Integrated multi-source data fusion geological disaster monitoring system and early warning method thereof

Abstract

The present application belongs to the field of geological disaster monitoring, Internet of Things sensor integration, artificial intelligence and multi-source data fusion technology, and relates to an integrated multi-source data fusion geological disaster monitoring system and its early warning method. The system comprises an integrated data acquisition network and an integrated monitoring station in the space, the sky and the earth, the data acquisition network comprises a space layer, an aviation layer, a ground layer and an underground layer, and is supplemented by a visual linkage module to form a multi-dimensional parameter stereoscopic acquisition architecture. The integrated monitoring station comprises an integrated ground and underground monitoring primary end, a hardware level normalization mechanism and a single digital communication bus topology mechanism. The present application realizes the unified access of the vibrating string sensor and the digital signal output type sensor by setting a frequency-digital conversion module; the digital communication bus is shared by each sensing component, thereby reducing the number of internal wiring and making the structure more simple; the device is modularized and highly integrated, thereby facilitating installation and maintenance; and the overall integration degree and reliability of the geological disaster monitoring system are improved.

Description

Technical Field

[0001] This invention belongs to the fields of geological disaster monitoring, Internet of Things sensor integration, artificial intelligence and multi-source data fusion technology. Specifically, it relates to an integrated multi-source data fusion comprehensive sensing geological disaster monitoring system that is applied to the full-cycle monitoring of refined investigation and early warning of complex geological disasters such as landslides, debris flows and landslides. Background Technology

[0002] Driven by both abnormal fluctuations in global climate change, frequent extreme weather events, and large-scale human engineering activities (such as basin-wide hydropower cascade development, high-grade mountain highway construction, and deep mining), the mechanical equilibrium of surface soil and rock is being disturbed like never before. The resulting geological disasters, such as landslides, debris flows, and collapses, are often highly concealed, sudden, and possess immeasurable destructive power. These geological disasters not only pose a serious threat to human life and property but also bring enormous risks to regional economic development and the operation of critical infrastructure.

[0003] Modern engineering geology and geotechnical mechanics theories indicate that the formation, occurrence, and evolution of geological hazards are essentially an extremely complex process involving multi-physics field coupling and nonlinear dynamic gradual change. Therefore, constructing a scientific, rigorous, and high-fidelity automated monitoring network for geological hazards requires breaking through the limitations of monitoring single physical quantities and comprehensively encompassing three core sensing dimensions: perception of environmental triggering factors, perception of deep stress state, and perception of surface morphological evolution. Only in this way can the entire chain of information regarding hazard evolution be captured.

[0004] Despite the increasingly sophisticated data communication standards at the macro level and the gradual evolution of cloud-based data processing algorithms towards deep learning, current engineering practices face extremely severe integration challenges in constructing multi-dimensional stereoscopic sensing underlying hardware networks that meet these high standards. The root of this challenge lies in the inherent physical differences of the measured physical quantities, leading to extreme fragmentation and high heterogeneity in the underlying signal systems of various sensors.

[0005] Firstly, in the field of deep stress and minute deformation monitoring, such as earth pressure gauges used to measure the internal squeezing pressure of soil and rock, anchor cable force gauges used to monitor the stress in anchoring projects, and rebar gauges, the engineering community has long widely and reliantly used vibrating wire sensors. The physical working principle of a vibrating wire sensor is based on a tensioned special steel wire. When external pressure causes a small relative displacement at both ends of the sensor, the tension of the internal steel wire changes accordingly, thus altering its inherent vibration frequency according to the laws of physical acoustics. These sensors have advantages such as good long-term stability and small zero-point drift, but their fatal weakness lies in outputting a non-standard analog damped sinusoidal attenuated signal. To extract this frequency characteristic, a dedicated reader or a large acquisition box equipped with a 100-volt high-voltage pulse excitation circuit and a high-speed analog detection circuit is required to complete the instantaneous oscillation, echo capture, and analysis of the signal. Although some existing technologies have developed local anti-interference circuits for vibrating wire sensors, these improvements are mostly limited to the analog signal conditioning level of the single sensor itself, failing to address the cross-protocol integration problem with other types of digital sensors from the perspective of the overall system architecture.

[0006] Secondly, in stark contrast to the ancient and complex analog system of vibrating wire sensors, modern landform monitoring, such as high-precision dual-axis tilt sensors based on microelectromechanical systems (MEMS) technology and environmental factor monitoring, such as optical rain gauges and high-precision illuminance sensors based on infrared optics, is highly digital. These intelligent sensors typically integrate a microprocessor (MCU), which can not only perform analog-to-digital conversion and filtering of data internally, but also directly output digital serial communication signals that fully comply with the RS-485 physical layer differential electrical standard and the Modbus-RTU application layer protocol specification.

[0007] Based on the underlying signal system of the aforementioned sensors, the fundamental separation and irreconcilability between high-voltage analog frequency signals and low-voltage digital serial signals have forced most existing automated field monitoring base stations for geological disasters to adopt a "piecemeal" system architecture design. To simultaneously access analog frequency signals and digital communication signals at a single monitoring station, system integrators typically need to deploy multiple completely independent data acquisition terminals in parallel within a protective enclosure: one terminal is dedicated to channel scanning, high-voltage excitation, and frequency measurement of analog signals; another terminal, such as a general-purpose remote terminal unit (RTU), is dedicated to node polling and data transmission of digital signals. This traditional architecture design, stemming from historical technological legacies, directly leads to multiple insurmountable and intertwined technical bottlenecks:

[0008] First, the extremely complex internal electrical topology of the system leads to severely compromised electromagnetic compatibility (EMC). In this patchwork architecture, the internal wiring presents a chaotic star or point-to-point mesh structure. Numerous cables transmitting weak analog frequency signals crisscross within the confined chassis space with digital communication cables transmitting high-frequency pulses, significantly increasing the overall physical size and static standby power consumption of the equipment, and causing fatal signal crosstalk problems. Weak analog frequency echo signals are highly susceptible to parasitic capacitance coupling interference from adjacent RS-485 digital high-frequency differential signals or external 4G / 5G wireless RF signals when transmitted over long distances or passing through dense terminal blocks, resulting in irregular fluctuations and baseline drift in the final resolved frequency readings. Once this distortion of the underlying hardware signals is uploaded to the cloud, it will directly cause serious misjudgments in the complex backend early warning model, generating a large number of false alarms.

[0009] Secondly, the system exhibits extremely poor adaptability to extreme outdoor environments, resulting in exceptionally high lifecycle maintenance costs. Geological disaster monitoring stations are typically deployed in harsh areas such as high mountains, deep valleys, steep slopes, and virgin forests—regions with extremely inaccessible transportation, drastic temperature and humidity fluctuations, and high susceptibility to lightning strikes. The cumbersome and densely packed cable connections within the modular chassis are highly susceptible to hidden contact problems after prolonged exposure to extreme thermal expansion and contraction, high humidity and moisture intrusion leading to oxidation and corrosion, and even minor vibrations from the mountainside. In the event of a data interruption, the lack of a unified underlying bus architecture necessitates maintenance personnel navigating a complex network of cables, carrying various testing instruments, to systematically troubleshoot analog links, digital links, and the two independent power supply systems. This extremely low fault location efficiency severely hinders the evolution of automated geological disaster monitoring networks towards large-scale, high-reliability, and low-maintenance-cost solutions.

[0010] In view of this, there is an urgent need to develop an innovative solution that can break down the physical isolation barrier between analog and digital signals at the root of the hardware architecture, so as to achieve high integration and bus-based access of multi-source heterogeneous data, and complete high-fidelity data fusion in real time at edge computing nodes. This has become a key research direction in the development of next-generation geological disaster monitoring instruments. Summary of the Invention

[0011] The purpose of this invention is to provide an integrated multi-source data fusion comprehensive sensing geological disaster monitoring system, and also to provide an early warning method for the aforementioned integrated multi-source data fusion comprehensive sensing geological disaster monitoring system, in order to solve the problems of unified access of heterogeneous sensors and system wiring redundancy, blind spots of multi-scale three-dimensional monitoring and scientific placement of sensors, spatiotemporal synchronization of multi-source data and feature alignment errors, high false alarms and missed alarms caused by static one-dimensional threshold judgment, and system survival and communication paralysis under extreme disaster conditions.

[0012] This invention is achieved through the following technical solution:

[0013] An integrated multi-source data fusion comprehensive sensing geological disaster monitoring system includes an integrated data acquisition network and an integrated monitoring station covering space, ground, and satellite.

[0014] The integrated data acquisition network includes a seamless satellite monitoring network for geophysical field detection satellite interfaces as the space layer, a semi-aerial UAV detection module as the airborne layer, and an integrated front-end sensing array as the ground and underground layer. Together with the visual linkage module, it forms a multi-dimensional, full-parameter, three-dimensional acquisition architecture. The semi-aerial UAV detection module uses a UAV system equipped with an electromagnetic source and transient electromagnetic receiving coil. The integrated front-end sensing array is used to capture the energy release from micro-fractures in rocks and includes surface morphology sensing components and environmental excitation factor sensing components deployed on the surface, as well as a microseismic sensor array, geostress sensor, and soil temperature and humidity sensor deployed underground. The visual linkage module is a video monitoring device with intelligent wake-up, support for infrared night vision, remote optical zoom, and panoramic elevation monitoring.

[0015] The integrated monitoring station is equipped with solar photovoltaic panels and a wide-temperature-range energy storage battery. It includes a protective chassis with built-in guide rails, on which are mounted a central processing unit, a multi-channel frequency-to-digital conversion module, and a power management module. Multiple vibrating wire sensors are connected to the multi-channel frequency-to-digital conversion module, which integrates excitation circuits and high-frequency signal acquisition circuits. The frequency-to-digital conversion module, the surface morphology sensing component, and the environmental excitation factor sensing component are connected to the central processing unit via the same digital communication bus.

[0016] Furthermore, the spatial data is used to obtain information on large-scale regional plate activity and macroscopic seismic events, extract regional tectonic stress background, and quantify the disturbance and induced effects of mid- and far-field seismic waves on specific geological disaster monitoring points, providing macroscopic background parameter boundaries for microscopic early warning models.

[0017] Furthermore, the semi-aerial unmanned aerial vehicle (UAV) detection module employs a ground-air collaborative electromagnetic ground detection system to depict the three-dimensional resistivity structure of the underground, identify the stratigraphic composition, deep fault zones, slip surface orientation, groundwater confluence channels, and underground cavities, and use the detection results as a direct design basis for the scientific placement of ground sensors and optimization of spatial coordinates.

[0018] Furthermore, the environmental excitation factor sensing components include a GNSS surface deformation sensor, a multi-axis tilt sensor, and a rain gauge; distributed optical fiber sensors are laid along the potential runoff path of debris flows to form a high-density sensing network.

[0019] Furthermore, the multi-channel vibrating wire sensor is used for underground micro-monitoring, and its output is a non-standard physical frequency signal; the frequency-to-digital conversion module is used to convert the frequency signal output by the vibrating wire sensor into a digital signal and then output it to the central processing unit. The excitation circuit of the frequency-to-digital conversion module is responsible for periodically sending sweep frequency or step pulse excitation signals to the multi-channel vibrating wire sensor. The frequency acquisition circuit captures the weak induced electromotive force generated by the resonance of the steel string in real time, and calculates and directly maps the frequency signal into a standard digital signal through the internal high-speed computing unit.

[0020] Furthermore, the output of the frequency-to-digital conversion module, the surface morphology sensing component that outputs digital signals, and the environmental excitation factor sensing component are all connected to the central processing unit via the same RS-485 or CAN digital communication bus in a daisy chain or parallel node configuration.

[0021] Furthermore, the central processing unit has a built-in microprocessor, including a Cortex-M series or FPGA and a level conversion matching circuit. Through polling and interrupt mechanisms, it collects heterogeneous data from all sensing components in a unified manner under time synchronization via a digital communication bus, and packages and transmits the data through a wireless communication module.

[0022] Furthermore, the power management module inside the protective enclosure not only supplies power to its own control circuit, but also has a voltage regulator and boost output interface, which directly provides stable power to the extended underground microseismic detector, ground stress sensor and high-definition video components;

[0023] The central processing unit has built-in edge computing capabilities and a multimodal communication architecture, enabling data to be transmitted back in real time via cellular networks such as 4G / 5G / NB-IoT. It can also use a built-in local self-organizing network module, where adjacent monitoring stations can act as relay nodes for data relay. The satellite communication early warning module uses BeiDou short message service or Tiantong satellite, which can directly transmit the highest level of disaster status and evacuation instructions to the command center via satellite channels.

[0024] An early warning method for an integrated multi-source data fusion geological disaster monitoring system includes the following steps:

[0025] Step 1: Sub-millisecond spatiotemporal synchronization and multi-dimensional feature extraction based on hardware clock:

[0026] Acquire multi-source heterogeneous raw data, perform sub-millisecond time synchronization on all sensor data on the front-end communication bus, and perform spatial registration based on UAV survey coordinates; set time point t, for the i-th sensor, extract its key physical features in the time and frequency domains, and construct a multi-dimensional local feature vector:

[0027] ;

[0028] Step 2, Dynamic weight allocation and global feature-level fusion:

[0029] Real-time calculation and assignment of a dynamic weight to each sensor feature vector Subsequently, a fusion algorithm is used to reduce the dimensionality of the high-dimensional feature vectors and synthesize them to generate a global feature vector that can comprehensively characterize the current health status of the geological body. :

[0030] ;

[0031] Step 3: Nonlinear risk assessment decision-making model based on machine learning:

[0032] To replace traditional static threshold comparisons, a risk assessment decision model is constructed using global feature vectors. Continuous input to machine learning decision functions deployed in the cloud In this process, a comprehensive geological hazard risk value reflecting the probability and severity of disaster occurrence is calculated:

[0033] ;

[0034] The decision function It integrates support vector machine, random forest or deep neural network algorithms, and uses a historical massive disaster database for supervised learning and online adaptive updates, which can keenly capture the nonlinear coupling relationship between multi-source data and weak precursor anomalies;

[0035] Step 4: Adaptive hierarchical early warning generation and visual linkage cross-validation:

[0036] Set dynamic risk warning thresholds When the real-time calculated risk value meets At that time, the early warning response procedure is automatically triggered; based on Based on the absolute magnitude and growth rate, the early warning model outputs four levels of warning signals: attention level, alert level, danger level, and extremely high risk level; and sends instructions to the front end to intelligently activate the video surveillance devices deployed at key potential hazard points; the cameras capture on-site images, and the visual images are compared with the risk values ​​calculated by the sensors. Perform multimodal cross-validation; issue early warning information after confirmation.

[0037] Further, in step one, key physical features are captured, including soil displacement rate and acceleration of surface deformation sensors, energy release rate and dominant frequency drift of earthquake and microseismic sensors, and soil cohesion and moisture content gradient of rainfall, soil temperature and humidity sensors.

[0038] Step four: GNSS and drone aerial cameras capture on-site images, including the opening of slope cracks, vegetation tilt, and turbidity of lake water.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] 1. The integrated multi-source data fusion geological disaster monitoring system of the present invention achieves unified access of vibrating wire sensors and digital signal output sensors by setting a frequency-to-digital conversion module;

[0041] 2. All sensing components share a digital communication bus, reducing the number of internal wirings and making the structure simpler;

[0042] 3. The device is modular and highly integrated, facilitating installation and maintenance;

[0043] 4. Improved the overall integration and reliability of the geological disaster monitoring system. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 This is a schematic diagram of the overall architecture of the integrated multi-source data fusion geological disaster monitoring system of the present invention;

[0046] Figure 2 The information collection and management module is shown in (a) and (b) is a flowchart of the information collection and management module and a schematic diagram of the real-time status monitoring interface of the monitoring node.

[0047] Figure 3 The data preprocessing module is shown in (a) and (b) is a flowchart of the data preprocessing module.

[0048] Figure 4 The data quality assessment module includes (a) a flowchart of the data quality assessment module, (b) a data quality distribution demonstration diagram, and (c) a schematic diagram of the spatial distribution estimation of equipment and environmental noise in the monitoring area.

[0049] Figure 5 This is a flowchart illustrating the machine learning-based intelligent algorithm for early warning and decision-making logic of the present invention.

[0050] Figure 6 A schematic diagram of the internal hardware modules and communication architecture of the integrated front-end monitoring station;

[0051] Figure 7This is a schematic diagram showing the connection between the frequency-to-digital conversion module and each sensing component.

[0052] Figure 8 A schematic diagram showing the installation of internal modules for protection. Detailed Implementation

[0053] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0054] Research has found the following problems with existing geological disaster monitoring technologies:

[0055] 1. Issues with unified access to heterogeneous sensors and redundant system wiring: In existing monitoring stations, frequency signals, such as deep stress vibrating wire sensors, have long been separated from digital signal equipment. This requires the configuration of multiple independent acquisition instruments, resulting in technical defects such as large internal size of the protective enclosure, extremely complex wiring, and susceptibility to electromagnetic crosstalk and physical connection failures.

[0056] 2. Problems with blind spots in multi-scale three-dimensional monitoring and scientific placement of sensors: The existing discrete monitoring network has a narrow coverage area and relies on subjective experience for sensor placement. It relies only on single surface displacement data and ignores deep geological structural features and macro-geological environmental inducing factors, resulting in a high rate of missed reports of hidden landslides and deep sliding surfaces.

[0057] 3. Problems with spatiotemporal synchronization and feature alignment of multi-source data: The traditional NTP time protocol has low synchronization accuracy with an error greater than 100ms, which leads to the problem that multidimensional physical field data cannot be accurately matched on the time scale, and the three-dimensional geological field reconstruction is distorted and the fusion calculation fails due to asynchronous sampling.

[0058] 4. High false alarm and false alarm problem caused by static one-dimensional threshold judgment: The traditional assessment based on a single static abrupt change threshold has limitations. Because it fails to dynamically integrate multi-source precursor characteristics such as soil temperature and humidity, microseismic energy, and surface deformation, it leads to technical bottlenecks such as weak generalization ability of early warning models and poor robustness of risk assessment.

[0059] 5. System survival and communication paralysis under extreme disaster conditions: In the instant of devastating geological disasters such as heavy rainfall and mudslides, the monitoring terminal loses power and data transmission capability due to the interruption of the municipal power network and the damage of public network communication base stations, resulting in the complete failure of the early warning system.

[0060] To this end, the present invention provides an integrated multi-source data fusion comprehensive sensing geological disaster monitoring system. The system architecture is divided into a spatial layer, a temporal layer, a sensor layer and a platform layer from macro to micro. The system includes a multi-dimensional full-parameter acquisition architecture, a heterogeneous sensor hardware-level normalization integration device, a redundancy protection mechanism for network and power outages in the event of a disaster, and an intelligent early warning model kernel based on multi-source data fusion built into the platform layer, realizing the evolution from underlying hardware aggregation and communication link reconstruction to cloud algorithm evolution.

[0061] Specifically, the integrated multi-source data fusion geological disaster monitoring system of the present invention includes an integrated data acquisition network and an integrated monitoring station. For example... Figure 1 As shown, the monitoring system comprises a multi-scale, three-dimensional monitoring network integrating space-air monitoring (satellites, drones), surface monitoring (solar-powered monitoring base stations), and deep underground monitoring (deep sensing equipment and DAS distributed fiber optic acoustic sensing system). Multi-source heterogeneous raw data collected by each node are transmitted in real-time to the local control center or cloud server via wireless communication base stations, providing the hardware foundation for subsequent data fusion and risk assessment. This integrated data acquisition network is a comprehensive "space-air-ground-underground" data acquisition network capable of fully capturing the spatiotemporal characteristics of geological disaster evolution. The integrated data acquisition network includes a space layer, an airborne layer, a ground layer, and an underground layer, and also includes a visual linkage module, forming a multi-dimensional, full-parameter, three-dimensional acquisition architecture.

[0062] like Figure 2 (a) and Figure 2 As shown in (b), the operational health status of each sensor node (such as node 1, node 2, and node 3) in the monitoring network is displayed. The system uses green, yellow, and red indicator lights to indicate the "normal status", "warning status", and "fault status" of the devices, respectively, thereby assisting maintenance personnel in timely and efficient management of the monitoring hardware.

[0063] The spatial layer is a satellite monitoring network that seamlessly connects to the interface of geophysical field detection satellites to acquire information on large-scale regional plate activity and macroscopic seismic events. This layer's data is used to extract regional tectonic stress background and quantify the disturbance and induced effects of mid- and far-field seismic waves on specific geological disaster monitoring points, providing macroscopic background parameter boundaries for microscopic early warning models.

[0064] The aforementioned airborne layer is a semi-airborne unmanned aerial vehicle (UAV) detection module. This invention introduces airborne electromagnetic detection technology. In the initial stage of system deployment or during high-risk flood seasons, a UAV system equipped with a high-precision electromagnetic source and transient electromagnetic receiving coil, such as a ground-air coordinated electromagnetic ground probing system, is used to conduct non-contact rapid exploration of the target area. The exploration depth is required to be no less than 100 meters below the surface. This module aims to accurately depict the three-dimensional resistivity structure of the underground, clearly identify the stratigraphic composition, deep fault zones, slip surface orientation, groundwater confluence channels, and underground cavities, and use the detection results as a direct design basis for the scientific placement and spatial coordinate optimization of ground sensors.

[0065] The surface and underground layers form an integrated front-end sensing array. This invention deploys high-precision GNSS surface deformation sensors, multi-axis tilt sensors, rain gauges, and other environmental excitation factor sensing components on the surface. Underground, it deploys a microseismic sensor array, a geostress sensor, and a soil and rock temperature and humidity sensor. The microseismic sensor array is used to capture the energy release from micro-fractures in the rock. Distributed fiber optic sensors (BOTDR / FBG) are laid along the potential debris flow path to form a high-density sensing network.

[0066] The visual linkage module is a video surveillance device integrated into the front-end equipment, featuring intelligent wake-up functionality and supporting infrared night vision, remote optical zoom, and panoramic tilt monitoring. This module works in conjunction with the sensing array to provide intuitive verification of the on-site physical state during numerical model-based early warnings.

[0067] The integrated monitoring station employs a hardware-level normalization integration device for heterogeneous sensors. To address the issues of redundant front-end equipment and chaotic wiring, this invention designs the internal hardware architecture of the monitoring station. Specifically, the integrated monitoring station includes an integrated ground and underground monitoring terminal, a hardware-level normalization mechanism, and a single digital communication bus topology. The integrated ground and underground monitoring terminal includes a high-protection-level (IP67 or higher) protective enclosure. Figure 8 As shown, the protective enclosure is equipped with standard DIN rails. The central processing unit (MCU), frequency-to-digital converter module, and intelligent power management module are all modularly installed on the standard DIN rails, facilitating quick plugging and unplugging and maintenance in the field.

[0068] like Figure 3 (a) and Figure 3As shown in (b), the dynamic fluctuation characteristics of multi-dimensional data sequences collected by various types of heterogeneous sensors within a set monitoring period are illustrated, and a QC report is generated, intuitively reflecting the temporal evolution of various physical indicators within the monitoring area. The hardware-level normalization mechanism includes multiple vibrating wire sensors for ground and underground microscopic monitoring, which output non-standard physical frequency signals. The frequency-to-digital conversion module adopts a multi-channel parallel structure design, which integrates an excitation circuit and a high-frequency signal acquisition circuit to convert the frequency signals output by the vibrating wire sensors into digital signals and output them to the central processing unit. The output terminal of the vibrating wire sensor is connected to the input terminal of the multi-channel frequency-to-digital conversion module, and the output terminal of the multi-channel frequency-to-digital conversion module is connected to the digital communication bus. Among them, the excitation circuit is responsible for periodically sending sweep frequency or step pulse excitation signals to the multiple vibrating wire sensors; the frequency acquisition circuit captures the weak induced electromotive force generated by the resonance of the steel wire in real time, and accurately calculates and directly maps the frequency signal into a standard digital signal (such as a digitized stress physical quantity) through the internal high-speed computing unit. This module acts as hardware middleware, completely eliminating the drawback of frequency signal transmission being susceptible to interference from distributed capacitance.

[0069] The single digital communication bus topology includes a digital communication bus, a frequency-to-digital conversion module, a surface morphology sensing component, and an environmental excitation factor sensing component, all connected to the central processing unit via the same digital communication bus. For example... Figure 7 As shown, the single digital communication bus topology includes a digital communication bus, a frequency-to-digital conversion module, a landform sensing component, and an environmental excitation factor sensing component, all connected to the central processing unit via the same digital communication bus. Traditional vibrating wire sensors are first connected to the frequency-to-digital conversion module; this module converts its non-standard signal into a standard "digital signal," which, along with the landform sensing component and environmental excitation factor sensing component that natively output "digital signals," is connected in parallel to the lower "digital communication bus (Bus)." Data from these heterogeneous sensing nodes is converged and transmitted to the lower "central processing unit" for centralized processing via this single digital communication bus. This bus architecture simplifies the electrical topology and solves the problem of unified access for multi-source heterogeneous sensors. Specifically, the output of the frequency-to-digital conversion module, along with the landform sensing component (tilt sensor, etc.) and environmental excitation factor sensing component (rain gauge, etc.) that natively output digital signals, are all connected to the central processing unit via the same RS-485 or CAN digital communication bus in a daisy-chain or parallel node configuration.

[0070] like Figure 6The diagram illustrates the electrical topology inside the protective enclosure: it includes external vibrating wire and digital sensors, and an internal central processing unit, power supply module, and frequency-to-digital converter. The central processing unit incorporates a microprocessor, such as a Cortex-M series or FPGA, and level conversion and matching circuitry. Through polling and interrupt mechanisms, it synchronously collects heterogeneous data from all sensing components via a digital communication bus, packages it, and transmits it via a wireless communication module. This topology significantly simplifies the cabling within the enclosure, facilitating centralized management of multi-source heterogeneous data from a single host unit.

[0071] To ensure the normal operation of the system under extreme conditions, this invention adopts a redundancy protection mechanism for disaster-prone network and power outages, and implements multiple redundancy designs at the energy supply and communication link levels.

[0072] First, independent passive power supply: All front-end integrated monitoring stations are equipped with high-power solar photovoltaic panels and wide-temperature-range energy storage batteries, achieving independent operation without any reliance on mains power. The power management module inside the chassis not only supplies power to its own control circuits but also has a regulated boost output interface, directly providing stable power to the expanded underground microseismic detectors, ground stress sensors, and high-definition video components.

[0073] Secondly, it employs edge computing and heterogeneous communication backbones: the central processing unit integrates edge computing capabilities and a multimodal communication architecture. Under normal conditions, data is transmitted back in real time via cellular networks such as 4G / 5G / NB-IoT. When mudslides or large-scale landslides damage public network base stations, the system automatically and seamlessly switches to disaster recovery communication mode. The system has a built-in local self-organizing network (Mesh network) module, where adjacent monitoring stations can act as relay nodes for data relay; simultaneously, it is equipped with a satellite communication early warning module, such as BeiDou short message service or Tiantong satellite. In extreme disaster situations involving power outages and network disruptions, the initial MCU activates a lightweight edge early warning algorithm. Once the risk is assessed as exceeding the limit, the highest level of disaster status and evacuation instructions are directly transmitted to the command center via satellite channels, maintaining the unobstructed flow of escape routes.

[0074] This invention discloses an early warning method for an integrated multi-source data fusion geological disaster monitoring system. The system's intelligent data center incorporates an "intelligent geological disaster digital early warning model kernel," which, through the integration of machine learning and big data mining technologies, achieves dynamic risk assessment. Specifically, the method includes the following steps:

[0075] Step one: Sub-millisecond spatiotemporal synchronization and multi-dimensional feature extraction based on hardware clock. For example... Figure 4 (a) in Figure 4As shown in (c), the preprocessed heterogeneous data undergoes feature extraction and quality assessment in this module. The system sets a time window to extract key physical features of the sensors in the time and frequency domains, constructing local feature vectors. To ensure data quality and the mathematical rigor of the fusion algorithm, the system's underlying layer uses a precise time protocol (IEEE 1588 PTP) or a built-in high-precision RTC combined with BeiDou time synchronization to perform strict sub-millisecond time synchronization on all sensor data on the front-end bus, and performs spatial registration based on UAV survey coordinates. At a given time point t, for the i-th sensor deployed in the system, its key physical features in the time and frequency domains (such as displacement rate and acceleration of deformation sensors, energy release rate and dominant frequency drift of microseismic sensors, and water content gradient of soil sensors) are extracted to construct multi-dimensional local feature vectors. .

[0076] Step two: Dynamic weight allocation and global feature-level fusion. Different sensors exhibit drastically different sensitivities at different stages of disaster evolution. The system introduces a dynamic weight evaluation mechanism (combining game theory, objective entropy weighting, and subjective GDEMATEL methods). Based on the historical stability of each sensor, the current signal-to-noise ratio, and surrounding environmental interference factors, a dynamic weight is calculated and assigned to each sensor's feature vector in real time. Subsequently, a fusion algorithm is used to reduce the dimensionality of the high-dimensional feature vectors and synthesize them, generating a global feature vector that can comprehensively characterize the current health status of the geological body. : .

[0077] Step 3: Machine Learning-Based Nonlinear Risk Assessment Decision Model. A risk assessment decision model is constructed to replace traditional static threshold comparisons. This involves using global feature vectors... Continuous input to machine learning decision functions deployed in the cloud In this process, a comprehensive geological hazard risk value reflecting the probability and severity of disaster occurrence is calculated: The decision function It integrates Support Vector Machine (SVM), Random Forest, or Deep Neural Network algorithms, and utilizes a massive historical disaster database for supervised learning and online adaptive updates. It can keenly capture the nonlinear coupling relationship between multi-source data and weak precursor anomalies.

[0078] Step four: Adaptive hierarchical early warning generation and visual linkage cross-validation. For example... Figure 5 As shown, the mathematical calculation path of the machine learning model is illustrated: the process begins with "acquiring multi-source heterogeneous raw data", followed by "millisecond-level spatiotemporal synchronization of hardware clock"; then "extracting multi-dimensional feature vectors". The system then dynamically allocates weights. And fuse global features Input this global feature into the machine learning model to "calculate the risk assessment value". Finally, the system performs a threshold determination.

[0079] The system sets dynamically assessed risk warning thresholds. When the real-time calculated risk value meets At that time, the system automatically triggers the early warning response procedure. Based on... Based on the absolute magnitude and growth rate, the early warning model outputs four levels of warning signals: blue (attention level), yellow (warning level), orange (danger level), and red (extremely high risk level). Simultaneously, the system sends commands to the front end, intelligently activating video surveillance devices deployed at key hazard points. The cameras utilize infrared night vision and optical zoom to quickly capture on-site images (such as opening slope cracks, leaning trees, turbid water, etc.), combining the visual images with the risk values ​​calculated by sensors. Multimodal cross-validation was conducted. After confirmation, the early warning information was rapidly distributed to the responsible persons of the city, town, and village-level mass monitoring and prevention network through multiple channels such as SMS, voice, APP push and emergency broadcast, to guide the tiered emergency response strategy, including large-scale personnel evacuation and intensive drone re-flight reconnaissance.

[0080] Example 1: Three-dimensional network deployment and scientific point arrangement based on "star-ground-space-inside" four-dimensional collaboration.

[0081] In response to the current systemic drawbacks of monitoring geological disasters, especially deep landslides and high-altitude concealed debris flows with complex causes, such as shallow, point-like, and large blind spots, this embodiment provides a four-dimensional spatial collaborative monitoring network deployment scheme that integrates multidisciplinary detection methods, and introduces a preliminary geophysical and geochemical diagnostic process in the site selection and deployment stage.

[0082] 1. Extraction of Macroscopic Background Field in Spatial Layer (Satellite Monitoring): The outbreak of geological disasters is often not only the result of localized rainfall or gravity, but also a localized release of long-term regional crustal tectonic stress. The system platform seamlessly connects to the API interface of geophysical field detection satellites to continuously monitor plate activity and macroscopic micro-seismic events over a large area surrounding the survey area. This approach breaks through the short-sighted limitation of traditional monitoring that only focuses on the "disaster body itself." In actual operation, small distant seismic waves or continuous crustal creep are very likely to be the "last straw" that triggers the instantaneous instability of steep slopes or water-rich landslides that have been in a long-term creep deformation stage. This spatial layer satellite data is input into the system's digital early warning model in real time, not only to generate regional disaster risk coefficients, but also to dynamically correct the background stress boundary conditions in the local landslide assessment model in the form of quantitative parameters.

[0083] 2. Precise Geophysical Exploration at the Airborne Layer (Semi-Aerial UAV Detection): Blindly deploying sensors is the root cause of monitoring network failures. In the initial exploration phase of this monitoring system's on-site deployment, or after exposure to high-risk environments such as continuous heavy rainfall, the system deploys UAV systems equipped with high-frequency transient electromagnetic transmitters and high-sensitivity receiving coils. The UAVs use electromagnetic pulses of a specific frequency as their excitation field source, flying low to the ground along a planned route, collecting high-density secondary induced magnetic field signals from the ground, thus achieving non-contact, high-resolution, and rapid exploration of the electrical structure of soil and rock under complex terrain. To understand the deep-seated causes of landslides in complex geological and geomorphological areas, the system platform inverts and calculates the massive amounts of frequency-domain and time-domain electromagnetic data collected by the UAVs, accurately depicting a three-dimensional distribution map of the underground resistivity structure. With this high-precision 3D model, geological experts and the system's built-in diagnostic algorithms can clearly delineate the exact location of the fault fracture zone, the undulation of the interface between the bedrock and the overburden, the orientation and dip angle of the main sliding surface, as well as the spatial distribution of water-rich areas (underground rivers, fissure water conduits) and underground mining caverns.

[0084] 3. Scientific Deployment and Linkage of Surface and Subsurface Sensors Based on Geophysical Diagnosis: After acquiring the aforementioned high-precision three-dimensional resistivity anomaly zones and geomechanical models, the expert system can identify the inherent mechanical weaknesses and key hydrogeological control nodes of "non-visible landslides" that are impossible to detect by conventional surface inspections. Based on this high-value diagnostic data, this system abandons the traditional "grid-based uniform deployment" or "experience-based random stake insertion" approach. Instead, it precisely anchors integrated surface monitoring stations, deep-hole inclinometers, and microseismic sensors to the calculated stress concentration zones, active groundwater confluence zones, and potential shedding points of the sliding surface. This effective targeted deployment strategy, guided by airborne geophysical exploration, significantly improves the signal-to-noise ratio of the sensor network from a physical source, ensuring that the system can sensitively capture extremely weak precursor signals such as deep rock micro-fractures in the earliest stages of disaster development.

[0085] Example 2: Hardware-level normalization access and bus topology optimization device for heterogeneous sensors.

[0086] This embodiment represents the core independent innovation of the present invention in the underlying architecture of the front-end physical hardware. It focuses on solving the persistent problems of signal incompatibility, equipment stacking due to "one machine, one test" in existing geological survey equipment, and the difficulties in field implementation and low reliability caused by extremely redundant wiring inside the box and poor anti-interference ability.

[0087] 1. Multi-channel Frequency-to-Digital Conversion Module: In the monitoring of deep structures and soil mechanics in geological disasters, the most core and reliable sensing element is the vibrating wire sensor (such as vibrating wire piezometers, anchor cable force gauges, and rebar gauges). Its working principle is based on the natural vibration frequency of the steel wire changing with the tension state. Because its output physical quantity is a pure frequency signal, it is not affected by the attenuation caused by changes in line resistance, capacitance, and distributed inductance due to long-distance cable transmission in the field, thus possessing unparalleled long-term stability and anti-electromagnetic interference capabilities. However, its output frequency signal is a purely analog physical quantity, which cannot be directly identified and read by conventional digital acquisition boards based on voltage and current sampling. To solve this industry pain point, this invention creatively designs and independently encapsulates a "multi-channel frequency-to-digital conversion module" inside an integrated protective enclosure. This hardware module highly integrates a high-voltage transient excitation circuit and a high-speed precision frequency acquisition circuit. During operation, the excitation circuit, under control, first sends a low-voltage sweep signal or a high-voltage step pulse excitation signal in a specific frequency band to the connected vibrating wire sensor, forcing the steel string inside the sensor to resonate. Subsequently, the high-speed frequency acquisition circuit quickly switches to receiving mode, accurately capturing the weak alternating induced electromotive force generated when the steel string cuts magnetic lines of force as it vibrates freely in the magnetic field. The high-performance microcontroller (or dedicated FPGA) integrated within the module uses a zero-crossing detection algorithm or Fast Fourier Transform (FFT) to calculate the steel string resonant frequency with extremely high precision. Combined with the pre-stored sensor factory calibration polynomial parameters, it directly converts the calculated value into a standard physical quantity (such as the actual pressure value in MPa or the displacement in mm) at the hardware level. In this architecture, this module perfectly acts as a hardware middleware for heterogeneous signals, completely bridging the gap between analog frequency signals and digital network architecture.

[0088] 2. Single Digital Communication Bus Parallel Topology Architecture: In traditional integrated monitoring stations, it is often necessary to configure a vibrating wire acquisition unit for vibrating wire sensors and a DTU for digital sensors. These instruments are connected by messy jumpers, forming an extremely complex star topology. This invention completely overturns this design concept, constructing an extremely simple underlying communication network within a protective enclosure. Specifically, the digital output of the aforementioned "frequency-to-digital conversion module," along with surface morphology sensing components that natively have built-in analog-to-digital conversion output digital signals (such as high-precision GNSS receivers and MEMS dual-axis tilt sensors), and environmental excitation factor sensing components (such as soil multi-parameter testers and tipping bucket rain gauges), are all connected to the same industrial-grade RS-485 bus or CAN digital communication bus, using a daisy-chain or standard bus parallel network topology.

[0089] 3. Highly Integrated Microcontroller and Standard DIN Rail Modular Layout: All core control components, including the central processing unit (MCU) with integrated routing and communication functions, the frequency-to-digital converter module, and the high-efficiency intelligent power management module, adopt a unified industrial standard housing and are directly fixed to the high-strength (IP67 or higher waterproof and dustproof rating) protective chassis backplate via standard DIN rail snap-fit. The central processing unit integrates a high-performance microprocessor (such as an industrial-grade Cortex-M series core) and a high-speed optocoupler level conversion isolation circuit. As the sole host of the system, the MCU systematically sends data acquisition scheduling commands to various heterogeneous sensor nodes connected to the same bus through a bus polling mechanism or an event interrupt mechanism, and obtains the returned standard data packets. Only a highly reliable waterproof aviation connector is retained on the outside of the chassis; external long-distance sensor signal lines are introduced through the connector and directly connected to the terminal blocks inside the chassis. This design, which uses hardware-level normalization and a unified digital bus, reduces the number of cables and connection terminals inside the protective chassis by more than 80%. This not only completely eliminates the serious electromagnetic crosstalk caused by the mixed wiring of analog small signals and high-speed digital signals, but also makes the system assembly, fault node diagnosis and module replacement in harsh field environments simple and efficient, and improves the long-term service life, system integration and survivability of the entire monitoring terminal.

[0090] Example 3: Spatiotemporal registration and dynamic fusion early warning of multi-source data based on machine learning.

[0091] This embodiment analyzes in detail how the system's cloud-based data processing platform and edge computing work closely together to overcome the high false alarm rate caused by traditional single static indicators, and uses rigorous mathematical models and cutting-edge AI deep learning algorithms to achieve highly robust intelligent early warning and judgment.

[0092] 1. Mathematical Construction of High-Precision Spatiotemporal Synchronization and Multidimensional Feature Vector Extraction: A geological hazard body is an extremely complex and nonlinear dynamic evolution system controlled by the coupling of internal and external dynamic geological processes. To achieve scientific prediction, the absolute alignment of massive multi-source data in the spatiotemporal dimension is a prerequisite. To avoid fusion failure caused by millisecond-level errors due to network jitter in the traditional NTP protocol, all data acquisition layers in this system employ IEEE 1588 PTP (Precise Time Protocol) hardware-level timestamp marking, or periodic calibration using a built-in high-precision RTC clock chip combined with BeiDou satellite timing pulses (PPS), ensuring that the absolute time synchronization error of all sensor data packets is strictly controlled at the sub-millisecond level. After receiving the synchronized and aligned multi-source raw data streams, the system's central processing unit and cloud do not directly use the raw scalar values ​​for judgment. Instead, based on the characteristics of different physical quantities, they extract high-level feature parameters in the time, frequency, or spatial domains within a time window t. For example, for microseismic sensor signals, the cumulative energy release rate per unit time, the frequency shift of the dominant frequency characteristic band, and the spatial clustering of micro-fracture events are extracted; for GNSS monitoring signals, not only is the absolute three-dimensional cumulative displacement extracted, but also the displacement rate, acceleration, and tangent angle parameters of the displacement-time curve are calculated; for geotechnical and hydrological sensors, the gradient change rate of deep soil moisture content and pore water pressure caused by rainfall infiltration is calculated. These characteristic parameters reflecting the dynamic state inside and outside the landslide are combined and encapsulated into a high-dimensional feature vector. For the i-th sensor deployed in the system, its feature vector is rigorously represented as: .

[0093] 2. Dynamic Weight Matrix Calculation and Global Feature-Level Mathematical Fusion: In different life stages of landslide evolution (such as the initial deformation stage, isokinetic creep stage, accelerated deformation stage, and even the pre-landslide instability stage), the representative meaning and sensitivity of physical indicators captured by different sensors are drastically different (for example, deep microseismic signal anomalies are most sensitive and precedent during the gestation period, while accelerated surface displacement characteristics become the dominant determining factor during the pre-landslide period). Therefore, using a traditional simple static arithmetic average would severely dilute the true precursor signals. To address this, this system introduces a dynamic weight evaluation mechanism. The system combines game theory, the entropy method of objective data, and the GDEMATEL method based on subjective expert experience in real time. Based on the historical data drift rate of each sensor node, the signal-to-noise ratio of the current sampling period, power supply status, and environmental meteorological interference factors, the system calculates and adaptively assigns a precise dynamic weight value to the feature vector of each sensor in real time. Subsequently, the system performs dimensionality reduction mapping and linear synthesis on the high-dimensional vectors of these heterogeneous data at the feature level, ultimately generating a global feature vector that can comprehensively and multidimensionally characterize the overall health status and energy accumulation level of the geological body. Its mathematical fusion expression is: In this formula, N represents the total number of monitoring sensors that are effectively involved in the current calculation.

[0094] 3. Machine Learning-Based Nonlinear Risk Assessment and Early Warning Decision Model: High-quality global feature vectors were constructed. Then, it is input into a pre-trained, large-scale machine learning early warning decision-making model deployed in the cloud data center to calculate the current real-time risk assessment value of geological disasters. The model calculation formula is as follows: The crucial multidimensional space mapping decision function. This is the core AI engine of the entire intelligent monitoring platform. This function doesn't rely solely on manually set analytical equations; instead, it deeply integrates powerful learning algorithms from the field of artificial intelligence: leveraging the support vector machine (SVM)'s exceptional ability to finely partition the boundaries of small-sample nonlinear spaces, combined with the excellent anti-overfitting and noise-resistant advantages of random forests when handling high-dimensional heterogeneous features, and even introducing deep neural network algorithms such as long short-term memory networks (LSTM) to extract time-series dependency features when conditions permit. Before going live, the model underwent supervised pre-training with massive amounts of samples using hundreds of thousands of real rainfall-induced and earthquake-induced landslide cases from the national historical geological disaster database, as well as long-term background noise multi-source dimensionality reduction data. This established a nonlinear approximation relationship between the current complex feature state vector and the probability of a disaster occurring within a future time window. During system operation, it also supports online adaptive learning, continuously fine-tuning the model parameters and network based on the latest monitored normal fluctuation baseline, resulting in increasingly accurate assessments.

[0095] 4. Intelligent Autonomous Judgment and Multi-Level Tiered Early Warning Distribution: Based on the powerful AI core mentioned above, the system completely abandons rigid, single-point static rules such as "an alarm is triggered when the crack displacement exceeds 5mm" which are prone to false alarms. Instead, it sets a comprehensive, macro-dynamic risk threshold. When the probability risk value calculated in real time on the cloud or edge exceeds the security boundary, i.e. At that time, the early warning model autonomously determines to trigger emergency response procedures at various levels. The system bases its decisions on the calculated... The absolute magnitude and differential evolution rate of the threat are used to precisely classify the threat, automatically outputting a four-level progressively strict warning signal: blue (attention level, requiring increased attention), yellow (warning level, requiring increased frequency of drone re-flight reconnaissance and ground patrols), orange (danger level, requiring local traffic control and material preparation), and red (extremely high risk level, judged to be in a state of imminent landslide, requiring immediate activation of the full evacuation plan). After the decision is made, a response within seconds is achieved. Through a multi-channel network of all media, including cloud-based government communication API interfaces, WeChat service accounts, automatic dialing voice systems, and high-powered air raid sirens on site, detailed disaster assessments, predicted impact ranges, and personalized evacuation route suggestions are directly and accurately delivered to the designated city, town (township), and village-level mass monitoring and prevention network personnel and threatened residents, constructing a solid digital defense line.

[0096] Example 4: A highly resilient redundant communication system for extreme disaster situations involving "network and power outages".

[0097] Recent massive landslides and torrential rains triggering large-scale mudslides demonstrate that natural disasters often involve the release of extremely destructive energy, typically destroying power grid transmission corridors in mountainous areas and severing communication base station fiber optic cables, creating extreme "power outages" and isolated environments. If early warning systems are unable to respond, the costly monitoring system will instantly fail at the critical moment when escape orders need to be issued. To address this "last mile" problem in geological disaster early warning, this invention incorporates extreme redundancy and edge self-rescue designs in both the energy and data communication links of the system hardware.

[0098] 1. Completely Passive and Cross-Node Power Supply Design: All integrated ground monitoring stations at all levels within the system utilize high-efficiency, high-power monocrystalline silicon solar photovoltaic arrays, combined with industrial-grade gel batteries (or high-density lithium iron phosphate battery packs) resistant to extreme cold and high temperature shocks, as the sole power source. This physically eliminates dependence on the mains power grid and power cables, achieving completely isolated and sustainable operation. Simultaneously, to address the power needs of high-power specialized instruments buried deep underground (whose installation locations preclude the use of solar energy), the intelligent power management module inside the monitoring station's protective enclosure not only meets the low-power operation requirements of its core MCU, wireless module, and connected bus sensors, but also features a specially designed boost / regulated DC-DC isolated output interface with robust overload protection. This interface directly provides a continuous, constant, and ripple-free power supply to surrounding high-sensitivity underground microseismic detectors, geostress transducers, and the aforementioned high-power video PTZ cameras. This daisy-chain energy architecture, where the central node supplies power to the surrounding child nodes, breaks through the engineering bottleneck of power transmission without power lines.

[0099] 2. Multimodal communication link network: Data transmission is the lifeblood of the system. This invention constructs a three-dimensional heterogeneous communication network that can resist the destruction of multiple nodes: Conventional backbone broadband link: Under normal monitoring conditions with good weather and intact infrastructure, the equipment will undoubtedly prioritize the use of high-bandwidth 4G / 5G or low-power wide area network (NB-IoT) cellular communication technology to perform high-frequency large data packet high-speed reporting and daily interaction to the cloud data center.

[0100] 3. Local Disaster Recovery Network (Mesh Resilient Self-Organizing Network): To prevent damage from regional base station power outages, each monitoring station's initial equipment integrates an independent long-range LoRa radio frequency module or broadband wireless Mesh self-organizing network module in parallel within its central processing unit. When a monitoring point located at the bottom of a deep mountain valley loses public WAN signal due to a landslide or base station power outage, this device can immediately activate the self-organizing network protocol and automatically search for other surviving monitoring station nodes within a radius of several kilometers. By using these as jumpers and relay routes, a relay transmission method can be used to weave a local data survival transmission network, independent of public network control, deep within the disaster area, transmitting underlying alarms to edge nodes with signals.

[0101] 4. Space-based satellite escape route alert channel during severe disasters: In the event of devastating damage such as super rainstorms or massive mudslides causing large-scale landslides, resulting in the physical destruction of all public network base stations and wired optical cables within a basin spanning hundreds of square kilometers, and even the loss of nearby Mesh relay nodes, the system will trigger its final defense mechanism. The control system within the integrated monitoring chassis will cut off power to other failed radio frequency modules within milliseconds, automatically waking up and switching to the BeiDou satellite short message system or the Tiantong high-orbit communication satellite early warning module.

[0102] Example 5: GIS spatiotemporal linkage visualization data management and quality control mechanism for seismic detection arrays.

[0103] To address the engineering management challenges of deploying, retrieving, and monitoring large-scale nodal microseismic / earthquake equipment in deep geological hazard or metal mine exploration, this system's software platform layer includes a built-in GIS visualization data management module. This module enables refined management of survey line projects across both temporal and spatial dimensions, and its specific implementation features include:

[0104] 1. Digital twin of real working environment: The system interface constructs a real satellite base map and seamlessly integrates high-resolution satellite imagery of mountains and vegetation, which can realistically reproduce the real physical working environment in the field at a 1:1 scale, providing an intuitive reference benchmark for the spatial positioning of front-end sensors.

[0105] 2. Spatiotemporal Linkage Filtering and Dynamic Presentation: The platform supports interactive operation in both time and space dimensions based on the UTC time window of the shot point. By dragging the timeline at the bottom of the interface, users can instantly highlight and filter the sensor nodes that are performing data acquisition within that specific time period, dynamically presenting the spatial relative positional relationship between the seismic source (such as the seismic source vehicle) and the geophone array.

[0106] 3. Real-time Quality Control (QC) Based on Heat Maps: The system provides a clear overview of equipment status and multi-node seismograph status monitoring. On the GIS map interface, different colored icons visually distinguish the health status of nodes and instruments (e.g., green dots indicate normal node data acquisition; gray dots represent communication failures such as dead or bad channels; purple square icons are used to track and locate the current spatial position of the seismic source vehicle in real time).

[0107] 4. Intelligent Compensation and Correction Algorithm for Missing or Incorrect Field Reports: Addressing the common problem of missing report records or incorrect node coordinates in harsh field construction environments, the system innovatively provides a fault-tolerant compensation mechanism. On one hand, it supports direct import of original station number reports for visual viewing and interactive modification; on the other hand, the system incorporates a two-step adaptive compensation algorithm—"Step 1: Spatial Distribution Fitting" and "Step 2: Automatic Station Number Allocation." Through optional manual drawing of survey lines combined with adaptive node fitting of the algorithm, the system can automatically correct errors and generate accurate survey line items and station number spatial arrangement matrices. This mechanism significantly improves the management efficiency and absolute reliability of data collection for large-scale field monitoring network projects.

[0108] It will be understood by those skilled in the art that the present invention is not limited to the specific embodiments described herein, and that various obvious changes, readjustments, and substitutions can be made by those skilled in the art without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include more other equivalent embodiments without departing from the concept of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. An integrated multi-source data fusion comprehensive sensing geological disaster monitoring system, characterized in that: This includes an integrated data acquisition network and integrated monitoring stations within the space-ground-space system; The integrated data acquisition network includes a seamless access satellite monitoring network for geophysical field detection satellite interface as the space layer, a semi-aerial UAV detection module as the aviation layer, and an integrated front-end sensing array as the ground and underground layers. Together with the vision linkage module, it forms a multi-dimensional, full-parameter, three-dimensional acquisition architecture. The semi-aerial drone detection module employs a drone system equipped with an electromagnetic source and a transient electromagnetic receiving coil; the integrated front-end sensing array is used to capture the energy release from micro-fractures in rocks, including surface morphology sensing components and environmental excitation factor sensing components arranged on the surface, and micro-seismic sensor arrays, geostress sensors, and soil and rock temperature and humidity sensors arranged underground; the visual linkage module is a video monitoring device with intelligent wake-up, support for infrared night vision, remote optical zoom, and panoramic elevation monitoring. The integrated monitoring station is equipped with solar photovoltaic panels and a wide-temperature-range energy storage battery. It includes a protective chassis with built-in guide rails, on which are mounted a central processing unit, a multi-channel frequency-to-digital conversion module, and a power management module. Multiple vibrating wire sensors are connected to the multi-channel frequency-to-digital conversion module, which integrates excitation circuits and high-frequency signal acquisition circuits. The frequency-to-digital conversion module, the surface morphology sensing component, and the environmental excitation factor sensing component are connected to the central processing unit via the same digital communication bus.

2. The integrated multi-source data fusion comprehensive sensing geological disaster monitoring system according to claim 1, characterized in that: The spatial layer data is used to obtain information on large-scale regional plate activity and macroscopic seismic events, extract regional tectonic stress background, and quantify the disturbance and induced effects of mid- and far-field seismic waves on specific geological disaster monitoring points, providing macroscopic background parameter boundaries for microscopic early warning models.

3. The integrated multi-source data fusion comprehensive sensing geological disaster monitoring system according to claim 1, characterized in that: The semi-aerial unmanned aerial vehicle (UAV) detection module employs a ground-air collaborative electromagnetic ground detection system to depict the three-dimensional resistivity structure of the underground, identify the stratigraphic composition, deep fault zones, slip surface orientation, groundwater confluence channels, and underground cavities, and use the detection results as a direct design basis for the scientific placement of ground sensors and optimization of spatial coordinates.

4. The integrated multi-source data fusion comprehensive sensing geological disaster monitoring system according to claim 1, characterized in that: The environmental excitation factor sensing components include a GNSS surface deformation sensor, a multi-axis tilt sensor, and a rain gauge; distributed optical fiber sensors are laid along the potential runoff path of debris flows to form a high-density sensing network.

5. The integrated multi-source data fusion comprehensive sensing geological disaster monitoring system according to claim 1, characterized in that: The multi-channel vibrating wire sensor is used for underground micro-monitoring and outputs non-standard physical frequency signals. The frequency-to-digital conversion module is used to convert the frequency signals output by the vibrating wire sensor into digital signals and output them to the central processing unit. The excitation circuit of the frequency-to-digital conversion module is responsible for periodically sending sweep frequency or step pulse excitation signals to the multi-channel vibrating wire sensor. The frequency acquisition circuit captures the weak induced electromotive force generated by the resonance of the steel string in real time, and calculates and directly maps the frequency signals into standard digital signals through the internal high-speed computing unit.

6. The integrated multi-source data fusion comprehensive sensing geological disaster monitoring system according to claim 1, characterized in that: The output of the frequency-to-digital conversion module, the surface morphology sensing component that outputs digital signals, and the environmental excitation factor sensing component are all connected to the central processing unit via the same RS-485 or CAN digital communication bus in a daisy chain or parallel node configuration.

7. The integrated multi-source data fusion comprehensive sensing geological disaster monitoring system according to claim 1, characterized in that: The central processing unit has a built-in microprocessor, including a Cortex-M series or FPGA and a level conversion matching circuit. Through polling and interrupt mechanisms, it collects heterogeneous data from all sensing components in a unified manner under time synchronization via a digital communication bus, and packages and transmits the data through a wireless communication module.

8. The integrated multi-source data fusion comprehensive sensing geological disaster monitoring system according to claim 1, characterized in that: The power management module inside the protective enclosure not only supplies power to its own control circuit, but also has a voltage regulator and boost output interface, which directly provides stable power to the extended underground microseismic detector, ground stress sensor and high-definition video components. The central processing unit has built-in edge computing capabilities and a multimodal communication architecture, and data can be transmitted back in real time through cellular networks such as 4G / 5G / NB-IoT; it can also use a built-in local self-organizing network module, where adjacent monitoring stations can act as relay nodes for data relay transmission. The satellite communication early warning module, using BeiDou short message service or TianTong satellite, can directly transmit the highest level of disaster status and evacuation instructions to the command center via satellite channel.

9. The early warning method for an integrated multi-source data fusion geological disaster monitoring system according to claim 1, characterized in that, Includes the following steps: Step 1: Sub-millisecond spatiotemporal synchronization and multi-dimensional feature extraction based on hardware clock: Acquire multi-source heterogeneous raw data, perform sub-millisecond time synchronization on all sensor data on the front-end communication bus, and perform spatial registration based on UAV survey coordinates; set time point t, for the i-th sensor, extract its key physical features in the time and frequency domains, and construct a multi-dimensional local feature vector: ； Step 2, Dynamic weight allocation and global feature-level fusion: Real-time calculation and assignment of a dynamic weight to each sensor feature vector Subsequently, a fusion algorithm is used to reduce the dimensionality of the high-dimensional feature vectors and synthesize them to generate a global feature vector that can comprehensively characterize the current health status of the geological body. : ； Step 3: Nonlinear risk assessment decision-making model based on machine learning: To replace traditional static threshold comparisons, a risk assessment decision model is constructed using global feature vectors. Continuous input to machine learning decision functions deployed in the cloud In this process, a comprehensive geological hazard risk value reflecting the probability and severity of disaster occurrence is calculated: ； The decision function It integrates support vector machine, random forest or deep neural network algorithms, and uses a historical massive disaster database for supervised learning and online adaptive updates, which can keenly capture the nonlinear coupling relationship between multi-source data and weak precursor anomalies; Step 4: Adaptive hierarchical early warning generation and visual linkage cross-validation: Set dynamic risk warning thresholds When the real-time calculated risk value meets At that time, the early warning response procedure is automatically triggered; based on The absolute magnitude and growth rate of the data determine the warning model's output of warning signals at four levels: attention level, warning level, danger level, and extremely high risk level. It also sends instructions to the front end to intelligently wake up the video surveillance devices deployed at key potential hazard points; The camera captures on-site images, and the visual images are cross-validated with the risk values ​​calculated by the sensors in a multimodal manner; The warning information will be issued after confirmation.

10. The early warning method for an integrated multi-source data fusion geological disaster monitoring system according to claim 9, characterized in that, Step 1: Capture key physical features including soil displacement rate and acceleration from surface deformation sensors, energy release rate and dominant frequency drift from earthquake and microseismic sensors, and soil cohesion and moisture content gradient from rainfall and soil temperature and humidity sensors. Step four: GNSS and drone aerial cameras capture on-site images, including the opening of slope cracks, vegetation tilt, and turbidity of lake water.

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