Superconducting optical fiber preparation method and high-sensitivity landslide monitoring method

Abstract

The invention discloses a superconducting optical fiber preparation method and a high-sensitivity landslide monitoring method, which are applied to the technical field of landslide monitoring. The monitoring method comprises the following steps: constructing a three-dimensional distributed sensing network based on a biological superconducting sensing optical fiber in combination with a multi-source sensor; emitting a detection light signal, and collecting a returned backscattering light signal; demodulating the collected optical signals to obtain strain, temperature or vibration parameters along optical fiber distribution points; in combination with multi-source data about the landslide mass and a landslide geomechanical model, the stable state of the landslide mass is evaluated through a threshold criterion or a machine learning algorithm, a prediction result is obtained by using a linear prediction model, and safety operation is executed based on the prediction result. According to the invention, the sensing of the landslide disaster from the millimeter level to the micro-nano level is realized, the sensitivity is high, the anti-interference capability is strong, and a brand new technical means is provided for the early precise early warning of geological disasters.

Description

Technical Field

[0001] This invention relates to the field of landslide monitoring technology, and more specifically to a method for preparing superconducting optical fibers and a highly sensitive landslide monitoring method. Background Technology

[0002] Landslides are common and highly destructive geological hazards in my country and globally. Due to their suddenness, numerous hidden danger points, high concealment, low monitoring accuracy, and difficulty in early warning, they pose a serious threat to people's lives and property and major engineering projects. Therefore, developing efficient, accurate, reliable, and intelligent landslide monitoring and early warning technologies is an urgent problem to be solved. Currently, relatively intelligent landslide monitoring technologies mainly include macroscopic inspection and mapping methods, GNSS (Global Navigation Satellite System) monitoring methods, inclinometer methods, and traditional fiber optic sensing methods (such as Rayleigh scattering and Brillouin scattering fibers). Most traditional monitoring technologies rely on human experience, are highly subjective, require drilling, are costly, cannot achieve real-time, quantitative monitoring, and suffer from delayed early warnings. In particular, prediction accuracy is usually at the centimeter level, insensitive to the millimeter- or even micrometer-level minute deformations in the early stages of a landslide, and cannot obtain deep displacement information within the landslide body. While modern intelligent monitoring technologies have advantages such as distributed measurement and resistance to electromagnetic interference, the monitoring process struggles to comprehensively capture the overall deformation field of the landslide body, and their real-time performance is poor. There is still room for improvement in the sensitivity of traditional optical fibers for parameters such as strain and temperature. Especially in complex geological environments, the signal-to-noise ratio and measurement accuracy are easily affected by environmental factors, resulting in insufficient ability to capture weak early precursor signals of landslides and a high rate of false alarms and missed alarms.

[0003] In recent years, biomaterials have shown potential in the field of sensing due to their unique physicochemical properties. For example, mucoproteins secreted by certain fungi or protein fibers extracted from marine organisms can exhibit excellent conductivity and signal transmission characteristics under specific conditions. However, how to combine these biomaterials with traditional fiber optic sensing technology to construct a novel, ultra-sensitive geological disaster monitoring sensor has not yet been reported domestically or internationally.

[0004] Therefore, how to provide a method for preparing superconducting optical fibers and a highly sensitive method for landslide monitoring is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method for preparing superconducting optical fibers and a highly sensitive landslide monitoring method, which combines bio-superconducting materials with traditional optical fibers, greatly improving the sensor's sensitivity to micro-strain and temperature, and can effectively capture weak signals in the early stage of landslide incubation.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A method for fabricating a bio-superconducting optical fiber, applied to the aforementioned bio-superconducting material thin film, includes the following steps: Select a section of standard single-mode communication optical fiber, strip the coating layer at the end of the fiber, perform ultrasonic cleaning on the exposed surface of the quartz optical fiber, and then treat it with oxygen plasma for 5 minutes to generate active hydroxyl groups on its surface. A thin film of bio-superconducting material was formed on the surface of the pretreated optical fiber and then cross-linked and cured under ultraviolet light. A flexible polyurethane micro-armor sheath is then coated onto the surface of the cured optical fiber to provide mechanical protection while allowing strain transfer.

[0007] Optionally, cysteine-rich protein Srp secreted by specific sulfur-loving bacteria is extracted and purified, conductive amino acid fragments are introduced into its sequence through genetic engineering, and it is self-assembled into nanofiber hydrogels under laboratory conditions. Biomolecular thin films with superconducting properties are grown on specific substrates using physical vapor deposition or chemical solution methods, and their properties are optimized by post-annealing to form bio-superconducting material thin films.

[0008] A highly sensitive landslide monitoring method based on bio-superconducting optical fiber includes the following steps: The prepared bio-superconducting sensing optical fiber was deployed in the landslide body and its stable bedrock to be monitored according to the predetermined topology, and a three-dimensional distributed sensing network was constructed by setting up multi-source sensors. The integrated monitoring host transmits detection light signals to the three-dimensional distributed sensor network and collects the backscattered light signals that return, carrying information about the landslide's status. By utilizing the extreme sensitivity of bio-superconducting materials to the phase, intensity, or frequency of light waves in optical fibers, the collected optical signals are demodulated, and the strain, temperature, or vibration parameters at the distribution points along the optical fiber are calculated. Acquire multi-source data and landslide geomechanical models of landslide bodies, combine strain, temperature or vibration parameters obtained from the solution, evaluate the stability of landslide bodies through threshold criteria or machine learning algorithms, and issue early warning information when the safety threshold is exceeded. A linear prediction model is constructed. When an early warning information is received, the landslide parameters are input into the linear prediction model to obtain the prediction results, and safe operations are performed based on the prediction results.

[0009] Optionally, constructing a three-dimensional distributed sensor network includes: One to three longitudinal monitoring profiles are set up along the main sliding direction of the landslide, and several transverse profiles are set up perpendicular to the main sliding direction. Sensors are deployed in the surface layer, shallow layer and deep layer of the borehole to directly capture the sliding surface; Sensors are densely deployed at key deformation points of the landslide to form a three-dimensional distributed sensor network.

[0010] Optionally, the acquired optical signal can be demodulated using a phase-sensitive optical time-domain reflectometer (Φ-OTDR) or Brillouin optical time-domain analysis (BOTDA) to identify characteristic spectral peaks or phase abrupt changes introduced by the bio-superconducting material.

[0011] Optionally, a linear prediction model based on Ginzburg-Landau theory is adopted. The core parameters of the linear prediction model are trained using density functional to determine the correlation between point displacement changes. The trained prediction model is then optimized based on the re-co-optimization objective function to obtain the optimized model.

[0012] Optionally, training the correlation between point displacement changes and core parameters of the linear prediction model using the density functional model includes: We extracted characteristic parameters related to landslide stability from a linear prediction model based on Ginzburg-Landau theory using density functional theory. These parameters included the amplitude, offset, and mean displacement of the characteristic frequency band of the biological response to fiber optic transmission. Soil physical parameters were then imported, and the amplitude and soil physical parameters were used as key parameters to construct a formula for the biological superconducting order parameter. , in, For biological superconducting order parameters, The linear coupling coefficients are... These are key physical parameters.

[0013] Optionally, the objective function expression for collaborative optimization is: , in, The model's prediction results; To identify changes in sensitivity; The predicted changes are modeled for the phase feature analysis channel and the DFT density loss channel, respectively. These are the linear coupling coefficients; For phase identification coefficients; Key physical parameters; This represents the stress gradient variation term.

[0014] As can be seen from the above technical solutions, compared with the prior art, the present invention provides a method for preparing superconducting optical fibers and a highly sensitive landslide monitoring method, which has the following beneficial effects: 1. Extremely high sensitivity: The innovative bio-superconducting material has an "amplification" effect on micro-strain and temperature changes at room temperature. When combined with optical fiber, it can improve the sensitivity of traditional optical fiber sensing technology by 1-2 orders of magnitude, and can detect nanoscale micro-strain, thereby capturing extremely weak "precursor" signals before landslide instability and realizing true early warning. 2. Rich sensing dimensions: Distributed measurement capabilities enable a single optical fiber to acquire strain / temperature information at thousands of points along the landslide body, achieving comprehensive three-dimensional sensing from "point" to "line" to "surface" and "volume", accurately locating the sliding surface and potential fracture zone; 3. Strong anti-interference ability: The combination of bio-superconducting materials and optical fibers enhances the transmission quality of signals in complex geological environments, effectively suppresses environmental noise, and improves the signal-to-noise ratio and the reliability of monitoring data. 4. Environmentally friendly and adaptive: The biomaterials used have good biocompatibility and environmental adaptability. Some materials can even self-repair or grow in rock and soil environments, extending the life of the sensor. 5. High level of intelligence: It integrates multi-source data fusion and intelligent algorithms, which can automatically identify the landslide evolution stage and realize the full-process automation from data collection to intelligent early warning, greatly reducing manual intervention; 6. Based on multi-dimensional superconducting monitoring samples, combined with biotransmission identification and data analysis, anomaly data are acquired simultaneously, establishing a three-dimensional distributed landslide monitoring sensor network integrating air, space, ground, and deep depth. This addresses the problems of poor accuracy and lagging data processing in traditional monitoring. Focusing on threshold characteristics related to landslide stability parameters, a superconducting linear basis function is constructed, breaking through traditional apparent parameter discrimination and reflecting the correlation characteristics of deep deformation. Geomechanical models combined with machine learning achieve precise mapping of deformation to key parameters, improving the intelligence of monitoring and the visualization of prediction. The real-time on-site prediction process significantly improves monitoring accuracy, enabling full-process management of landslide disasters from "appearance monitoring" to "mechanism analysis" and then to "intelligent early warning," maximizing the protection of people's lives and property. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0016] Figure 1 This is a flowchart of the highly sensitive landslide monitoring method based on bio-superconducting optical fiber according to the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] This invention discloses a method for fabricating biological superconducting optical fibers, comprising the following steps: Select a section of standard single-mode communication optical fiber, strip the coating layer at the end of the fiber, perform ultrasonic cleaning on the exposed surface of the quartz optical fiber, and then treat it with oxygen plasma for 5 minutes to generate active hydroxyl groups on its surface. A thin film of bio-superconducting material was formed on the surface of the pretreated optical fiber and cross-linked and cured under mild ultraviolet light; A flexible polyurethane micro-armor sheath is then coated onto the surface of the cured optical fiber to provide mechanical protection while allowing strain transfer.

[0019] Furthermore, bio-superconducting thin films include: The cysteine-rich protein Srp secreted by a specific sulfur-loving bacterium was extracted and purified. Conductive amino acid fragments were introduced into its sequence through genetic engineering technology, and it was then self-assembled into a nanofiber hydrogel under laboratory conditions. Biomolecular thin films with superconducting properties are grown on specific substrates using physical vapor deposition or chemical solution methods, and their properties are optimized by post-annealing to form bio-superconducting material thin films.

[0020] Furthermore, the nanofiber hydrogel uses nanocellulose / polyvinyl alcohol (CNFs / PVA) composite hydrogel produced by Hubei Huada Fine Chemical Co., Ltd.

[0021] Furthermore, other bio-superconducting materials could include niobium-based superconducting nanowires, λ-type BEDT-TTF salt organic superconductors, superconducting nanowires produced by Geobacterium, and glycine-modified bismuth chalcogenide superconductors.

[0022] A highly sensitive landslide monitoring method based on bio-superconducting optical fiber, referring to Figure 1 As shown, it includes the following steps: The prepared bio-superconducting sensing optical fiber was deployed in the landslide body and its stable bedrock to be monitored according to the predetermined topology, and a three-dimensional distributed sensing network was constructed by setting up multi-source sensors. The integrated monitoring host transmits detection light signals to the three-dimensional distributed sensor network and collects the backscattered light signals that return, carrying information about the landslide's status. By utilizing the extreme sensitivity of bio-superconducting materials to the phase, intensity, or frequency of light waves in optical fibers, the collected optical signals are demodulated, and the strain, temperature, or vibration parameters at the distribution points along the optical fiber are calculated. Acquire multi-source data and landslide geomechanical models of the landslide body, combine the strain, temperature or vibration parameters obtained from the calculation, evaluate the stability of the landslide body through threshold criteria or machine learning algorithms, and issue early warning information when the safety threshold is exceeded.

[0023] Furthermore, constructing a three-dimensional distributed sensor network includes: One to three longitudinal monitoring profiles are set up along the main sliding direction of the landslide, and several transverse profiles are set up perpendicular to the main sliding direction. GNSS receivers, crack gauges, rain gauges, and surface inclinometers are deployed in the surface layer; soil moisture sensors and shallow inclinometers are deployed in the shallow surface layer; and fixed inclinometers, pore water pressure gauges, and deep settlement meters are deployed in the deep layers of the borehole to directly capture the sliding surface. Sensors are densely deployed at key deformation sites such as the rear edge, front edge, and side edge of the landslide to form a three-dimensional distributed sensor network.

[0024] Furthermore, the acquired optical signals are demodulated using a phase-sensitive optical time-domain reflectometer (Φ-OTDR) or Brillouin optical time-domain analysis (BOTDA) to identify characteristic spectral peaks or phase abrupt changes introduced by the bio-superconducting material, including: By fixing bio-superconducting materials as sensing units at the detection interface, abrupt changes in macroscopic optical responses (such as reflection spectra or interference phases) caused by microscopic changes can be detected, resulting in sharp characteristic spectral peaks or significant phase step characteristic parameters. The demodulation system is used to suppress background noise interference, improve overall demodulation sensitivity, continuously monitor abrupt signals, and capture steep and easily identifiable abrupt signals. The system achieves ultrasensitive discrimination and quantitative detection of microbial events by accurately locating mutation points with high signal-to-noise ratios.

[0025] Further assessment of the stability of the landslide body includes: Geological exploration, drilling, geophysical exploration and other methods are used to obtain the geometric morphology of the landslide body, such as slope, slope height, burial depth and shape of the sliding surface, location, thickness and strength characteristics of the sliding zone soil; combined with the key mechanical parameters of the rock and soil body such as friction angle, cohesion and unit weight determined by the test; A landslide geomechanical model based on the finite element method was constructed to simulate the stress-strain field within the landslide body, dynamically displaying the development and penetration process of the plastic zone. The model integrates static physical parameters with dynamic hydrological data for mechanical quantitative calculation. The system achieves automated steady-state identification through threshold criteria or machine learning algorithms. Critical thresholds are set for key indicators (such as cumulative rainfall, pore water pressure, surface displacement rate, or safety factor calculated by the model). Once the monitoring data or calculation results exceed the threshold, the system will issue an early warning.

[0026] Furthermore, a linear prediction model based on Ginzburg-Landau theory is adopted. The core parameters of the linear prediction model are trained on the correlation of point displacement changes using density functionals. The trained prediction model is then optimized based on the re-co-optimization objective function to obtain the optimized model.

[0027] Specifically, density functional theory (DFT) can determine the variation characteristics of key regions without directly solving the extremely complex Schrödinger equation, and calculate the minimum energy state properties of the system, such as geometric structure, band structure, and density of states. The accuracy of DFT calculation depends on the approximation degree of the selected exchange-correlation functional.

[0028] Furthermore, training the correlation between point displacement changes and core parameters of the linear prediction model using density functional theory includes: We extracted characteristic parameters related to landslide stability from a linear prediction model based on Ginzburg-Landau theory using density functional theory. These parameters included the amplitude, offset, and mean displacement of the characteristic frequency band of the biological response to fiber optic transmission. Soil physical parameters were then imported, and the amplitude and soil physical parameters were used as key parameters to construct a formula for the biological superconducting order parameter. , in, For biological superconducting order parameters, The linear coupling coefficients are... These are key physical parameters.

[0029] Specifically, biological superconducting sequence parameters It is used to describe the “intensity” or “degree” of superconducting states in biological systems. The measurable signal intensity related to the Meissner effect reflects the density of superconducting electron pairs and the size of the superconducting band gap. Linear coupling coefficient It is closely related to temperature, the structural integrity of biomolecules, and the state of water hydration. By using the scaling factor of a specific constant of the system, the physical parameter P is obtained in relation to the superconducting order parameter. The impact on efficiency; Key physical parameters It is an independent variable that affects the superconducting state of a organism, driven by a tiny externally applied current or magnetic field.

[0030] Furthermore, the expression for the collaborative optimization objective function is: , in, The model's prediction results; To identify changes in sensitivity; The predicted changes are modeled for the phase feature analysis channel and the DFT density loss channel, respectively. These are the linear coupling coefficients; For phase identification coefficients; Key physical parameters; This represents the stress gradient variation term.

[0031] Furthermore, performing safety operations based on the prediction results includes: Compare the prediction result R with the safety factor (Fs=1): when At that time, it was determined that the stability of the landslide met the safety requirements and no treatment was necessary; when When the landslide is determined to be unstable and does not meet safety requirements, reinforcement treatment is necessary. when If the stability of the landslide is determined to be completely unsafe, evacuation should be carried out immediately, and engineering design and treatment should be implemented.

[0032] In one specific embodiment, the fabrication of the bio-superconducting sensing fiber includes: 1. Fiber pretreatment: Select a standard single-mode communication fiber, remove the coating layer of about 10 cm at the end, and use acetone, ethanol and deionized water to ultrasonically clean the surface of the bare quartz fiber in sequence. Then treat it with oxygen plasma for 5 minutes to generate active hydroxyl groups (-OH) on its surface. 2. Preparation of bio-superconducting materials: A cysteine-rich protein (Srp) secreted by a specific sulfur-loving bacterium was extracted and purified. Conductive amino acid fragments were introduced into its sequence using genetic engineering techniques, and the protein was then self-assembled into a nanofiber hydrogel under laboratory conditions. This hydrogel exhibits extremely high conductivity at room temperature, displaying superconducting-like properties. 3. Biomimetic Modification: The pretreated optical fiber was immersed in the above-mentioned Srp protein nanofiber hydrogel and left to stand for 12 hours under constant temperature and humidity conditions. Through the strong interaction between the thiol groups (-SH) of Srp protein and the active sites on the optical fiber surface, a uniform and dense bio-superconducting composite film with a thickness of about 500 nanometers was formed on the surface of the optical fiber. 4. Curing and Encapsulation: The optical fiber is removed and cross-linked under gentle ultraviolet light for 30 minutes to enhance the stability and strength of the film. Finally, a flexible polyurethane micro-armor is coated on the outside of the biofilm to provide mechanical protection while allowing strain transfer.

[0033] In another specific embodiment, taking a steep landslide along a road as an example: Three vertical monitoring holes, each 30 meters deep, were drilled at the rear, front, and middle edges of the landslide. Three prepared 100-meter-long bio-superconducting sensing optical fibers were vertically inserted into the holes using specialized guides, and backfilled with a mixture of bentonite and cement grout to ensure tight coupling between the fibers and the surrounding soil and rock. Simultaneously, a 200-meter-long zigzag sensing optical fiber was laid along the main sliding direction on the landslide surface and secured with anchor piles. All deployed optical fibers are led to a monitoring box at the foot of the slope and connected to the optical module of the monitoring host unit. The monitoring host is powered by solar panels and batteries and has a built-in 4G communication module. The monitoring host is set to automatically collect data every 10 minutes. It employs a combined demodulation technique based on a phase-sensitive optical time-domain reflectometer (Φ-OTDR) and Brillouin optical time-domain analysis (BOTDA), with a spatial resolution of 1 meter. The theoretical strain measurement accuracy can reach ±0.5 με, and the temperature accuracy ±0.1℃. After a month of continuous monitoring, sustained tensile strain accumulation was detected in the sensing fiber optic section at a depth of 15-18 meters in the central borehole, exceeding the preset primary threshold (50 με). An automatic "Caution" level warning was issued, urging close attention to the area. Over the following week, the strain rate in the area accelerated, accompanied by a slight increase in localized temperature (possibly related to frictional heating), prompting the platform to upgrade to a "Warning" level warning. Upon receiving the warning, management implemented measures such as surface drainage and load limiting, successfully preventing a landslide. Subsequent verification confirmed that this depth was precisely the location of the landslide's main sliding surface.

[0034] The various embodiments in this specification are described 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. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.

[0035] Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for fabricating a biological superconducting optical fiber, characterized in that, Includes the following steps: Select a section of standard single-mode communication optical fiber, strip the coating layer at the end of the fiber, perform ultrasonic cleaning on the exposed surface of the quartz optical fiber, and then treat it with oxygen plasma for 5 minutes to generate active hydroxyl groups on its surface. A thin film of bio-superconducting material was formed on the surface of the pretreated optical fiber and then cross-linked and cured under ultraviolet light. A flexible polyurethane micro-armor sheath is then coated onto the surface of the cured optical fiber to provide mechanical protection while allowing strain transfer.

2. The method for fabricating a bio-superconducting optical fiber according to claim 1, characterized in that, Bio-superconducting thin films include: The cysteine-rich protein Srp secreted by a specific sulfur-loving bacterium was extracted and purified. Conductive amino acid fragments were introduced into its sequence through genetic engineering technology, and it was then self-assembled into a nanofiber hydrogel under laboratory conditions. Biomolecular thin films with superconducting properties are grown on specific substrates using physical vapor deposition or chemical solution methods, and their properties are optimized by post-annealing to form bio-superconducting material thin films.

3. A highly sensitive landslide monitoring method based on bio-superconducting optical fiber, characterized in that, Includes the following steps: The prepared bio-superconducting sensing optical fiber was deployed in the landslide body and its stable bedrock to be monitored according to the predetermined topology, and a three-dimensional distributed sensing network was constructed by setting up multi-source sensors. The integrated monitoring host transmits detection light signals to the three-dimensional distributed sensor network and collects the backscattered light signals that return, carrying information about the landslide's status. By utilizing the extreme sensitivity of bio-superconducting materials to the phase, intensity, or frequency of light waves in optical fibers, the collected optical signals are demodulated, and the strain, temperature, or vibration parameters at the distribution points along the optical fiber are calculated. Acquire multi-source data and landslide geomechanical models of landslide bodies, combine strain, temperature or vibration parameters obtained from the solution, evaluate the stability of landslide bodies through threshold criteria or machine learning algorithms, and issue early warning information when the safety threshold is exceeded. A linear prediction model is constructed. When an early warning information is received, the landslide parameters are input into the linear prediction model to obtain the prediction results, and safe operations are performed based on the prediction results.

4. The highly sensitive landslide monitoring method based on bio-superconducting optical fiber according to claim 3, characterized in that, Constructing a three-dimensional distributed sensor network includes: One to three longitudinal monitoring profiles are set up along the main sliding direction of the landslide, and several transverse profiles are set up perpendicular to the main sliding direction. Sensors are deployed in the surface layer, shallow layer and deep layer of the borehole to directly capture the sliding surface; Sensors are densely deployed at key deformation points of the landslide to form a three-dimensional distributed sensor network.

5. The highly sensitive landslide monitoring method based on bio-superconducting optical fiber according to claim 3, characterized in that, The acquired optical signals are demodulated using a phase-sensitive optical time-domain reflectometer (Φ-OTDR) or Brillouin optical time-domain analysis (BOTDA) to identify characteristic spectral peaks or phase abrupt changes introduced by bio-superconducting materials.

6. The highly sensitive landslide monitoring method based on bio-superconducting optical fiber according to claim 3, characterized in that, A linear prediction model based on Ginzburg-Landau theory is adopted. The core parameters of the linear prediction model are trained using density functional to determine the correlation between point displacement changes. The trained prediction model is then optimized based on the re-co-optimization objective function to obtain the optimized model.

7. The highly sensitive landslide monitoring method based on bio-superconducting optical fiber according to claim 6, characterized in that, Training the core parameters of a linear prediction model using density functional regression to correlate point displacement changes includes: We extracted characteristic parameters related to landslide stability from a linear prediction model based on Ginzburg-Landau theory using density functional theory. These parameters included the amplitude, offset, and mean displacement of the characteristic frequency band of the biological response to fiber optic transmission. Soil physical parameters were then imported, and the amplitude and soil physical parameters were used as key parameters to construct a formula for the biological superconducting order parameter. ， in, For biological superconducting order parameters, The linear coupling coefficients are... These are key physical parameters.

8. The highly sensitive landslide monitoring method based on bio-superconducting optical fiber according to claim 6, characterized in that, The objective function expression for collaborative optimization is: ， in, The model's prediction results; To identify changes in sensitivity; The predicted changes are modeled for the phase feature analysis channel and the DFT density loss channel, respectively. These are the linear coupling coefficients; Phase identification coefficient; Key physical parameters; This represents the stress gradient variation term.

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