A geological survey monitoring and early warning system and method

Abstract

The present invention relates to the field of big data technology, particularly a geological survey, monitoring and early warning system and method, comprising: a data governance module; a data optimization module, which communicates with the data governance module to evaluate the health status of a data lake; provides metadata and data tracking capabilities; executes data management strategies to provide data lifecycle management functions; defines optimization goals and constraints to provide decision support for data management strategies and resource allocation; obtains data value scores through a data value assessment model; an intelligent analysis engine, which communicates with the data governance module and the data optimization module to select corresponding intelligent analysis methods to analyze data in the data lake according to data type; generates an analysis report and compares it with data expectations; dynamically adjusts the weight of the analysis method according to the comparison results; and a deep reinforcement learning decision system, which communicates with the data optimization module and the intelligent analysis engine to jointly ensure the quality and effective use of data.

Description

Technical Field

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

[0002] In recent years, with the acceleration of urbanization and the increase in extreme weather events, the frequency and intensity of geological disasters have increased, posing a serious threat to people's lives and property. Against this backdrop, the importance of geological survey, monitoring, and early warning systems has become increasingly prominent. Traditional geological survey and monitoring methods rely primarily on manual inspections and single-sensor monitoring. These methods suffer from low monitoring frequency, limited coverage, and delayed data analysis, making them inadequate for modern geological disaster prevention and control.

[0003] With technological advances, a number of new geological survey, monitoring, and early warning systems have gradually emerged. These systems typically use a combination of multiple sensors, such as GNSS, tilt sensors, and deep displacement meters, to obtain more comprehensive monitoring data. However, these systems still have many shortcomings in data processing and analysis. First, the fusion and noise reduction of multi-source heterogeneous data remains a challenge, and existing methods often fail to effectively remove various noise interferences in complex environments. Second, in terms of data analysis, most systems still use simple statistical methods or shallow machine learning algorithms, which make it difficult to capture the nonlinear and non-stationary characteristics of geological processes. In addition, existing risk assessment models are often oversimplified and fail to fully account for the complexity and dynamics of geological systems, making it difficult to ensure the accuracy and timeliness of early warnings.

[0004] The closest existing technology typically uses multi-source data collection combined with machine learning for geological disaster early warning. While this approach has improved the comprehensiveness of monitoring and the accuracy of early warnings to a certain extent, the following issues remain: First, data preprocessing capabilities are limited, making it difficult to effectively handle noise interference in complex environments; second, machine learning models are often black-box in nature, lacking a deep understanding of the physical mechanisms of geological processes; third, early warning models lack generalizability and are prone to failure in new and complex geological environments; and fourth, the system's lack of real-time performance means that the time from data collection to warning generation 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 survey monitoring and early warning systems in data processing, analysis and early warning, and to provide a more comprehensive, accurate and timely geological disaster early warning solution.

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

[0007] Multi-dimensional perception module for:

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

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

[0010] A data processing module is communicatively connected to the multi-dimensional perception module and is used to:

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

[0012] Based on the multi-source data, performing data denoising, fusion and nonlinear feature extraction;

[0013] A dynamic analysis module is in communication with the data processing module and is used to:

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

[0015] performing time series analysis and non-stationary process modeling based on the processed data;

[0016] The risk assessment module is in communication with the dynamic analysis module and is used to:

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

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

[0019] The early warning decision module is in communication with the risk assessment module and is used to:

[0020] Receiving a risk assessment report output by the risk assessment module;

[0021] Based on the risk assessment report, the warning level is determined and warning information is issued.

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

[0023] Surface monitoring unit, used to collect high-precision GNSS displacement data, tilt sensor data, and crack monitoring data;

[0024] Underground monitoring unit, used to collect deep displacement data, groundwater level data, and stress and strain data;

[0025] Environmental parameter monitoring unit, used to collect rainfall data, temperature data and seismic wave data.

[0026] Preferably, the data processing module includes:

[0027] A denoising and fusion unit for performing denoising and fusion of multi-source data based on random matrix theory;

[0028] The feature extraction unit is used to extract nonlinear features based on a topological data analysis method.

[0029] Preferably, the noise reduction fusion unit adopts a random matrix-based singular spectrum analysis method, comprising the following steps:

[0030] 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;

[0031] 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;

[0032] Generate the denoised data matrix X clean =UΣ λ V T , where Σ λ is the matrix after setting the singular values ​​smaller than λ 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 of singular value decomposition, U is the left singular vector matrix, Σ 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.

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

[0035] Define the filter function f:X clean →R;

[0036] 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;

[0037] Extract persistence graph D k The salient features F={(b i ,di )|(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.

[0038] Preferably, the dynamic analysis module includes:

[0039] Time series analysis unit, used to analyze the dynamic characteristics of time series based on fractional calculus and chaos theory;

[0040] The non-stationary process modeling unit is used to construct non-stationary random process models based on quantum probability theory and non-commutative geometry.

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

[0042] Defining fractional derivatives Where 0<α<1, 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 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;

[0045] 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.

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

[0047] Define a non-commutative probability space in is a von Neumann algebra, For status;

[0048] Constructing quantum random processes Among them Ut is a unitary operator family that satisfies U s +t=U s U t ;

[0049] Computing moments of quantum random processes

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

[0051] 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.

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

[0053] Defining the risk manifold and observation space Constructing fiber bundles

[0054] Define the tangent bundle TE and the cotangent bundle T * E. Constructing symplectic structures

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

[0056] Solving Hamilton's equations The risk evolution trajectory γ(t) = (x(t), p(t)) is obtained;

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

[0058] 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.

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

[0060] Collect surface deformation data, underground monitoring data and environmental parameter data through multi-dimensional perception modules;

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

[0062] 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;

[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, builds a risk assessment model based on algebraic topology and category theory, calculates risk metrics and generates a risk assessment report;

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

[0066] From a macro perspective, the geological survey, monitoring, and early warning system of this invention integrates advanced technologies such as multi-dimensional perception, intelligent data processing, dynamic analysis, and risk assessment to create a complete, intelligent geological disaster early warning system. This system can comprehensively perceive 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 acquisition, the multidimensional perception module of the present invention achieves comprehensive perception of surface deformation, underground monitoring, and environmental parameters. This multi-source data acquisition approach not only improves the comprehensiveness of monitoring but also enhances its reliability through the complementary use of data from different sensors. For example, the combination of surface GNSS monitoring and deep displacement monitoring can fully capture the deformation characteristics of geological bodies, effectively avoiding the one-sidedness that can result from a single monitoring approach.

[0069] Secondly, in terms of data processing, this invention employs a noise reduction and fusion method based on random matrix theory, effectively solving the challenge of processing multi-source heterogeneous data. This method not only removes various complex noise interferences but also extracts more valuable information through data fusion. This method exhibits significant advantages when processing high-dimensional, non-stationary geological monitoring data, providing a high-quality data foundation for subsequent analysis.

[0070] In terms of data analysis, this 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 nonlinearity and long-range correlations in geological data, thereby more accurately describing and predicting the evolution of geological processes. The method of this invention demonstrates significant advantages, especially when dealing with complex geological systems that are difficult to address with traditional methods.

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

[0072] Finally, in terms of overall system performance, this invention achieves intelligent integration of the entire process, from data collection to early warning decision-making, through close collaboration between modules. This collaboration not only improves the overall efficiency of the system but also achieves a qualitative leap in early warning performance through optimization and complementarity of various links. For example, efficient data processing provides high-quality input for dynamic analysis, while 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 survey, monitoring, and early warning system of the present invention effectively addresses several existing issues 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 to new directions for technological development in related fields. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 It is a logic block diagram of the entire system of the present invention.

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

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

[0077] Figure 4 This is a logic block diagram of the dynamic analysis module of the present invention.

[0078] Figure 5 This is a logic block diagram of the risk assessment module of the present invention.

[0079] Figure 6 This is a logic block diagram of the early warning decision module of the present invention. DETAILED DESCRIPTION

[0080] See Figure 1-6 The present invention provides a geological survey, monitoring, and early warning system and method. The system includes a multidimensional 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 throughout the entire process, from data collection to early warning decision-making, 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] Multidimensional 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 data processing module 2. This module can fully perceive changes in the geological environment and provide rich raw data for subsequent analysis.

[0083] The data processing module 2 is in communication with the multi-dimensional perception module 1 and is used to receive the multi-source data sent by the multi-dimensional perception module 1 and perform data noise reduction, fusion, and nonlinear feature extraction based on this data. Through these processes, the data quality can be significantly improved, laying the foundation for subsequent analysis.

[0084] The dynamic analysis module 3 is in communication with the data processing module 2 and is used to receive the processed data output by the data processing module 2 and perform time series analysis and non-stationary process modeling based on this data. This step can deeply explore the dynamic characteristics and potential patterns in the data.

[0085] The risk assessment module 4 is in communication with the dynamic analysis module 3 and is configured to receive the analysis results output by the dynamic analysis module 3 and calculate 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 in communication with the risk assessment module 4 and is used to receive the risk assessment report output by the risk assessment module 4. Based on the report, it determines the early warning level and issues early warning information. This is the final output of the system and provides 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 tiny angular changes (typically 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, groundwater level data, and stress and strain data. For example, deep displacement can be monitored using a borehole inclinometer with an accuracy of up to 0.1 mm / m; groundwater level can be measured using a pressure gauge with an accuracy of ±0.1% FS; and stress and strain data can be obtained using 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 the rain gauge can reach 0.1mm, the accuracy of the temperature sensor can reach ±0.1°C, and seismic wave monitoring can use a wide-band seismograph with a frequency range of 0.01Hz to 50Hz.

[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 random matrix theory. This method is particularly suitable for processing high-dimensional, 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] Among them, X i ∈R m represents the m-dimensional observation data of the i-th sensor. For example, for GNSS data, X i It may represent the displacement time series in the xy and z directions. Then, perform singular value decomposition:

[0096] Y=UΣV T ,

[0097] Among them, 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] Among them, σ i is a 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 between signal and noise components.

[0100] Finally, generate the denoised data matrix:

[0101] X clean =UΣ λ V T ,

[0102] Among them, Σ λ is the matrix after setting the singular values ​​smaller than λ to zero.

[0103] In geological survey, monitoring, and early warning systems, multi-source heterogeneous data (such as surface deformation data, underground monitoring data, and environmental parameter data) requires fusion and noise reduction. We use singular value decomposition (SVD) from random matrix theory to perform data denoising and fusion. In geological survey, monitoring, and early warning systems, 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 millimeter-level accuracy; borehole inclinometers are used to collect deep displacement data with an accuracy of 0.1 mm / m; and rain gauges are used to collect rainfall data with a resolution of 0.1 mm. This multi-source data is input into the data processing module, where singular value decomposition and noise reduction are used to remove noise and fuse the multi-source data to produce high-quality raw data. Noise reduction can significantly reduce the noise component in the data, thereby improving the accuracy of subsequent analysis. Fusion of multi-source data provides more comprehensive information, contributing to a better understanding of changes in the geological environment.

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

[0106] First, define the filter 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 or stress).

[0107] Then, compute the persistence coherence:

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

[0109] Among them, H * represents the k-order homology group, and ∈ is the filtering parameter. This step can reveal the topological structure of the data. Finally, the persistence graph D is extracted. k Notable features of:

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

[0111] Among them, (b i ,d i ) are the birth-death pairs in the persistence graph, and δ is the persistence threshold. Empirically, δ can be chosen to be the median or mean of the persistence values.

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

[0113] For example, high-precision GNSS equipment is used to collect surface displacement data, while strain gauges or fiber optic sensors are used to measure stress changes. This data is then fed into a feature extraction module, where a persistent coherence algorithm extracts nonlinear features, revealing the complex structure and dynamic characteristics of the geological system. This persistent coherence algorithm can uncover complex geological structural features that traditional linear methods might overlook, facilitating a more accurate assessment of geological risks. These subtle structural and dynamic features can be used to develop more precise predictive models, improving the accuracy and timeliness of early warnings.

[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 foundation for subsequent dynamic analysis and risk assessment. Compared with traditional data processing methods, this method can better handle nonlinear 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 a random matrix-based singular spectrum analysis method, which performs well when processing high-dimensional, multi-source geological monitoring data.

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

[0116] In another embodiment of the present invention, the feature extraction unit 22 employs a persistent homology algorithm based on topological data analysis. This algorithm is particularly well suited for extracting nonlinear features from geological data and can capture complex geological structural information that traditional methods may overlook. 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 the time series based on fractional calculus and chaos theory. This method is particularly suitable for processing the long-range correlation and nonlinear characteristics commonly seen in geological processes. The specific steps are as follows:

[0118] First, define the fractional derivative:

[0119]

[0120] Here, Γ is the gamma function, and α is the fractional-order parameter. In geological monitoring, α is typically between 0.1 and 0.9. For example, for surface deformation data, α ≈ 0.5 often well describes 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 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] Among them, λ max is the maximum Lyapunov exponent, h is the Columbus entropy, and C is the correlation dimension. These parameters together describe the complexity and difficulty of prediction of the time series. For example, max >0 indicates that the system has chaotic characteristics and is difficult to predict.

[0126] Fractional calculus and chaos theory are used to analyze the dynamic characteristics of time series. By calculating fractional derivatives and autocorrelation functions, dynamic features of time series can be extracted. In geological survey, monitoring, and early warning systems, these steps can deeply explore the dynamic characteristics of time series data.

[0127] For example, high-precision GNSS equipment is used to collect surface displacement data, while strain gauges or fiber optic sensors are used to measure stress changes. This time series data is then fed into a dynamic analysis module, where dynamic features are extracted using fractional calculus and chaos theory to reveal the dynamic characteristics of the geological system. Fractional calculus and chaos theory can reveal the complex dynamic characteristics of geological systems, facilitating a more accurate understanding and prediction of geological processes. These captured dynamic features can be used to develop more precise early warning models, improving the accuracy and timeliness of early warnings.

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

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

[0130]

[0131] Among them, U t is a unitary operator family that satisfies U s+t =U s U t This process can describe the dynamic evolution of geological systems. Next, calculate the moment of the quantum random process:

[0132]

[0133] These moments contain information about 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 characteristics can be used to analyze the nonlinear and non-Gaussian nature of geological processes. For example, m3 is the third-order moment. When m3 ≠ 0, it indicates that the process is skewed, which may indicate that the geological system is in a non-equilibrium state and requires special attention.

[0137] Quantum probability theory and noncommutative geometry are used to construct a nonstationary stochastic process model. By defining a noncommutative probability space and quantum stochastic processes, the nonstationary dynamic evolution of geological systems can be described. These steps can be used to describe the nonstationary dynamic evolution of geological systems in geological exploration, monitoring, and early warning systems.

[0138] For example, high-precision GNSS equipment is used to collect surface displacement data, while strain gauges or fiber optic sensors are used to measure stress changes. This data is then fed into the non-stationary process modeling module, where quantum probability theory and non-commutative geometry are used to describe the non-stationary dynamic evolution of the system, revealing its nonlinear and non-Gaussian properties. Quantum probability theory and non-commutative geometry can capture the non-stationary characteristics of geological systems, facilitating more accurate description and prediction of geological processes. These captured non-stationary characteristics can then be used to develop more precise early warning models, improving the accuracy and timeliness of early warnings.

[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 the complex characteristics of geological processes such as nonlinearity, non-stationarity, and long-range correlations, greatly improving the accuracy of analysis and predictive capabilities.

[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 geological systems. 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, while O represents the space of observation data.

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

[0143]

[0144] Among them, x i and p i are the position and momentum coordinates respectively. This structure can describe the dynamic characteristics of geological systems.

[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 kinetic 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 kinetic energy of the geological body, while V(x) can represent gravitational potential energy or strain energy, etc.

[0148] Solving Hamilton's equations The risk evolution trajectory γ(t) = (x(t), p(t)) is obtained;

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

[0150] 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. This risk metric can quantify the potential risk of geological hazards.

[0151] A risk assessment model based on algebraic topology and category theory is constructed. By solving the Hamiltonian equation and calculating risk metrics, risk assessment and early warning decisions are implemented. In a geological survey, monitoring, and early warning system, these steps enable the entire process from raw data to risk assessment. For example, high-precision GNSS equipment is used to collect surface displacement data, and strain gauges or fiber optic sensors are used to measure stress changes. This data is then input into the risk assessment module, where it is solved by solving the Hamiltonian equation and calculating risk metrics. Solving the Hamiltonian equation and calculating risk metrics can provide more accurate risk assessment and early warning information, facilitating timely response measures. Early warning information based on risk metrics can provide direct action guidance to decision makers, improving the effectiveness of geological disaster prevention and control.

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

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

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

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

[0156] The dynamic analysis module 3 then 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 patterns 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 risk metrics, and generates a risk assessment report. This step transforms the complex mathematical analysis results into understandable risk information.

[0158] Finally, the early warning decision 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 intelligent processing of the entire process from data collection 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 nonlinear and non-stationary geological processes, provide more reliable early warning information, and provide strong technical support for geological disaster prevention and control. In order to verify the effectiveness and superiority of the geological survey, monitoring and early warning system and method of the present invention, we selected a typical landslide monitoring scenario for simulation experiments. The scenario is located in a mountainous area with an area of ​​about 2 square kilometers. It has complex geological structures and frequent rainfall characteristics. It is a typical landslide high-incidence area.

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

[0161] Ten high-precision GNSS monitoring points, five deep displacement monitoring points, three groundwater level monitoring points, and two automatic weather stations were deployed in the area. The system employed the multidimensional perception module, data processing module, dynamic analysis module, risk assessment module, and early warning decision-making module developed in the present invention to continuously monitor the area for one year.

[0162] Comparative Example 1: Traditional Monitoring Method

[0163] A combination of conventional GNSS monitoring and manual inspections is used to collect and analyze data weekly, with experts conducting risk assessments and early warnings based on their experience.

[0164] Comparative Example 2: Simple Machine Learning Method

[0165] The same monitoring equipment as in Example 1 is used, but a simple machine learning algorithm (such as a support vector machine) is used for data analysis and early warning, which does not include the advanced mathematical models and algorithms of the present invention.

[0166] We selected the following indicators for testing:

[0167] 1. Warning accuracy: number of correct warnings / total number of warnings;

[0168] 2. Warning lead time: the difference between the time the warning is issued and the time the landslide occurs;

[0169] 3. False alarm rate: number of false alarms / total number of alarms;

[0170] 4. Missing reporting rate: number of landslide events without warning / total number of landslide events;

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

[0172] Detection method:

[0173] Early warning accuracy and false alarm rate: The early warning information issued by the recording system is compared with the actual landslide events.

[0174] Warning lead time: record the time when the warning is issued and compare it with the actual time when the landslide occurs.

[0175] Missing reporting rate: Count the actual landslide events and compare them with the system warnings.

[0176] Data processing time: records the time interval from the start of data collection to the generation of 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: The system of the present invention (Example 1) significantly outperforms the traditional method (Comparative Example 1) and the simple machine learning method (Comparative Example 2). This is primarily due to the multi-dimensional sensing technology and advanced mathematical models used in the present invention, which can more comprehensively and accurately capture landslide precursors.

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

[0182] 3. False alarm rate and missed alarm rate: Example 1 performs well in both indicators, significantly 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 of Example 1 is only 10 minutes, which is much faster than the other two methods. This real-time performance is crucial for early warning of sudden geological disasters.

[0184] The advantages of the system of the present invention are mainly reflected in the following aspects:

[0185] 1. Multi-dimensional perception capability: By integrating data from multiple sensors, the system can fully grasp changes in the geological environment.

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

[0187] 3. Advanced analytical models: Using advanced mathematical tools such as fractional calculus and chaos theory, we 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 geological systems.

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

[0190] Overall, the geological survey, monitoring, and early warning system presented in this paper significantly outperforms traditional methods and simple machine learning approaches in terms of accuracy, timeliness, and reliability. This advantage is particularly pronounced in complex geological environments, providing strong technical support for geological disaster prevention and emergency management.

[0191] It should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present invention should be included in the scope of protection 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 communicatively connected to the multi-dimensional perception module and is used to: Receiving multi-source data sent by the multi-dimensional perception module; Based on the multi-source data, performing data denoising, fusion and nonlinear feature extraction; A dynamic analysis module is in communication with the data processing module and is used to: receiving processed data output by the data processing module; performing time series analysis and non-stationary process modeling based on the processed data; 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 in communication with the risk assessment module and is used to: Receiving a risk assessment report output by the risk assessment module; Based on the risk assessment report, determine the warning level and issue warning information; The data processing module includes: A denoising and fusion unit for performing denoising and fusion of multi-source data based on random matrix theory; A feature extraction unit, used for extracting nonlinear features based on a topological data analysis method; The noise reduction fusion unit adopts a random matrix-based singular spectrum analysis method, including 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 ​​smaller 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, Σ 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; 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 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.

2. The system according to claim 1, wherein: The multi-dimensional perception module includes: 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 and strain data; Environmental parameter monitoring unit, used to collect rainfall data, temperature data and seismic wave data.

3. The system according to claim 1, wherein: 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.

4. The system according to claim 3, 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.

5. The system according to claim 4, 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 t∈R, where 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.

6. The system according to claim 1, wherein: 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.

7. A geological survey monitoring and early warning method, based on the system according to any one of claims 1 to 6, 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, 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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