A multi-source data fusion geological anomaly monitoring and early warning method, device and medium

Abstract

This invention discloses a method, equipment, and medium for monitoring and early warning of geological anomalies based on multi-source data fusion, relating to the field of geological disaster monitoring and early warning data processing technology. The method includes: simultaneously acquiring the three-dimensional spatial position vector and pore water pressure of a geological body; performing first- and second-order time-dimension differentiation analysis on the three-dimensional spatial position vector to obtain instantaneous velocity and instantaneous acceleration vectors, respectively; calculating spatial trajectory geometric constraint evolution characteristic parameters using the instantaneous velocity and instantaneous acceleration vectors; constructing hydraulic coupling parameters using the pore water pressure, the instantaneous velocity vector, and the spatial trajectory geometric constraint evolution characteristic parameters; and performing trend determination on the time series of the continuously generated hydraulic coupling parameters, outputting a geological anomaly instability early warning command when the first and second derivatives of the hydraulic coupling parameters are simultaneously greater than zero and remain above zero for a specific time window. This invention overcomes the conventional blind spot of relying solely on the magnitude of displacement scalars for threshold judgment, by accurately capturing the topological abrupt change in spatial motion trajectory from curved to straight, and self-consistently integrating it with the groundwater dynamic state, effectively filtering false alarms caused by local disturbances.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring and early warning data processing technology, and in particular to a method for monitoring and early warning of geological anomalies by multi-source data fusion. Background Technology

[0002] Geological disaster monitoring and early warning technologies are a crucial line of defense for ensuring the safety of people's lives and property and the smooth implementation of major projects. While current monitoring methods are relatively abundant thanks to the development of sensor technology and the Internet of Things, there are still significant shortcomings in in-depth data mining and the construction of early warning logic.

[0003] Existing methods for monitoring and warning of geological anomalies typically rely on deploying various sensors in the affected area to collect physical quantities directly. For example, GNSS receivers are used to acquire three-dimensional displacement data from surface monitoring points, or piezometers are installed deep in boreholes to obtain underground pore water pressure data. In terms of data processing and warning determination, traditional techniques usually calculate the scalar rate of displacement or the absolute change in pore water pressure independently. When the absolute value or rate of change of these individual monitoring indicators exceeds a pre-set empirical threshold, the system triggers and issues a warning.

[0004] However, the aforementioned traditional early warning technologies based on a single scalar and empirical thresholds have significant limitations in practical and complex engineering applications, and face technical pain points that are easily overlooked and difficult to overcome: First, traditional methods are highly susceptible to frequent false alarms due to localized, non-instability deformation. During the evolution of complex geological bodies, especially in the lead-up to complete instability, the geological body undergoes a lengthy and complex process of internal displacement, micro-fracture compaction, and local shearing of locked sections. During this internal stress adjustment phase, influenced by localized soil softening or heavy rainfall, surface monitoring points often exhibit extremely high local scalar displacement rates. Conventional early warning methods immediately trigger false alarms once this rate exceeds a fixed empirical threshold. However, in reality, such displacement is often merely spatially multi-directional creep or localized internal adjustment; no overall, continuous failure surface has formed within the geological body. These frequent false alarms not only waste emergency resources but also severely weaken the effectiveness of the early warning system.

[0005] Secondly, traditional methods neglect the geometric constraint evolution characteristics of displacement trajectories in three-dimensional space. The deformation of a geological body is a typical three-dimensional spatial vector evolution process, but conventional methods only extract the scalar magnitude of the displacement vector, directly discarding crucial directional evolution and spatial topological information. From the perspective of geomechanics, the destruction of a geological body is controlled by the friction and locking constraints of the three-dimensional geological structure. Only when the fracture surfaces within the geological body are completely connected, forming a unified macroscopic master sliding surface, will the trajectory of the monitoring point lose its normal constraint, transforming from a restricted, complex, meandering curve into a linear, directional acceleration along the macroscopic sliding surface. Existing technologies only focus on the rate of displacement, completely ignoring the geometric and topological abrupt change in the spatial trajectory from curved to straight—a characteristic of the connection of the sliding surface.

[0006] Finally, because existing technologies cannot effectively establish a deep coupling relationship between the macroscopic spatial trajectory geometric constraint characteristics and the dynamic state of deep groundwater, the universality and robustness of existing early warning models are difficult to guarantee when facing application scenarios with different geological structures and different rock and soil properties.

[0007] In summary, how to break away from conventional scalar threshold thinking, accurately extract the geometric constraint evolution characteristics of spatial motion trajectories, and construct a coupled model with deep fluid dynamics states with clear physical meaning, thereby achieving a leap from observation of apparent displacement to deduction of the evolution of essential physical states, is a pressing problem to be solved in the field of geological disaster monitoring and early warning. Summary of the Invention

[0008] To achieve the above-mentioned objectives, this invention provides a method for monitoring and early warning of geological anomalies based on multi-source data fusion, comprising the following steps: Step S1: Synchronously acquire the three-dimensional spatial position vector and pore water pressure of the geological body; Step S2: Analyze the instantaneous velocity vector by performing the first derivative of the three-dimensional spatial position vector in the time dimension, and analyze the instantaneous acceleration vector by performing the second derivative; Step S3: Calculate the spatial trajectory geometric constraint evolution characteristic parameters using the instantaneous velocity vector and the instantaneous acceleration vector; Step S4: Construct the hydraulic coupling parameters using the pore water pressure, the instantaneous velocity vector, and the spatial trajectory geometric constraint evolution characteristic parameters; Step S5: Perform trend determination on the time series of the continuously generated water turbine coupling parameters. When the first and second derivatives of the water turbine coupling parameters are both greater than zero and a time window is continuously set, output a geological anomaly instability warning command.

[0009] Furthermore, before performing first-order time-division analytic analysis on the three-dimensional spatial position vector to obtain the instantaneous velocity vector, the method further includes: performing temporal smoothing filtering on the three-dimensional spatial position vector, and performing first-order time-division analytic analysis on the filtered three-dimensional spatial position vector to obtain the instantaneous velocity vector.

[0010] Optionally, the spatial trajectory geometric constraint evolution characteristic parameters are calculated, specifically including: calculating the cross product of the instantaneous velocity vector and the instantaneous acceleration vector to obtain the spatial trajectory geometric constraint evolution characteristic parameters.

[0011] Optionally, constructing water-mechanism coupling parameters specifically includes: obtaining the water-mechanism coupling parameters based on the pore water pressure, the instantaneous velocity vector, and the spatial trajectory geometric constraint evolution characteristic parameters.

[0012] Optionally, the three-dimensional spatial position vector and pore water pressure of the geological body can be collected simultaneously, specifically including: collecting the absolute spatial coordinates of the surface monitoring point at the corresponding time as the three-dimensional spatial position vector, and obtaining the seepage expansion pressure generated by deep groundwater on the rock and soil skeleton as the pore water pressure.

[0013] To achieve the above-mentioned objectives, this invention also provides a geological anomaly monitoring and early warning device based on multi-source data fusion, employing the geological anomaly monitoring and early warning method based on multi-source data fusion as described above, comprising: The basic parameter acquisition module synchronously acquires the three-dimensional spatial position vector and pore water pressure of the geological body; The kinematic feature analysis module performs first-order time-dimension differentiation on the three-dimensional spatial position vector to obtain the instantaneous velocity vector, and second-order differentiation to obtain the instantaneous acceleration vector; The spatial feature analysis module calculates the spatial trajectory geometric constraint evolution feature parameters using the instantaneous velocity vector and the instantaneous acceleration vector; The coupling parameter construction module constructs hydraulic turbine coupling parameters using the pore water pressure, the instantaneous velocity vector, and the spatial trajectory geometric constraint evolution characteristic parameters. The early warning logic generation module performs trend determination on the continuously generated time series of the water turbine coupling parameters. When the first and second derivatives of the water turbine coupling parameters are both greater than zero and a time window is continuously set, a geological anomaly instability early warning command is output.

[0014] To achieve the above-mentioned objectives, the present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements a geological anomaly monitoring and early warning method based on multi-source data fusion as described above.

[0015] This application provides a method, equipment, and medium for monitoring and early warning of geological anomalies based on multi-source data fusion, which has the following advantages compared to existing technologies: The beneficial effects of this invention are as follows: By analytically differentiating the three-dimensional spatial position vector in the time dimension to obtain the instantaneous velocity and acceleration vectors, and further calculating the geometric constraint evolution characteristic parameters of the spatial trajectory, this invention overcomes the limitation of conventional techniques that blindly rely on displacement rate scalars. This technical solution maps external kinematic features to the three-dimensional spatial geometric dimension, objectively extracting the topological features of the internal structural evolution of geological bodies, and establishing a direct correlation between the macroscopic sliding surface connection and the nonlinear dimensional reduction morphological changes of the spatial motion trajectory. Utilizing the extracted normal geometric constraint features, the system can naturally identify and filter false high displacement interference caused by local stress adjustments or non-destructive high-speed creep of geological bodies, significantly reducing the false alarm rate from the underlying physical evolution mechanism without relying on artificial threshold intervention.

[0016] By nonlinearly multiplying and fusing pore water pressure (reflecting fluid state), instantaneous velocity vector (reflecting motion speed), and spatial trajectory geometric constraint evolution characteristic parameters (reflecting spatial resistance), a hydraulic coupling parameter is constructed. This invention achieves purely physical self-consistent fusion of multi-source data. This technical solution deeply physical couples fluid driving force and spatial geometric constraint force back to their essential energy and work dimensions. This data architecture characterizes the true energy release state of geological bodies along their instantaneous motion direction, ensuring that this early warning model possesses extremely high universality and robustness in data processing when facing complex and varied geological structures and soil properties.

[0017] By dynamically determining the convexity and concavity of the continuously generated time series of water turbine coupling parameters, and only outputting warning commands when both the first and second derivatives are simultaneously greater than zero and remain so for a specific time window, this technical solution avoids the static alarm upper limit setting that is susceptible to environmental interference and instead utilizes the objective physical divergence characteristics of energy release within the system to assess the disaster state. This trend determination logic effectively absorbs and avoids numerical fluctuations and short-term disturbances in the monitoring data series, pinpointing the critical mechanical abrupt change point where the geological structure transforms from internal microscopic fracturing to macroscopic overall instability, thus comprehensively improving the accuracy of disaster warnings. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a geological anomaly monitoring and early warning method based on multi-source data fusion as described in this invention. Detailed Implementation

[0019] The present invention will now be described in detail with reference to the specific embodiments shown in the accompanying drawings. However, these embodiments do not limit the present invention, and any structural, methodological, or functional modifications made by those skilled in the art based on these embodiments are included within the scope of protection of the present invention.

[0020] If the present invention involves orientation (e.g., up, down, left, right, front, back, outside, inside, etc.) when described, then the orientations involved need to be defined.

[0021] The scope of the embodiments described herein includes the entire scope of the claims and all available equivalents thereof. Throughout this document, the terms “first,” “second,” etc., are used only to distinguish one element from another without requiring or implying any actual relationship or order between the elements. Indeed, a first element can also be referred to as a second element, and vice versa. Furthermore, the terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a structure, apparatus, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a structure, apparatus, or device. Without further limitations, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the structure, apparatus, or device that includes said element. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably.

[0022] The terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer" used in this document to indicate orientation or positional relationships are based on the orientation or positional relationships shown in the accompanying drawings and are used only for the convenience of describing this document and simplifying the description. They do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as limiting the invention. In the description herein, unless otherwise specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two elements, or direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0023] The following is in conjunction with the appendix Figure 1 This application provides a detailed description of a geological anomaly monitoring and early warning method based on multi-source data fusion.

[0024] Step S1: Synchronously acquire the three-dimensional spatial position vector and pore water pressure of the geological body.

[0025] The system synchronously collects external deformation characteristic parameters and internal seepage characteristic parameters through basic sensor equipment deployed on site.

[0026] As an optional implementation, the absolute spatial coordinates of the surface monitoring points at the corresponding time are collected as a three-dimensional spatial position vector, in the form of: This data was directly collected by a GNSS monitoring station. Simultaneously, the seepage expansion pressure exerted by deep groundwater on the rock and soil skeleton was obtained as the pore water pressure, denoted as... This data is read directly from a vibrating wire piezometer buried deep within the borehole.

[0027] This synchronous acquisition not only ensures the temporal correspondence of the data, but also provides the most basic state input for the subsequent construction of an energy flow density model with clear physical entity significance.

[0028] Step S2: Analyze the instantaneous velocity vector by performing the first derivative of the three-dimensional spatial position vector in the time dimension, and analyze the instantaneous acceleration vector by performing the second derivative.

[0029] Furthermore, considering the ionospheric interference in the field environment, the system performs time-series smoothing filtering on the original acquired three-dimensional spatial position vectors before performing differentiation to remove white noise.

[0030] Subsequently, the time dimension of the filtered spatial location vector is differentiated analytically to obtain the transient kinematic characteristics of the geological body. The specific logic is as follows: For spatial position vectors Perform first-order time derivative to output the instantaneous velocity vector. : Instantaneous velocity vector It characterizes the instantaneous speed and absolute direction of the spatial displacement of a geological body, and its direction is always along the tangent of the trajectory of the monitoring point in three-dimensional space.

[0031] Next, the instantaneous velocity vector Perform second-order time derivative to output the instantaneous acceleration vector. : Instantaneous acceleration vector It characterizes the intensity of changes in the movement state of a geological body and the state of internal mechanical imbalance.

[0032] Step S3: Calculate the spatial trajectory geometric constraint evolution characteristic parameters using instantaneous velocity vector and instantaneous acceleration vector.

[0033] As an optional embodiment, step S3 specifically includes calculating the magnitude of the cross product of the instantaneous velocity vector and the instantaneous acceleration vector to obtain the spatial trajectory geometric constraint evolution characteristic parameters. The core calculation formula is: Instantaneous acceleration vector It can be orthogonally decomposed into tangential acceleration vectors in the natural coordinate system. and normal acceleration vector ,Right now: Tangential acceleration With velocity vector Collinearity only represents the change in the magnitude of the velocity of geological bodies; normal acceleration With velocity vector Vertical, pointing to the center of curvature of the trajectory, characterizes the change in the direction of geological body movement, and is controlled by the spatial geometric constraints of the rock and soil body.

[0034] Substituting the orthogonal decomposition of acceleration into the cross product formula, and applying the distributive law and the algebraic property that the cross product of parallel vectors is zero, we obtain: Since tangential acceleration is parallel to velocity, their cross product is zero, therefore it is equivalent to: The magnitude of the normal acceleration satisfies ,in Let be the instantaneous radius of curvature of the spatial trajectory. The derivation ultimately yields: By utilizing the objective theorem that the cross product of parallel vectors is zero, tangential motion characteristics are eliminated, while normal motion characteristics, which characterize the strength of spatial constraints, are separated and amplified. This parameter can capture the topological abrupt change in the spatial trajectory from curved to straight when a sliding surface within a geological body is connected.

[0035] Step S4: Construct the hydraulic coupling parameters by using pore water pressure, instantaneous velocity vector and spatial trajectory geometric constraint evolution characteristic parameters.

[0036] As an optional embodiment, the water turbine coupling parameters of the system are constructed. The formula is: Extrapolating from the aforementioned steps Substituting into the formula, we can obtain its equivalent analytical form at the physical level: The unidirectional physical logic and dimensionality reduction analysis of each basic parameter are as follows: Fluid-driven surface power factor pore water pressure Displacement of geological bodies When the energy is applied by the pore fluid through permeation and expansion and is converted into deformation kinetic energy, it is converted into an active work term. This factor objectively characterizes the instantaneous linear power flux of the pore fluid.

[0037] Spatial geometry release operator The geometric dimensions that constitute the energy release channel are: the smaller the radius of curvature, the stronger the physical locking in the normal direction; the larger the radius of curvature, the smoother the channel.

[0038] This model defines the nonlinear product coupling of fluid driving force and geometric constraint force as the power density along the track. This enables the fusion of multi-source data into a dynamic index with a definite physical entity.

[0039] Step S5: Determine the trend of the continuously generated time series of hydraulic turbine coupling parameters. When the first and second derivatives of the hydraulic turbine coupling parameters are both greater than zero and the time window is continuously set, output a geological anomaly instability warning command.

[0040] The system generates continuously The time series analysis does not set an absolute value alarm upper limit, but instead judges the convexity-concavity evolution of the function itself. This is done when the first derivative is simultaneously satisfied. and the second derivative This indicates that the energy flux density exhibits an upward-convex, accelerating divergence state. If this state persists for a specific time window, it is determined to be a physical abrupt change, and a warning is issued.

[0041] The length of the set time window can be configured differently according to the physical medium type of the target geological body being monitored. For brittle rock structures, shear failure is often extremely sudden, and the acceleration time after the macroscopic sliding surface is connected is very short. Therefore, the corresponding set time window is set to a smaller interval. For cohesive soil or accumulated structures, plastic deformation characteristics are obvious, and the critical divergence process is relatively gradual. The set time window is set to a larger interval to avoid short-term numerical fluctuations caused by local plastic yielding of the soil.

[0042] The length of the time window is typically set as a continuous multiple of the sampling period of the underlying monitoring equipment. Specifically, if the system's data sampling and time dimension parsing period is... Then set a time window Set as ,in This refers to the number of valid data frames that continuously trigger the determination condition. In a preferred embodiment, The value range is set to 3 to 5. This logic can adapt to monitoring devices with different sampling frequencies, ensuring the timeliness of early warning commands while filtering out single or occasional numerical jump errors.

[0043] In scenarios involving the boundary between localized creep and water pressure disturbance, such as a monitoring point on a steep slope in the early stage of localized stress adjustment during geological anomaly development, the area has experienced continuous heavy rainfall. Surface water has infiltrated along existing tension cracks, causing the shallow soil to soften and resulting in localized uneven settlement. Simultaneously, deep pore water has been replenished, leading to an increase in pore water pressure.

[0044] At this stage, the core locking section deep within the slope remains stable, while the main control sliding surface has not yet been penetrated. The system's bottom-level sensors acquire the sliding displacement of the shallow soil, resolving a high instantaneous displacement rate. Simultaneously, the pore water pressure acquired by the deep piezometer is also high. If conventional early warning technology based on a single scalar threshold is used, the system will output an early warning signal due to the excessive displacement rate. However, in the architecture of this application, because the deep sliding surface is not penetrated, the surface displacement is constrained by the normal force of the undamaged internal soil and rock mass. Its trajectory in three-dimensional space continuously bypasses the obstruction point, exhibiting a meandering spatial form. The system's spatial feature analysis module determines that the instantaneous radius of curvature of the trajectory is small at this time, and the normal acceleration component is dominant. Therefore, the extracted spatial trajectory geometric constraint evolution characteristic parameter, i.e., the cross product modulus, remains at a high non-zero level.

[0045] After substituting the above parameters into the fluid-structure interaction physical model, the numerical expansion of the hydraulic coupling parameter is strongly suppressed due to the small radius of curvature. Over a continuous time period, the first derivative of the hydraulic coupling parameter fluctuates alternately between positive and negative values ​​with rainfall and local stress adjustments, while its second derivative oscillates around zero, failing to form and maintain a divergent state greater than zero.

[0046] Based on this, the system determined that the hydrodynamic driving energy was being dissipated by the geometric friction of the internal structure of the geological body, and that a macroscopic slip channel had not yet formed. Ultimately, the system did not issue a warning, classifying the deformation as a non-instability local disturbance, effectively filtering out high-frequency false alarms caused by severe weather conditions.

[0047] In scenarios involving critical instability and the expected connection of the sliding surface, such as when deep micro-cracks within the slope expand and merge under continuous shear stress as geological evolution progresses, eventually shearing off the core locking segment, a unified macroscopic sliding surface is formed throughout the slope. The landslide body loses its surrounding normal geometric constraints and enters a stage of overall directional accelerated sliding.

[0048] During the critical instability stage, the internal spatial topology undergoes a sudden change. The rock and soil mass slides unimpeded along the continuous sliding surface, and its trajectory changes from a restricted curve to a straight line or a smooth great circle along the sliding surface. The system determines that the radius of curvature diverges sharply at this point. Since the geological body is mainly subjected to tangential gravity and sliding driving force, the velocity vector and acceleration vector are highly collinear in three-dimensional space, and the cross product modulus calculated by the system converges rapidly and approaches zero. This sudden change in the underlying mechanical state allows the internally accumulated water flow driving surface power to obtain a fully open release channel.

[0049] After the system substituted the parameters into the model, the hydraulic turbine coupling parameters exhibited an irreversible exponential divergence surge. In a continuous time series, the system determined that both the first and second derivatives of the hydraulic turbine coupling parameters had jumped and were strictly greater than zero. When the state of the second derivative being continuously greater than zero exceeded the aforementioned specific time window, the system, based on the physical divergence characteristics of the disappearance of spatial obstruction and the rapid release of energy, determined that the geological structure had reached the mechanical critical point of transformation from quantitative change to qualitative change, and automatically output a geological anomaly instability warning command.

[0050] The architecture of a geological anomaly monitoring and early warning device based on multi-source data fusion provided in this application embodiment will be described in detail below.

[0051] The device includes the following modules: Basic parameter acquisition module: It interfaces with GNSS and piezometer to obtain the original absolute spatial coordinate matrix and pore water pressure time-series flow.

[0052] Kinematic feature analysis module: It has a built-in Kalman filter unit and a differential operator to perform first and second derivatives on the coordinate matrix and separate the velocity matrix and acceleration matrix.

[0053] Spatial Feature Analysis Module: As the core of three-dimensional spatial analysis, it performs vector orthogonal decomposition and cross product modulus calculation to extract geometric variables that reflect trajectory normal constraints.

[0054] Coupled parameter construction module: Using the fluid-structure reduction product equation calculation unit, pore pressure, velocity and geometric operators are integrated into a unique power density parameter along the track.

[0055] Early warning logic generation module: It uses a second-order differential operator to determine the concavity and convexity of the power density function in real time and controls the external communication relay to issue commands.

[0056] This application also provides a computer-readable storage medium and an electronic terminal.

[0057] The electronic terminal includes a processor, a memory, and an internal communication bus. The memory stores computer program code, and the communication bus connects the processor to the network interface of the data acquisition module. When the computer program stored in the memory is read and executed by the processor, the processor allocates the computing power of each module to implement the aforementioned decision-making logic.

[0058] Storage media can be tangible devices that hold and store instructions for use by instruction execution devices. Storage media can include, for example, electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof.

[0059] It should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

[0060] The detailed descriptions listed above are merely specific descriptions of feasible embodiments of the present invention, and are not intended to limit the scope of protection of the present invention. All equivalent embodiments or modifications made without departing from the spirit of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring and early warning of geological anomalies by multi-source data fusion, characterized in that, The method comprises the following steps: Step S1: synchronously collecting a three-dimensional spatial position vector and pore water pressure of a geological body; Step S2: performing first-order derivation analysis on the three-dimensional spatial position vector in the time dimension to obtain an instantaneous velocity vector, and performing second-order derivation analysis to obtain an instantaneous acceleration vector; Step S3: calculating a spatial trajectory geometric constraint evolution characteristic parameter through the instantaneous velocity vector and the instantaneous acceleration vector; Step S4: constructing a water-machine coupling parameter through the pore water pressure, the instantaneous velocity vector, and the spatial trajectory geometric constraint evolution characteristic parameter; Step S5: performing trend determination on a time sequence of the continuously generated water-machine coupling parameter, and outputting a geological abnormal instability early warning instruction when the first-order derivative and the second-order derivative of the water-machine coupling parameter are both greater than zero and last for a set time window. 2.The multi-source data fusion geological anomaly monitoring and early warning method according to claim 1, characterized in that, In step S3, the spatial trajectory geometric constraint evolution characteristic parameter is calculated by calculating the cross product module length of the instantaneous velocity vector and the instantaneous acceleration vector. 3.The multi-source data fusion geological anomaly monitoring and early warning method according to claim 1, characterized in that, In step S4, the water-machine coupling parameter is constructed according to the pore water pressure, the instantaneous velocity vector, and the spatial trajectory geometric constraint evolution characteristic parameter.

4. The multi-source data fusion geological anomaly monitoring and early warning method according to claim 1, characterized in that, In step S1, the three-dimensional spatial position vector and the pore water pressure of the geological body are synchronously collected, specifically, the absolute spatial coordinates of a surface monitoring point at a corresponding time are collected as the three-dimensional spatial position vector, and the seepage expansion pressure of deep groundwater on the rock-soil skeleton is obtained as the pore water pressure.

5. A multi-source data fusion geological anomaly monitoring and early warning device, characterized in that, The application of the multi-source data fusion geological anomaly monitoring and early warning method according to any one of claims 1 to 4 comprises: a basic parameter acquisition module that synchronously collects a three-dimensional spatial position vector and pore water pressure of a geological body; a kinematic characteristic analysis module that performs first-order derivation analysis on the three-dimensional spatial position vector in the time dimension to obtain an instantaneous velocity vector, and performs second-order derivation analysis to obtain an instantaneous acceleration vector; a spatial characteristic analysis module that calculates a spatial trajectory geometric constraint evolution characteristic parameter through the instantaneous velocity vector and the instantaneous acceleration vector; a coupling parameter construction module that constructs a water-machine coupling parameter through the pore water pressure, the instantaneous velocity vector, and the spatial trajectory geometric constraint evolution characteristic parameter; an early warning logic generation module that performs trend determination on a time sequence of the continuously generated water-machine coupling parameter, and outputs a geological abnormal instability early warning instruction when the first-order derivative and the second-order derivative of the water-machine coupling parameter are both greater than zero and last for a set time window.

6. A computer readable storage medium characterized by, The computer program stored in the storage is executed by the processor to implement the multi-source data fusion geological anomaly monitoring and early warning method according to any one of claims 1 to 4.

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