component seismometer or accelerometer with a sensitivity of up to 1000V / m / s and a frequency response range of 1-200Hz. The station layout follows the specific topological structure requirements. For SPAC method processing, a ring array topology is adopted. Usually 6-8 receiving points are evenly distributed on a circle with a radius of r, and a reference point is set at the center of the circle. The station spacing design principle is that the minimum spacing is not less than 1 / 10 of the target burial depth, and the maximum spacing is not more than 2 times the target burial depth. For larger area detection, multiple ring arrays or cross arrays can be used. Each station uses a tripod bracket for horizontal correction to ensure good coupling between the sensor and the ground.

[0058] Set a synchronization time reference for each receiving station in the ring array topology and establish a signal acquisition time window. Time synchronization is the key to multi-station data analysis. GPS time synchronization technology is usually used to ensure that the time synchronization error of each station is less than 1 millisecond. Establish a unified signal acquisition time window, including the background noise acquisition window before excitation (usually 10-30 seconds), the signal acquisition window during excitation (consistent with the frequency band excitation duration) and the signal attenuation window after excitation (usually 10-20 seconds). The sampling rate is set to above 1000Hz to ensure that high-frequency signals are not distorted. The length of the time window is adjusted according to the detection depth. For targets buried within 5 meters, the total window length is usually set to 100-200 seconds.

[0059] Through the signal acquisition time window, the excitation signal of each frequency band is recorded with high precision, and the environmental background noise is collected as a comparison benchmark. High-precision recording includes recording the complete waveform signal received by each receiving point during the excitation process of each frequency band, and recording the environmental background noise as a comparison benchmark. Background noise collection is carried out before excitation to record the signal characteristics of interference sources such as natural micro-motion and traffic vibration in the environment. During the data acquisition process, the signal quality is monitored in real time, including parameters such as signal-to-noise ratio and waveform integrity. For frequency bands with poor signal quality, repeated acquisition is performed. Each recording point simultaneously collects vibration signals of three components (vertical, north-south, and east-west) for comprehensive analysis.

[0060] The high-precision records are integrated with the comparison benchmark to generate the original micro-motion signal data with timestamps. The integration process pre-checks the recorded data of each station and removes the records with obvious abnormalities. Then the valid records of all stations in the same frequency band are time-aligned to establish a unified time reference system. The aligned data is added with precise timestamps, including year, month, day, hour, minute, second and millisecond information. The background noise data is used as a comparison benchmark and packaged together with the actual excitation signal to form a complete original micro-motion signal data packet. The data packet is organized and identified by frequency band and station number to facilitate subsequent analysis and processing. The final generated original micro-motion signal data contains excitation parameter information, geographic location information, time information and complete waveform data.

[0061] In a power pipeline culvert detection project, the actual operation is as follows: for a target with a buried depth of about 2 meters, the 15-60Hz frequency range is divided into 9 frequency bands, each with 5Hz. The amplitude is set to ±4mm for the low frequency band 15-30Hz, and the amplitude is set to ±2mm for the high frequency band 45-60Hz. Eight receiving stations are arranged in the target area to form a circular array with a radius of 3 meters, and the station spacing is about 2.3 meters. All stations use GPS time synchronization, and the sampling rate is set to 2000Hz. Each frequency band is excited for 40 seconds, and the background noise and attenuation signal are recorded for 20 seconds before and after. The data collected in this way successfully captured the response characteristics of the underground structure to the micro-motion signal in different frequency bands, providing high-quality original data for subsequent spatial autocorrelation analysis.

[0062] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0063] (1) Identify and mark abnormal sections with obvious interference in the original micro-motion signal data to form a valid data judgment threshold;

[0064] (2) Eliminate abnormal data segments according to the effective data judgment threshold, process the remaining data in segments, and construct a micro-motion signal slice set;

[0065] (3) applying a bandpass filter to the micromotion signal slice set to remove high-frequency noise and low-frequency drift, and generate frequency band purified micromotion data;

[0066] (4) performing time domain normalization processing on the frequency band purified micromotion data to balance the signal intensity variation and obtain a normalized micromotion sequence;

[0067] (5) Apply fast Fourier transform to the normalized micro-motion sequence and calculate the power spectrum and cross-power spectrum matrix between stations;

[0068] (6) Based on the power spectrum and cross-power spectrum matrix, the spatial autocorrelation function of any two stations is calculated, and the spatial autocorrelation coefficient is obtained through azimuth averaging.

[0069] Specifically, after obtaining the original micro-motion signal data, systematic data processing is performed and the spatial autocorrelation coefficient is calculated. The abnormal sections with obvious interference in the original micro-motion signal data are identified and marked to form a valid data judgment threshold. The abnormal section identification adopts the short-time window energy analysis method, which divides the original signal into short-time windows of 1-2 seconds, calculates the signal energy value in each window, and sets the abnormal judgment threshold by comparing it with the background noise baseline energy. Short-time window energy analysis not only considers the signal amplitude, but also calculates the spectral energy distribution of the signal, with special attention to the energy concentration in the 15-60Hz detection frequency band. For the detection of buried power pipeline box culverts, signal segments with energy mutations exceeding 5 times the background noise are usually marked as abnormal sections. At the same time, combined with the spectral feature judgment, such as when sharp power frequency interference (50Hz) or pulse interference signals appear in the spectrum, they are also marked as abnormal. This dual judgment method forms a valid data judgment threshold, providing a clear standard for subsequent data screening.

[0070] According to the effective data judgment threshold, the abnormal data segments are eliminated, and the remaining data is processed in segments to construct a micro-motion signal slice set. The elimination process removes the data segments marked as abnormal, and then checks the length of continuous effective data to ensure that each segment has sufficient length (usually 10-20 seconds) for subsequent spectrum analysis. The micro-motion signal is divided into several segments of the same time length, and each segment of data is allowed to overlap partially. The actual operation is to segment the remaining effective data according to a fixed length, and 25%-50% overlap is allowed between adjacent segments to improve data utilization and spectrum estimation stability. The segment length is determined according to the lowest frequency of the target frequency band to ensure that it contains at least 3-5 complete cycles. For example, for a signal with a minimum frequency of 10Hz, the segment length needs to be at least 0.3-0.5 seconds. All segmented effective data constitute a micro-motion signal slice set, each slice contains the same number of sampling points, and keeps the original sampling rate unchanged, which is convenient for subsequent unified processing.

[0071] A bandpass filter is applied to the micromotion signal slice set to remove high-frequency noise and low-frequency drift, and generate band-purified micromotion data. The bandpass filter design uses a Butterworth filter, and usually selects 4-8 orders to ensure good flatness characteristics in the passband and an appropriate attenuation slope at the cutoff frequency. For the detection of buried power pipeline box culverts, the filter passband is usually set to 1-80Hz. The low cutoff frequency of 1Hz is used to remove the DC component and low-frequency drift in the data, and the high cutoff frequency of 80Hz is used to suppress high-frequency noise. The filtering process uses zero-phase filtering technology to eliminate phase distortion through forward and reverse filtering to ensure that the phase characteristics of the filtered signal remain unchanged, which is crucial for subsequent correlation analysis. The filtered data is called band-purified micromotion data, and its signal energy is mainly concentrated in the target frequency band, which effectively improves the signal-to-noise ratio. The band-purified micromotion data is normalized in the time domain to balance the signal intensity changes and obtain a normalized micromotion sequence. Time domain normalization calculates the root mean square value (RMS value) of each slice data, and then uses this as a benchmark for amplitude standardization so that each slice has a uniform energy level. This process eliminates the influence of changes in signal strength at different time periods. The normalized micro-motion sequence maintains the original phase characteristics and relative spectrum distribution, but the amplitude information is standardized, which facilitates comparative analysis of data from different time periods and different stations.

[0072] Fast Fourier transform is applied to the normalized micro-motion sequence to calculate the power spectrum and cross-power spectrum matrix between stations. Fast Fourier transform is performed on each segment of data to calculate the power spectrum and cross-power spectrum. The spatial autocorrelation function C of any two stations in the array is calculated according to the following formula: ij , calculated as follows:

[0073]

[0074] Among them, S i and S j The Fourier spectra of the micro-motion signals of the i-th station and the j-th station are respectively represented by complex conjugate. This formula calculates the normalized cross spectrum, that is, the correlation between the signals of the two stations. Autopower spectrum Represents the energy distribution of the signal itself, the cross power spectrum Represents the mutual relationship between two signals. By calculating the spatial autocorrelation function of all pairs of stations in the array, a complete power spectrum and cross-power spectrum matrix are constructed.

[0075] Based on the power spectrum and cross-power spectrum matrix, the spatial autocorrelation function of any two stations is calculated, and the spatial autocorrelation coefficient is obtained through azimuth averaging. For each segment of data, the spatial autocorrelation coefficient is calculated as:

[0076]

[0077] Where ω is the angular frequency, r represents the horizontal distance between the two stations, that is, the array radius. θ is the azimuth angle, and Real means taking the real part of the complex number. This process is to average the spatial autocorrelation functions of the two stations with the same distance but different directions in the azimuth direction to obtain the spatial autocorrelation coefficient. According to the theoretical derivation of the spatial autocorrelation method, the spatial autocorrelation coefficient satisfies the following formula:

[0078] ρ(ω,r)=J 0 (rk) = J 0 (2πfr / V r (f))

[0079] Where f is the frequency, k is the wave number, V r (f) is the phase velocity of Rayleigh wave, J 0 is the first kind of zero-order Bessel function. This theoretical relationship is the basis for the SPAC method to extract dispersion curves, indicating that there is a clear mathematical connection between the spatial autocorrelation coefficient and the Rayleigh wave phase velocity.

[0080] In a buried power pipeline culvert detection project, the micro-motion signal preprocessing and spatial autocorrelation coefficient calculation examples are as follows: the original micro-motion signal data sampling rate is 2000Hz, there are 8 stations, and the recording time is 120 seconds. Through short-time window energy analysis, three abnormal sections (located at 32-37 seconds, 59-63 seconds and 98-102 seconds, respectively) are identified. These anomalies are mainly caused by nearby construction vibration and vehicle passing. After removing the abnormal sections, the remaining data are segmented into 15-second lengths and 50% overlap rates, and 14 effective micro-motion signal slices are constructed. A 4th-order Butterworth bandpass filter (passband 5-70Hz) is applied to process each slice to remove low-frequency drift and high-frequency noise. The filtered data is normalized in the time domain so that the RMS value of each slice is unified to 1. The spectral characteristics of each slice are analyzed by 4096-point FFT, and the power spectrum and cross-power spectrum matrix between stations are calculated. For a circular array with a radius of 3 meters, there are 12 pairs of stations with equal spacing, and the spatial autocorrelation coefficient is calculated by azimuth averaging. The results show that in the 25-35Hz frequency band, the spatial autocorrelation coefficient exhibits obvious Bessel function characteristics, indicating that this frequency band contains rich underground structure information.

[0081] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0082] (1) The spatial autocorrelation coefficients are grouped and sorted according to the array radius and frequency to form a spatial autocorrelation data matrix;

[0083] (2) Identify the low-frequency maximum area in the spatial autocorrelation data matrix and determine the effective frequency range boundary;

[0084] (3) Perform variable conversion on the spatial autocorrelation coefficient within the effective frequency range boundary and the relationship between the first-kind zero-order Bessel function to establish the frequency-variable correspondence;

[0085] (4) Through the frequency-quantity correspondence, the quantity value is inversely calculated for different frequency points to construct a quantity sampling point set;

[0086] (5) For the set of sample points, the weighted least square method is used to perform curve fitting to generate a frequency-phase velocity fitting curve;

[0087] (6) Extract the phase velocity value corresponding to each frequency from the frequency-phase velocity fitting curve to generate the phase velocity dispersion curve.

[0088] Specifically, after obtaining the spatial autocorrelation coefficient, it is necessary to extract the dispersion curve through the SPAC method, use the weighted least squares method to optimize the fitting accuracy, and obtain the phase velocity dispersion curve. The spatial autocorrelation coefficients are grouped and sorted according to the array radius and frequency to form a spatial autocorrelation data matrix. The specific operation is to arrange the calculated spatial autocorrelation coefficients in two dimensions according to different array radius values ​​(fixed values ​​for a single radius circular array and multiple values ​​for multiple circular arrays) and frequency points to construct a spatial autocorrelation data matrix. The rows of the matrix represent different frequency points, the columns represent different array radii, and each element value in the matrix is ​​the spatial autocorrelation coefficient under the corresponding frequency and radius conditions. This matrix organization method facilitates the subsequent systematic analysis of the spatial autocorrelation characteristics under different frequencies and different radii, especially when using the ESPAC method for multi-radius arrays. This organizational form is particularly important.

[0089] Identify the low-frequency maximum area in the spatial autocorrelation data matrix and determine the effective frequency range boundary. The maximum value of the spatial autocorrelation coefficient in the low-frequency band (the part with a frequency lower than the first zero value) should not be less than 0.8, and identify the corresponding area in the matrix. The low-frequency maximum area usually appears in the frequency range before the first zero point of the Bessel function. By scanning the coefficient values ​​of each row in the matrix (corresponding to different frequencies), find the continuous frequency segment with a coefficient value above 0.8, and determine its upper limit frequency as the effective frequency range boundary. For the detection of buried power pipeline box culverts, the effective frequency range is usually between 10-60Hz, and the specific boundary depends on the actual detection conditions and signal quality. The purpose of determining the effective frequency range boundary is to screen out the frequency band with high signal-to-noise ratio and satisfy the theoretical relationship, so as to avoid introducing noise interference in subsequent analysis. Perform variable conversion on the spatial autocorrelation coefficient within the effective frequency range boundary and the relationship between the first-order zero-order Bessel function, and establish the frequency-quantity correspondence. There is a clear relationship between the spatial autocorrelation coefficient and the first-order zero-order Bessel function. The key to variable conversion is to determine the variable of the Bessel function, where the variable is the independent variable of the Bessel function, which is related to the frequency, array radius and phase velocity. Through this conversion, the corresponding relationship between frequency and variable is established, which provides a theoretical basis for the subsequent inverse calculation of phase velocity. The variable conversion process takes into account the matching relationship between the array geometric dimensions and the wavelength, ensuring that the variable value can accurately reflect the wave propagation characteristics of the underground medium within the effective frequency range.

[0090] Through the frequency-quantity correspondence, the quantity values ​​at different frequency points are inversely calculated to construct a quantity sampling point set. When the array radius is r, the spatial autocorrelation coefficients at different frequencies are first calculated; then the quantity at the frequency is inversely calculated by the formula. This process is to find the first-order zero-order Bessel function table for each frequency point through the spatial autocorrelation coefficient value to find the corresponding quantity value. Since the Bessel function is an oscillating function, a spatial autocorrelation coefficient value may correspond to multiple quantity values. Therefore, it is necessary to combine frequency continuity and physical constraints for screening to ensure that the selected quantity value is within a reasonable range and changes smoothly with the quantity values ​​of adjacent frequency points. By performing this operation on all frequency points within the effective frequency range, a quantity sampling point set is constructed, and each sampling point contains a frequency value and a corresponding quantity value.

[0091] For the set of sample points, the weighted least squares method is used to perform curve fitting to generate a frequency-phase velocity fitting curve. The weighted least squares method is an optimization algorithm that assigns different weights to different data points to make the fitting curve closer to the data points with high credibility. In this method, the weight assignment is usually based on the reliability of the spatial autocorrelation coefficient. High weights are given to high-correlation areas in the low-frequency band, and low weights are given to high-frequency bands or areas with low correlation. The fitting process uses the relationship between the sample and the frequency, array radius and phase velocity. The corresponding phase velocity value is calculated from the known sample value and frequency value to form a frequency-phase velocity fitting curve. The model used in the fitting process is usually a polynomial or spline function to capture the nonlinear characteristics of the dispersion curve.

[0092] The phase velocity values ​​corresponding to each frequency are extracted from the frequency-phase velocity fitting curve to generate the phase velocity dispersion curve. The phase velocity dispersion curve is a curve that describes the change of Rayleigh wave phase velocity with frequency and is a direct reflection of the layered structural characteristics of the underground medium. The extraction process uniformly samples on the fitting curve to obtain the phase velocity values ​​corresponding to a series of frequency points. These discrete points are then smoothed, such as using moving average or spline interpolation, to ensure the continuity and smoothness of the dispersion curve. Finally, the processed dispersion curve is quality evaluated to ensure that it conforms to physical laws, such as low-frequency phase velocity is usually greater than high-frequency phase velocity (normal dispersion characteristics), and the phase velocity value is within a reasonable range. When the extended space autocorrelation method is used for Bessel function fitting, the corrected goodness of fit should be greater than 0.8, which is an important indicator for evaluating the quality of the dispersion curve.

[0093] In a buried power pipeline box culvert detection project, the SPAC method dispersion curve extraction example is as follows: the spatial autocorrelation coefficient data obtained by a circular array with a radius of 3 meters is sorted into a spatial autocorrelation data matrix according to the frequency (5-70Hz, interval 0.5Hz) and the array radius (fixed at 3 meters). By scanning the matrix, it is found that in the frequency range of 8-45Hz, the maximum value of the spatial autocorrelation coefficient reaches 0.87, which meets the quality requirement of not less than 0.8, so 8-45Hz is determined as the effective frequency range boundary. For the spatial autocorrelation coefficient in this frequency range, the variable conversion is performed according to the Bessel function relationship to establish the frequency-volume correspondence relationship. By looking up the Bessel function table, the corresponding volume value of each frequency point is obtained. For example, at the frequency point of 25Hz, the spatial autocorrelation coefficient is 0.65, and the corresponding volume value is about 1.34. For all the volume sampling points in the entire effective frequency range, the weighted least squares method is used for fitting, and the weight is set to the square of the spatial autocorrelation coefficient, so that the high correlation area has a greater impact on the fitting result. The fitted frequency-phase velocity relationship shows that the phase velocity in the frequency range of 8-15Hz is about 650-550m / s, the phase velocity in the frequency range of 15-30Hz is about 550-450m / s, and the phase velocity in the frequency range of 30-45Hz is about 450-350m / s, showing a typical normal dispersion characteristic. This phase velocity dispersion curve clearly reflects the layered structure characteristics of the underground medium. The higher low-frequency phase velocity indicates that there is a hard layer in the deep, while the lower high-frequency phase velocity indicates that the shallow medium is relatively soft. This variation characteristic is consistent with the complex geological environment in which the buried power pipeline box culvert is located.

[0094] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0095] (1) Construct hierarchical structural parameters based on the geological background information of the survey area to form an initial underground velocity structure model;

[0096] (2) The phase velocity dispersion curve is used as the target feature input, the S-wave velocity and layer thickness search range is set, and the inversion parameter space is established;

[0097] (3) Forward calculation of theoretical dispersion curves of model samples in the inversion parameter space to generate a model dispersion feature set;

[0098] (4) By comparing the fit between the model dispersion feature set and the phase velocity dispersion curve, the fitness value of each sample is quantitatively evaluated;

[0099] (5) Use the divergence genetic algorithm to optimize the fitness value iteratively, select the optimal parameter combination, and construct the velocity structure profile;

[0100] (6) The velocity structure profile is processed continuously in the horizontal and vertical directions to generate the underground S-wave velocity structure.

[0101] Specifically, in the environmental detection and analysis method based on the simulated micro-vibration signal excitation generator, after obtaining the phase velocity dispersion curve, it is necessary to reconstruct the underground S-wave velocity structure through inversion technology. The hierarchical structure parameters are constructed based on the geological background information of the survey area to form an initial underground velocity structure model. The establishment of the initial model is the starting point of the inversion calculation, and it is necessary to comprehensively consider the geological conditions, drilling data and existing experience of the survey area. For the detection of buried power pipeline culverts, the underground medium is usually divided into 3-6 horizontal layers, and each layer is given an initial estimated thickness, S-wave velocity, P-wave velocity and density parameters. The initial S-wave velocity value is usually set based on an empirical range, such as 100-300m / s for the surface soil, 300-500m / s for the middle soil, and 500-1500m / s for the deep bedrock. The location of the culvert may show a local high-speed anomaly, so this feature must also be considered in the initial model. The P-wave velocity is usually derived from the S-wave velocity by setting the Poisson's ratio (about 0.25-0.45), and the density is associated with the velocity using an empirical formula. The accuracy of the initial model does not need to be very high, but it should be as close to the actual situation as possible to accelerate the convergence of subsequent inversion.

[0102] The phase velocity dispersion curve is used as the target feature input, the S-wave velocity and layer thickness search range are set, and the inversion parameter space is established. The inversion parameter space refers to the multidimensional space where all the parameters to be determined may take values, mainly including the S-wave velocity and thickness of each layer. In order to avoid the inversion process falling into meaningless solutions, it is necessary to set a reasonable parameter search range. The S-wave velocity search range is usually set to 0.5-2 times the initial estimate. Considering the characteristics of the box culvert of the buried power pipeline, the upper limit of the velocity of the box culvert layer can be appropriately relaxed to 3 times. The layer thickness search range is determined according to the target burial depth and estimated size, usually 0.7-1.5 times the initial estimate. For the surface layer and semi-infinite bottom layer, only the velocity is optimized without optimizing the thickness. The division of the parameter space adopts logarithmic uniform sampling to ensure a more detailed sampling density in the low-value area. In addition, constraints can be set between the parameters, such as the law of velocity increasing with depth (except for the box culvert), the velocity change of adjacent layers does not exceed a certain proportion, etc. These constraints help to narrow the search space and accelerate convergence.

[0103] The theoretical dispersion curves are forward calculated for the model samples in the inversion parameter space to generate a model dispersion feature set. Forward calculation is the process of deriving theoretical responses from given model parameters. In this method, it refers to calculating theoretical dispersion curves based on the underground velocity structure model. The forward algorithm is based on the elastic wave propagation theory. By solving the boundary value problem of the wave equation in the layered medium, the frequency-phase velocity relationship is obtained. The specific calculation adopts the transfer matrix method or the stiffness matrix method, inputs the parameters of each layer (thickness, S-wave velocity, P-wave velocity, density), and outputs the theoretical dispersion curve. For each model sample in the parameter space, a corresponding forward calculation is performed to form a theoretical dispersion feature set. In order to improve the computational efficiency, parallel processing technology is usually used in the forward calculation to calculate the theoretical curves of multiple model samples at the same time. The calculated frequency points of the theoretical dispersion curve are consistent with the measured dispersion curve, which is convenient for subsequent fit comparison.

[0104] The fitness value of each sample is quantitatively evaluated by comparing the fit between the model dispersion feature set and the phase velocity dispersion curve. The fit is an indicator to measure the degree of agreement between the theoretical curve and the measured curve. Common evaluation methods include root mean square error (RMSE), normalized error or weighted error. In this method, the frequency-weighted root mean square error is used as the fit indicator. The low-frequency band is given a higher weight and the high-frequency band is given a lower weight. This treatment takes into account that the low-frequency information is more closely related to the deep structure and the low-frequency signal is usually of higher quality. The fit is calculated for the theoretical dispersion curve and the measured dispersion curve of each model sample, and an error value is obtained. The inverse of the error value is used as the fitness value of the sample. The higher the fitness value, the closer the model is to the actual underground structure. In this way, the inversion problem is transformed into an optimization problem of the fitness function, laying the foundation for the subsequent application of genetic algorithms.

[0105] The divergent genetic algorithm is used to optimize the fitness value iteratively, select the optimal parameter combination, and construct the velocity structure profile. The divergent genetic algorithm is an improved version of the standard genetic algorithm, which is particularly suitable for multi-peak optimization problems and can effectively prevent premature convergence. The algorithm randomly generates an initial population (usually 100-300 individuals), each of which represents a set of model parameters. Then, through genetic operations such as selection, crossover, and mutation, a new generation of population is iteratively generated. The selection operation is based on the fitness value, and the tournament selection or roulette selection method is used to retain individuals with high fitness. The crossover operation combines the parameters of two parent individuals according to certain rules to generate new offspring individuals. The mutation operation randomly changes certain parameters of the individual to increase population diversity. The characteristic of the divergent genetic algorithm is that it introduces niche technology, and avoids all individuals from gathering to a single optimal solution through a fitness sharing mechanism, so that multiple possible solutions can be explored. After multiple generations of iterations (usually 30-50 generations), the algorithm converges to multiple local optimal solutions, from which the parameter combination with the highest fitness is selected as the final result to construct the velocity structure profile.

[0106] The velocity structure profile is processed continuously in the horizontal and vertical directions to generate the underground S-wave velocity structure. Since the velocity structure obtained by inversion is a layered discrete model, it needs to be processed continuously to more realistically reflect the changes in the underground medium. The longitudinal continuity uses interpolation smoothing technology, such as linear interpolation or spline interpolation, to convert the discrete layered model into a continuously changing velocity-depth function. For local abnormal bodies such as buried power pipeline box culverts, horizontal continuity processing is also required. The horizontal continuity is based on the inversion results of multiple measuring points, and a three-dimensional S-wave velocity distribution model is established through methods such as bilinear or Kriging interpolation. During the continuous processing, special attention is paid to maintaining the sharp features of the box culvert boundary to avoid excessive smoothing that causes the loss of target features. By reasonably setting the interpolation weights and boundary constraints, an underground S-wave velocity structure that maintains the main structural features and has physical rationality is finally generated.

[0107] In a buried power pipeline culvert detection project, the phase velocity dispersion curve inversion example is as follows: Based on local geological data and previous drilling data, a 4-layer initial velocity structure model was established, including surface soil (thickness 1m, S wave velocity 200m / s), middle soil (thickness 2m, S wave velocity 350m / s), suspected culvert layer (thickness 1.5m, S wave velocity 700m / s) and base layer (semi-infinite, S wave velocity 500m / s). The S wave velocity search range is set to 0.6-2.5 times the initial value of each layer, and the thickness search range is set to 0.8-1.2 times the initial value. Through theoretical forward calculation, the dispersion feature set of 200 initial model samples is generated, and the fitness value is calculated by comparing with the measured dispersion curve. The divergence genetic algorithm is applied for optimization, and the population size is set to 150, 40 generations are iterated, the crossover probability is 0.8, the mutation probability is 0.1, and the shared radius is 0.05. The optimal model finally obtained shows: surface soil (thickness 0.9m, S wave velocity 180m / s), middle soil (thickness 2.1m, S wave velocity 370m / s), box culvert layer (thickness 1.6m, S wave velocity 780m / s) and base layer (S wave velocity 520m / s). This result clearly identifies the high-speed abnormal layer at a depth of about 3m, which is basically consistent with the position and size of the actual box culvert, verifying the effectiveness of this method in the detection of urban underground facilities.

[0108] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0109] (1) Perform numerical filtering on the underground S-wave velocity structure and extract the velocity gradient distribution map;

[0110] (2) Determine the velocity anomaly threshold in the velocity gradient distribution map and divide the S-wave velocity anomaly area;

[0111] (3) Apply edge detection algorithm to the S-wave velocity anomaly area to extract the velocity change boundary line and form the contour features of the box body;

[0112] (4) Quantify the plane position coordinates and burial depth parameters from the contour features of the box culvert to construct the spatial positioning information of the box culvert;

[0113] (5) Compare and analyze the spatial positioning information of the inclusion body with the S-wave velocity of the surrounding medium to evaluate the medium velocity difference ratio;

[0114] (6) The position parameters and structural status of the buried power pipeline box culvert are determined by comparing the medium velocity difference ratio and the dispersion characteristic mode.

[0115] Specifically, after obtaining the underground S-wave velocity structure, the environmental detection and analysis method based on the simulated micro-vibration signal excitation generator needs to determine the specific parameters of the buried power pipeline box culvert through edge detection and anomaly analysis. The underground S-wave velocity structure is numerically filtered to extract the velocity gradient distribution map. The numerical filtering process aims to smooth the small fluctuations in the original S-wave velocity structure while retaining the main velocity change characteristics. The processing adopts Gaussian filtering or median filtering method, and the filter window size is usually set to 1 / 5 to 1 / 3 of the expected target minimum size, which can eliminate noise without over-smoothing the target boundary. After filtering, the spatial gradient value of the velocity, that is, the rate of change of the velocity, is calculated. The gradient calculation adopts the central difference method to calculate the velocity change rate in the horizontal and vertical directions respectively, and then synthesize the total gradient value. The area with high gradient value corresponds to the location where the velocity changes sharply, which is usually the interface of different media or the boundary of the abnormal body. The velocity gradient distribution map is presented in two-dimensional or three-dimensional form, which intuitively shows the changing characteristics of the underground velocity structure.

[0116] The velocity anomaly threshold is calibrated in the velocity gradient distribution map to divide the S-wave velocity anomaly area. The determination of the velocity anomaly threshold is a key step in identifying the anomaly body, which requires comprehensive consideration of the velocity distribution characteristics of the background medium and the expected characteristics of the target body. The S-wave velocity of the buried power pipeline box culvert is usually 30%-60% higher than that of the surrounding medium, so the anomaly threshold can be set to 1.3-1.6 times the local background velocity. The specific operation is to statistically analyze the S-wave velocity distribution of each depth layer in the study area, calculate the average value and standard deviation, and then determine the anomaly judgment standard suitable for the current detection environment based on the statistical characteristics and empirical threshold. For complex geological environments, the adaptive threshold method can be used, that is, the threshold is dynamically adjusted according to the velocity distribution characteristics of the local area. The area exceeding the threshold is marked as the S-wave velocity anomaly area, forming an abnormal area mask map, which provides the target area for subsequent edge detection. The edge detection algorithm is applied to the S-wave velocity anomaly area to extract the velocity change boundary line and form the contour feature of the box culvert. The edge detection algorithm is used to accurately locate the boundary position of the anomaly body. Commonly used algorithms include Sobel operator, Canny edge detector or gradient vector flow. Considering that the box culvert of buried power pipelines usually has a regular geometric shape, morphological processing, such as opening and closing operations, can be applied before edge detection to remove small noise and fill internal holes. The edge detection process calculates the gradient amplitude and direction within the mask range of the abnormal area, and then extracts continuous edge lines through non-maximum suppression and double threshold processing. For the box culvert of buried power pipelines, its edge features are usually expressed as closed polygons or curves. In order to improve the continuity of the edge, edge tracking and connection algorithms can also be applied to deal with edge breaks caused by noise or insufficient data. The final contour feature of the box culvert is a set of boundary points or parameterized curves that describe the target geometric shape.

[0117] The plane position coordinates and burial depth parameters are quantified from the contour features of the box culvert to construct the spatial positioning information of the box culvert. Based on the extracted contour features, the spatial position and geometric parameters of the box culvert are quantitatively determined through shape analysis and parameter fitting. The plane position coordinates are obtained by calculating the geometric center or centroid of the contour. For irregular shapes, the minimum circumscribed rectangle or ellipse fitting method can be used to determine the center position. The burial depth parameters are determined by analyzing the distribution of contour features in the depth direction, including the top depth, bottom depth and vertical thickness of the box culvert. For box culverts with complex shapes, three-dimensional reconstruction can also be performed to obtain more complete spatial structure information through multi-section joint analysis or voxel model construction. During the spatial positioning process, special attention is paid to the consistency of the coordinate system to ensure that all position parameters refer to a unified reference point and direction. The final constructed box culvert spatial positioning information contains key parameters such as the plane coordinates, burial depth range, geometric dimensions and attitude angle of the target. The box culvert spatial positioning information is compared and analyzed with the S-wave velocity of the surrounding medium to evaluate the medium velocity difference ratio. This step aims to verify whether the physical characteristics of the identified target meet the expected characteristics of the buried power pipeline box culvert through velocity comparison. The S-wave velocity values ​​are counted in the determined box culvert area, and the average value and coefficient of variation are calculated. Then, reference points are selected in the same depth area around the box culvert to count the S-wave velocity distribution characteristics of the background medium. The medium velocity difference ratio is obtained by calculating the ratio of the average velocity in the box culvert area to the average velocity of the background medium. The S-wave velocity of the box culvert material (such as concrete or metal) of the buried power pipeline is usually 30%-60% higher than that of the surrounding soil, so the expected velocity difference ratio should be in the range of 1.3-1.6. In addition, the uniformity and change trend of the velocity inside the box culvert can also be analyzed. These characteristics may reflect the internal structure or integrity of the box culvert.

[0118] By comparing the medium velocity difference ratio with the dispersion characteristic mode, the location parameters and structural status of the buried power pipeline box culvert are determined. The last step is to make a final judgment by combining the results of various analyses. Verify whether the medium velocity difference ratio is within the expected range and confirm that the identification target is indeed the buried power pipeline box culvert rather than other underground facilities or natural geological anomalies. Then, compare the location of the box culvert with the known pipeline network distribution map to verify the rationality of the detection results. Further, analyze the dispersion characteristic mode of the box culvert area, such as the difference characteristics of the phase velocity dispersion curve between the measuring point above the box culvert and the measuring point in the non-box culvert area. These differences may be manifested as changes in the shape of the dispersion curve, displacement of characteristic frequency points, or the appearance of abnormal resonance peaks. By comparing with the dispersion characteristic mode library of typical box culverts, the structural status of the box culvert can be evaluated, such as judging whether there are abnormal conditions such as cracks, cavities or water accumulation. Based on the comprehensive analysis results, the location parameters (plane coordinates and burial depth) and structural status evaluation of the buried power pipeline box culvert are finally determined to provide a basis for subsequent maintenance decisions.

[0119] In a buried power pipeline culvert detection project, an example of edge detection and parameter determination is as follows: Gaussian filtering (window size 0.3m) is applied to the inverted three-dimensional S-wave velocity structure, and the velocity gradient distribution is calculated. It is found that there is an obvious high-gradient zone in the middle of the detection area. Through statistical analysis, it is determined that the average S-wave velocity of the background soil is about 350m / s, and the velocity anomaly threshold is set to 1.4 times the background value (i.e. 490m / s). The Canny edge detection algorithm is applied to the area exceeding the threshold, and a closed contour approximately rectangular is extracted with an area of ​​about 2.5m 2 . Through the minimum circumscribed rectangle analysis, it was determined that the plane position of the box culvert was 15.3m northeast of the origin of the survey area and 7.8m north, and the angle between the long axis and the east-west direction was about 15 degrees. Depth analysis showed that the top of the box culvert was buried at a depth of about 1.8m and a vertical thickness of about 1.5m. The average S-wave velocity in the box culvert area was 530m / s, and the ratio to the surrounding soil was 1.51, which was consistent with the typical characteristics of the power pipeline box culvert. Dispersion curve analysis showed that the dispersion curve of the measuring point above the box culvert showed a significant deviation in the range of 25-35Hz, which was manifested as a local increase in the phase velocity, but no obvious discontinuity or sharp resonance peak was observed, indicating that the box culvert structure was intact and had no obvious internal defects. Comprehensively judging, this location is indeed a buried power pipeline box culvert, the structure is in good condition, and no immediate maintenance is required.

[0120] The above describes the environmental detection and analysis method based on the simulated micro-motion signal excitation generator in the embodiment of the present application. The following describes the environmental detection and analysis system based on the simulated micro-motion signal excitation generator in the embodiment of the present application. Figure 2 In the embodiment of the present application, an environment detection and analysis system based on a simulated micro-motion signal excitation generator includes:

[0121] A generation module is used to generate a vertical vibration signal through a constant resistance vibration exciter and a dedicated coupling base integrated structure to obtain a simulated micro-motion signal excitation source;

[0122] The excitation module is used to perform multi-band micro-motion signal excitation and station array arrangement according to the frequency parameter combination of the simulated micro-motion signal excitation source to obtain the original micro-motion signal data;

[0123] The processing module is used to perform bandpass filtering and time domain normalization preprocessing on the original micro-motion signal data, calculate the power spectrum and cross-power spectrum through fast Fourier transform, and obtain the spatial autocorrelation coefficient;

[0124] An extraction module is used to extract dispersion curves based on spatial autocorrelation coefficients by using the SPAC method, and to optimize the fitting accuracy using the weighted least squares method to obtain phase velocity dispersion curves;

[0125] The inversion module is used to use the phase velocity dispersion curve to perform inversion calculation on the initial underground velocity structure model through the divergence genetic algorithm to generate the underground S-wave velocity structure;

[0126] The identification module is used to identify and analyze the S-wave velocity abnormal area based on the underground S-wave velocity structure using an edge detection algorithm to determine the position parameters and structural status of the buried power pipeline box culvert.

[0127] Through the coordinated cooperation of the above-mentioned components, vertical vibration signals are generated through the integrated structure of constant resistance vibrator and special coupling base, which realizes the lossless transmission and uniform distribution of vibration energy, effectively avoids the secondary coupling interference of traditional seismic sources, and enables the analog micro-motion signal excitation source to provide a stable and controllable vibration field, overcoming the limitation of hard surface on the propagation of traditional artificial seismic source signals in urban environments; multi-band micro-motion signal excitation and station array arrangement are carried out according to the frequency parameter combination of the analog micro-motion signal excitation source, and accurate detection of targets at different depths is achieved by targeted excitation of signals in different frequency bands, thereby improving the system's detection depth adaptability and resolution; the original micro-motion signal data is pre-processed by bandpass filtering and time domain normalization, and the power spectrum and cross-power spectrum are calculated by fast Fourier transform, which effectively eliminates the electromagnetic intrusion in urban environments. Noise and non-target vibration sources are eliminated, which improves data quality and signal-to-noise ratio; based on the spatial autocorrelation coefficient, the dispersion curve is extracted by the SPAC method, and the weighted least squares method is used to optimize the fitting accuracy, which significantly enhances the stability and reliability of the dispersion curve in a multi-source noise environment, providing high-quality basic data for underground structure inversion; using the phase velocity dispersion curve, the initial underground velocity structure model is inverted and calculated by the divergence genetic algorithm, which avoids the problem that traditional inversion methods are prone to fall into local optimality, enhances the ability to recognize complex stratigraphic structures, and makes the reconstruction of underground S-wave velocity structure more accurate; based on the underground S-wave velocity structure, the edge detection algorithm is used to identify and analyze the S-wave velocity abnormal area, and the position parameters and structural status of the buried power pipeline box culvert are accurately determined, meeting the accuracy requirements of urban underground facility management. It is particularly worth emphasizing that in this scheme, the application of divergence genetic algorithm fully considers the characteristics of multi-peak optimization problems. By introducing niche technology and fitness sharing mechanism, the algorithm is prevented from falling into a single solution space, effectively overcoming the limitations of traditional optimization algorithms in complex underground structure inversion; at the same time, the edge detection algorithm processes the S-wave velocity anomaly area, making full use of the sensitivity of the Canny detector to weak edges and its ability to accurately describe the real boundary, so that the geometric characteristics and structural status of the buried power pipeline box culvert can be accurately identified in the complex underground environment of the city. The clever combination of these algorithm features makes this scheme show significant technical advantages in the field of urban underground facility detection, and realizes the comprehensive benefits of non-invasive detection, anti-interference optimization, cost reduction, safety and environmental protection.

[0128] Specifically, Figure 3 The figure is a schematic diagram of the structure of the integrated excitation source device of the present application, the overall structure of which is composed of an upper exciter and an excitation coupling base integrally connected to the lower end of the exciter, and includes the above-mentioned environmental detection and analysis system based on the analog micro-motion signal excitation generator. The main parameters and indicators of the upper exciter are shown in Table 1 below.

[0129] Table 1 Main parameters and indicators of constant resistance oscillator

[0130] Maximum force: >500N(Newton) Force constant: 13.8N / A Maximum amplitude: ±5mm Maximum allowable current: <38Ap (peak value) Operating frequency: 0-2000Hz Zeroth order resonant frequency: 24±2Hz Internal coil DC resistance: 0.34Ω Internal coil inductance: 0.072mH Mover quality: 34.8Kg Total mass: 37.6Kg Dimensions (main body): φ210×228(mm)

[0131] The above-mentioned integrated excitation source device transmits the vibration signal to the designed base through the vibration of the vibrator, avoiding the secondary coupling connection methods such as traditional screws and nuts, and further avoiding the possible secondary and multiple vibration signals, and vibrating and transmitting the vibration signal downward as losslessly and without interference as possible. Its wider base design avoids the possibility of single-point signal transmission as much as possible during the vibration process. Through its wide signal transmission, it plays a good role in the physical constraint of the downward vertical signal of the source signal of geophysical exploration. When it is working: when the upper end vibrator generates vibrations of different frequencies, different amplitudes, and different energy intensities, it drives the lower end base to conduct vibration, and its integrated process structure maximizes and optimizes the vibration conduction effect. The base achieves the purpose of uniform vertical vibration conduction by directly coupling to the ground or other vibration conduction coupling. Since the equipment is mostly used for vibration monitoring and detection, its production process has been improved and invented in a targeted manner. The wide-face design concept is adopted for the base module to achieve signal uniformity and amplification constraint. Subsequently, heavy objects can be placed on the two arms of the upper exciter to achieve stronger source energy. It is suitable for vertical seismic waves in different urban surface environments, forming a stable and applicable small green vibration non-destructive detection equipment for cities.

[0132] Reference Figure 4 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0133] Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0134] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0135] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synch link) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0136] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0137] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0138] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. An environmental detection and analysis method based on an analog micro-vibration signal excitation generator, characterized in that: include: The vertical vibration signal is generated by integrating the constant resistance vibration exciter and the special coupling base to obtain the simulated micro-motion signal excitation source; Perform multi-band micro-motion signal excitation and station array arrangement according to the frequency parameter combination of the simulated micro-motion signal excitation source to obtain original micro-motion signal data; The original micro-motion signal data is subjected to band-pass filtering and time-domain normalization preprocessing, and the power spectrum and the cross-power spectrum are calculated by fast Fourier transform to obtain the spatial autocorrelation coefficient; Based on the spatial autocorrelation coefficient, the dispersion curve is extracted by SPAC method, and the fitting accuracy is optimized by weighted least square method to obtain the phase velocity dispersion curve; Using the phase velocity dispersion curve, an initial underground velocity structure model is inverted and calculated by a divergence genetic algorithm to generate an underground S-wave velocity structure; Based on the underground S-wave velocity structure, an edge detection algorithm is used to identify and analyze the S-wave velocity abnormal area to determine the position parameters and structural status of the buried power pipeline box culvert.

2. The environmental detection and analysis method based on the simulated micro-vibration signal excitation generator according to claim 1 is characterized in that: The vertical vibration signal is generated by integrating a constant resistance exciter and a dedicated coupling base to obtain a simulated micro-motion signal excitation source, including: The constant resistance vibration exciter with a preset force constant generates an original vibration signal according to the working frequency range to obtain the initial excitation energy; The initial excitation energy is transmitted to the conductive base with a wide surface design to eliminate secondary coupling interference and form a uniform vibration conductive medium; Applying a vertical restraining force to the conductive base to control the vibration amplitude within a predetermined range, thereby generating a directional vertical vibration transmission channel; According to the directional vertical vibration transmission channel, the ratio of the upper end exciter mover mass to the total mass is adjusted to construct a stable excitation waveform; Through the direct coupling of the stable excitation waveform and the conductive base, a vertical vibration field with wide surface distribution is formed on the ground, generating a simulated micro-motion signal excitation source.

3. The environmental detection and analysis method based on the simulated micro-vibration signal excitation generator according to claim 1 is characterized in that: The method of performing multi-band micro-motion signal excitation and station array arrangement according to the frequency parameter combination of the simulated micro-motion signal excitation source to obtain original micro-motion signal data includes: According to the working frequency range of the simulated micro-motion signal excitation source, a plurality of detection frequency bands are divided to generate a frequency parameter scanning sequence; According to the characteristics of each frequency band in the frequency parameter scanning sequence, the amplitude of the excitation signal is dynamically adjusted to form a frequency band excitation scheme; Based on the frequency band excitation scheme, high-sensitivity seismic signal receiving stations are arranged in the detection area to construct a ring array topology structure; Setting a synchronization time reference for each receiving station in the ring array topology structure and establishing a signal acquisition time window; Through the signal acquisition time window, the excitation signals of each frequency band are recorded with high precision, and the environmental background noise is collected as a comparison benchmark; The high-precision record is integrated with the comparison reference to generate raw micro-motion signal data including a time stamp.

4. The environmental detection and analysis method based on the simulated micro-vibration signal excitation generator according to claim 1 is characterized in that: The raw micro-motion signal data is subjected to bandpass filtering and time domain normalization preprocessing, and the power spectrum and cross-power spectrum are calculated by fast Fourier transform to obtain the spatial autocorrelation coefficient, including: Identify and mark abnormal sections with obvious interference in the original micro-motion signal data to form a valid data determination threshold; Eliminate abnormal data segments according to the effective data determination threshold, process the remaining data in segments, and construct a micro-motion signal slice set; Applying a bandpass filter to the micromotion signal slice set to remove high-frequency noise and low-frequency drift, and generating frequency-band purified micromotion data; Performing time domain normalization processing on the frequency band purified micromotion data to balance the signal intensity variation and obtain a normalized micromotion sequence; Applying fast Fourier transform to the normalized micro-motion sequence to calculate the power spectrum and cross-power spectrum matrix between stations; Based on the power spectrum and the cross-power spectrum matrix, the spatial autocorrelation function of any two stations is calculated, and the spatial autocorrelation coefficient is obtained through azimuth averaging.

5. The environmental detection and analysis method based on the simulated micro-vibration signal excitation generator according to claim 1 is characterized in that: The method of extracting dispersion curves based on the spatial autocorrelation coefficient by SPAC method and optimizing fitting accuracy by weighted least square method to obtain phase velocity dispersion curves includes: The spatial autocorrelation coefficients are grouped and sorted according to the array radius and frequency to form a spatial autocorrelation data matrix; Identifying the low frequency band maximum value region in the spatial autocorrelation data matrix and determining the effective frequency range boundary; Performing variable conversion on the spatial autocorrelation coefficient within the boundary of the effective frequency range and the relationship between the first-kind zero-order Bessel function to establish a frequency-variable correspondence relationship; Through the frequency-quantity correspondence, the quantity values ​​at different frequency points are inversely calculated to construct a quantity sampling point set; For the set of the quantity sampling points, a weighted least square method is used to perform curve fitting to generate a frequency-phase velocity fitting curve; The phase velocity value corresponding to each frequency is extracted from the frequency-phase velocity fitting curve to generate a phase velocity dispersion curve.

6. The environmental detection and analysis method based on the simulated micro-vibration signal excitation generator according to claim 1 is characterized in that: The method of using the phase velocity dispersion curve to invert and calculate the initial underground velocity structure model through a divergence genetic algorithm to generate an underground S-wave velocity structure includes: Construct hierarchical structural parameters based on the geological background information of the survey area to form an initial underground velocity structure model; The phase velocity dispersion curve is used as a target feature input, the S-wave velocity and layer thickness search range is set, and an inversion parameter space is established; Performing forward calculation of theoretical dispersion curves on the model samples in the inversion parameter space to generate a model dispersion feature set; By comparing the fit between the model dispersion feature set and the phase velocity dispersion curve, the fitness value of each sample is quantitatively evaluated; The fitness value is optimized and iterated by using a divergence genetic algorithm, an optimal parameter combination is selected, and a velocity structure profile is constructed; The velocity structure profile is continuously processed in the horizontal and vertical directions to generate an underground S-wave velocity structure.

7. The environmental detection and analysis method based on the simulated micro-vibration signal excitation generator according to claim 1 is characterized in that: Based on the underground S-wave velocity structure, an edge detection algorithm is used to identify and analyze the S-wave velocity abnormal area to determine the position parameters and structural state of the buried power pipeline box culvert, including: Perform numerical filtering on the underground S-wave velocity structure and extract the velocity gradient distribution map; In the velocity gradient distribution diagram, the velocity anomaly threshold is calibrated to divide the S-wave velocity anomaly area; Apply edge detection algorithm to the S-wave velocity anomaly area to extract velocity change boundary lines and form the contour features of the box inclusion body; Quantify the plane position coordinates and burial depth parameters from the contour features of the box culvert to construct the spatial positioning information of the box culvert; Compare and analyze the spatial positioning information of the inclusion body with the S-wave velocity of the surrounding medium to evaluate the medium velocity difference ratio; By comparing the medium velocity difference ratio with the dispersion characteristic mode, the position parameters and structural status of the buried power pipeline box culvert are determined.

8. An environment detection and analysis system based on an analog micro-motion signal excitation generator, used to implement the environment detection and analysis method based on an analog micro-motion signal excitation generator as described in any one of claims 1 to 7, characterized in that: The system comprises: A generation module is used to generate a vertical vibration signal through a constant resistance vibration exciter and a dedicated coupling base integrated structure to obtain a simulated micro-motion signal excitation source; An excitation module, used to perform multi-band micro-motion signal excitation and station array arrangement according to the frequency parameter combination of the simulated micro-motion signal excitation source, and obtain original micro-motion signal data; A processing module, used for performing bandpass filtering and time domain normalization preprocessing on the original micro-motion signal data, calculating the power spectrum and the cross-power spectrum by fast Fourier transform, and obtaining the spatial autocorrelation coefficient; An extraction module is used to extract the dispersion curve based on the spatial autocorrelation coefficient by SPAC method, and optimize the fitting accuracy by weighted least square method to obtain the phase velocity dispersion curve; An inversion module is used to use the phase velocity dispersion curve to perform inversion calculation on the initial underground velocity structure model through a divergence genetic algorithm to generate an underground S-wave velocity structure; The identification module is used to identify and analyze the S wave velocity abnormal area based on the underground S wave velocity structure by using an edge detection algorithm to determine the position parameters and structural status of the buried power pipeline box culvert.

9. A computer device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and is characterized in that when the processor executes the computer program, the environmental detection and analysis method based on the simulated micro-motion signal excitation generator as described in any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the processor is enabled to execute the environment detection and analysis method based on a simulated micro-motion signal excitation generator according to any one of claims 1 to 7.

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