A weak current pipeline settlement monitoring system based on multi-source data fusion

Abstract

The present application provides a weak current pipeline settlement monitoring system based on multi-source data fusion. The system includes: a ground sensor array, including multiple fiber grating static levels, strain sensing cables and multiple MEMS three-axis accelerometers, which are used to collect horizontal displacement, deformation stress and vertical settlement in turn; a satellite remote sensing module, including a synthetic aperture radar module and a GNSS module, which are used to obtain surface deformation data and elevation data of the monitoring area in turn; a data analysis platform, which is used to extract features from the data collected by the ground sensor array, and use a multi-source data fusion algorithm to fuse the extracted features with the satellite remote sensing data obtained by the satellite remote sensing module, and perform settlement monitoring and early warning based on the fusion results. The present application solution solves the problems of insufficient accuracy and delayed response of traditional single monitoring, can significantly improve the reliability of pipeline settlement early warning, and effectively prevent the risk of pipeline rupture.

Description

Technical Field

[0001] The present application relates to a weak current pipeline settlement monitoring system based on multi-source data fusion, belonging to the technical field of construction engineering safety monitoring. Background Art

[0002] In the application scenario where weak current pipelines enter buildings, foundation settlement often causes the mechanical stress on the optical cable to exceed the limit, resulting in breakage. Therefore, it is necessary to monitor the pipeline settlement to detect abnormalities in a timely manner.

[0003] Traditional monitoring methods, such as levels and total stations, rely on manual operation and cannot achieve 24 / 7 continuous monitoring, resulting in relatively low monitoring efficiency. When using only fiber-optic sensing monitoring, conventional fiber Bragg grating systems can only support a limited number of monitoring points, making it difficult to meet the demand for monitoring points on ultra-long pipelines. Satellite monitoring, such as InSAR technology, can obtain deformation data over a wide range, but the lack of verification with ground-based measured data leads to a high rate of false alarms. Summary of the Invention

[0004] The present application provides a foundation settlement monitoring device for construction projects, aiming to solve the problems of existing settlement monitoring technology being single, inaccurate, and having delayed response.

[0005] In a first aspect, an embodiment of the present application provides a weak current pipeline settlement monitoring system based on multi-source data fusion, comprising:

[0006] Ground sensor array, including multiple fiber Bragg grating static levels, strain sensing cables and multiple MEMS triaxial accelerometers, used to collect horizontal displacement, deformation stress and vertical settlement in sequence;

[0007] Satellite remote sensing module, including synthetic aperture radar module and GNSS module, is used to obtain surface deformation data and elevation data of the monitoring area in turn;

[0008] A data analysis platform for extracting features from the data collected by the ground sensor array, fusing the extracted features with the satellite remote sensing data acquired by the satellite remote sensing module using a multi-source data fusion algorithm, and performing subsidence monitoring and early warning based on the fusion results;

[0009] The ground sensor array is distributed in a three-dimensional grid along the weak current pipeline entrance to the building, buried in layers at different depths below the surface in the vertical direction, and radiated outward from the pipeline entrance to the building in the horizontal direction within a set range, with a group of sensor nodes set at each set distance.

[0010] The synthetic aperture radar module periodically acquires surface deformation data of the monitoring area and generates a sedimentation rate map through differential interferometry technology; the GNSS module acquires elevation data of the monitoring area in real time;

[0011] The data analysis platform includes an edge computing node, a cloud server and an early warning terminal. The edge computing node is deployed at the monitoring site and is equipped with a lightweight LSTM model to process the data collected by the ground sensor array in real time and extract settlement characteristics. The cloud server obtains satellite remote sensing data, constructs a unified time-space coordinate system, and uses a weighted Kalman filter algorithm to fit multi-source data to generate a comprehensive settlement risk map. The early warning terminal stores risk thresholds for different pipeline materials. When the settlement amount is determined to meet the set early warning conditions based on the comprehensive settlement risk map and the risk threshold, an alarm is automatically triggered and the positioning information of the early warning area is sent to the operation and maintenance personnel.

[0012] Based on the above system, optionally, the fiber Bragg grating static level adopts a temperature compensation structure, a built-in PT-100 platinum resistor, and eliminates the interference of ambient temperature on sedimentation measurement through a dual-parameter demodulation algorithm.

[0013] Based on the above system, optionally, the installation structure of the ground sensor array includes:

[0014] The fiber Bragg grating (FBG) static level and the strain sensing cable are installed in a spiked settlement tube. Barbed structures are provided on the outside of the tube to enhance coupling with the soil. Each settlement tube is 1.5 meters long and connected via flanges to form a continuous monitoring network. Fiber Bragg grating (FBG) sensor strings are embedded in the tube, with monitoring points set every 0.3 meters to cover full-depth deformation monitoring.

[0015] The MEMS three-axis accelerometer module is arranged with multiple monitoring nodes along the pipeline axis at preset intervals. Each monitoring node includes a sealed and protected MEMS three-axis accelerometer, a temperature compensation unit and a wireless transmission module; the accelerometer is vertically fixed above the pipeline.

[0016] Based on the above system, optionally, the monitoring period of the synthetic aperture radar module is 7 days; after the data analysis platform updates the comprehensive settlement map, it automatically compares it with the historical comprehensive settlement map, identifies abnormal deformation areas and marks them as high-risk areas.

[0017] Based on the above system, optionally, in the satellite remote sensing module, the GNSS module deploys at least one base station and multiple monitoring stations within the monitoring area to form an observation network, and each station is equipped with a multi-frequency GNSS receiver and an anti-multipath antenna; the monitoring stations are arranged in a grid along the direction of the weak current pipeline entering the building.

[0018] Based on the above system, optionally, the data analysis platform integrates a BIM model, overlays the monitoring data with the building structure model, and supports three-dimensional visual settlement trend simulation and pipeline stress simulation analysis.

[0019] Based on the above system, optionally, the cloud server constructs a unified spatiotemporal coordinate system and establishes a conversion model between the WGS84 coordinate system and the local coordinate system:

[0020]

[0021] Where, is the rotation matrix, which is obtained by solving the baseline vectors of the three GNSS reference stations, and T is the translation vector, which is determined by measuring the ground control points;

[0022] 、 and is the three-dimensional coordinate in the local coordinate system, 、 and It is the three-dimensional coordinate in the WGS84 coordinate system.

[0023] Based on the above system, optionally, the multi-source data fusion algorithm includes:

[0024] (1) Constructing the equation of state

[0025]

[0026] Where, Represents the state vector, which is used to describe the system at time status, is the vertical settlement in mm; is the pipeline axial strain, in units of ; is the vertical acceleration in g; is the satellite-retrieved sedimentation rate in mm / month;

[0027]

[0028] Where, Represents the observation vector, which represents the original signal measured by the sensor and needs to be associated with the state vector through transformation. is the optical fiber wavelength offset, in pm; is the strain cable phase difference, in rad; is the second derivative of the vertical component of the MEMS triaxial accelerometer, unit ; is the InSAR interferometric phase, in rad;

[0029] (2) Adaptive weight allocation

[0030] Calculate the confidence of satellite data:

[0031]

[0032] Where, is the satellite measurement error, with a typical value of 1.2 mm;

[0033] is the combined error of the ground sensors, which is 0.05 mm for the fiber Bragg grating static level and 0.1 mm for the MEMS triaxial accelerometer;

[0034] is the satellite data weight, and the upper limit is taken when the signal-to-noise ratio is greater than 15dB;

[0035] is the sensor weight, when the temperature gradient When forced ;

[0036] (2) Weighted Kalman filter iteration:

[0037] In the prediction stage, the calculation is based on the following formula:

[0038]

[0039]

[0040] Where, Indicates the The predicted state vector at time t; represents the state transition matrix; Indicates the The predicted state vector at time t; represents the control input matrix; represents the external control vector; Indicates the The moment-to-moment forecast error covariance matrix; Indicates the The error covariance matrix at time t; represents the transposed matrix of the state transition matrix; Represents the process noise matrix, which is dynamically adjusted according to the pipeline burial depth;

[0041] During the update phase, the calculation is done according to the following formula:

[0042]

[0043]

[0044]

[0045] Where, Indicates the The Kalman gain matrix at time t; Represents the observation matrix, which contains the geometric relationship parameters between the sensor and the satellite; represents the transposed matrix of the observation matrix; Indicates the weight of satellite data; represents the ground sensor weight, =1- ; represents the satellite observation noise covariance matrix; represents the ground sensor observation noise covariance matrix; Indicates the The updated state vector at each moment; Indicates the The observation vector at time instant; Indicates the observed value corresponding to the predicted state, and the measured value The difference is the basis for updating; Indicates the The error covariance matrix after time update; I represents the identity matrix.

[0046] Based on the above system, optionally, the calculation formula of the risk threshold is:

[0047]

[0048] Where, This is the basic threshold, 2.5mm / month for PVC pipes and 3.5mm / month for steel pipes; =0.05 / m, is the depth correction coefficient, d is the depth; It is the material coefficient, which is 0 for PVC pipe and 0.2 for steel pipe.

[0049] The weak current pipeline settlement monitoring system based on multi-source data fusion provided by this application has the following beneficial effects:

[0050] This application utilizes a three-dimensional grid layout with vertically layered sensors combined with horizontally radiated sensors, combined with simultaneous monitoring at different depths to accurately capture the compressive deformation characteristics of soil at different depths. Strain sensing optical cables are routed closely along weak current pipelines, breaking through the limitations of traditional discrete point monitoring to achieve continuous linear stress distribution measurement, improving the comprehensiveness and accuracy of monitoring.

[0051] 2. This application uses multi-source data fusion technology to integrate the high-precision real-time data provided by the ground sensor array with the large-scale macro deformation covered by satellite remote sensing to improve the reliability of monitoring.

[0052] 3. This application sets up an early warning mechanism, which will alert the operation and maintenance personnel when the settlement risk threshold is exceeded. Upon receiving the alarm information, the operation and maintenance personnel can take corresponding measures (such as activating the backup optical cable route) to minimize the impact of pipeline settlement. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the description, serve to explain the principles of the present application. In addition, these drawings and the description are not intended to limit the scope of the concept of the present application in any way, but rather to illustrate the concept of the present application for those skilled in the art by reference to specific embodiments.

[0054] Figure 1 A schematic diagram of the structure of a weak current pipeline settlement monitoring system based on multi-source data fusion provided in one embodiment of the present application;

[0055] Figure 2 A schematic diagram of the installation structure of a fiber Bragg grating static level and a strain sensing optical cable provided in one embodiment of the present application;

[0056] Figure 3 A schematic diagram of the installation structure of a MEMS tri-axis accelerometer provided in one embodiment of the present application;

[0057] Figure 4 A schematic diagram of the ground site layout of the GNSS module provided in one embodiment of the present application. DETAILED DESCRIPTION

[0058] To make the purpose, technical solutions and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0059] This invention provides a weak-current pipeline settlement monitoring system based on multi-source data fusion, aiming to address the limitations of existing settlement monitoring technologies, which suffer from limited accuracy and delayed response. This system significantly improves the reliability of pipeline settlement warnings, effectively prevents the risk of pipeline rupture, ensures campus network stability, and safeguards the smooth operation of university education.

[0060] Reference Figure 1 The present invention provides a weak current pipeline settlement monitoring system based on multi-source data fusion. The system mainly includes: a ground sensor array, a satellite remote sensing module and a data analysis platform.

[0061] Among them, the ground sensor array, including multiple fiber grating (Weak-reflection Fiber Bragg Grating, WFBG) static levels, strain sensing optical cables and multiple MEMS (Micro-Electro-Mechanical System, micro-electromechanical system) three-axis accelerometers, are used to collect horizontal displacement, deformation stress and vertical settlement in turn.

[0062] The satellite remote sensing module, including the Synthetic Aperture Radar (SAR) module and the Global Navigation Satellite System (GNSS) module, is used to obtain surface deformation data and elevation data of the monitored area.

[0063] The data analysis platform is used to extract features from the data collected by the ground sensor array, and use a multi-source data fusion algorithm to fuse the extracted features with the satellite remote sensing data obtained by the satellite remote sensing module, and perform subsidence monitoring and early warning based on the fusion results.

[0064] Among them, such as Figure 2-4 As shown, the ground sensor array is distributed in a three-dimensional grid along the weak current pipeline entrance to the building, and is buried in layers at multiple different depths below the surface in the vertical direction (for example, 0.5m, 1m, and 1.5m). Horizontally, it is centered on the pipeline entrance to the building and radiates outward within a set range (for example, a radius of 10 meters), with a group of sensor nodes set at a set distance (for example, 2m).

[0065] In some embodiments, the strain sensing optical cable is laid along the pipeline, with monitoring points (each equipped with a strain sensor) positioned at regular intervals (e.g., 1 meter) to capture horizontal displacement and deformation stress. Additionally, MEMS triaxial accelerometer modules are deployed at intervals of 5-8 meters along the weak current pipeline's entrance to the building to capture vertical settlement.

[0066] Furthermore, in some embodiments, the mounting structure of the ground sensor array includes:

[0067] The fiber Bragg grating static level and the strain sensing optical cable are installed in a thorny settlement tube. A barb structure is provided on the outside of the settlement tube to enhance coupling with the soil. Each settlement tube is 1.5 meters long and is connected by a flange to form a continuous monitoring network. A fiber Bragg grating sensor string is embedded in the tube, with a monitoring point set every 0.3 meters to cover full-depth deformation monitoring.

[0068] The MEMS three-axis accelerometer module is arranged with multiple monitoring nodes along the pipeline axis at preset intervals. Each monitoring node includes a sealed and protected MEMS three-axis accelerometer, a temperature compensation unit and a wireless transmission module; the accelerometer is vertically fixed above the pipeline.

[0069] In some embodiments, the fiber Bragg grating static level adopts a temperature compensation structure and a built-in PT-100 platinum resistor, and eliminates the interference of ambient temperature on sedimentation measurement through a dual-parameter demodulation algorithm.

[0070] The synthetic aperture radar module periodically acquires surface deformation data in the monitored area and generates a sedimentation rate map using differential interferometry (D-InSAR) technology. In some embodiments, a satellite equipped with a synthetic aperture radar periodically acquires surface deformation data in the monitored area and generates a sedimentation rate map using differential interferometry technology with a spatial resolution better than 5 mm.

[0071] The GNSS module acquires elevation data of the monitored area in real time. Specifically, the GNSS module includes a GNSS reference network that can acquire elevation data of a large area in real time. In some embodiments, the GNSS module deploys at least one reference station and multiple monitoring stations within the monitored area to form an observation network, each station equipped with a multi-frequency GNSS receiver and an anti-multipath antenna; the monitoring stations are arranged in a grid pattern along the direction of the weak current pipeline entering the building.

[0072] The data analysis platform includes edge computing nodes, cloud servers and early warning terminals.

[0073] The edge computing node, deployed at the monitoring site and equipped with a lightweight LSTM model (32 hidden layer neurons), processes the data collected by the ground sensor array in real time and extracts subsidence features. Because the edge computing node is deployed at the monitoring site, processing efficiency is improved while reducing the amount of data transmitted to the cloud server.

[0074] The cloud server acquires satellite remote sensing data, constructs a unified space-time coordinate system, and uses a weighted Kalman filter algorithm to fit multi-source data to generate a comprehensive subsidence risk map.

[0075] The early warning terminal stores risk thresholds for different pipeline materials. When the settlement amount is determined to meet the set early warning conditions based on the comprehensive settlement risk map and the risk threshold, it automatically triggers an alarm and sends the location information of the early warning area to the operation and maintenance personnel.

[0076] In some embodiments, the synthetic aperture radar module has a monitoring period of 7 days. After the data analysis platform updates the comprehensive subsidence map, it automatically compares it with historical comprehensive subsidence maps, identifies areas of abnormal deformation, and marks them as high-risk areas. This facilitates subsequent review.

[0077] In some embodiments, the data analysis platform integrates a BIM (Building Information Modeling) model, overlays the monitoring data with the building structure model, and supports three-dimensional visual settlement trend simulation and pipeline stress simulation analysis.

[0078] In some embodiments, the cloud server constructs a unified spatiotemporal coordinate system and establishes a conversion model between the WGS84 coordinate system (Earth's center of mass coordinate system) and the local coordinate system:

[0079]

[0080] Where, is the rotation matrix, which is calculated by solving the baseline vectors of three GNSS reference stations. It is used to describe the rotation relationship between the two coordinate systems and eliminate the effects of earth curvature, azimuth deviation, etc.; T is the translation vector, which is determined by measuring ground control points (measured points with known precise coordinates) and is used to adjust the origin offset of the coordinate system to ensure absolute matching of spatial positions;

[0081] 、 and is the three-dimensional coordinate in the local coordinate system, 、 and It is the three-dimensional coordinate in the WGS84 coordinate system.

[0082] Obtaining the rotation matrix R:

[0083] Utilize at least three GNSS reference stations (arranged in a triangulated network) to calculate the coordinates of each station in the WGS84 coordinate system by receiving satellite signals. Then, based on the baseline vectors (vector direction and length) between the stations, calculate the coordinate system rotation parameters to ensure that the coordinate axis direction of the local coordinate system is consistent with the actual geographic orientation (such as true north).

[0084] Determination of translation vector T:

[0085] Several ground control points (such as fixed building corners and measurement landmarks) are selected within the monitoring area. Their local coordinate system coordinates are measured using a total station or other equipment, and the WGS84 coordinates measured by GNSS are simultaneously obtained. The offset between the two sets of coordinates is calculated using the least squares method to obtain the translation vector T.

[0086] By accurately calculating the rotation matrix and translation vector, the spatial position of sensor data (such as fiber Bragg grating settlement and strain values) and satellite images (InSAR settlement rate maps and GNSS elevation data) can be unified. The error can be controlled at the millimeter level, providing a reliable spatial reference for the subsequent weighted Kalman filter algorithm.

[0087] In addition, after unifying the coordinate system, the monitoring data can be superimposed on the BIM model to intuitively display the settlement trend in three-dimensional space (such as the spatial distribution of pipeline vertical settlement and horizontal displacement), and predict the pipeline deformation risk through stress simulation analysis, assisting operation and maintenance personnel to quickly locate high-risk areas.

[0088] In some embodiments, the multi-source data fusion algorithm includes:

[0089] (1) Constructing the equation of state

[0090]

[0091] Where, Represents the state vector, which is used to describe the system at time Status;

[0092] is the vertical settlement, in mm, reflecting the vertical deformation of the soil or pipeline;

[0093] is the pipeline axial strain, in units of , used to monitor the tensile or compressive stress of pipelines caused by settlement;

[0094] is the vertical acceleration, in g, which can be converted into sedimentation velocity or displacement by integration;

[0095] It is the satellite-derived sedimentation rate in mm / month, which provides a macro trend of surface deformation over a large area.

[0096]

[0097] Where, Represents the observation vector, which represents the original signal measured by the sensor and needs to be associated with the state vector through transformation;

[0098] is the optical fiber wavelength offset, in pm. According to the fiber Bragg grating principle, the wavelength offset is linearly related to the sedimentation.

[0099] is the strain cable phase difference, in rad, used to calculate the distribution of pipeline axial strain;

[0100] is the second derivative of the vertical component of the MEMS triaxial accelerometer, unit , reflecting the rate of change of sedimentation acceleration;

[0101] is the InSAR interferometric phase, in rad, which can be converted into surface deformation displacement through phase unwrapping;

[0102] (2) Adaptive weight allocation

[0103] Calculate the confidence of satellite data:

[0104]

[0105] Where, is the satellite measurement error, with a typical value of 1.2 mm, which is affected by factors such as atmospheric delay and radar resolution;

[0106] It is the combined error of ground sensors, which is 0.05mm for fiber Bragg grating static level and 0.1mm for MEMS triaxial accelerometer, reflecting the accuracy of local single-point monitoring;

[0107] is the satellite data weight, and the upper limit is taken when the signal-to-noise ratio is greater than 15dB;

[0108] is the sensor weight, when the temperature gradient When forced .

[0109] Among them, the weight logic is:

[0110] When satellite data errors The smaller, The larger it is (upper limit 0.8), the more reliable the satellite data is;

[0111] On the contrary, the ground sensor has smaller error or temperature gradient When the sensor weight is forced =1− =0.9, reducing the impact of satellite data (avoiding misjudgment caused by temperature interference).

[0112] (2) Weighted Kalman filter iteration:

[0113] 1. In the prediction stage, the following formula is used for calculation:

[0114] 1.1 State prediction formula:

[0115]

[0116] Where, : No. The predicted state vector at the moment (including the predicted values ​​of parameters such as settlement and strain);

[0117] : state transfer matrix, which describes the change of system state over time (such as the time transfer relationship of sedimentation rate);

[0118] : No. The predicted state vector at time t;

[0119] : Control input matrix (if the system has no external control, this value can be considered as 0);

[0120] : External control vector (usually 0, indicating no active intervention).

[0121] 1.2 Error covariance prediction formula:

[0122]

[0123] Where, : No. The moment-by-moment forecast error covariance matrix measures the uncertainty of the forecast state;

[0124] : No. The error covariance matrix at time t;

[0125] : transposed matrix of the state transfer matrix;

[0126] : The process noise matrix is ​​dynamically adjusted according to the pipeline burial depth (the greater the burial depth, the more complex the soil disturbance, and the higher the noise weight), reflecting the uncertainty in the system dynamic process.

[0127] 2. During the update phase, the calculation is based on the following formula:

[0128] 2.1 Kalman gain calculation formula:

[0129]

[0130] Where, : No. The Kalman gain matrix at the moment is used to balance the weights of the predicted value and the observed value;

[0131] : Observation matrix, which contains the geometric relationship parameters between the sensor and the satellite (such as coordinate conversion coefficients, measurement angles, etc.), and maps the state vector to the observation space;

[0132] : transposed matrix of the observation matrix;

[0133] : Satellite data weight (range 0.2~0.8, 0.8 when SNR>15dB);

[0134] : Ground sensor weight ( =1- ), when the temperature gradient is greater than 5°C / m, it is forced to be 0.9);

[0135] : Satellite observation noise covariance matrix, determined by the satellite measurement error (1.2 mm);

[0136] : The ground sensor observation noise covariance matrix is ​​determined by the accuracy of fiber Bragg grating (0.05mm) and MEMS (0.1mm).

[0137] 2.2 State update formula:

[0138]

[0139] Where, : No. The updated state vector (optimal estimate after integrating observation data);

[0140] : No. The observation vector at the time (including raw signals such as fiber wavelength offset and InSAR phase);

[0141] : The observed value corresponding to the predicted state and the measured value The difference is used as the basis for updating.

[0142] 2.3 Error covariance update formula:

[0143]

[0144] Where, : No. The error covariance matrix after constant update reflects the uncertainty of the fusion state;

[0145] I: Identity matrix, ensuring consistent dimensions of matrix operations.

[0146] Based on the above algorithm, through the state transfer matrix , observation matrix Establish mathematical associations between multi-source data and use weights Dynamically balance the credibility of satellite and ground sensors, and ultimately obtain the optimal settlement estimate through iterative calculation.

[0147] In some embodiments, the risk threshold is calculated as follows:

[0148]

[0149] Where, This is the basic threshold, 2.5mm / month for PVC pipes and 3.5mm / month for steel pipes; =0.05 / m, is the depth correction factor (the greater the depth, the more complex the soil stress distribution, and the threshold needs to be lowered), d is the depth; It is the material coefficient, which is 0 for PVC pipes and 0.2 for steel pipes (the steel pipe material has a higher rigidity, which allows a certain degree of settlement compensation (the threshold can be increased by 0.2 times), while there is no correction for PVC pipes).

[0150] In some embodiments, a multi-level alarm is used:

[0151] Yellow warning: triggered when single-point settlement is greater than 2mm / month and lasts for 2 weeks;

[0152] Red alert: average settlement > 3mm / month and deformation gradient > 0.5mm / m.

[0153] Among them, deformation gradient refers to the settlement difference within unit length (such as the settlement difference between the two ends of a 1-meter pipeline is greater than 0.5mm), which reflects the uneven settlement and is a key indicator of pipeline rupture.

[0154] The above scheme sets the foundation safety boundary based on engineering characteristics such as material and burial depth, and realizes graded warning through the dual indicators of "single-point settlement rate + deformation gradient" to avoid false alarms and missed alarms.

[0155] The weak current pipeline settlement monitoring system based on multi-source data fusion provided by this application has the following beneficial effects:

[0156] This application utilizes a three-dimensional grid layout with vertically layered sensors combined with horizontally radiated sensors, combined with simultaneous monitoring at different depths to accurately capture the compressive deformation characteristics of soil at different depths. Strain sensing optical cables are routed closely along weak current pipelines, breaking through the limitations of traditional discrete point monitoring to achieve continuous linear stress distribution measurement, improving the comprehensiveness and accuracy of monitoring.

[0157] 2. This application uses multi-source data fusion technology to integrate the high-precision real-time data provided by the ground sensor array with the large-scale macro deformation covered by satellite remote sensing to improve the reliability of monitoring.

[0158] 3. This application sets up an early warning mechanism, which will alert the operation and maintenance personnel when the settlement risk threshold is exceeded. Upon receiving the alarm information, the operation and maintenance personnel can take corresponding measures (such as activating the backup optical cable route) to minimize the impact of pipeline settlement.

[0159] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0160] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0161] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0162] Those skilled in the art will understand that all or part of the steps carried out in the method for implementing the above-mentioned embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one of the steps of the method embodiment or a combination thereof.

[0163] Furthermore, the functional units in the various embodiments of the present invention may be integrated into a single processing module, each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in either hardware or software functional modules. If the integrated modules are implemented as software functional modules and sold or used as standalone products, they may also be stored in a computer-readable storage medium. The aforementioned storage medium may be a read-only memory, a magnetic disk, or an optical disk, etc.

[0164] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0165] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A weak current pipeline settlement monitoring system based on multi-source data fusion, characterized in that: include: Ground sensor array, including multiple fiber Bragg grating static levels, strain sensing cables and multiple MEMS triaxial accelerometers, used to collect horizontal displacement, deformation stress and vertical settlement in sequence; Satellite remote sensing module, including synthetic aperture radar module and GNSS module, is used to obtain surface deformation data and elevation data of the monitoring area in turn; A data analysis platform for extracting features from the data collected by the ground sensor array, fusing the extracted features with the satellite remote sensing data acquired by the satellite remote sensing module using a multi-source data fusion algorithm, and performing subsidence monitoring and early warning based on the fusion results; The ground sensor array is distributed in a three-dimensional grid along the weak current pipeline entrance to the building, buried in layers at different depths below the surface in the vertical direction, and radiated outward from the pipeline entrance to the building in the horizontal direction within a set range, with a group of sensor nodes set at each set distance. The synthetic aperture radar module periodically acquires surface deformation data of the monitoring area and generates a sedimentation rate map through differential interferometry technology; the GNSS module acquires elevation data of the monitoring area in real time; The data analysis platform includes edge computing nodes, cloud servers and early warning terminals. The edge computing nodes are deployed at the monitoring site and equipped with a lightweight LSTM model to process the data collected by the ground sensor array in real time and extract settlement characteristics. The cloud server acquires satellite remote sensing data, constructs a unified space-time coordinate system, and uses a weighted Kalman filter algorithm to fit multi-source data to generate a comprehensive settlement risk map; the early warning terminal stores risk thresholds for different pipeline materials. When the settlement amount is determined to meet the set early warning conditions based on the comprehensive settlement risk map and the risk threshold, an alarm is automatically triggered and the positioning information of the early warning area is sent to the operation and maintenance personnel.

2. The system according to claim 1, wherein: The fiber Bragg grating static level adopts a temperature compensation structure and a built-in PT-100 platinum resistor, and eliminates the interference of ambient temperature on sedimentation measurement through a dual-parameter demodulation algorithm.

3. The system according to claim 1, wherein: The mounting structure of the ground sensor array includes: The fiber Bragg grating (FBG) static level and the strain sensing cable are installed in a spiked settlement tube. Barbed structures are provided on the outside of the tube to enhance coupling with the soil. Each settlement tube is 1.5 meters long and connected via flanges to form a continuous monitoring network. Fiber Bragg grating (FBG) sensor strings are embedded in the tube, with monitoring points set every 0.3 meters to cover full-depth deformation monitoring. The MEMS three-axis accelerometer module is arranged with multiple monitoring nodes along the pipeline axis at preset intervals. Each monitoring node includes a sealed and protected MEMS three-axis accelerometer, a temperature compensation unit and a wireless transmission module; the accelerometer is vertically fixed above the pipeline.

4. The system according to claim 1, wherein: The monitoring cycle of the synthetic aperture radar module is 7 days; after the data analysis platform updates the comprehensive settlement map, it automatically compares it with the historical comprehensive settlement map, identifies abnormal deformation areas and marks them as high-risk areas.

5. The system according to claim 1, wherein: The GNSS module deploys at least one base station and multiple monitoring stations within the monitoring area to form an observation network. Each station is equipped with a multi-frequency GNSS receiver and an anti-multipath antenna; the monitoring stations are arranged in a grid along the direction of the weak current pipeline entering the building.

6. The system according to claim 1, wherein: The data analysis platform integrates the BIM model, overlays the monitoring data with the building structure model, and supports three-dimensional visual settlement trend simulation and pipeline stress simulation analysis.

7. The system according to claim 1, wherein: The cloud server constructs a unified space-time coordinate system and establishes a conversion model between the WGS84 coordinate system and the local coordinate system: Where, is the rotation matrix, which is obtained by solving the baseline vectors of the three GNSS reference stations, and T is the translation vector, which is determined by measuring the ground control points; 、 and is the three-dimensional coordinate in the local coordinate system, 、 and It is the three-dimensional coordinate in the WGS84 coordinate system.

8. The system according to claim 1, wherein: The multi-source data fusion algorithm includes: (1) Constructing the equation of state Where, Represents the state vector, which is used to describe the system at time status, is the vertical settlement in mm; is the pipeline axial strain, in units of ; is the vertical acceleration in g; is the satellite-retrieved sedimentation rate in mm / month; Where, Represents the observation vector, which represents the original signal measured by the sensor and needs to be associated with the state vector through transformation. is the optical fiber wavelength offset, in pm; is the strain cable phase difference, in rad; is the second derivative of the vertical component of the MEMS triaxial accelerometer, unit ; is the InSAR interferometric phase, in rad; (2) Adaptive weight allocation Calculate the confidence of satellite data: Where, is the satellite measurement error, with a typical value of 1.2 mm; is the combined error of the ground sensors, which is 0.05 mm for the fiber Bragg grating static level and 0.1 mm for the MEMS triaxial accelerometer; is the satellite data weight, and the upper limit is taken when the signal-to-noise ratio is greater than 15dB; is the sensor weight, when the temperature gradient When forced ; (2) Weighted Kalman filter iteration: In the prediction stage, the calculation is based on the following formula: Where, Indicates the The predicted state vector at time t; represents the state transition matrix; Indicates the The predicted state vector at time t; represents the control input matrix; represents the external control vector; Indicates the The moment-to-moment forecast error covariance matrix; Indicates the The error covariance matrix at time t; represents the transposed matrix of the state transition matrix; Represents the process noise matrix, which is dynamically adjusted according to the pipeline burial depth; During the update phase, the calculation is based on the following formula: Where, Indicates the The Kalman gain matrix at time t; Represents the observation matrix, which contains the geometric relationship parameters between the sensor and the satellite; represents the transposed matrix of the observation matrix; Indicates the weight of satellite data; represents the ground sensor weight, =1- ; represents the satellite observation noise covariance matrix; represents the ground sensor observation noise covariance matrix; Indicates the The updated state vector at each moment; Indicates the The observation vector at time instant; Indicates the observed value corresponding to the predicted state, and the measured value The difference is the basis for updating; Indicates the The error covariance matrix after time update; I represents the identity matrix.

9. The system according to claim 1, wherein: The formula for calculating the risk threshold is: Where, This is the basic threshold, 2.5mm / month for PVC pipes and 3.5mm / month for steel pipes; =0.05 / m, is the depth correction coefficient, d is the depth; It is the material coefficient, which is 0 for PVC pipe and 0.2 for steel pipe.

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