An underground cavern microstructure surface quantum sensing detection device

Abstract

The application provides a kind of underground cavern microstructure surface quantum sensing detection device, including nanoscale piezoelectric sensor array, data acquisition unit, signal processing unit and structure surface identification unit;Nanoscale piezoelectric sensor array is deployed in surrounding rock surface or borehole, and microseismic signal is detected;Data acquisition unit collects and converts signal into digital signal;Signal processing unit pre-processes and extracts feature data;Structure surface identification unit identifies the position, dip angle and length of microstructure surface based on feature data.The application can detect underground cavern microstructure surface with high sensitivity and high precision, provide important basis for rock mass stability evaluation and engineering design, reduce geological disaster risk and ensure the safety of underground engineering.

Description

Technical Field

[0001] This invention relates to the field of underground engineering geological exploration technology, and more specifically, to a quantum sensing detection device for the hidden microstructure of underground caverns. Background Technology

[0002] In the field of underground engineering, accurately detecting the hidden microstructures of underground caverns is crucial for assessing rock mass stability and designing safe underground structures. Traditional detection methods, such as core drilling, ground-penetrating radar (GPR), and seismic wave detection, while providing some information about rock mass structure, have limitations. Core drilling is a direct method, but it only provides limited discrete information and is costly. While GPR and seismic wave detection can provide continuous detection results, their accuracy in detecting hidden microstructures is limited, and their sensitivity to microseismic signals is insufficient, making it difficult to accurately identify minute structural features.

[0003] In implementing the embodiments of the present invention, the prior art has at least the following problems or defects: the prior art cannot detect the hidden micro-structures of underground caverns with high sensitivity and high precision, and it is difficult to identify the position, inclination and length of the structure in real time and accurately. This limits the comprehensive understanding and evaluation of underground rock mass structures, and thus affects the safety and reliability of underground engineering. Summary of the Invention

[0004] This invention provides a quantum sensing detection device for the hidden microstructure of underground caverns, comprising: The system comprises a nanoscale piezoelectric sensor array, a data acquisition unit, a signal processing unit, and a structural surface identification unit. The nanoscale piezoelectric sensor array is configured to be deployed on the surface of the surrounding rock of an underground cavern or inside a borehole to detect microseismic signals generated by hidden microstructures. The data acquisition unit is electrically connected to the nanoscale piezoelectric sensor array and is used to acquire the microseismic signals and convert them into digital signals. The signal processing unit is connected to the data acquisition unit and is used to preprocess and extract features from the digital signals to generate feature data. The structural surface identification unit is connected to the signal processing unit and is used to identify the position, tilt angle, and length parameters of the hidden microstructures based on the feature data.

[0005] Furthermore, the nanoscale piezoelectric sensor array includes multiple nanoscale piezoelectric sensors, each having a piezoelectric sensitive layer with a thickness of 1 nanometer to 100 nanometers; the nanoscale piezoelectric sensor array is arranged in a two-dimensional grid on a flexible substrate with a grid spacing of 0.1 meters to 1 meter; each nanoscale piezoelectric sensor in the array includes a quantum dot enhancement structure configured to improve the sensitivity and signal-to-noise ratio of the micro-vibration signal.

[0006] Furthermore, the data acquisition unit includes an analog-to-digital converter and a signal amplifier; the analog-to-digital converter is configured to convert the micro-vibration signal from an analog signal to a digital signal; the signal amplifier is configured to amplify the micro-vibration signal before conversion, with a gain factor of 10 to 100 times; the data acquisition unit also includes a multiplexer configured to sequentially switch signals from multiple nanoscale piezoelectric sensors for acquisition.

[0007] Furthermore, the signal processing unit includes a preprocessing module and a feature extraction module; the preprocessing module is configured to perform bandpass filtering on the digital signal, the bandpass filtering frequency range being 100 Hz to 10 kHz; the feature extraction module is configured to extract frequency features and time-domain features from the preprocessed signal; the frequency features include the main frequency and frequency band energy, and the time-domain features include signal amplitude and duration.

[0008] Furthermore, the feature extraction module calculates the dominant frequency of the microseismic signal using the following formula:

[0009] in, This is the dominant frequency of the microseismic signal. Let be the amplitude of the i-th frequency component. Let be the frequency value of the i-th frequency component, and N be the total number of frequency components.

[0010] Furthermore, the feature extraction module also uses the following formula to calculate the frequency band energy of the microseismic signal:

[0011] in, For frequency band energy, This is the spectrum of the microseismic signal. This is the lower limit frequency of the frequency band. This is the upper limit frequency of the frequency band.

[0012] Furthermore, the structural surface identification unit includes a machine learning model, which is a support vector machine model; the structural surface identification unit is configured to use the support vector machine model to classify the type of hidden microstructures based on the feature data, the type including joints, fissures and faults; the structural surface identification unit is also configured to output the three-dimensional coordinates and azimuth of the hidden microstructures.

[0013] Furthermore, it also includes a deployment unit configured to install the nanoscale piezoelectric sensor array onto the surrounding rock surface of the underground cavern or into a borehole; the deployment unit includes a robotic arm and a positioning system, the positioning system using GPS or a laser rangefinder to determine the sensor location; the deployment unit is also configured to adjust the sensor deployment density based on the results of a pre-analysis of the geological structure of the underground cavern.

[0014] Furthermore, it also includes a display unit, which is connected to the structure surface identification unit and is used to visualize the location and parameters of the identified hidden microstructure surfaces; the display unit is configured to generate a three-dimensional geological model and overlay the hidden microstructure surfaces on the three-dimensional geological model.

[0015] Furthermore, it also includes a rock mass grading unit, which is connected to the structural surface identification unit and is used to calculate rock mass quality indicators based on the identified hidden microstructural surface parameters; the rock mass grading unit is configured to calculate rock mass quality indicators using the BQ grading method or the RMR grading method; the rock mass grading unit is also configured to output rock mass quality grading results and stability evaluation reports.

[0016] The embodiments of the present invention have at least the following beneficial effects: 1. By employing a nanoscale piezoelectric sensor array, this device can improve the detection sensitivity and signal-to-noise ratio of microseismic signals, thereby achieving high-precision detection of hidden microstructures in underground caverns. This high-sensitivity detection capability allows the device to capture extremely weak signals, thus more accurately identifying the existence of hidden microstructures and solving the problem that traditional detection methods are unable to detect minute structures due to insufficient sensitivity.

[0017] 2. By combining the preprocessing and feature extraction functions of the signal processing unit, this device can effectively remove noise interference and extract key features of the microseismic signal, such as frequency and time-domain features. This process not only improves the signal quality but also provides a more accurate data foundation for subsequent structural surface identification, enabling more precise identification of parameters such as the type, location, dip angle, and length of the structural surface. This addresses the shortcomings of traditional methods in signal processing and improves the reliability and accuracy of the detection results.

[0018] 3. This device employs machine learning models, such as support vector machines, for the identification and classification of structural surfaces. This advanced identification technology can quickly and accurately classify the types of hidden structural surfaces based on extracted feature data and output their three-dimensional coordinates and azimuth. This not only improves identification efficiency but also enhances adaptability to complex geological structures, overcoming the limitations of traditional methods in structural surface identification and classification, and providing more comprehensive and accurate information for geological assessment of underground engineering. Attached Figure Description

[0019] The above and other objects, features, and advantages of exemplary embodiments of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings. Several embodiments of the invention are illustrated in the drawings by way of example, not limitation, in which: Figure 1 This is a schematic diagram of the structure of a quantum sensing and detection device for the hidden microstructure of an underground cavern, provided in an embodiment of the present invention. Detailed Implementation

[0020] The principles and spirit of the invention will now be described with reference to several exemplary embodiments. It should be understood that these embodiments are provided merely to enable those skilled in the art to better understand and implement the invention, and are not intended to limit the scope of the invention in any way. Rather, these embodiments are provided to make the invention more thorough and complete, and to fully convey the scope of the invention to those skilled in the art.

[0021] Those skilled in the art will understand that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0022] It should be noted that the number of any elements in the accompanying drawings is for illustrative purposes only and not as a limitation, and any naming is for distinction only and has no limiting meaning.

[0023] The following is for reference. Figure 1 , Figure 1 This is a schematic diagram of a quantum sensing and detection device for the hidden microstructure of an underground cavern, provided in an embodiment of the present invention. Figure 1 As shown, a quantum sensing detection device for the hidden microstructure of underground caverns includes: The system includes a nanoscale piezoelectric sensor array 101, a data acquisition unit 102, a signal processing unit 103, and a structural surface recognition unit 104. The nanoscale piezoelectric sensor array is configured to be deployed on the surface of the surrounding rock of an underground cavern or inside a borehole to detect microseismic signals generated by hidden microstructures. The data acquisition unit is electrically connected to the nanoscale piezoelectric sensor array to acquire the microseismic signals and convert them into digital signals. The signal processing unit is connected to the data acquisition unit to preprocess and extract features from the digital signals to generate feature data. The structure identification unit is connected to the signal processing unit to identify the position, tilt angle, and length parameters of the hidden microstructures based on the feature data.

[0024] This invention proposes a quantum sensing detection device for the hidden microstructure of underground caverns. This device achieves high-precision detection of the hidden microstructure of underground caverns through the coordinated operation of a nanoscale piezoelectric sensor array, a data acquisition unit, a signal processing unit, and a structure recognition unit.

[0025] Nanoscale piezoelectric sensor arrays are highly sensitive sensor arrays with piezoelectric sensitive layers at the nanometer level, effectively capturing microseismic signals. The data acquisition unit converts the microseismic signals detected by the sensor array into digital signals for subsequent processing. The signal processing unit preprocesses and extracts features from the digital signals, generating feature data that can be used for identification. The structure identification unit uses this feature data and a machine learning model to identify specific parameters of hidden microstructures, such as location, tilt angle, and length.

[0026] Specifically, the nanoscale piezoelectric sensor array comprises multiple nanoscale piezoelectric sensors, each with a piezoelectric sensitive layer ranging in thickness from 1 nanometer to 100 nanometers. These sensors are arranged in a two-dimensional grid on a flexible substrate with a grid spacing of 0.1 meters to 1 meter. Each sensor also includes a quantum dot enhancement structure to improve the sensitivity and signal-to-noise ratio of the microseismic signal. The data acquisition unit includes an analog-to-digital converter and a signal amplifier. The analog-to-digital converter converts the microseismic signal from analog to digital, and the signal amplifier amplifies the microseismic signal before conversion with a gain factor of 10 to 100 times.

[0027] In addition, the data acquisition unit also includes a multiplexer for sequentially switching signals from multiple nanoscale piezoelectric sensors for acquisition. The signal processing unit includes a preprocessing module and a feature extraction module. The preprocessing module performs bandpass filtering on the digital signal, with a frequency range of 100 Hz to 10 kHz. The feature extraction module extracts frequency and time-domain features from the preprocessed signal, including the dominant frequency, band energy, signal amplitude, and duration.

[0028] Preferably, the structure surface recognition unit employs a support vector machine (SVM) model for the identification and classification of hidden microstructure surfaces. This model is constructed using a training dataset, with input parameters including frequency features and temporal features obtained from the feature extraction module. During model construction, the training data is first normalized to eliminate dimensional differences between different features. Then, optimal model parameters, such as penalty parameters and kernel function parameters, are selected using cross-validation. In practical applications, the extracted feature data is input into the trained SVM model, which outputs parameters such as the type, three-dimensional coordinates, and azimuth of the hidden microstructure surface.

[0029] Furthermore, the feature extraction module in the signal processing unit uses specific formulas to calculate the dominant frequency and band energy of the microseismic signal. The dominant frequency is calculated as the amplitude-weighted average of the frequency components of the microseismic signal, and the band energy is calculated as the square integral of the microseismic signal spectrum within a specific band. These calculation methods ensure the accuracy and reliability of the feature data, providing a solid foundation for subsequent structural surface identification.

[0030] In some embodiments, the nanoscale piezoelectric sensor array includes a plurality of nanoscale piezoelectric sensors, each having a piezoelectric sensitive layer with a thickness of 1 nanometer to 100 nanometers; the nanoscale piezoelectric sensor array is arranged in a two-dimensional grid on a flexible substrate with a grid spacing of 0.1 meters to 1 meter; each nanoscale piezoelectric sensor in the nanoscale piezoelectric sensor array includes a quantum dot enhancement structure configured to improve the sensitivity and signal-to-noise ratio of the micro-vibration signal.

[0031] It should be noted that the nanoscale piezoelectric sensor array in this invention is a key component for detecting microseismic signals from the hidden microstructures of underground caverns. The nanoscale piezoelectric sensor array converts mechanical energy into electrical signals through its piezoelectric sensitive layer, thereby achieving highly sensitive detection of microseismic signals. The thickness of the piezoelectric sensitive layer is between 1 nanometer and 100 nanometers, a thickness range that ensures the sensor's high responsiveness to minute mechanical stresses. The sensor array is arranged in a two-dimensional grid on a flexible substrate. This arrangement not only increases the sensor's coverage but also enhances its adaptability to different terrains. Furthermore, each sensor incorporates a quantum dot reinforcement structure. Quantum dots are nanomaterials with quantum size effects, which can significantly improve the sensor's sensitivity and signal-to-noise ratio, thus capturing microseismic signals more accurately.

[0032] Specifically, each sensor in the nanoscale piezoelectric sensor array has a piezoelectric sensitive layer with a thickness ranging from 1 nanometer to 100 nanometers. This thickness range is chosen based on the physical properties of piezoelectric materials; a thinner piezoelectric sensitive layer can respond more sensitively to minute changes in mechanical stress, thereby improving the sensor's ability to detect microseismic signals. The sensor array is arranged in a two-dimensional grid on a flexible substrate with a grid spacing of 0.1 meters to 1 meter. This arrangement can be adjusted according to the specific size and shape of the underground cavern to achieve optimal detection results. The use of a flexible substrate allows the sensor array to better conform to the surrounding rock surface of the underground cavern or the borehole wall, thereby improving the accuracy and reliability of the detection. The quantum dot enhancement structure is an important component of the sensor. Quantum dots can significantly improve the sensor's sensitivity and signal-to-noise ratio through their quantum size effect. The size of quantum dots is typically between a few nanometers and tens of nanometers, and their unique optical and electrical properties enable the sensor to more effectively convert microseismic signals into electrical signals.

[0033] Preferably, the arrangement and parameter settings of the nanoscale piezoelectric sensor array can be optimized according to specific detection requirements. For example, for smaller underground caverns, the grid spacing can be appropriately reduced to improve detection resolution; while for larger underground caverns, the grid spacing can be appropriately increased to cover a wider area. In practical applications, the appropriate piezoelectric sensitive layer thickness and quantum dot size can be selected based on the geological conditions of the underground cavern and the detection target.

[0034] Furthermore, the flexible substrate of the sensor array can be made of flexible materials such as polyimide. These materials not only possess good flexibility but also high mechanical strength and chemical stability, enabling them to adapt to complex underground environments. During the sensor manufacturing process, the size and distribution of the piezoelectric sensitive layer and quantum dots can be precisely controlled through nanofabrication technology, thereby ensuring that the sensor performance meets design requirements. Through these optimization measures, the nanoscale piezoelectric sensor array can more effectively detect microseismic signals generated by the hidden microstructures of underground caverns, providing high-quality data support for subsequent data processing and structural identification.

[0035] In some embodiments, the data acquisition unit includes an analog-to-digital converter and a signal amplifier; the analog-to-digital converter is configured to convert the micro-vibration signal from an analog signal to a digital signal; the signal amplifier is configured to amplify the micro-vibration signal before conversion, with a gain factor of 10 to 100 times; the data acquisition unit further includes a multiplexer configured to sequentially switch signals from multiple nanoscale piezoelectric sensors for acquisition.

[0036] It should be noted that the data acquisition unit is a key component of the quantum sensing detection device for the hidden microstructure of underground caverns. Its main function is to convert the microseismic signals detected by the nanoscale piezoelectric sensor array from analog signals to digital signals and perform necessary signal amplification. The data acquisition unit includes an analog-to-digital converter (ADC), a signal amplifier, and a multiplexer. The ADC converts continuous analog signals into discrete digital signals for subsequent signal processing. The signal amplifier amplifies the weak microseismic signals before conversion to improve signal strength and signal-to-noise ratio. The multiplexer sequentially switches the signals from multiple nanoscale piezoelectric sensors, enabling efficient acquisition of signals from multiple sensors. These components work together to ensure high-quality signal acquisition and transmission.

[0037] Specifically, the analog-to-digital converter (ADC) in the data acquisition unit is a key component that converts analog signals into digital signals. The conversion accuracy of an ADC is typically determined by its bit depth; for example, a 12-bit or 16-bit ADC can provide higher conversion accuracy. The gain factor of the signal amplifier is set between 10 and 100, and this range is chosen based on the intensity of the micro-vibration signal and the requirements of subsequent processing.

[0038] In practical applications, the gain coefficient can be adjusted according to the signal strength and noise level to achieve the best signal amplification effect. The function of a multiplexer is to sequentially switch the signals from multiple nanometer-scale piezoelectric sensors, ensuring that the signal from each sensor is acquired sequentially. The number of channels in the multiplexer should be selected based on the size of the sensor array; for example, if the sensor array has 100 sensors, the multiplexer should have at least 100 channels. The specific parameter settings and selection of these components are crucial for ensuring high-quality signal acquisition.

[0039] Preferably, the signal amplifier of the data acquisition unit can employ automatic gain control (AGC) technology to adapt to micro-vibration signals of varying intensities. AGC technology automatically adjusts the amplifier gain based on the intensity of the input signal, thereby maintaining a stable output signal even when signal intensity varies significantly. Furthermore, the sampling rate of the analog-to-digital converter (ADC) should be selected based on the frequency characteristics of the micro-vibration signal. For example, for signals with a frequency range of 100 Hz to 10 kHz, the ADC sampling rate should be at least twice the highest frequency of the signal to meet the requirements of the Nyquist theorem.

[0040] In practical applications, the sampling rate of the ADC can be set to 20 kHz or higher to ensure signal integrity and accuracy. The switching speed of the multiplexer should also be matched with the ADC's sampling rate to avoid signal distortion. Through these optimizations, the data acquisition unit can more effectively acquire and process the micro-vibration signals detected by the nanoscale piezoelectric sensor array, providing high-quality data support for subsequent signal processing and structural surface identification.

[0041] In some embodiments, the signal processing unit includes a preprocessing module and a feature extraction module; the preprocessing module is configured to perform bandpass filtering on the digital signal, the bandpass filtering frequency range being 100 Hz to 10 kHz; the feature extraction module is configured to extract frequency features and time-domain features from the preprocessed signal; the frequency features include the dominant frequency and frequency band energy, and the time-domain features include signal amplitude and duration.

[0042] It should be noted that the signal processing unit plays a crucial role in the quantum sensing detection device for the hidden microstructures of underground caverns. Its main function is to preprocess and extract features from the acquired digital signals, generating feature data that can be used for structural surface identification. The preprocessing module removes noise components from the signal through bandpass filtering, ensuring signal purity. The feature extraction module extracts frequency and time-domain features from the preprocessed signal, including dominant frequency, frequency band energy, signal amplitude, and duration. This feature data can effectively characterize the properties of the microseismic signal, providing crucial information for subsequent structural surface identification.

[0043] Specifically, the signal processing unit includes a preprocessing module and a feature extraction module. The main function of the preprocessing module is to perform bandpass filtering on the digital signal, with a frequency range set from 100 Hz to 10 kHz. This frequency range is chosen based on the frequency characteristics of the microseismic signal, effectively removing low-frequency and high-frequency noise while retaining the main components of the signal. The bandpass filtering can be implemented using digital filters, such as Butterworth or Chebyshev filters. The feature extraction module extracts frequency and time-domain features from the preprocessed signal. Frequency features include the dominant frequency and band energy. The dominant frequency refers to the frequency component with the most concentrated energy in the signal, while the band energy represents the energy distribution of the signal within a specific frequency band. Time-domain features include signal amplitude and duration. Signal amplitude reflects the signal strength, while duration represents the signal's duration. These feature parameters can be extracted using signal processing methods such as Fourier transform, thus providing accurate feature data for the structural surface identification unit.

[0044] Preferably, the feature extraction module can employ specific algorithms when calculating the dominant frequency and band energy. The dominant frequency can be calculated by weighted averaging of the signal's spectrum, with the amplitude of each frequency component serving as the weight. The band energy can be calculated by integrating the square of the signal spectrum over a specific band. These calculation methods ensure the accuracy and reliability of the feature data.

[0045] In practical applications, the signal processing unit can further optimize the filter design and feature extraction algorithm based on the signal characteristics and detection requirements. For example, the parameters of the bandpass filter can be adjusted according to the signal's frequency distribution, or more complex feature extraction methods, such as wavelet transform, can be used to improve the accuracy of feature extraction. Through these optimization measures, the signal processing unit can process the acquired digital signals more effectively, providing high-quality feature data for the structural surface recognition unit, thereby improving the performance and reliability of the entire detection device.

[0046] In some embodiments, the feature extraction module calculates the dominant frequency of the microseismic signal using the following formula:

[0047] in, This is the dominant frequency of the microseismic signal. Let be the amplitude of the i-th frequency component. Let be the frequency value of the i-th frequency component, and N be the total number of frequency components.

[0048] It should be noted that the feature extraction module in the signal processing unit is used to calculate the dominant frequency of the microseismic signal, which is achieved through a specific formula. The dominant frequency refers to the frequency component in the signal where the energy is most concentrated; it reflects the main frequency characteristics of the signal. The parameters in the formula include the amplitude and frequency value of each frequency component, and the dominant frequency is calculated by weighted averaging of these parameters. This method can effectively extract the frequency characteristics of the signal, providing important basis for subsequent structural surface identification.

[0049] Specifically, the formula for calculating the dominant frequency in the feature extraction module is: the dominant frequency equals the sum of the products of the amplitudes and frequency values ​​of all frequency components divided by the sum of the amplitudes of all frequency components. In this formula, Indicates the main frequency. This represents the amplitude of the i-th frequency component. This represents the frequency value of the i-th frequency component, while N represents the total number of frequency components. Amplitude This reflects the signal strength at a specific frequency; the frequency value. This represents the frequency components of the signal. The dominant frequency of the signal can be obtained by multiplying the amplitude of each frequency component by its frequency value, summing the results, and then dividing by the sum of the amplitudes of all frequency components. This calculation process effectively extracts the main frequency characteristics of the signal, providing crucial information for subsequent signal analysis and processing.

[0050] Preferably, when calculating the dominant frequency, the feature extraction module first performs a Fourier transform on the signal to obtain its spectrum. The Fourier transform converts a time-domain signal into a frequency-domain signal, allowing the extraction of the signal's frequency components and their corresponding amplitudes. After obtaining the signal's spectrum, the dominant frequency is calculated using the aforementioned formula. Specific steps include: performing a Fourier transform on the signal to extract its frequency components and amplitudes; calculating the product of the amplitude and frequency value of each frequency component; summing the products of all frequency components; calculating the sum of the amplitudes of all frequency components; and finally, dividing the sum of the products by the sum of the amplitudes to obtain the dominant frequency. This process can be implemented using digital signal processing algorithms, such as the Fast Fourier Transform (FFT) algorithm, to improve computational efficiency. Through these steps, the feature extraction module can accurately calculate the dominant frequency of the microseismic signal, providing high-quality feature data for the structural surface identification unit, thereby improving the performance and reliability of the entire detection device.

[0051] In some embodiments, the feature extraction module further calculates the frequency band energy of the microseismic signal using the following formula:

[0052] in, For frequency band energy, This is the spectrum of the microseismic signal. This is the lower limit frequency of the frequency band. This is the upper limit frequency of the frequency band.

[0053] It should be noted that the feature extraction module in the signal processing unit is also used to calculate the band energy of the microseismic signal. Band energy refers to the energy distribution of a signal within a specific frequency band, reflecting the degree of energy concentration within that band. By calculating the band energy, the frequency characteristics of the signal can be further analyzed, providing richer feature information for structural surface identification. This calculation process is achieved through a specific formula that involves the signal's spectrum and the upper and lower limits of the band frequency.

[0054] Specifically, the formula for calculating band energy in the feature extraction module is: Band energy equals the integral of the square of the signal spectrum within a specific band. In this formula, Indicates frequency band energy. This represents the spectrum of the microseismic signal, while and These represent the lower and upper frequency limits of the frequency band, respectively. (Spectrum) Frequency band energy is the frequency domain representation of a signal, reflecting its amplitude distribution at different frequencies. It is calculated by integrating the square of the spectrum within a specified frequency band to obtain the total energy of the signal within that band. This calculation method effectively extracts the energy characteristics of a signal within a specific frequency band, providing crucial information for subsequent signal analysis and processing.

[0055] Preferably, when calculating the band energy, the feature extraction module first needs to perform a Fourier transform on the signal to obtain its spectrum. The Fourier transform converts a signal in the time domain into a signal in the frequency domain, thereby allowing the extraction of the signal's frequency components and their corresponding amplitudes. After obtaining the signal's spectrum, the band energy is calculated according to the formula described above. Specific steps include: performing a Fourier transform on the signal to extract its spectrum; and determining the upper and lower limits of the band frequency. and The frequency band energy is obtained by integrating the square of the spectrum within the frequency band. This process can be implemented using digital signal processing algorithms, such as the Fast Fourier Transform (FFT) algorithm, to improve computational efficiency. Through these steps, the feature extraction module can accurately calculate the frequency band energy of the microseismic signal, providing high-quality feature data for the structural surface identification unit, thereby improving the performance and reliability of the entire detection device.

[0056] In some embodiments, the structure surface identification unit includes a machine learning model, wherein the machine learning model is a support vector machine model; the structure surface identification unit is configured to use the support vector machine model to classify the type of hidden microstructures based on the feature data, wherein the type includes joints, fissures and faults; the structure surface identification unit is further configured to output the three-dimensional coordinates and azimuth of the hidden microstructures.

[0057] It should be noted that the structural surface identification unit in the quantum sensing detection device for hidden microstructures in underground caverns is responsible for analyzing feature data using a machine learning model to identify parameters such as the type, location, dip angle, and length of the hidden microstructures. Machine learning models, particularly Support Vector Machines (SVMs), are chosen due to their efficiency and accuracy in classification tasks. This model can classify hidden microstructures based on input feature data, such as frequency and temporal features, and output detailed information such as their three-dimensional coordinates and azimuth. This process not only improves the accuracy of identification but also enhances adaptability to complex geological structures, providing crucial support for the safety assessment of underground engineering projects.

[0058] Specifically, the machine learning model in the structural surface identification unit, namely the Support Vector Machine (SVM) model, is a supervised learning model based on statistical learning theory, used for data classification. The SVM model separates data points of different categories by finding the optimal separating hyperplane, thereby classifying the types of hidden microstructures. In this embodiment, the input parameters of the SVM model include frequency and temporal features extracted from the signal processing unit, such as dominant frequency, band energy, signal amplitude, and duration. These feature data, after preprocessing and feature extraction, are input into the SVM model. The model learns the feature patterns of different types of hidden microstructures, such as joints, fissures, and faults, through the training dataset, thus enabling accurate identification of the types of hidden microstructures in practical applications. Furthermore, the SVM model can also output the three-dimensional coordinates and azimuth of the hidden microstructures; these parameters are of great significance for understanding the spatial distribution and geological structure of the structural surfaces.

[0059] Preferably, the process of constructing an SVM model includes data preprocessing, model training, and parameter optimization. First, the collected feature data is normalized to eliminate dimensional differences between different features and improve the model's training performance. Then, the SVM model is trained using a labeled training dataset. During training, optimal model parameters, such as the penalty parameter C and kernel function parameters, are selected through cross-validation. Commonly used kernel functions include linear kernels, polynomial kernels, and radial basis function kernels. Choosing a suitable kernel function can improve the model's ability to classify complex data. After model training is complete, the extracted feature data is input into the trained SVM model, and the model outputs parameters such as the type of hidden structure surface, three-dimensional coordinates, and azimuth angle. This process not only improves the accuracy and efficiency of recognition but also enhances the model's adaptability to different geological conditions, providing reliable technical support for the safety assessment and design of underground engineering projects.

[0060] In some embodiments, a deployment unit is further included, the deployment unit being configured to install the nanoscale piezoelectric sensor array onto the surrounding rock surface of an underground cavern or into a borehole; the deployment unit includes a robotic arm and a positioning system, the positioning system using GPS or a laser rangefinder to determine the sensor location; the deployment unit is also configured to adjust the sensor deployment density based on the results of a pre-analysis of the geological structure of the underground cavern.

[0061] It should be noted that the deployment unit in the quantum sensing detection device for the hidden microstructure of underground caverns is used to install a nanoscale piezoelectric sensor array onto the surrounding rock surface or inside boreholes of the underground cavern. The purpose of the deployment unit is to ensure that the sensor array can be accurately installed in the predetermined location and to adjust the sensor deployment density based on the pre-analysis results of the geological structure. The deployment unit includes a robotic arm and a positioning system, which can use GPS or a laser rangefinder to determine the sensor's position. This deployment method not only improves the accuracy of sensor installation but also enhances adaptability to complex terrain, ensuring high-precision detection of microseismic signals.

[0062] Specifically, the robotic arm in the deployment unit is used to physically install the nanoscale piezoelectric sensor array. Its operational precision and flexibility directly affect the sensor installation effect. The positioning system is responsible for accurately determining the sensor installation location, and can choose to use either GPS or a laser rangefinder. GPS provides global positioning services and is suitable for sensor deployment in open areas; laser rangefinders are more accurate in underground environments and are suitable for areas with weak GPS signals, such as underground caverns. The deployment unit also adjusts the sensor deployment density based on the pre-analysis results of the underground cavern's geological structure. This means that in areas with complex geological conditions or dense structural surfaces, the sensor deployment density will be increased accordingly to improve detection resolution and accuracy. The adjustment of deployment density can be determined by analyzing geological reports, historical data, or previous exploration results to ensure that the sensor array can cover key areas, thereby improving the overall effectiveness of the detection system.

[0063] Preferably, the operation process of the deployment unit can be further refined. First, based on the results of the geological structure pre-analysis, the deployment plan for the sensors is determined, including the deployment location and density. Then, using a positioning system, such as a laser rangefinder, the size and shape of the underground cavern are accurately measured, providing precise coordinate information for sensor installation. Next, a robotic arm installs the sensor array onto the surrounding rock surface or into the borehole based on this coordinate information. During installation, the robotic arm can be equipped with a visual feedback system to ensure accurate sensor placement. After installation, functional tests are performed on the sensors to ensure that each sensor functions properly and accurately collects data. Through these steps, the deployment unit can efficiently and accurately complete the installation of the sensor array, providing a solid foundation for high-precision detection of the hidden microstructure of underground caverns.

[0064] In some embodiments, a display unit is further included, which is connected to the structure surface identification unit and is used to visualize the location and parameters of the identified hidden microstructure surfaces; the display unit is configured to generate a three-dimensional geological model and overlay the hidden microstructure surfaces in the three-dimensional geological model.

[0065] It should be noted that the display unit in the quantum sensing detection device for the hidden microstructure surfaces of underground caverns is used to visualize and display the location and parameters of the identified hidden microstructure surfaces. This unit generates a three-dimensional geological model and overlays the hidden microstructure surfaces onto the model, providing geological engineers and researchers with intuitive geological structural information. This visualization method not only improves the understanding of underground structures but also facilitates further engineering design and safety assessments. The implementation of the display unit relies on advanced graphics processing technology and data visualization algorithms, ensuring the accuracy and readability of the information.

[0066] Specifically, the display unit's functions include generating a 3D geological model and overlaying microstructural information. The 3D geological model is obtained by modeling geological data from underground caverns, which may include geological exploration data, borehole data, and microseismic signal data collected by sensor arrays. The model generation process typically involves steps such as topographic reconstruction, geological stratification, and structural surface location. The overlay display of microstructural surfaces involves graphically overlaying parameters such as the position, dip angle, and length of the microstructural surfaces output by the structural surface identification unit onto the 3D geological model. The display unit can use rendering techniques from computer graphics, such as ray tracing or rasterization, to achieve high-quality visualization effects. Furthermore, the display unit provides interactive functions, allowing users to view different parts of the model through zooming, rotating, and panning operations, thereby gaining a more comprehensive understanding of the geological structure.

[0067] Preferably, the implementation process of the display unit can be further refined. First, based on the collected geological data and microseismic signal data, a three-dimensional geological model is generated using professional geological modeling software. The model generation process includes steps such as data preprocessing, mesh generation, and surface reconstruction. In the data preprocessing stage, the collected data is cleaned and corrected to ensure its accuracy and consistency. Then, based on the distribution and characteristics of the geological data, geological layers are divided, and a three-dimensional mesh is constructed for each layer. Next, using the hidden structural surface parameters output by the structural surface identification unit, such as location, dip angle, and length, hidden structural surfaces are drawn in the three-dimensional geological model. The drawing process can be achieved by defining geometry and texture to ensure that the hidden structural surfaces are clearly visible in the model. Finally, the display unit provides a user interface, allowing users to view different perspectives and details of the model through mouse and keyboard operations. Through these steps, the display unit can effectively transform complex geological data into intuitive three-dimensional visualization information, providing strong support for the planning and design of underground engineering.

[0068] In some embodiments, a rock mass grading unit is further included, which is connected to the structural surface identification unit and is used to calculate rock mass quality indicators based on the identified hidden microstructural surface parameters; the rock mass grading unit is configured to calculate rock mass quality indicators using the BQ grading method or the RMR grading method; the rock mass grading unit is also configured to output rock mass quality grading results and stability evaluation reports.

[0069] It should be noted that the rock mass grading unit in the quantum sensing detection device for hidden microstructure surfaces in underground caverns is used to calculate rock mass quality indicators based on identified hidden microstructure surface parameters, and output rock mass quality grading results and stability evaluation reports. Rock mass grading is a crucial step in assessing the safety and stability of underground engineering projects. By using standardized grading methods, such as the BQ grading method or the RMR grading method, the quality of the rock mass can be quantified, providing a scientific basis for engineering design and construction. The implementation of the rock mass grading unit relies on the accurate identification of hidden microstructure surface parameters and a reasonable grading algorithm to ensure the reliability and practicality of the grading results.

[0070] Specifically, the function of the rock mass classification unit includes calculating rock mass quality indicators using the BQ classification method or the RMR classification method. The BQ classification method is a classification method based on factors such as the uniaxial compressive strength of the rock, the rock mass integrity index, and structural surface conditions. Its calculation formula is as follows: ,in For the uniaxial compressive strength of rock, The RMR (Rock Mass Integrity Index) is a classification method that comprehensively considers factors such as rock strength, joint spacing, joint conditions, groundwater, and joint orientation. Its calculation formula is: ,in to Each factor corresponds to a score. The rock mass grading unit calculates scores for these factors based on the microstructural parameters output by the structural surface identification unit, such as location, dip angle, and length, and ultimately derives the rock mass quality index. Furthermore, the rock mass grading unit is also responsible for outputting rock mass quality grading results and stability evaluation reports, which can provide important reference for the design and construction of underground engineering projects.

[0071] Preferably, the implementation process of the rock mass classification unit can be further refined. First, based on the hidden microstructure parameters output by the structure surface identification unit, the uniaxial compressive strength of the rock is calculated. and rock mass integrity index These parameters can be obtained through laboratory testing or field exploration data. Then, using the formulas for the BQ grading method or the RMR grading method, these parameters are substituted into the formulas to calculate the rock mass quality index. During the calculation process, an appropriate grading method can be selected based on specific geological conditions and engineering requirements. For example, in areas with high rock strength, the BQ grading method can be used preferentially; while in areas with well-developed joints, the RMR grading method may be more suitable. Finally, based on the calculated rock mass quality index, the rock mass quality grading results and stability evaluation report are output. The report may include the rock mass grading category, stability evaluation, and corresponding engineering recommendations. Through these steps, the rock mass grading unit can provide scientific and accurate technical support for the safety assessment and design of underground engineering projects.

[0072] The above embodiments of the present invention have the following beneficial effects: 1. By employing a nanoscale piezoelectric sensor array, this device can improve the detection sensitivity and signal-to-noise ratio of microseismic signals, thereby achieving high-precision detection of hidden microstructures in underground caverns. This high-sensitivity detection capability allows the device to capture extremely weak signals, thus more accurately identifying the existence of hidden microstructures and solving the problem that traditional detection methods are unable to detect minute structures due to insufficient sensitivity.

[0073] 2. By combining the preprocessing and feature extraction functions of the signal processing unit, this device can effectively remove noise interference and extract key features of the microseismic signal, such as frequency and time-domain features. This process not only improves the signal quality but also provides a more accurate data foundation for subsequent structural surface identification, enabling more precise identification of parameters such as the type, location, dip angle, and length of the structural surface. This addresses the shortcomings of traditional methods in signal processing and improves the reliability and accuracy of the detection results.

[0074] 3. This device employs machine learning models, such as support vector machines, for the identification and classification of structural surfaces. This advanced identification technology can quickly and accurately classify the types of hidden structural surfaces based on extracted feature data and output their three-dimensional coordinates and azimuth. This not only improves identification efficiency but also enhances adaptability to complex geological structures, overcoming the limitations of traditional methods in structural surface identification and classification, and providing more comprehensive and accurate information for geological assessment of underground engineering.

[0075] Furthermore, the storage medium in the embodiments of this application stores program instructions capable of implementing all the above methods. These program instructions can be stored in the storage medium in the form of a software product, including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks, or terminal devices such as computers, servers, mobile phones, and tablets.

[0076] The above description is merely an explanation of some preferred embodiments of the present invention and the technical principles employed. Those skilled in the art should understand that the scope of the invention as described in the embodiments of the present invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A quantum sensing and detection device for the hidden microstructure of underground caverns, characterized in that, It includes a nanoscale piezoelectric sensor array, a data acquisition unit, a signal processing unit, and a structure surface recognition unit; The nanoscale piezoelectric sensor array is configured to be deployed on the surface of the surrounding rock of an underground cavern or inside a borehole to detect microseismic signals generated by hidden microstructures. The data acquisition unit is electrically connected to the nanoscale piezoelectric sensor array and is used to acquire the micro-vibration signal and convert the micro-vibration signal into a digital signal. The signal processing unit is connected to the data acquisition unit and is used to preprocess and extract features from the digital signal to generate feature data; The structure surface recognition unit is connected to the signal processing unit and is used to identify the position, tilt angle, and length parameters of the hidden microstructure surface based on the feature data.

2. The quantum sensing detection device for the hidden microstructure of underground caverns as described in claim 1, characterized in that, The nanoscale piezoelectric sensor array includes multiple nanoscale piezoelectric sensors, each having a piezoelectric sensitive layer with a thickness of 1 nanometer to 100 nanometers. The nanoscale piezoelectric sensor array is arranged in a two-dimensional grid on a flexible substrate with a grid spacing of 0.1 meters to 1 meter. Each nanoscale piezoelectric sensor in the array includes a quantum dot enhancement structure configured to improve the sensitivity and signal-to-noise ratio of the micro-vibration signal.

3. The quantum sensing and detection device for the hidden microstructure of underground caverns as described in claim 1, characterized in that, The data acquisition unit includes an analog-to-digital converter and a signal amplifier; the analog-to-digital converter is configured to convert the micro-vibration signal from an analog signal to a digital signal; the signal amplifier is configured to amplify the micro-vibration signal before conversion, with a gain factor of 10 to 100 times; the data acquisition unit also includes a multiplexer configured to sequentially switch signals from multiple nanoscale piezoelectric sensors for acquisition.

4. The quantum sensing and detection device for the hidden microstructure of underground caverns as described in claim 1, characterized in that, The signal processing unit includes a preprocessing module and a feature extraction module; The preprocessing module is configured to perform bandpass filtering on the digital signal, with the bandpass filtering frequency range being 100 Hz to 10 kHz; the feature extraction module is configured to extract frequency features and time-domain features from the preprocessed signal; the frequency features include the main frequency and frequency band energy, and the time-domain features include the signal amplitude and duration.

5. The quantum sensing detection device for the hidden microstructure of underground caverns as described in claim 4, characterized in that, The feature extraction module uses the following formula to calculate the dominant frequency of the microseismic signal: in, This is the dominant frequency of the microseismic signal. Let be the amplitude of the i-th frequency component. Let be the frequency value of the i-th frequency component, and N be the total number of frequency components.

6. The quantum sensing and detection device for the hidden microstructure of underground caverns as described in claim 4, characterized in that, The feature extraction module also uses the following formula to calculate the frequency band energy of the microseismic signal: in, For frequency band energy, This is the spectrum of the microseismic signal. This is the lower limit frequency of the frequency band. This is the upper limit frequency of the frequency band.

7. The quantum sensing and detection device for the hidden microstructure of underground caverns as described in claim 1, characterized in that, The structural surface identification unit includes a machine learning model, which is a support vector machine model; the structural surface identification unit is configured to use the support vector machine model to classify the type of hidden microstructures based on the feature data, the type including joints, fissures and faults; the structural surface identification unit is also configured to output the three-dimensional coordinates and azimuth of the hidden microstructures.

8. The quantum sensing detection device for the hidden microstructure of underground caverns as described in claim 1, characterized in that, It also includes a deployment unit configured to install the nanoscale piezoelectric sensor array onto the surrounding rock surface of the underground cavern or into a borehole; the deployment unit includes a robotic arm and a positioning system, the positioning system using GPS or a laser rangefinder to determine the sensor location; the deployment unit is also configured to adjust the sensor deployment density based on the results of a pre-analysis of the geological structure of the underground cavern.

9. The quantum sensing detection device for the hidden microstructure of underground caverns as described in claim 1, characterized in that, It also includes a display unit, which is connected to the structure surface identification unit and is used to visualize the location and parameters of the identified hidden microstructure surfaces; the display unit is configured to generate a three-dimensional geological model and overlay the hidden microstructure surfaces on the three-dimensional geological model.

10. The quantum sensing detection device for the hidden microstructure of underground caverns as described in claim 1, characterized in that, It also includes a rock mass grading unit, which is connected to the structural surface identification unit and is used to calculate rock mass quality indicators based on the identified hidden microstructural surface parameters; the rock mass grading unit is configured to calculate rock mass quality indicators using the BQ grading method or the RMR grading method; the rock mass grading unit is also configured to output rock mass quality grading results and stability evaluation reports.

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