An intelligent monitoring system for pipe jacking grouting state of integrated distributed optical fiber sensing network
By integrating a distributed optical fiber sensor network, the problem of precise monitoring of grout distribution during pipe jacking grouting was solved, enabling real-time and accurate assessment of grouting status, generating intuitive spatial distribution maps, and providing overall quality assessment.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA RAILWAY TENTH BUREAU GROUP URBAN RAIL TRANSIT ENGINEERING CO LTD
- Filing Date
- 2026-03-09
- Publication Date
- 2026-07-21
Smart Images
Figure CN122432518A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of grouting status monitoring technology, and in particular to an intelligent monitoring system for pipe jacking grouting status using a distributed optical fiber sensor network. Background Technology
[0002] Pipe jacking is a trenchless underground pipeline laying technology widely used in urban pipeline network construction. To ensure the safety and quality of pipe jacking construction, reduce friction between pipe sections and the soil, stabilize the excavation face, and prevent ground settlement, it is usually necessary to inject grout in a timely and sufficient manner into the annular gap formed at the tail of the pipe section. The quality of grouting, especially the degree of filling and compaction of the grout in the annular gap, is crucial to the long-term stability and safety of the pipe jacking structure. Insufficient grouting may lead to pipe section deformation, leakage, or settlement of the soil above; uneven grouting may cause stress concentration, affecting the safety of the pipe segment structure; excessive grouting will result in material waste and environmental pollution.
[0003] Traditional grouting monitoring methods primarily rely on monitoring macroscopic parameters such as total grout volume, grouting pressure, and grouting flow rate. While these parameters reflect the overall grouting situation, they struggle to provide detailed spatial information on the grout distribution along the circumference and longitudinal direction of the tunnel lining segments within the annular voids. Some methods may indirectly assess grouting effectiveness by deploying settlement observation points on the ground, but these methods suffer from latency and cannot directly reflect the grouting filling status around the tunnel lining segments. Current technology lacks an effective means to assess the spatial distribution of grout around the outer wall of the tunnel jacking segments (e.g., the presence of voids, density, and relative thickness differences) in real-time or near-real-time with high precision.
[0004] Distributed fiber optic sensing technology, with its advantages of continuous measurement of physical quantities along the fiber length, resistance to electromagnetic interference, and intrinsic security, has shown potential in the field of geotechnical engineering monitoring. By pre-laying distributed optical fibers on the outer wall of pipe jacking segments, distributed sensing data related to the physical state of the grouting body, such as temperature and strain, can be collected during the grouting process. However, how to transform this massive amount of data, distributed along one-dimensional optical fibers and varying over time, into an accurate and deterministic assessment of the grouting state (especially reflecting the density / thickness) in the two-dimensional spatial region surrounding the outer wall of the segments remains a technical challenge. Existing methods for analyzing distributed fiber optic data may struggle to extract quantitative parameters directly related to the physical state of the grouting body (such as thermal conduction and thermal diffusion characteristics), and rarely systematically consider the continuity and mutual influence of the grouting body in space. This makes the assessment results susceptible to interference from local accidental factors and difficult to provide a comprehensive regional and overall state assessment. Summary of the Invention
[0005] This application provides an intelligent monitoring system for grouting status of pipe jacking that integrates a distributed optical fiber sensor network. This system aims to improve the technical problems in related technologies, such as the difficulty in obtaining detailed spatial distribution information of grouting, the influence of local accidental factors on evaluation results, and the lack of overall evaluation.
[0006] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:
[0007] This application provides an intelligent monitoring system for grouting status of pipe jacking with an integrated distributed optical fiber sensor network, comprising: a distributed optical fiber sensing unit for acquiring distributed optical fiber sensing data, the distributed optical fiber sensing data including time-series sensing values at different locations along a predetermined distributed optical fiber laid on the outer wall of a pipe jacking segment within a preset time period; a data organization and mapping module configured to map the optical fiber length coordinates at different locations of the distributed optical fiber to the corresponding spatial coordinates of the outer wall surface of the pipe jacking segment, and organize the sensing values into a spatiotemporal dataset of sensing values corresponding to the spatial coordinates and time of the outer wall surface of the pipe jacking segment; and a local analysis and processing module, configured to... For each preset spatial location on the outer wall surface of the jacking pipe segment, a curve showing the change of the sensed value over time at the preset spatial location is determined. The decay stage representing the decrease in sensed value over time is identified in the curve. Based on the time-series data of the sensed value in the decay stage, a preset thermal decay model is fitted to obtain the decay parameter at the preset spatial location. Based on the decay parameter at the preset spatial location and a preset criterion, a local grouting state index at the preset spatial location is determined. A map generation module is used to generate a grouting state distribution map representing the spatial distribution of the local grouting state index on the outer wall surface of the jacking pipe segment.
[0008] The system of this application can perform local analysis on distributed optical fiber sensing data, extract quantitative parameters (attenuation parameters) that reflect the physical characteristics of the grouting body, and determine the local grouting state based on these parameters, generating an intuitive spatial distribution map, thus overcoming the shortcomings of traditional methods that lack fine spatial information.
[0009] In one possible implementation, the distributed optical fiber sensing unit is a distributed temperature sensing unit used to acquire distributed temperature sensing data, where the sensed value is a temperature value. Utilizing DTS data, the heat release and dissipation process of grouting hydration can be directly sensed. This temperature signal is closely related to the density, thickness, and other characteristics of the grout body, providing a data foundation for accurately assessing the grouting status.
[0010] In one possible implementation, the local analysis processing module is configured such that the preset thermal decay model is a single exponential decay model, expressed as T(t) = A*exp(-k*t) + C, where k is the decay parameter. This module is configured to fit the temperature time-series data of the cooling stage to determine the decay parameter k. The decay parameter k obtained by fitting the single exponential decay model can quantify the heat dissipation rate of the local grout body and is an effective indicator reflecting density / thickness.
[0011] In one possible implementation, the local analysis processing module is configured to fit the temperature time series data of the cooling phase using the least squares method to determine the attenuation parameter k.
[0012] In one possible implementation, the data organization and mapping module is configured to establish a deterministic mapping relationship between the fiber length coordinates and the circumferential and longitudinal coordinates of the outer wall surface of the jacking pipe segment, based on a preset laying geometry of the distributed optical fiber on the outer wall of the jacking pipe segment. This precise geometric mapping ensures that the sensed data can be accurately correlated to the specific spatial location on the pipe segment surface.
[0013] In one possible implementation, the outer surface of the jacking segment is divided into a predetermined number of two-dimensional spatial regions as the preset spatial locations. Discretizing the continuous segment surface into manageable two-dimensional spatial regions facilitates regional data processing, analysis, and condition assessment.
[0014] In one possible implementation, the two-dimensional spatial region is a region in the form of a rectangular grid.
[0015] In one possible implementation, the system further includes: a spatial impact calculation module configured to determine adjacent preset spatial locations of the preset spatial location and obtain a spatial impact factor of the preset spatial location based on the attenuation parameter of the adjacent preset spatial locations; and a regional comprehensive evaluation module configured to deterministically combine the attenuation parameter and the spatial impact factor at the preset spatial location through a preset combination function and a preset spatial impact weighting factor to obtain a regional comprehensive evaluation score at the preset spatial location; wherein, the local analysis processing module is configured to determine the local grouting status index at the preset spatial location based on the regional comprehensive evaluation score at the preset spatial location and a preset regional determination criterion.
[0016] This application uses local physical quantification results (attenuation parameters) as a foundation, introduces physical quantification results from adjacent regions to construct spatial influence factors, and combines the two to form a higher-level regional comprehensive evaluation score. The local state is then determined based on this comprehensive score. This allows the local evaluation results to not only reflect its own characteristics but also consider the influence of the surrounding environment, improving the robustness of the evaluation and its ability to reflect the actual grouting filling state, thus overcoming the limitations of purely local evaluations.
[0017] In one possible implementation, the system further includes: an overall index aggregation module configured to aggregate the regional comprehensive evaluation scores or the local grouting state indices at all the preset spatial locations to obtain an overall grouting state index; and an output module for generating monitoring output based on the overall grouting state index. By aggregating the evaluation results of all regions, a macroscopic, single numerical evaluation of the grouting state of the entire monitoring area is provided, facilitating users to quickly grasp the overall grouting quality and generating intuitive monitoring output.
[0018] In one possible implementation, the system further includes a parameter adjustment module configured to adjust the value of the preset spatial influence weighting factor based on a comparison between the overall grouting status index and at least one preset overall threshold. By introducing a feedback mechanism, the system can adaptively optimize the spatial influence weighting parameter in the regional assessment according to the actual monitored overall situation (overall grouting status), improving the adaptability and accuracy of the method and reducing the empirical dependence of parameter setting. Attached Figure Description
[0019] Figure 1 This is a schematic diagram of the structure of an intelligent monitoring system provided for some embodiments of this application. Detailed Implementation
[0020] The technical solutions of the embodiments of this application will be described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.
[0021] In the following description, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.
[0022] Furthermore, in this application, directional terms such as "upper," "lower," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and may change accordingly depending on the orientation of the components in the accompanying drawings.
[0023] In this application, unless otherwise expressly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a direct connection or an indirect connection through an intermediate medium. Furthermore, the term "electrical connection" can refer to the manner in which an electrical connection is used to achieve signal transmission.
[0024] As used herein, “about,” “approximately,” or “approximately” includes the stated value and a reference value within an acceptable range of deviation from the given value, characterized in that the acceptable range of deviation is determined by a person skilled in the art taking into account the measurement under discussion and the error associated with the measurement of the given quantity (i.e., the limitations of the measurement method).
[0025] This application provides an intelligent monitoring system for grouting status of pipe jacking that integrates a distributed optical fiber sensor network. This system addresses the problems in existing technologies where grouting monitoring struggles to obtain detailed spatial distribution information of grouting, and where evaluation results are affected by local factors and lack a holistic assessment.
[0026] This application achieves monitoring of grouting status by performing spatiotemporal analysis on the distributed optical fiber sensing data pre-laid on the outer wall of the jacking pipe segment, especially by obtaining local physical parameters through fitting a physical attenuation model, and combining spatial correlation information for comprehensive regional evaluation.
[0027] like Figure 1 As shown, the system includes a distributed optical fiber sensing unit 10, a data organization and mapping module 20, a local analysis and processing module 30, and a map generation module 40. These modules can be independent hardware units, software modules, or combinations thereof, and are connected and communicate with each other through a data interface.
[0028] The distributed optical fiber sensing unit 10 is configured to acquire distributed optical fiber sensing data. The distributed optical fiber sensing data includes time-series sensing values at different locations along a predetermined path of distributed optical fiber laid on the outer wall of the jacking pipe segment within a preset time period. The sensing unit may include a distributed optical fiber sensing cable and a distributed optical fiber sensing demodulator. The cable is laid along a predetermined path on the outer wall of the jacking pipe segment, such as spiral, circumferential, or longitudinal. The demodulator measures physical quantities at different locations along the optical fiber by sending optical signals and analyzing the scattered light (such as Brillouin scattering, Rayleigh scattering, or Raman scattering) returned along the fiber length.
[0029] For example, during the grouting process of pipe jacking, the distributed optical fiber sensing unit 10 periodically (e.g., every 1 minute, 5 minutes or 10 minutes) collects sensing data along the entire length of the optical fiber, forming a series of sensing value curves distributed along the length of the optical fiber at different times.
[0030] In some embodiments, the distributed optical fiber sensing unit 10 is a distributed temperature sensing (DTS) unit, used to acquire distributed temperature sensing (DTS) data, wherein the sensed value is a temperature value. It is understood that the heat release during cement hydration is a major physical phenomenon in the grouting process, and temperature changes can directly reflect the presence and activity level of the grouting body.
[0031] The data organization and mapping module 20 is configured to map the fiber length coordinates at different locations of the distributed optical fiber to the corresponding spatial coordinates of the outer wall surface of the jacking pipe segment, and organize the sensed values into a spatiotemporal dataset of sensed values corresponding to the spatial coordinates and time of the outer wall surface of the jacking pipe segment. Since the geometry of the optical fiber laying on the outer wall of the segment can be obtained in advance, this module can establish a deterministic mathematical mapping relationship between the fiber length coordinate l and the two-dimensional spatial coordinates (x,y) of the outer wall surface of the segment. The spatial coordinates can be the circumferential angle and longitudinal distance of a cylindrical coordinate system, or they can be unfolded into a plane rectangular coordinate system. Based on this mapping relationship, the original data S(l,t) (where S is the sensed value) is converted into a spatiotemporal dataset S(x,y,t) indexed by the spatial position (x,y) and containing the sensed values of that position at different time points.
[0032] For example, the preset spatial location can be a discrete region obtained by dividing the outer wall surface of the jacking pipe segment into a predetermined number of two-dimensional spatial regions. The two-dimensional spatial region can be a region in the form of a rectangular grid, that is, the rectangular surface of the unfolded pipe segment is divided into M*N small rectangular grids along its length and circumference. The data organization and mapping module 20 associates the fiber optic sensing data falling into each grid region (e.g., averaging or interpolating the data of all fiber optic points in that region) with that region to form a spatiotemporal dataset S(i,j,t) indexed by region index (i,j) and time t.
[0033] The local analysis and processing module 30 is configured to process the spatiotemporal dataset of the sensed values. For each preset spatial location (e.g., each grid region) on the outer wall surface of the jacking pipe segment, the module performs the following steps:
[0034] Determine the curve of the sensing value changing over time at the preset spatial location. Extract the sensing value sequence {S(x,y,t_1),S(x,y,t_2),...,S(x,y,t_N)} at a specific spatial location (x,y) throughout the entire monitoring period from the spatiotemporal dataset.
[0035] The module identifies the decay phase in the curve representing the change of the sensed value over time. For the temperature rise caused by the heat release from grouting hydration, the decay phase refers to the stage where the temperature gradually decreases from the peak. This module can be configured to identify the temperature peak point and select a period of time after the peak point as the decay phase. For example, identifying the decay phase includes determining the time point t_peak when the sensed value reaches its peak value, and determining a period of time-series data after the peak time point as the sensed value time-series data of the decay phase, such as the time range [t_peak, t_end_analysis].
[0036] Based on the time-series data of the sensed values during the attenuation phase, a preset thermal attenuation model is fitted to obtain the attenuation parameter at the preset spatial location. The preset thermal attenuation model is a temperature attenuation model, and the local physical parameter is the attenuation parameter k. This model can adopt a single exponential attenuation model T(t) = A*exp(-k*t) + C, where T(t) is the change in temperature value during the attenuation phase with time t relative to t_peak, A and C are model parameters, and k is the attenuation parameter. The fitting step includes fitting the time-series temperature data of the cooling phase to determine the attenuation parameter k. This module is configured to use a standard fitting algorithm, such as the least squares method, to fit the time-series temperature data of the attenuation phase and calculate a unique attenuation parameter k(x,y) as the local physical parameter at that location.
[0037] This attenuation parameter quantifies the rate of heat loss at that location, reflecting the heat storage and conduction capacity of the grout, and is closely related to the density and thickness of the grout. The smaller the k value, the slower the attenuation, indicating that the grout is thicker, denser, or has poor heat dissipation conditions; the larger the k value, the faster the attenuation, indicating that the grout is thinner, less dense, or has voids.
[0038] Based on the attenuation parameter at the preset spatial location and a preset criterion, a local grouting state index is determined at the preset spatial location. The preset criterion consists of multiple parameter thresholds (k_th1, k_th2, ...) preset based on the attenuation parameter. The module is configured to determine the grouting state into a preset discrete numerical category, such as 0, 1, 2, 3..., representing different states such as void, insufficient, normal, and full, based on the deterministic comparison result between the calculated attenuation parameter k(x,y) and these thresholds. This index is a single numerical value representing a preliminary assessment of the grouting state at that location.
[0039] The map generation module 40 is used to generate a grouting state distribution map representing the spatial distribution of the local grouting state index on the outer wall surface of the jacking pipe segment. This module receives the local grouting state index at all preset spatial locations output by the local analysis and processing module 30, and visualizes these indices at the corresponding spatial locations.
[0040] For example, the map generation module 40 generates a two-dimensional pseudo-color map, where different color regions represent different local grouting status index categories. For instance, index 0 is displayed as red (warning), 1 as yellow (caution), 2 as green (normal), and 3 as blue (good), thereby providing on-site construction personnel with intuitive spatial distribution information on grouting quality.
[0041] In some embodiments, the system further includes a spatial impact calculation module 60 and a regional comprehensive assessment module 50.
[0042] The spatial impact calculation module 60 is configured to determine the adjacent preset spatial locations of the preset spatial location, and obtain the spatial impact factor of the preset spatial location based on the attenuation parameter of the adjacent preset spatial locations. This module is used to quantify the impact of the grouting status of the surrounding area on the current area assessment.
[0043] The preset spatial location can be a rectangular grid region. The spatial influence calculation module 60 is configured to determine the four-neighbor or eight-neighbor domains of each preset spatial location as its adjacent preset spatial locations based on the spatial division structure of the preset spatial location (e.g., the topological relationship of the rectangular grid).
[0044] For example, for a central region (i,j), its neighboring regions can be (i±1,j), (i,j±1) (four-neighborhood), or (i±1,j±1) (eight-neighborhood). For regions located at boundaries or corners, the number of their neighboring regions will be reduced accordingly.
[0045] The spatial influence calculation module 60 is configured to calculate the spatial influence factor of the preset spatial location based on the attenuation parameters of these adjacent preset spatial locations. The spatial influence factor is used to reflect the influence of the local physical characteristics of adjacent areas on the current area.
[0046] For example, the spatial influence calculation module 60 is configured to calculate the weighted average of the attenuation parameters of the adjacent preset spatial locations, and use the weighted average as the spatial influence factor SIF(x,y) or SIF(i,j). The formula can be expressed as SIF(i,j)=Sum(w_nb*k_nb), where Sum represents the summation of all adjacent regions 'nb' of region (i,j), k_nb is the attenuation parameter of the adjacent region nb, and w_nb is the weight of the adjacent region nb to the current region (i,j). The weight w_nb can be a preset constant (e.g., a weight of 1 / N_neighbors, where N_neighbors is the number of adjacent regions), or determined according to distance or other preset rules.
[0047] The regional comprehensive assessment module 50 is configured to deterministically combine the attenuation parameter and the spatial influence factor at the preset spatial location using a preset combination function and a preset spatial influence weighting factor to obtain a regional comprehensive assessment score at the preset spatial location. This score ZGCS(x,y) or ZGCS(i,j) integrates the local physical characteristics of the location itself (attenuation parameter k) and the influence of the physical characteristics of the surrounding environment (spatial influence factor SIF), thereby providing a more comprehensive regional assessment of the grouting filling status.
[0048] For example, the region comprehensive evaluation module 50 is configured to use a combination function. The combination function can be multiplicative or additive. A multiplicative combination can be expressed as ZGCS(i,j)=(k(i,j)+epsilon)*(SIF(i,j)+epsilon_s)*weight, where k(i,j) is the attenuation parameter of region (i,j), SIF(i,j) is its spatial influence factor, weight is a preset spatial influence weight factor, and epsilon and epsilon_s are small positive numbers used to avoid zero values causing the product to be zero. An additive combination can be expressed as ZGCS(i,j)=(k(i,j)*(1-weight')+SIF(i,j)*weight')*C, where weight' is another weight factor (e.g., between 0 and 1), and C is a scaling constant. The preset spatial influence weight factor weight or weight' can initially be a preset constant.
[0049] In some embodiments, the local analysis and processing module 30 revises the method for determining the local grouting status index. Based on the regional comprehensive evaluation score at the preset spatial location and the preset regional determination criteria, the local grouting status index at the preset spatial location is determined.
[0050] Exemplarily, the preset area determination criterion presets multiple determined score thresholds (ZGCS_th1, ZGCS_th2,...) based on the comprehensive area evaluation score. According to the deterministic comparison relationship between the calculated comprehensive area evaluation score ZGCS(i,j) and these thresholds, the grouting state index is determined as a certain preset discrete numerical category. For example:
[0051] If ZGCS(i,j) > ZGCS_threshold1, it is determined as state category 0;
[0052] If ZGCS_threshold2 < ZGCS(i,j) <= ZGCS_threshold1, it is determined as state category 1.
[0053] Each position obtains a unique local grouting state index GSI_local(i,j). In this way, the determination of the local grouting state is no longer based solely on isolated local physical parameters, but comprehensively considers its own characteristics and the influence of the surrounding environment, making the evaluation result more spatially consistent and robust. The map generation module 40 also generates a distribution map based on these local grouting state indices obtained through comprehensive area evaluation.
[0054] In some embodiments, the system may further include an overall index aggregation module 70 and an output module 80.
[0055] The overall index aggregation module 70 is configured to aggregate the comprehensive area evaluation scores or the local grouting state indices at all the preset spatial positions to obtain an overall grouting state index. This index is used to indicate the overall grouting state profile of the entire monitoring area.
[0056] Exemplarily, the overall index aggregation module 70 is configured to calculate the average value of the comprehensive area evaluation scores ZGCS(i,j) at all preset spatial positions, or calculate the average value or weighted average value of all local grouting state indices GSI_local(i,j) as the overall grouting state index GSI_overall.
[0057] The output module 80 is used to generate a monitoring output based on the overall grouting state index. The output can be a numerical display, a status level display, or a report generation.
[0058] Exemplarily, the output module 80 can display GSI_overall in numerical form on the user interface, or map it to a preset overall grouting state level (such as "overall seriously insufficient", "overall good", "overall overfilled") and display it with text or color identification. This module can also generate a monitoring report including the overall index and the local distribution map.
[0059] The system may further include a parameter adjustment module. This module is configured to adjust the value of the preset spatial influence weighting factor based on a comparison between the overall grouting status index and at least one preset overall threshold. This module enables the system to adaptively optimize the spatial influence weight in the regional comprehensive assessment based on the overall monitoring results. The preset spatial influence weighting factor may initially be a preset constant.
[0060] The parameter adjustment module is configured to periodically acquire the latest overall grouting status index GSI_overall during the monitoring process or when a specific event occurs (e.g., after grouting of a certain pipe jacking section is completed), and compare it with a preset overall threshold (e.g., insufficient grouting threshold T_low, overfilled grouting threshold T_high).
[0061] For example, the parameter adjustment module can be configured to increase the value of the preset space influence weight factor (weight or weight') when GSI_overall is lower than a preset insufficient grouting threshold T_low; and decrease the value of the weight factor when GSI_overall is higher than a preset overfilling threshold T_high. Increasing the weight factor can enhance the positive influence of the good condition of adjacent areas on the current area assessment, helping to identify areas that are being filled; decreasing the weight factor focuses more on the locally detected physical parameters. The step size or function for increasing or decreasing the weight factor can be a preset deterministic rule.
[0062] The parameter adjustment module can also be configured to adjust the value of the preset spatial influence weighting factor based on the comparison results of the spatial distribution characteristics of the grouting status index or the regional comprehensive evaluation score at all preset spatial locations with at least one preset distribution threshold. The spatial distribution characteristics can be the variance, standard deviation, spatial autocorrelation, or specific patterns (such as the area of continuous void regions) of these indices / scores. When the overall state is acceptable, but the local distribution is uneven (e.g., excessive variance), the parameter adjustment module can fine-tune the spatial influence weighting factor according to preset rules, such as slightly increasing or decreasing its value, to attempt to optimize the model's ability to capture local unevenness or to indicate distribution anomalies. The fine-tuning rules are preset deterministic functions or lookup tables.
[0063] Understandably, the data processing and analysis modules in the system (data organization and mapping module 20, local analysis and processing module 30, spatial impact calculation module 60, regional comprehensive assessment module 50, overall index aggregation module 70, and parameter adjustment module) can run as software programs or algorithms on the data processing unit (such as an industrial control computer, server, or high-performance embedded computing platform). The distributed fiber optic sensing unit 10 is a dedicated sensing device. The map generation module 40 and output module 80 can be implemented by a display, printer, network interface, or report generation software connected to the data processing unit.
[0064] Through the above description of the embodiments, those skilled in the art can clearly understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above.
[0065] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another apparatus, or some features may be ignored or not executed. Furthermore, the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.
[0066] The units described as separate components may or may not be physically separate. A component shown as a unit can be one physical unit or multiple physical units; that is, it can be located in one place or distributed in multiple different places. Depending on actual needs, some or all of the units can be selected to achieve the purpose of this embodiment.
[0067] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware.
[0068] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A smart monitoring system for grouting status in pipe jacking integrating a distributed optical fiber sensor network, characterized in that, include: A distributed optical fiber sensing unit is used to acquire distributed optical fiber sensing data, which includes time series sensing values at different locations along a preset distributed optical fiber laid on the outer wall of the jacking pipe segment within a preset time period. The data organization and mapping module is configured to map the fiber length coordinates at different locations of the distributed optical fiber to the corresponding spatial coordinates of the outer wall surface of the jacking pipe segment, and to organize the sensed values into a spatiotemporal dataset of sensed values corresponding to the spatial coordinates and time of the outer wall surface of the jacking pipe segment. The local analysis and processing module is configured to, for each preset spatial position on the outer wall surface of the jacking pipe segment, determine the curve of the sensing value changing with time at the preset spatial position, and identify the decay stage in the curve of the sensing value changing with time that represents the decay of the sensing value with time. Based on the time-series data of the sensed values during the attenuation phase, the attenuation parameter at the preset spatial location is obtained, and based on the attenuation parameter at the preset spatial location and the preset criteria, the local grouting state index at the preset spatial location is determined. as well as The map generation module is used to generate a grouting state distribution map representing the spatial distribution of the local grouting state index on the outer wall surface of the jacking pipe segment.
2. The system according to claim 1, characterized in that, The distributed optical fiber sensing unit is a distributed temperature sensing unit used to acquire distributed temperature sensing data, wherein the sensing value is the temperature value.
3. The system according to claim 2, characterized in that, The local analysis and processing module obtains the attenuation parameter at the preset spatial location based on a preset thermal attenuation model, which is expressed as T(t)=A*exp(-k*t)+C, where k is the attenuation parameter. The local analysis and processing module is configured to fit the temperature time series data of the cooling stage to determine the attenuation parameter.
4. The system according to claim 3, characterized in that, The local analysis and processing module is configured to use the least squares method to fit the temperature time series data of the cooling stage in order to determine the decay parameter.
5. The system according to claim 1, characterized in that, The data organization and mapping module is configured to establish a deterministic mapping relationship between the fiber length coordinates and the circumferential and longitudinal coordinates of the outer wall surface of the jacking pipe segment based on the preset laying geometry of the distributed optical fiber on the outer wall of the jacking pipe segment.
6. The system according to claim 1, characterized in that, The outer wall surface of the jacking pipe segment is divided into a predetermined number of two-dimensional spatial regions as the preset spatial locations.
7. The system according to claim 6, characterized in that, The two-dimensional spatial region is a region in the form of a rectangular grid.
8. The system according to claim 1, characterized in that, Also includes: The spatial influence calculation module is configured to determine the adjacent preset spatial locations of the preset spatial location, and obtain the spatial influence factor of the preset spatial location based on the attenuation parameter of the adjacent preset spatial locations; as well as The regional comprehensive assessment module is configured to combine the attenuation parameter and the spatial influence factor at the preset spatial location through a preset combination function and a preset spatial influence weighting factor to obtain the regional comprehensive assessment score at the preset spatial location. The local analysis and processing module is configured to determine the local grouting status index at the preset spatial location based on the regional comprehensive evaluation score at the preset spatial location and the preset regional judgment criteria.
9. The system according to claim 8, characterized in that, Also includes: The overall index aggregation module is configured to aggregate the regional comprehensive evaluation scores or the local grouting status index at all the preset spatial locations to obtain the overall grouting status index. as well as The output module is used to generate monitoring output based on the overall grouting status indicators.
10. The system according to claim 9, characterized in that, The system also includes a parameter adjustment module, configured to adjust the value of the preset spatial influence weighting factor based on the comparison result between the overall grouting status index and at least one preset overall threshold.