Geological investigation monitoring and early warning system and method

Abstract

The invention relates to the technical field of big data, in particular to a geological survey monitoring and early warning system and method. The data optimization module is in communication connection with the data governance module and is used for evaluating the health condition of the data lake; metadata and data tracking capability are provided; executing a data management strategy, and providing a life cycle management function of the data; defining an optimization target and a constraint condition, and providing decision support for a data management strategy and resource allocation; obtaining a data value score through the data value evaluation model; the intelligent analysis engine is in communication connection with the data governance module and the data optimization module and selects a corresponding intelligent analysis method to analyze the data in the data lake according to the data type; generating an analysis report and comparing the analysis report with the data expectation; dynamically adjusting the weight of the analysis method according to the comparison result; and the deep reinforcement learning decision-making system is in communication connection with the data optimization module and the intelligent analysis engine to jointly ensure the quality and effective utilization of the data.

Description

Technical Field

[0001] The present invention relates to the technical field of geological exploration and monitoring, and particularly to a geological exploration and monitoring early warning system and method. Background Art

[0002] In recent years, with the acceleration of the urbanization process and the increase in extreme weather events, the frequency and intensity of geological disasters have shown an upward trend, posing a serious threat to people's lives and property. Against this background, the importance of geological exploration and monitoring early warning systems has become increasingly prominent. Traditional geological exploration and monitoring methods mainly rely on manual inspections and single-sensor monitoring. Such methods have problems such as low monitoring frequency, limited coverage, and lagging data analysis, and are difficult to meet the needs of modern geological disaster prevention and control.

[0003] With the progress of technology, some new types of geological exploration and monitoring early warning systems have gradually emerged. These systems usually adopt a combination of multiple sensors, such as GNSS, tilt sensors, deep displacement gauges, etc., to obtain more comprehensive monitoring data. However, there are still many deficiencies in these systems in terms of data processing and analysis. First, the fusion and noise reduction of multi-source heterogeneous data are still a challenge, and existing methods often have difficulty effectively removing various noise interferences in complex environments. Second, in terms of data analysis, most systems still use simple statistical methods or shallow machine learning algorithms, and are difficult to capture the non-linear and non-stationary characteristics in geological processes. In addition, existing risk assessment models are usually too simplified and cannot fully consider the complexity and dynamics of geological systems, resulting in difficulties in ensuring the accuracy and timeliness of early warnings.

[0004] The closest prior art usually uses a method of combining multi-source data collection with machine learning for geological disaster early warning. Although this method improves the comprehensiveness of monitoring and the accuracy of early warning to a certain extent, there are still the following problems: First, the data preprocessing ability is limited, and it is difficult to effectively process noise interference in complex environments; second, machine learning models are often black-box, lacking in-depth understanding of the physical mechanisms of geological processes; third, the generalization ability of early warning models is insufficient, and they are prone to failure when facing new and complex geological environments; fourth, the real-time performance of the system is insufficient, and the time from data collection to generating early warning information is long, making it difficult to respond to sudden geological disasters. Summary of the Invention

[0005] The present invention aims to solve the deficiencies of existing geological exploration and monitoring early warning systems in data processing, analysis, and early warning, and provides a more comprehensive, accurate, and timely geological disaster early warning solution.

[0006] The present invention proposes a geological exploration and monitoring early warning system, including:

[0007] A multi-dimensional perception module, for:

[0008] Collect surface deformation data, underground monitoring data, and environmental parameter data;

[0009] Transmit the collected multi-source data to the data processing module;

[0010] The data processing module, communicatively connected to the multi-dimensional perception module, is configured to:

[0011] Receive the multi-source data sent by the multi-dimensional perception module;

[0012] Based on the multi-source data, perform data denoising, fusion, and non-linear feature extraction;

[0013] The dynamic analysis module, communicatively connected to the data processing module, is configured to:

[0014] Receive the processed data output by the data processing module;

[0015] Based on the processed data, perform time series analysis and non-stationary process modeling;

[0016] The risk assessment module, communicatively connected to the dynamic analysis module, is configured to:

[0017] Receive the analysis result output by the dynamic analysis module;

[0018] Based on the analysis result, calculate the risk metric and generate a risk assessment report;

[0019] The early warning decision-making module, communicatively connected to the risk assessment module, is configured to:

[0020] Receive the risk assessment report output by the risk assessment module;

[0021] Based on the risk assessment report, determine the early warning level and issue an early warning message.

[0022] Preferably, the multi-dimensional perception module includes:

[0023] The surface monitoring unit, configured to collect high-precision GNSS displacement data, tilt sensor data, and crack monitoring data;

[0024] The underground monitoring unit, configured to collect deep displacement data, underground water level data, and stress and strain data;

[0025] The environmental parameter monitoring unit, configured to collect rainfall data, temperature data, and seismic wave data.

[0026] Preferably, the data processing module includes:

[0027] The denoising and fusion unit, configured to perform denoising and fusion of multi-source data based on the random matrix theory;

[0028] A feature extraction unit for extracting non - linear features based on topological data analysis method.

[0029] Preferably, the noise reduction and fusion unit adopts the singular spectrum analysis method based on random matrix, including the following steps:

[0030] Construct the input data matrix \(X = [X_1,X_2,\cdots,X n \in R m×n , where \(X i \in R m represents the m - dimensional observation data of the i - th sensor;

[0031] Perform singular value decomposition \(Y = U\Sigma V T ; Define the noise reduction threshold \(\lambda=\text{median}(\sigma i ) + c\cdot\text{MAD}(\sigma i ), where \(\sigma i is the singular value, c is a constant, and MAD is the median absolute deviation;

[0032] Generate the noise - reduced data matrix \(X clean = U\Sigma λ V T , where \(\Sigma λ is the matrix with singular values less than \(\lambda\) set to zero,

[0033] where \(X\) is the input data matrix, \(X i is the observation data of the i - th sensor, \(Y\) is the result after singular value decomposition, \(U\) is the left singular vector matrix, \(\Sigma\) is the singular value matrix, \(V\) is the right singular vector matrix, \(\lambda\) is the noise reduction threshold, and \(X clean is the noise - reduced data matrix.

[0034] Preferably, the feature extraction unit adopts the persistent homology algorithm based on topological data analysis, including the following steps:

[0035] Define the filtration function \(f:X clean \to R\);

[0036] Calculate the persistent homology \(PH * (X clean )=\{H * (f -1 (-\infty,\epsilon])\to H * (f -1 (-\infty,\epsilon'])|\epsilon\leq\epsilon'\}, where \(H * represents the k - th homology group, and \(\epsilon\) is the filtration parameter;

[0037] Extract the significant features \(F\) in the persistence diagram \(D k =\{(b i ,di )|(b i ,d i ) ∈ D k ,d i -b i > δ}, where (b i ,d i ) is a birth-death pair in the persistence diagram, and δ is the persistence threshold.

[0038] Preferably, the dynamic analysis module includes:

[0039] A time series analysis unit for analyzing the dynamic characteristics of a time series based on fractional calculus and chaos theory;

[0040] A non-stationary process modeling unit for constructing a non-stationary random process model based on quantum probability theory and non-commutative geometry.

[0041] Preferably, the time series analysis unit performs the following steps:

[0042] Define the fractional derivative where 0 < α < 1 and Γ is the gamma function;

[0043] Calculate the fractional autocorrelation function R α (τ) = E[D α X(t) · D α X(t + τ)];

[0044] Extract the dynamic feature vector V = [λ max , h, C, R α (0), R α (τ max )], where λ max is the largest Lyapunov exponent, h is the Kolmogorov entropy, and C is the correlation dimension;

[0045] where D α is the fractional derivative, α is the fractional parameter, R α (0) is the value of the autocorrelation function at zero delay, and R α (τ max ) is the value of the autocorrelation function at the maximum delay.

[0046] Preferably, the non-stationary process modeling unit performs the following steps:

[0047] Define the non-commutative probability space where is a von Neumann algebra, is the state;

[0048] Construct the quantum stochastic process where Ut is a family of unitary operators satisfying U s +t = U s U t ;

[0049] Calculating the moments of a quantum stochastic process

[0050] Extracting non-stationary features G = {m2(t, t + τ), m3(t, t + τ1, t + τ2), m4(t, t + τ1, t + τ2, t + τ3)},

[0051] where m2(t, t + τ) is the second moment, m3(t, t + τ1, t + τ2) is the third moment, and m4(t, t + τ1, t + τ2, t + τ3) is the fourth moment.

[0052] Preferably, the risk assessment module constructs a risk assessment model based on algebraic topology and category theory, including the following steps:

[0053] Defining a risk manifold and an observation space Constructing a fiber bundle

[0054] Defining the tangent bundle TE and the cotangent bundle T * E, and constructing a symplectic structure

[0055] Defining the Hamiltonian function H: T * E → R, H(x, p) = K(p) + V(x);

[0056] Solving the Hamilton equations to obtain the risk evolution trajectory γ(t) = (x(t), p(t));

[0057] Calculating the risk metric where g ij is the Riemannian metric on the risk manifold;

[0058] where,[[]] is the time derivative of the position variable, is the time derivative of the momentum variable, p i is the momentum variable, x i is the position variable, M -1 is the inverse of the mass matrix, and k is the potential energy coefficient.

[0059] A geological exploration monitoring and early warning method, based on the system, includes the following steps:

[0060] Collecting surface deformation data, underground monitoring data, and environmental parameter data through the multi-dimensional perception module;

[0061] Transfer the collected multi-source data to the data processing module;

[0062] The data processing module receives the multi-source data and performs data denoising, fusion, and non-linear feature extraction based on random matrix theory and topological data analysis methods;

[0063] The dynamic analysis module receives the processed data and performs time series analysis and non-stationary process modeling based on fractional calculus, chaos theory, quantum probability theory, and non-commutative geometry;

[0064] The risk assessment module receives the analysis results, constructs a risk assessment model based on algebraic topology and category theory, calculates the risk metric, and generates a risk assessment report;

[0065] The early warning decision-making module receives the risk assessment report, determines the early warning level, and issues early warning information.

[0066] From a macroscopic perspective, the geological exploration monitoring and early warning system of the present invention constructs a complete and intelligent geological disaster early warning system by integrating advanced technologies such as multi-dimensional perception, intelligent data processing, dynamic analysis, and risk assessment. This system can comprehensively perceive the changes in the geological environment, deeply analyze the complex dynamic characteristics of geological processes, and provide accurate and timely early warning information, thereby greatly improving the efficiency and effectiveness of geological disaster prevention and control.

[0067] Specifically, the beneficial effects of the present invention are mainly reflected in the following aspects:

[0068] First, in terms of data collection, the multi-dimensional perception module of the present invention realizes the comprehensive perception of surface deformation, underground monitoring, and environmental parameters. This multi-source data collection method not only improves the comprehensiveness of monitoring but also enhances the reliability of monitoring through the complementarity of data from different sensors. For example, the combination of surface GNSS monitoring and deep displacement monitoring can comprehensively grasp the deformation characteristics of geological bodies and effectively avoid the one-sidedness that may be brought by a single monitoring method.

[0069] Second, in terms of data processing, the present invention adopts a noise reduction and fusion method based on random matrix theory, effectively solving the problem of processing multi-source heterogeneous data. This method can not only remove various complex noise interferences but also extract more valuable information through data fusion. Especially when processing high-dimensional and non-stationary geological monitoring data, this method shows significant advantages, providing a high-quality data basis for subsequent analysis.

[0070] In terms of data analysis, the present invention combines advanced mathematical tools such as fractional calculus, chaos theory, and quantum probability theory to construct a powerful dynamic analysis engine. This engine can deeply explore complex features such as non-linearity and long-range correlation in geological data, thereby more accurately describing and predicting the evolution of geological processes. Especially when dealing with complex geological systems that are difficult to handle by traditional methods, the method of the present invention shows obvious advantages.

[0071] In terms of risk assessment, the present invention constructs an innovative risk assessment model based on algebraic topology and category theory. This model can capture the complex topological structure and dynamic characteristics of geological systems, thereby providing more comprehensive and accurate risk assessment results. Compared with traditional simple threshold methods or statistical methods, this method can better identify potential high-risk areas and critical time points, providing strong support for early warning decision-making.

[0072] Finally, in terms of the overall system performance, the present invention realizes the full-process intelligence from data acquisition to early warning decision-making through the close cooperation between various modules. This cooperation not only improves the overall efficiency of the system, but also realizes a qualitative leap in early warning performance through the optimization and complementarity of each link. For example, efficient data processing provides high-quality input for dynamic analysis, and accurate dynamic analysis provides a reliable basis for risk assessment, ultimately forming an efficient and accurate early warning decision-making chain.

[0073] In summary, the geological exploration monitoring and early warning system of the present invention effectively solves a number of problems existing in the prior art through innovative technical solutions, significantly improving the accuracy, timeliness, and reliability of geological disaster early warnings. This not only provides strong technical support for geological disaster prevention and control, but also points out a new direction for the technical development of related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is the logical block diagram of the overall system of the present invention.

[0075] Figure 2 It is the logical block diagram of the multi-dimensional perception module of the present invention.

[0076] Figure 3 It is the logical block diagram of the data processing module of the present invention.

[0077] Figure 4 It is the logical block diagram of the dynamic analysis module of the present invention.

[0078] Figure 5 It is the logical block diagram of the risk assessment module of the present invention.

[0079] Figure 6 It is the logical block diagram of the early warning decision-making module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0080] See also Figure 1-6 The present invention provides a geological survey monitoring and early warning system and method, which includes a multi-dimensional perception module 1, a data processing module 2, a dynamic analysis module 3, a risk assessment module 4 and an early warning decision module 5. These modules work together to achieve intelligent processing of the entire process from data collection to early warning decision, greatly improving the accuracy and timeliness of geological disaster monitoring and early warning.

[0081] Specifically, the geological survey monitoring and early warning system of the present invention includes the following modules:

[0082] The multi-dimensional perception module 1 is used to collect surface deformation data, underground monitoring data and environmental parameter data, and transmit the collected multi-source data to the data processing module 2. This module can fully perceive the changes in the geological environment and provide rich raw data for subsequent analysis.

[0083] The data processing module 2 is connected to the multi-dimensional perception module 1 for receiving multi-source data sent by the multi-dimensional perception module 1 and performing data noise reduction, fusion and non-linear feature extraction based on the data. Through these processes, the data quality can be significantly improved, laying the foundation for subsequent analysis.

[0084] The dynamic analysis module 3 is connected to the data processing module 2 for receiving the processed data output by the data processing module 2 and performing time series analysis and non-stationary process modeling based on the data. This step can deeply explore the dynamic characteristics and potential laws in the data.

[0085] The risk assessment module 4 is in communication with the dynamic analysis module 3 and is used to receive the analysis results output by the dynamic analysis module 3, and calculate the risk metrics based on these results and generate a risk assessment report. This step converts the analysis results into understandable risk information.

[0086] The early warning decision module 5 is connected to the risk assessment module 4 for receiving the risk assessment report output by the risk assessment module 4, and determining the early warning level and issuing early warning information based on the report. This is the final output of the system, providing direct action guidance for decision makers.

[0087] Preferably, the multi-dimensional perception module 1 of the present invention includes a surface monitoring unit 11 , an underground monitoring unit 12 and an environmental parameter monitoring unit 13 .

[0088] The surface monitoring unit 11 is used to collect high-precision GNSS displacement data, tilt sensor data and crack monitoring data. For example, a high-precision GNSS receiver can achieve millimeter-level displacement monitoring, a tilt sensor can detect small angle changes (usually with an accuracy of up to 0.01°), and a crack monitor can measure micron-level crack changes.

[0089] The underground monitoring unit 12 is used to collect deep displacement data, underground water level data, and stress-strain data. For example, deep displacement can be monitored by an inclinometer with an accuracy of up to 0.1 mm / m; the underground water level can be measured by a pressure water level gauge with an accuracy of up to ±0.1% FS; stress-strain data can be obtained by strain gauges or fiber optic sensors with an accuracy of up to 1 με (microstrain).

[0090] The environmental parameter monitoring unit 13 is used to collect rainfall data, temperature data, and seismic wave data. For example, the resolution of a rain gauge can reach 0.1 mm, the accuracy of a temperature sensor can reach ±0.1 °C, and seismic wave monitoring can use a broadband seismograph with a frequency range covering 0.01 Hz to 50 Hz.

[0091] The data processing module 2 of the present invention includes a noise reduction and fusion unit 21 and a feature extraction unit 22.

[0092] The noise reduction and fusion unit 21 is used to perform noise reduction and fusion of multi-source data based on the random matrix theory. This method is particularly suitable for processing high-dimensional and heterogeneous geological monitoring data. Specifically, the following steps can be adopted:

[0093] First, construct the input data matrix:

[0094] X = [X1, X2,..., X n ∈ R m×n ,

[0095] where, X i ∈ R m represents the m-dimensional observation data of the i-th sensor. For example, for GNSS data, X i may represent the displacement time series in the xy and z directions. Then, perform singular value decomposition:

[0096] Y = UΣV T ,

[0097] where, U and V are the left and right singular vector matrices respectively, and Σ is the singular value matrix. Next, define the noise reduction threshold:

[0098] λ = median(σ i ) + c · MAD(σ i ),

[0099] where, σ i is the singular value, c is a constant (the empirical value is usually between 2 and 3), and MAD is the median absolute deviation. This threshold selection method can effectively distinguish signal and noise components.

[0100] Finally, generate the noise-reduced data matrix:

[0101] X clean = UΣ λ V T ,

[0102] where Σ λ is the matrix with singular values less than λ set to zero.

[0103] In the geological exploration monitoring and early warning system, multi-source heterogeneous data (such as surface deformation data, underground monitoring data, and environmental parameter data) need to be fused and denoised. We use singular value decomposition (SVD) in random matrix theory for data denoising and fusion. In the geological exploration monitoring and early warning system, these steps can significantly improve data quality and remove various noise interferences.

[0104] For example, high-precision GNSS equipment is used to collect surface displacement data with an accuracy of up to millimeters; borehole inclinometers are used to collect deep displacement data with an accuracy of up to 0.1 mm / m; rain gauges are used to collect rainfall data with a resolution of up to 0.1 mm. These multi-source data are input into the data processing module, and through singular value decomposition and denoising processing, noise is removed and multi-source data are fused to obtain high-quality original data. Through denoising processing, the noise components in the data can be significantly reduced, thus improving the accuracy of subsequent analysis. Fusing multi-source data can provide more comprehensive information and help better understand the changes in the geological environment.

[0105] The feature extraction unit 22 is used to extract non-linear features based on topological data analysis methods. This method can capture complex geological structure features that may be ignored by traditional linear methods. The specific steps include:

[0106] First, define the filtering function f: X clean → R. In geological monitoring, this function can be the magnitude or rate of change of a certain physical quantity (such as displacement, stress).

[0107] Then, calculate the persistent homology:

[0108] PH * (X clean ) = {H * (f -1 (-∞, ∈]) → H * (f -1 (-∞, ∈′]) | ∈ ≤ ∈′},

[0109] where H * represents the k-th homology group and ∈ is the filtering parameter. This step can reveal the topological structure of the data. Finally, extract the significant features in the persistence diagram D k :

[0110] F = {(b i , d i)(b i , d i ) ∈ D k , d i -b i > δ},

[0111] where (b i , d i ) is a birth - death pair in the persistence diagram, and δ is the persistence threshold. Empirically, δ can be chosen as the median or mean of the persistence values.

[0112] In the geological exploration monitoring and early warning system, these steps can capture the subtle structures and dynamic features in the data.

[0113] For example, use high - precision GNSS devices to collect surface displacement data, and use strain gauges or fiber optic sensors to measure stress change data. Input these data into the feature extraction module, and extract non - linear features through the persistent homology algorithm to reveal the complex structure and dynamic characteristics of the geological system. Through the persistent homology algorithm, complex geological structure features that may be ignored by traditional linear methods can be discovered, which helps to more accurately evaluate geological risks. The captured subtle structures and dynamic features can be used to establish a more accurate prediction model, improving the accuracy and timeliness of early warning.

[0114] Through the above - mentioned processing, the system of the present invention can effectively extract key information from complex geological monitoring data, providing a reliable basis for subsequent dynamic analysis and risk assessment. Compared with traditional data - processing methods, this method can better handle non - linear and non - stationary geological processes, improving the accuracy and comprehensiveness of feature extraction. In a preferred embodiment of the present invention, the noise reduction and fusion unit 21 adopts the singular spectrum analysis method based on random matrices, which performs excellently in processing high - dimensional and multi - source geological monitoring data.

[0115] The advantage of this method is that it can process data from different sensors simultaneously, such as GNSS data, tilt sensor data of the surface monitoring unit 11, and deep displacement data of the underground monitoring unit 12, etc. In this way, the system of the present invention can effectively remove the noise in various monitoring data, improving the accuracy of subsequent analysis.

[0116] In another embodiment of the present invention, the feature extraction unit 22 adopts the persistent homology algorithm based on topological data analysis. This algorithm is particularly suitable for extracting non - linear features in geological data and can capture complex geological structure information that may be ignored by traditional methods. The dynamic analysis module 3 of the present invention includes a time - series analysis unit 31 and a non - stationary process modeling unit 32. These two units work together to deeply explore the dynamic characteristics of geological monitoring data.

[0117] The time series analysis unit 31 analyzes the dynamic characteristics of time series based on fractional calculus and chaos theory. This method is particularly suitable for dealing with the long-range correlation and non-linear characteristics commonly found in geological processes. The specific steps are as follows:

[0118] First, define the fractional derivative:

[0119]

[0120] where Γ is the gamma function and α is the fractional order parameter. In geological monitoring, the value of α usually ranges from 0.1 to 0.9. For example, for surface deformation data, when α ≈ 0.5, it can often well describe its long-range correlation characteristics.

[0121] Next, calculate the fractional autocorrelation function:

[0122] R α (τ) = E[D α X(t)·D α X(t + τ)],

[0123] This function can reveal the correlation of the time series at different time scales. Finally, extract the dynamic feature vector:

[0124] V = [λ max , h, C, R α (0), R α (τ max )],

[0125] where λ max is the largest Lyapunov exponent, h is the Kolmogorov entropy, and C is the correlation dimension. These parameters together describe the complexity and predictability of the time series. For example, λ max > 0 indicates that the system has chaotic characteristics and is more difficult to predict.

[0126] Fractional calculus and chaos theory are used to analyze the dynamic characteristics of time series. By calculating the fractional derivative and autocorrelation function, the dynamic features of the time series can be extracted. In the geological exploration monitoring and early warning system, these steps can deeply mine the dynamic features in the time series data.

[0127] For example, use high-precision GNSS equipment to collect surface displacement data, and use strain gauges or fiber optic sensors to measure stress change data. Input these time series data into the dynamic analysis module, and extract dynamic features through fractional calculus and chaos theory to reveal the dynamic characteristics of the geological system. Through fractional calculus and chaos theory, the complex dynamic characteristics of the geological system can be revealed, which helps to more accurately understand and predict geological processes. The captured dynamic features can be used to establish a more accurate early warning model to improve the accuracy and timeliness of early warning.

[0128] Preferably, the non-stationary process modeling unit 32 of the present invention constructs a non-stationary stochastic process model by using a method based on quantum probability theory and non-commutative geometry. This method can effectively handle the non-stationarity and uncertainty in geological processes. The specific steps are as follows:

[0129] First, define a non-commutative probability space where is a von Neumann algebra, is a state. This provides a mathematical framework for describing non-stationary processes. Then, construct a quantum stochastic process:

[0130]

[0131] where U t is a family of unitary operators satisfying U s+t = U s U t . This process can describe the dynamic evolution of the geological system. Next, calculate the moments of the quantum stochastic process:

[0132]

[0133] These moments contain information on the statistical characteristics of the process.

[0134] Finally, extract non-stationary features:

[0135] G = {m2(t, t + τ), m3(t, t + τ1, t + τ2), m4(t, t + τ1, t + τ2, t + τ3)},

[0136] These features can be used to analyze the non-linear and non-Gaussian characteristics of geological processes. For example, m3 is the third moment. When m3 ≠ 0, it indicates that the process has skewness, which may imply that the geological system is in a non-equilibrium state and requires special attention.

[0137] Construct a non-stationary stochastic process model using quantum probability theory and non-commutative geometry. By defining a non-commutative probability space and a quantum stochastic process, the non-stationary dynamic evolution of the geological system can be described. In a geological exploration monitoring and early warning system, these steps can describe the non-stationary dynamic evolution of the geological system.

[0138] For example, high-precision GNSS devices are used to collect surface displacement data, and strain gauges or fiber optic sensors are used to measure stress change data. These data are input into the non-stationary process modeling module, and the non-stationary dynamic evolution of the system is described through quantum probability theory and non-commutative geometry, revealing the non-linear and non-Gaussian characteristics of the system. Through quantum probability theory and non-commutative geometry, the non-stationary characteristics of the geological system can be captured, which helps to more accurately describe and predict geological processes. The captured non-stationary characteristics can be used to establish a more accurate early warning model, improving the accuracy and timeliness of early warning.

[0139] Through the above steps, the system of the present invention can comprehensively analyze the dynamic characteristics of geological monitoring data, providing in-depth data support for subsequent risk assessment. Compared with traditional time series analysis methods, this method can better handle complex characteristics such as non-linearity, non-stationarity, and long-range correlation in geological processes, greatly improving the accuracy of analysis and prediction ability.

[0140] The risk assessment module 4 of the present invention constructs a risk assessment model based on algebraic topology and category theory. This method can effectively capture the complex topological structure and dynamic characteristics of the geological system. The specific steps are as follows:

[0141] First, define the risk manifold M and the observation space O, and construct the fiber bundle π: E → M. In geological monitoring, M can represent the state space of the geological body, and O represents the space of observation data.

[0142] Then, define the tangent bundle TE and the cotangent bundle T * E, and construct the symplectic structure:

[0143]

[0144] where x i and p i are the position and momentum coordinates respectively. This structure can describe the dynamic characteristics of the geological system.

[0145] Next, define the Hamiltonian function H: T * E → R:

[0146] H(x, p) = K(p) + V(x),

[0147] H(x, p) is the Hamiltonian function, representing the total energy of the system. K(p) is the kinetic energy term, representing the motion energy of the geological body. V(x) is the potential energy term, representing gravitational potential energy or strain energy, etc. In geological monitoring, K(p) can represent the motion energy of the geological body, and V(x) can represent gravitational potential energy or strain energy, etc.

[0148] Solve the Hamilton equation Obtain the risk evolution trajectory γ(t) = (x(t), p(t));

[0149] Calculate the risk metric where g ij is the Riemannian metric on the risk manifold;

[0150] where, is the time derivative of the position variable, is the time derivative of the momentum variable, p i is the momentum variable, x i is the position variable, M -1 is the inverse matrix of the mass matrix, and k is the potential energy coefficient. This risk metric can quantify the potential risk of geological disasters.

[0151] Construct a risk assessment model based on algebraic topology and category theory. Through solving the Hamilton equation and calculating the risk metric, risk assessment and early warning decision-making are realized. In the geological exploration and monitoring early warning system, these steps can achieve the full-process processing from the original data to the risk assessment. For example, use high-precision GNSS equipment to collect surface displacement data, and use strain gauges or fiber optic sensors to measure stress change data. Input these data into the risk assessment module, and through solving the Hamilton equation and calculating the risk metric, risk assessment and early warning decision-making are realized; by solving the Hamilton equation and calculating the risk metric, more accurate risk assessment and early warning information can be provided, which helps to take timely countermeasures. The early warning information based on the risk metric can provide direct action guidance for decision-makers and improve the effect of geological disaster prevention and control.

[0152] The present invention also provides a geological exploration and monitoring early warning method corresponding to the above system. The method includes the following steps:

[0153] First, collect surface deformation data, underground monitoring data and environmental parameter data through the multi-dimensional perception module 1. For example, use high-precision GNSS equipment to collect surface displacement data with an accuracy of up to millimeters; use borehole inclinometers to collect deep displacement data with an accuracy of up to 0.1 mm / m; use rain gauges to collect rainfall data with a resolution of up to 0.1 mm.

[0154] Then, transmit the collected multi-source data to the data processing module 2. The data transmission can be carried out in a wired or wireless manner, and a suitable transmission method is selected according to the on-site conditions.

[0155] Next, the data processing module 2 receives the multi-source data and performs data denoising, fusion and non-linear feature extraction based on the random matrix theory and topological data analysis method. This step can significantly improve the data quality and remove various noise interferences.

[0156] Then, the dynamic analysis module 3 receives the processed data and performs time series analysis and non-stationary process modeling based on fractional calculus, chaos theory, quantum probability theory, and non-commutative geometry. This step can deeply explore the dynamic characteristics and potential laws in the data.

[0157] Next, the risk assessment module 4 receives the analysis results and constructs a risk assessment model based on algebraic topology and category theory, calculates the risk metric, and generates a risk assessment report. This step transforms the complex mathematical analysis results into understandable risk information.

[0158] Finally, the early warning decision-making module 5 receives the risk assessment report, determines the early warning level, and issues early warning information. The early warning level can be divided into multiple levels, such as normal, attention, warning, danger, etc., and each level corresponds to different response measures.

[0159] Through the above steps, the method of the present invention realizes the full-process intelligent processing from data acquisition to early warning decision-making, greatly improving the accuracy and timeliness of geological disaster monitoring and early warning. Compared with traditional methods, the method of the present invention can better handle complex non-linear and non-stationary geological processes, provide more reliable early warning information, and provide strong technical support for geological disaster prevention and control. To verify the effectiveness and superiority of the geological exploration monitoring and early warning system and method of the present invention, we selected a typical landslide monitoring scenario for simulation experiments. This scenario is located in a mountainous area, with an area of about 2 square kilometers, having a complex geological structure and frequent rainfall characteristics, and is a typical high-incidence area of landslides.

[0160] Example 1: Geological exploration monitoring and early warning system of the present invention

[0161] 10 high-precision GNSS monitoring points, 5 deep displacement monitoring points, 3 groundwater level monitoring points, and 2 automatic weather stations were deployed in this area. The system uses the multi-dimensional perception module, data processing module, dynamic analysis module, risk assessment module, and early warning decision-making module of the present invention to continuously monitor this area for one year.

[0162] Comparative Example 1: Traditional monitoring method

[0163] A method combining conventional GNSS monitoring and manual inspections was adopted, with data collection and analysis carried out once a week, and experts conducted risk assessment and early warning based on experience.

[0164] Comparative Example 2: Simple machine learning method

[0165] The same monitoring equipment as in Example 1 was used, but simple machine learning algorithms (such as support vector machines) were used for data analysis and early warning, without including the advanced mathematical models and algorithms of the present invention.

[0166] We select the following indicators for testing:

[0167] 1. Early warning accuracy rate: Number of correct early warnings / Total number of early warnings;

[0168] 2. Early warning lead time: Difference between the time when the early warning is issued and the time when the landslide occurs;

[0169] 3. False alarm rate: Number of false early warnings / Total number of early warnings;

[0170] 4. Missed alarm rate: Number of landslide events not warned / Total number of landslide events;

[0171] 5. Data processing time: Time required from data collection to generating early warning information;

[0172] Detection method:

[0173] Early warning accuracy rate and false alarm rate: By recording the early warning information issued by the system and verifying it against the actual landslide events that occur.

[0174] Early warning lead time: Record the time when the early warning is issued and compare it with the actual landslide occurrence time.

[0175] Missed alarm rate: Count the actual landslide events that occur and compare them with the system's early warning situation.

[0176] Data processing time: Record the time interval from the start of data collection to generating early warning information.

[0177] The following are the test results during the one-year monitoring period:

[0178]

[0179] Analysis and discussion:

[0180] 1. Early warning accuracy rate: The system of the present invention (Example 1) is significantly superior to the traditional method (Comparative Example 1) and the simple machine learning method (Comparative Example 2). This is mainly due to the multi-dimensional perception technology and advanced mathematical model adopted by the present invention, which can capture the precursors of landslides more comprehensively and accurately.

[0181] 2. Early warning lead time: The average early warning lead time of Example 1 reaches 72 hours, far exceeding the other two methods. This shows that the system of the present invention can identify potential risks earlier and gain precious time for disaster prevention and mitigation.

[0182] 3. False alarm rate and missed alarm rate: Example 1 performs excellently in these two indicators, greatly reducing the probability of false alarms and missed alarms. This not only improves the credibility of the early warning system but also avoids unnecessary economic losses and potential dangers.

[0183] 4. Data processing time: The data processing time in Example 1 is only 10 minutes, much faster than the other two methods. This real-time performance is crucial for early warning of sudden geological disasters.

[0184] The superiority of the system of the present invention is mainly reflected in the following aspects:

[0185] 1. Multi-dimensional perception ability: By integrating various sensor data, the system can comprehensively grasp the changes in the geological environment.

[0186] 2. Efficient data processing: The data noise reduction and fusion technology based on random matrix theory greatly improves the data quality.

[0187] 3. Advanced analysis models: By using advanced mathematical tools such as fractional calculus and chaos theory, it can better describe complex geological processes.

[0188] 4. Intelligent risk assessment: The risk assessment model based on algebraic topology and category theory can capture the complex topological structure and dynamic characteristics of the geological system.

[0189] 5. Real-time early warning ability: Thanks to efficient algorithms and models, the system can achieve near-real-time data processing and early warning.

[0190] Generally speaking, the geological exploration, monitoring and early warning system of the present invention is significantly superior to traditional methods and simple machine learning methods in terms of early warning accuracy, timeliness and reliability. This advantage is particularly obvious in complex geological environments, providing strong technical support for the prevention and emergency management of geological disasters.

[0191] It should be noted that the above are only the preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A geological survey monitoring and early warning system, characterized in that: include: Multi-dimensional perception module for: Collect surface deformation data, underground monitoring data and environmental parameter data; Transmitting the collected multi-source data to the data processing module; A data processing module is connected to the multi-dimensional perception module for: Receiving multi-source data sent by the multi-dimensional perception module; Based on the multi-source data, perform data denoising, fusion and nonlinear feature extraction; A dynamic analysis module is connected to the data processing module for: Receiving processed data output by the data processing module; Based on the processed data, performing time series analysis and non-stationary process modeling; The risk assessment module is in communication with the dynamic analysis module and is used to: Receiving the analysis result output by the dynamic analysis module; Based on the analysis results, calculate risk metrics and generate a risk assessment report; The early warning decision module is connected to the risk assessment module for: Receiving the risk assessment report output by the risk assessment module; Based on the risk assessment report, the warning level is determined and warning information is issued.

2. The system according to claim 1, characterized in that The multi-dimensional perception module comprises: Surface monitoring unit, used to collect high-precision GNSS displacement data, tilt sensor data and crack monitoring data; Underground monitoring unit, used to collect deep displacement data, groundwater level data and stress-strain data; Environmental parameter monitoring unit, used to collect rainfall data, temperature data and seismic wave data.

3. The system according to claim 1, characterized in that The data processing module comprises: A denoising and fusion unit, used to perform denoising and fusion of multi-source data based on random matrix theory; The feature extraction unit is used to extract nonlinear features based on a topological data analysis method.

4. The system according to claim 3, characterized in that The noise reduction fusion unit adopts a singular spectrum analysis method based on a random matrix, comprising the following steps: Construct the input data matrix X = [X1, X2, ..., X n ]∈R m×n , where X i ∈R m represents the m-dimensional observation data of the i-th sensor; Perform singular value decomposition Y = UΣV T ; Define the noise reduction threshold λ = median(σ i )+c·MAD(σ i ), where σ i is a singular value, c is a constant, and MAD is the median absolute deviation; Generate the denoised data matrix X clean =UΣ λ V T , where Σ λ is the matrix after setting the singular values ​​less than λ to zero, Where X is the input data matrix, X i is the observation data of the i-th sensor, Y is the result of singular value decomposition, U is the left singular vector matrix, ∑ is the singular value matrix, V is the right singular vector matrix, λ is the noise reduction threshold, X clean is the data matrix after denoising.

5. The system according to claim 3, characterized in that The feature extraction unit adopts a continuous homology algorithm based on topological data analysis, including the following steps: Define the filter function f:X clean →R; Calculate continuous coherent PH * (X clean )={H * (f -1 (-∞,ε])→H * (f -1 (-∞,ε′])|ε≤ε′}, where H * represents the k-order homology group, ε is the filtering parameter; Extract persistence graph D k The salient features in F={(b i ,d i )|(b i ,d i )∈D k ,d i -b i >δ}, where (b i ,d i ) are birth-death pairs in the persistence graph, and δ is the persistence threshold.

6. The system according to claim 1, characterized in that The dynamic analysis module includes: Time series analysis unit, used to analyze the dynamic characteristics of time series based on fractional calculus and chaos theory; The non-stationary process modeling unit is used to construct non-stationary random process models based on quantum probability theory and non-commutative geometry.

7. The system according to claim 6, characterized in that The time series analysis unit performs the following steps: Defining fractional derivatives Where 0<α<1, Γ is the gamma function; Calculate the fractional autocorrelation function R α (τ)=E[D α X(t)·D α X(t+τ)]; Extract dynamic feature vector V = [λ max ,h,C,R α (0),R α (τ max )], where λ max is the maximum Lyapunov exponent, h is the Columbus entropy, and C is the correlation dimension; Among them, D α is the fractional derivative, α is the fractional parameter, R α (0) is the value of the autocorrelation function at zero delay, R α (τ max ) is the value of the autocorrelation function at the maximum delay.

8. The system according to claim 6, characterized in that The non-stationary process modeling unit performs the following steps: Define a non-commutative probability space in is a von Neumann algebra, for status; Constructing quantum random processes Among them U t is a unitary operator family that satisfies U s +t=U s U t ; Computing moments of quantum random processes Extract non-stationary features G = {m2(t,t+τ),m3(t,t+τ1,t+τ2),m4(t,t+τ1,t+τ2,t+τ3)}, Among them, m2(t,t+τ) is the second-order moment, m3(t,t+τ1,t+τ2) is the third-order moment, and m4(t,t+τ1,t+τ2,t+τ3) is the fourth-order moment.

9. The system according to claim 1, characterized in that The risk assessment module constructs a risk assessment model based on algebraic topology and category theory, including the following steps: Defining the Risk Manifold and observation space Constructing fiber bundles Define the tangent bundle TE and the cotangent bundle T * E. Constructing symplectic structures Define the Hamiltonian function H:T * E→R,H(x,p)=K(p)+V(x); Solving Hamilton's equations The risk evolution trajectory γ(t) = (x(t), p(t)) is obtained; Calculating risk metrics where g ij is the Riemannian metric on the risk manifold; in, is the time derivative of the position variable, is the time derivative of the momentum variable, p i is the momentum variable, x i is the position variable, M -1 is the inverse matrix of the mass matrix, and k is the potential energy coefficient.

10. A geological survey monitoring and early warning method, based on the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Collect surface deformation data, underground monitoring data and environmental parameter data through multi-dimensional perception modules; Transmitting the collected multi-source data to the data processing module; The data processing module receives multi-source data and performs data denoising, fusion and nonlinear feature extraction based on random matrix theory and topological data analysis methods; The dynamic analysis module receives the processed data and performs time series analysis and non-stationary process modeling based on fractional calculus, chaos theory, quantum probability theory, and non-commutative geometry; The risk assessment module receives the analysis results and builds a risk assessment model based on algebraic topology and category theory, calculates risk metrics and generates a risk assessment report; The early warning decision module receives the risk assessment report, determines the warning level and issues warning information.

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