Slurry diffusion tracking method and system fusing optical fiber sensing and resistance chromatography technologies

Abstract

The invention provides a slurry diffusion tracking method and system fusing optical fiber sensing and resistance chromatography technologies, and relates to the technical field of geotechnical engineering, and the method comprises the steps: recognizing a high-complexity region of geological conditions based on a geological exploration result of a to-be-grouted region; resistivity data are obtained in real time through electrode nets which are globally and uniformly arranged on the surface of the tunnel face, and global resistivity change data in the grouting process are obtained through the resistance chromatography technology; the method comprises the following steps of: acquiring optical fiber sensing data in real time by utilizing an optical fiber sensor arranged in a to-be-grouted drill hole aiming at a high-complexity region of a geological condition, so as to obtain local strain, temperature and pressure data; performing data fusion analysis based on global resistivity change data and local strain, temperature and pressure data to finally obtain a slurry diffusion range; on the basis of obtaining the global resistivity, local strain, temperature and pressure data in the slurry diffusion process are monitored through optical fiber sensing, and global and local refined tracking of slurry is achieved.

Description

Technical Field

[0001] The present disclosure relates to the field of geotechnical engineering technology, and in particular to a slurry diffusion tracking method and system integrating optical fiber sensing and electrical resistance tomography technology. Background Art

[0002] In recent years, significant progress has been made in the field of tunnel transportation, especially in the development of transportation hubs, mountain railways and urban underground space. Tunnel construction has become an important means to improve transportation capacity and promote regional economic development. Common geological disasters in tunnel construction include water inrush, mud inrush, collapse and rock burst, especially in weak surrounding rock, high ground stress and water-rich strata. Water inrush and mud inrush are caused by excessive groundwater pressure or insufficient surrounding rock strength, collapse is mostly caused by poor surrounding rock stability or improper support, and rock burst is caused by the release of stress in high ground stress rocks. Effective geological survey, grouting reinforcement and construction adjustment are the key to preventing these disasters.

[0003] The main function of grouting technology is to fill the cracks or voids in the stratum by injecting slurry materials into the stratum to achieve the purpose of strengthening the stratum, reducing permeability, controlling deformation, etc. Grouting technology can not only improve the bearing capacity of the surrounding rock, but also effectively control the seepage of groundwater. It is an important means in tunnel construction and stratum reinforcement projects. Efficient grouting construction requires real-time monitoring of the diffusion state of the slurry to ensure that the slurry can evenly and fully fill the target area, avoiding engineering quality problems caused by insufficient or excessive grouting.

[0004] However, the existing grouting monitoring technology has many shortcomings. For example, traditional monitoring methods mainly rely on sensors installed on the surface or in boreholes, which are difficult to fully reflect the diffusion behavior of slurry under complex geological conditions. In addition, the spatial resolution and sensitivity of traditional methods are low, making it difficult to accurately obtain real-time dynamic information on slurry diffusion, especially in deep and complex formations. The accuracy and reliability of monitoring are greatly limited. Therefore, how to effectively track the diffusion path and concentration distribution of slurry has become an urgent problem to be solved in the engineering community.

[0005] Fiber optic sensing technology has attracted wide attention in the application of slurry diffusion monitoring due to its advantages of high sensitivity, anti-electromagnetic interference and long-distance distributed monitoring. Fiber optic sensing tracks the diffusion path of slurry by monitoring changes in temperature or strain. However, it is often difficult to accurately obtain the specific concentration distribution information and spatial diffusion morphology imaging information of slurry in complex geological environments by relying solely on fiber optic sensing technology. In addition, the measurement accuracy may be affected to a certain extent in complex formations with strong interference sources.

[0006] Therefore, the existing slurry diffusion tracking technology lacks stability and accuracy. Summary of the invention

[0007] In order to solve the above problems, the present invention proposes a slurry diffusion tracking method and system that integrates optical fiber sensing and electrical resistance tomography technology. On the basis of obtaining the global resistivity, optical fiber sensing is used to monitor the local strain, temperature and pressure data during the slurry diffusion process to achieve global and local refined tracking of the slurry.

[0008] According to some embodiments, the present disclosure adopts the following technical solutions:

[0009] The slurry diffusion tracking method integrating fiber optic sensing and electrical resistance tomography technology includes:

[0010] Based on the geological exploration results of the area to be grouting, identify the area with high geological complexity in the area to be grouting;

[0011] The resistivity data is acquired in real time through the electrode network uniformly arranged on the surface of the tunnel face, and the global resistivity change data during the grouting process is obtained by using the electrical resistance tomography technology;

[0012] For areas with highly complex geological conditions, fiber optic sensors arranged in the borehole to be grouted are used to obtain fiber optic sensor data in real time, thereby obtaining local strain, temperature and pressure data;

[0013] Based on the global resistivity change data and the local strain, temperature and pressure data, data fusion analysis is performed to finally obtain the slurry diffusion range.

[0014] According to some embodiments, the present disclosure adopts the following technical solutions:

[0015] The slurry diffusion tracking system that integrates fiber optic sensing and electrical resistance tomography technology includes area module, resistance module, fiber optic module and range module:

[0016] The area module is configured to: identify an area with high geological complexity in the area to be grouting based on geological exploration results of the area to be grouting;

[0017] The resistance module is configured to: obtain resistivity data in real time through an electrode network uniformly arranged on the surface of the tunnel face, and obtain global resistivity change data during the grouting process using electrical resistance tomography technology;

[0018] The optical fiber module is configured to: for areas with high geological complexity, use optical fiber sensors arranged in the borehole to be grouted to obtain optical fiber sensor data in real time, and then obtain local strain, temperature and pressure data;

[0019] The slurry diffusion range analysis module is configured to perform data fusion analysis based on global resistivity change data and local strain, temperature and pressure data, and finally obtain the slurry diffusion range.

[0020] According to some embodiments, the present disclosure adopts the following technical solutions:

[0021] A computer program product comprises a computer program, wherein when the computer program is executed by a processor, the slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography is implemented.

[0022] According to some embodiments, the present disclosure adopts the following technical solutions:

[0023] A non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology is implemented.

[0024] According to some embodiments, the present disclosure adopts the following technical solutions:

[0025] An electronic device comprises: a processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the slurry diffusion tracking method that integrates optical fiber sensing and electrical resistance tomography technology.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The disclosed slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology arranges resistive ERT electrodes on the surface of the tunnel face to globally capture resistivity change data, arranges optical fibers in boreholes in areas with complex local formation conditions to monitor local strain, temperature and pressure data during slurry diffusion, and ultimately achieves global and local refined tracking of the slurry through data fusion analysis of global resistivity change data and local strain, temperature and pressure data.

[0028] The present invention provides two methods for data fusion analysis. One is the diffusion range prediction based on feature extraction and fusion, which uses intelligent methods such as machine learning models and deep learning models to perform feature extraction and fusion, and introduces constraints based on physical laws to constrain the training of the model, thereby improving accuracy and real-time performance. The other is the diffusion range construction based on data analysis, which uses data analysis to first determine the rough diffusion range based on the preprocessed global resistivity change data, and then determine the precise slurry front position and slurry diffusion boundary based on the preprocessed local strain, temperature and pressure data, and accumulate the further obtained diffusion path in time to refine the rough diffusion range. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings constituting a part of the present disclosure are used to provide a further understanding of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.

[0030] Figure 1 The present invention is a method flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The present disclosure is further described below in conjunction with the accompanying drawings and embodiments.

[0032] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present disclosure belongs.

[0033] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0034] Terminology explanation:

[0035] The diffusion path is the dynamic grouting route, and the diffusion range is the slurry diffusion area within a certain period of time, which is the final grouting result.

[0036] Example 1

[0037] In one embodiment of the present disclosure, a slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology is provided, comprising:

[0038] Based on the geological exploration results of the area to be grouting, identify the area with high geological complexity in the area to be grouting;

[0039] The resistivity data is acquired in real time through the electrode network uniformly arranged on the surface of the tunnel face, and the global resistivity change data during the grouting process is obtained by using the electrical resistance tomography technology;

[0040] For areas with highly complex geological conditions, fiber optic sensors arranged in the borehole to be grouted are used to obtain fiber optic sensor data in real time, thereby obtaining local strain, temperature and pressure data;

[0041] Based on the global resistivity change data and the local strain, temperature and pressure data, data fusion analysis is performed to finally obtain the slurry diffusion range.

[0042] As an embodiment, the slurry diffusion tracking method of the present disclosure, which integrates optical fiber sensing and electrical resistance tomography technology, uses optical fiber sensing to monitor local strain, temperature and pressure data during the slurry diffusion process on the basis of obtaining global resistivity, so as to achieve global and local refined tracking of the slurry. Figure 1 The specific implementation process is as follows:

[0043] Step S1: Based on the geological exploration results of the area to be grouting, the area with high geological complexity in the area to be grouting is identified.

[0044] Furthermore, the areas with high geological complexity are marked for stratum complexity based on underground structure and stratum characteristic data obtained from geological exploration results, and areas with different geological conditions are divided into high-complexity and low-complexity areas.

[0045] Specifically, carry out preliminary geological exploration and identification of complex areas, obtain underground structure and formation characteristic data based on the results of preliminary geological exploration, mark the complex formation areas, and divide the areas with different geological conditions into high-complexity and low-complexity areas. High-complexity areas include but are not limited to: fracture zones, broken zones, water-rich formations, low-permeability rock formations, soft formations, heterogeneous rock formations, etc., to provide a basis for the subsequent electrode arrangement of optical fiber and electrical resistance tomography.

[0046] Step S2: Obtain resistivity data in real time through an electrode network uniformly arranged on the surface of the tunnel face, and use electrical resistance tomography to obtain global resistivity change data during the grouting process.

[0047] Furthermore, the electrical resistance tomography technology uses the Gauss-Newton inversion algorithm to infer the resistivity distribution of underground strata based on resistivity data, and through a continuous inversion process, obtains the resistivity distribution map at each moment, and then obtains the global resistivity change data during the grouting process.

[0048] Specifically, the electrode grid is globally and evenly arranged on the surface of the tunnel face, and the layout range and density of the electrodes can cover the expected slurry diffusion range.

[0049] Before grouting, the resistivity data of the initial stage is collected and the initial resistivity distribution diagram is calculated. During the grouting process, the resistivity data is collected in real time and the resistivity distribution diagram at each moment is calculated to obtain the global resistivity change data during the grouting process.

[0050] Among them, the Gauss-Newton inversion algorithm is used to infer the resistivity distribution of the underground strata. Through a continuous inversion process, the resistivity distribution map at each moment can be obtained; and the resistivity change data is used to determine the areas where the resistivity drops significantly. These areas correspond to the slurry injection areas and the range where the slurry diffuses.

[0051] Step S3: For areas with highly complex geological conditions, optical fiber sensors arranged in the borehole to be grouted are used to obtain optical fiber sensing data in real time, thereby obtaining local strain, temperature and pressure data.

[0052] Specifically, on the premise of global fiber optic sensing arrangement on the face, for areas with highly complex geological conditions, after drilling the grouting borehole, optical fiber is arranged on the borehole wall. For boreholes using small duct grouting, optical fiber is also arranged on the small duct wall. For boreholes with severe hole collapse and large amount of water and sand gushing, the optical fiber in the borehole is arranged more densely.

[0053] Before grouting, the optical fiber signal is calibrated based on the initial resistivity distribution map.

[0054] The calibration here includes two aspects:

[0055] (1) Calibrate the measurement parameters of the optical fiber signal, including measurement accuracy, signal output accuracy and stability.

[0056] (2) Perform a signal baseline test on the optical fiber before grouting, and use the optical fiber signal generated by the natural environment of the formation and environmental changes as a baseline to separate it from the optical fiber signal generated during the grouting process.

[0057] Step S4: Based on the global resistivity change data and the local strain, temperature and pressure data, data fusion analysis is performed to finally obtain the slurry diffusion range.

[0058] Specifically, during the grouting process, resistivity data and fiber optic sensing data are collected in real time. The global resistivity change data of the ERT system is integrated and analyzed with the local strain, temperature and pressure data of the fiber optic sensor to obtain the diffusion range of the slurry. The specific steps are as follows:

[0059] 1. Preprocessing

[0060] Ensure the time synchronization between the electrical resistance tomography data acquisition and the fiber optic sensor, and ensure that the global resistivity and local strain, temperature, and pressure data are aligned on the time axis.

[0061] The fiber optic sensing data is filtered and noise is eliminated, and the spatial resolution and temporal resolution of different data are resampled to ensure consistency in space and time.

[0062] 2. Data fusion after preprocessing

[0063] This embodiment provides two methods: a method based on feature extraction and fusion and a method based on data analysis, which are described separately below.

[0064] Method 1: Diffusion range prediction based on feature extraction and fusion

[0065] In three-dimensional space, the spatial mapping relationship between data points is established by using the spatial distribution of the electrode positions and optical fiber layout of the ERT system. The global resistivity change data and the local strain, temperature, and pressure data are spliced ​​into a multidimensional feature vector, which is input into a machine learning model (such as a decision tree or support vector machine) or a deep learning model (such as a multimodal neural network) for fusion processing to obtain the diffusion range. The specific steps are as follows:

[0066] (1) First, the resistivity data is preprocessed and converted into two-dimensional or three-dimensional grid data. The global spatial diffusion feature vector F is extracted through a convolutional neural network (CNN). Resistivity ; The local strain, temperature, pressure and other data monitored by the optical fiber are processed in time series, and the local time series feature vector F is extracted using the long short-term memory network Fiber .

[0067] (2) The attention mechanism is used to fuse the global and local feature vectors, giving different weights to the global feature vector and the local feature vector (resistivity data weight w Resistivity , fiber data weight w Fiber ), and output the fused feature vector F Fusion .

[0068] w Resistivity +w Fiber =1

[0069] F Fusion =w Resistivity ·F Resistivity +w Fiber ·F Fiber

[0070] (3) The fused feature vector is input into the fully connected layer to achieve continuous output of the slurry diffusion range.

[0071] In the training process of the above learning model, constraints based on physical laws are added. The resistivity diffusion characteristics must comply with the diffusion equation constraints, and the optical fiber strain and temperature and pressure signals must satisfy Hooke's law. The constraints are realized by adding regular terms to the loss function, and the loss function related to the physical laws is obtained, which is expressed as follows:

[0072] L physics =λ1·L diffusion +λ2·L strain

[0073] Among them, λ1 and λ2 are physical constraint weight coefficients, L diffusion With L strain Represents the loss term for the diffusion equation and strain constraint.

[0074] Finally, by minimizing the total loss function, we ensure that the model can not only accurately fit the data, but also follow the actual physical laws, thereby improving the accuracy and physical consistency of the prediction. The total loss function is:

[0075] L total =L data (F Fusion )+L physics

[0076] Among them, L total Represents the total loss, L data is the loss of data in the fitting process, L physics Losses related to physical laws.

[0077] Method 2: Diffusion range construction based on data analysis

[0078] The rough construction and fine construction are combined, specifically:

[0079] (1) Based on the preprocessed global resistivity change data, the area where the resistivity drops significantly is determined, which corresponds to the injection area of ​​the slurry and the range where the slurry diffuses, and a rough diffusion range is obtained.

[0080] (2) Based on the preprocessed local strain, temperature and pressure data, the abnormal change points of strain, temperature and pressure are extracted, and the precise slurry front position and slurry diffusion boundary are determined in combination with the grouting theory to refine the rough diffusion range.

[0081] Based on the slurry diffusion range obtained from the resistivity data, in order to finely characterize the slurry diffusion front, the data of abnormal change points of temperature and pressure captured by the optical fiber sensing data are cleaned and preprocessed, and a low-pass filtering algorithm is used to remove high-frequency noise. The data for a long period of time are divided into multiple time windows or space windows for local extraction and analysis, and the spatial coordinate positions of the data points are spatially calibrated with the resistivity data to ensure that the data change rate is obtained by calculation under the same spatial coordinates. When the change rate exceeds a certain critical value, the point is considered to be an abnormal change point, the abnormal change point is extracted, and the abnormal change point of the optical fiber sensing path is projected onto the resistivity imaging, and finally the precise slurry front position is determined.

[0082] In order to refine the slurry diffusion boundary, the strain, temperature and pressure monitoring change data extracted along the grouting path and the extracted abnormal change points are analyzed, and the strain gradient of the strain data is used to identify the area with large strain changes. The data is normalized with the parameters such as the surrounding rock permeability and elastic modulus along the optical fiber layout obtained in the previous geological exploration, and each data is given a weight. The weight is obtained through repeated experiments. For example, a high permeability area may have a greater impact on the slurry diffusion, so the permeability of this area may need to be given a higher weight; in a low permeability area, the strain change may be more sensitive, so a higher weight should be given; for hard rock formations, the elastic modulus has a greater impact, so the weight of the elastic modulus can be appropriately increased. For example, the optical fiber monitoring strain data, surrounding rock permeability and elastic modulus are used to achieve the refinement of the boundary. The weighted calculation formula can be:

[0083] W total =w ∈ ·E ∈ +w K ·E K +w E ·E E

[0084] Among them, W total Represents the final weighted value, E ∈ represents the normalized strain, E K represents the normalized permeability, E E represents the normalized elastic modulus, w ∈ 、w K 、w E denote the weights of strain, permeability and elastic modulus, respectively.

[0085] To build a physical deformation model:

[0086] ΔX=f(∈,σ,K,E)

[0087] Among them, ΔX represents the deformation of the diffusion boundary, and f(∈,σ,K,E) represents the deformation function with respect to strain, stress, permeability, and elastic modulus.

[0088] The boundary position after deformation is:

[0089] X new =X old +ΔX

[0090] Among them, X old Represented as the position of the original diffusion boundary, X new Indicates the position of the boundary after deformation.

[0091] The deformed boundary position is projected onto the resistivity image to determine the precise slurry diffusion boundary.

[0092] Based on the determined slurry front position and slurry diffusion boundary, the diffusion path at a certain moment is determined, and the diffusion path is accumulated over time to obtain the final diffusion range, thereby achieving accurate capture of the slurry diffusion process.

[0093] Example 2

[0094] In one embodiment of the present disclosure, a slurry diffusion tracking system integrating optical fiber sensing and electrical resistance tomography technology is provided, including a region module, a resistance module, an optical fiber module and a range module:

[0095] The area module is configured to: identify an area with high geological complexity in the area to be grouting based on geological exploration results of the area to be grouting;

[0096] The resistance module is configured to: obtain resistivity data in real time through an electrode network uniformly arranged on the surface of the tunnel face, and obtain global resistivity change data during the grouting process using electrical resistance tomography technology;

[0097] The optical fiber module is configured to: for areas with high geological complexity, use optical fiber sensors arranged in the borehole to be grouted to obtain optical fiber sensor data in real time, and then obtain local strain, temperature and pressure data;

[0098] The range module is configured to perform data fusion analysis based on the global resistivity change data and the local strain, temperature and pressure data, and finally obtain the slurry diffusion range.

[0099] Example 3

[0100] In one embodiment of the present disclosure, a computer program product is provided, including a computer program, which, when executed by a processor, implements the slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology.

[0101] Example 4

[0102] In one embodiment of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by a processor, the slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology is implemented.

[0103] Example 5

[0104] In one embodiment of the present disclosure, an electronic device is provided, including: a processor, a memory, and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory, so that the electronic device executes the slurry diffusion tracking method that implements the fusion of optical fiber sensing and electrical resistance tomography technology.

[0105] The present disclosure is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present disclosure. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0106] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0107] Although the above describes the specific implementation methods of the present disclosure in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present disclosure. Technical personnel in the relevant field should understand that on the basis of the technical solution of the present disclosure, various modifications or variations that can be made by those skilled in the art without creative work are still within the scope of protection of the present disclosure.

Claims

1. A slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology, characterized in that: include: Based on the geological exploration results of the area to be grouting, identify the area with high geological complexity in the area to be grouting; The resistivity data is acquired in real time through the electrode network uniformly arranged on the surface of the tunnel face, and the global resistivity change data during the grouting process is obtained by using the electrical resistance tomography technology; For areas with highly complex geological conditions, fiber optic sensors arranged in the borehole to be grouted are used to obtain fiber optic sensor data in real time, thereby obtaining local strain, temperature and pressure data; Based on the global resistivity change data and the local strain, temperature and pressure data, data fusion analysis is performed to finally obtain the slurry diffusion range.

2. The slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as claimed in claim 1, characterized in that: The areas with high geological complexity are marked with stratum complexity based on underground structure and stratum characteristic data obtained from geological exploration results, and areas with different geological conditions are divided into high-complexity and low-complexity areas.

3. The slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as claimed in claim 1, characterized in that: The electrical resistance tomography technology uses the Gauss-Newton inversion algorithm to infer the resistivity distribution of underground strata based on resistivity data. Through a continuous inversion process, the resistivity distribution map at each moment is obtained, and then the global resistivity change data during the grouting process is obtained.

4. The slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as claimed in claim 1, characterized in that: The optical fiber sensor arranged in the borehole to be grouted includes: After drilling the borehole to be grouted, arrange the optical fiber on the borehole wall; For the boreholes that use small conduit grouting, the optical fiber is arranged on the wall of the small conduit; For boreholes with severe collapse and large amounts of water and sand gushing, the optical fibers in the boreholes are arranged more densely.

5. The slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as claimed in claim 1, characterized in that: The data fusion analysis includes: Preprocessing of global resistivity variation data to align them in time and space with local strain, temperature, and pressure data; Feature extraction is performed on the preprocessed global resistivity change data and local strain, temperature and pressure data respectively; Fusion of extracted features; Based on the fused features, the spread range of the serous fluid is predicted.

6. The slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as claimed in claim 1, characterized in that: The data fusion analysis includes: Preprocessing of global resistivity variation data to align them in time and space with local strain, temperature, and pressure data; Based on the pre-processed global resistivity change data, the area where the resistivity drops significantly is determined, which corresponds to the injection area of ​​the slurry and the range where the slurry diffuses to obtain a rough diffusion range; Based on the pre-processed local strain, temperature and pressure data, the abnormal change points of strain, temperature and pressure are extracted to determine the precise slurry front position and slurry diffusion boundary. The further obtained diffusion path is accumulated in time to refine the rough diffusion range.

7. A slurry diffusion tracking system integrating optical fiber sensing and electrical resistance tomography technology, characterized in that: include: The area module is configured to: identify an area with high geological complexity in the area to be grouting based on geological exploration results of the area to be grouting; The resistance module is configured to: obtain resistivity data in real time through an electrode network uniformly arranged on the surface of the tunnel face, and obtain global resistivity change data during the grouting process using electrical resistance tomography technology; The optical fiber module is configured to: for areas with high geological complexity, use optical fiber sensors arranged in the borehole to be grouted to obtain optical fiber sensor data in real time, and then obtain local strain, temperature and pressure data; The range module is configured to perform data fusion analysis based on the global resistivity change data and the local strain, temperature and pressure data, and finally obtain the slurry diffusion range.

8. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as described in any one of claims 1 to 6 is implemented.

9. A non-transitory computer-readable storage medium, characterized in that: The non-transitory computer-readable storage medium is used to store computer instructions. When the computer instructions are executed by the processor, the slurry diffusion tracking method integrating optical fiber sensing and electrical resistance tomography technology as described in any one of claims 1 to 6 is implemented.

10. An electronic device, characterized in that: include: A processor, a memory and a computer program; wherein the processor is connected to the memory, the computer program is stored in the memory, and when the electronic device is running, the processor executes the computer program stored in the memory so that the electronic device executes the slurry diffusion tracking method of integrating optical fiber sensing and electrical resistance tomography technology as described in any one of claims 1 to 6.

Images