GNSS and buried depth fusion gas pipeline settlement monitoring method
By using a projection geometry correction model and a dynamic burial depth evolution model that integrates GNSS and burial depth, combined with multi-source data fusion, the problems of data silos, model staticization, and accuracy distortion in GNSS pipeline settlement monitoring have been solved, enabling accurate monitoring and real-time early warning of underground pipeline settlement.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- NANZHI (CHONGQING) ENERGY TECH CO LTD
- Filing Date
- 2026-06-30
- Publication Date
- 2026-07-28
AI Technical Summary
Existing GNSS pipeline settlement monitoring technologies suffer from data silos, static models, inaccurate data, insufficient real-time response capabilities, and barriers to the fusion of multi-source heterogeneous data, making it impossible to achieve accurate monitoring of underground pipeline settlement.
By integrating GNSS data with burial depth data, a projection geometry correction model is constructed to decouple the coupling effect between surface displacement and burial depth variation. Combined with a dynamic burial depth evolution model and multi-source heterogeneous data fusion, accurate inversion of the actual pipe settlement is achieved.
It achieves accurate inversion of the actual pipe settlement, reduces systematic errors, improves monitoring accuracy and real-time response capability, reduces false alarms and missed alarms, and supports the transformation of pipeline operation and maintenance from passive emergency response to proactive prevention.
Smart Images
Figure CN122468047A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of intelligent geological disaster monitoring and early warning, safety monitoring of oil and gas storage and transportation projects, and precision measurement and multi-source information fusion, and particularly to a millimeter-level precision monitoring method for long-distance natural gas pipelines based on GNSS and dynamic burial depth fusion. Background Technology
[0002] As a vital national energy artery, long-distance natural gas pipelines face risks such as uneven settlement and soil slippage in complex geological environments, directly threatening the operational safety of the pipeline network. Accurate monitoring and early warning of pipeline settlement are core requirements for the full life-cycle management of energy infrastructure. Currently, GNSS high-precision positioning technology is widely used for deformation monitoring of open structures such as bridges, dams, and railways, enabling real-time perception of millimeter-level three-dimensional displacement on the ground surface, providing fundamental technical support for underground pipeline settlement monitoring.
[0003] However, existing GNSS-based gas pipeline settlement monitoring technologies have significant shortcomings and cannot meet the monitoring requirements of "accurate visibility, clear assessment, and early reporting" for underground pipelines: First, the problem of data silos is prominent. GNSS displacement data and pipeline burial depth information are stored and processed independently, lacking an effective fusion interface. The value of the data is severely fragmented, making it difficult to form a unified understanding of the pipeline status.
[0004] Second, the static nature of the models is a serious problem. Most settlement models treat the burial depth parameters as static initial values in the construction drawings, ignoring the dynamic changes in burial depth caused by factors such as incomplete backfilling, long-term soil compaction, seasonal freeze-thaw cycles, rainwater infiltration, and disturbances from adjacent projects. As a result, the vertical displacement inversion deviation can reach more than ±30%.
[0005] Third, the accuracy distortion problem is significant. Existing technology does not consider the spatial projection relationship between GNSS observation points and the actual pipe body, does not correct for Z-axis errors caused by changes in burial depth, and cannot decouple the coupling effect of "overall ground settlement (affecting the actual pipe body)" and "compaction of surface backfill soil (not affecting the pipe body but changing the burial depth)," which easily leads to misjudgments of "artificial height settlement," especially in soft soil or landslide areas where the error is more obvious.
[0006] Fourth, real-time response capabilities are insufficient. Most systems rely on offline post-processing and lack edge computing support, resulting in warning delays of up to hours, making it difficult to support emergency decision-making.
[0007] Fifth, the barriers to fusion of multi-source heterogeneous data are high. Sensors such as GNSS, InSAR, hydrostatic level, and ground-penetrating radar have different spatiotemporal resolutions, accuracy, and applicable scenarios. Existing technologies lack a unified spatiotemporal reference framework and an adaptive weighted fusion mechanism. The weight allocation is highly subjective and prone to false alarms or missed alarms.
[0008] To address the aforementioned technical issues, no effective solution has yet been developed to achieve deep integration of GNSS displacement and dynamic burial depth, thus failing to fundamentally resolve the core problem of "inaccurate observation" in underground pipeline settlement monitoring. Summary of the Invention
[0009] The purpose of this invention is to propose a GNSS-based gas pipeline settlement monitoring method that integrates burial depth and GNSS parameters. This method addresses the problem of insufficient monitoring accuracy caused by static burial depth parameters and uncorrected projection deviations in existing GNSS pipeline settlement monitoring. By using a dual-parameter driving mechanism of "GNSS displacement combined with dynamic burial depth", a projection geometric correction model is constructed to decouple the coupling effect of surface displacement and burial depth changes, thereby achieving accurate inversion of the actual pipeline settlement.
[0010] To achieve the above objectives, this invention discloses a GNSS-integrated gas pipeline settlement monitoring method, the key of which includes the following steps: S1. Deploy a GNSS observation network to acquire the GNSS three-dimensional displacement sequence of each observation point within the monitoring area in real time, and extract the vertical displacement component. ; S2. Construct a dynamic burial depth evolution model (DDBM), and combine initial burial depth data from BIM / GIS with a measured feedback mechanism to calculate the dynamic burial depth change of the pipeline. ; S3. Establish a projected geometric correction model and calculate the actual pipe settlement using the formula. : ; in This is the projection attenuation function, used to correct the Z-axis projection deviation caused by changes in burial depth.
[0011] Furthermore, the dynamic burial depth change mentioned in step S2 Let t be the actual burial depth of the pipeline. With design burial depth The difference, the actual burial depth of the pipeline The mathematical expression is: ; In the formula: Pipeline design burial depth; Construction backfilling deviation; Long-term soil compaction settlement; : Changes in burial depth caused by seasonal disturbances; Changes in burial depth caused by external engineering disturbances.
[0012] Furthermore: The long-term compaction settlement of the soil Expressed using an exponential model: ; in For the maximum settlement, The decay rate can be obtained through experimental training. The change in burial depth caused by the seasonal disturbance Represented using a sinusoidal modulation exponential model: ; in For seasonal fluctuations, Angular frequency, This is the initial phase. The attenuation coefficient; The change in burial depth caused by external engineering disturbance Represented using a piecewise step function: ; in Let J be the settlement increment of the j-th disturbance. For unit step function, Let be the time when the j-th disturbance occurs.
[0013] Furthermore, the projection attenuation function described in step S3 The mathematical expression is: ; in The projection coupling coefficient is determined through on-site calibration or finite element simulation.
[0014] Furthermore, it also includes a multi-source heterogeneous data fusion step: collecting monitoring data from InSAR, hydrostatic level, and ground-penetrating radar, and using an adaptive spatiotemporal fusion engine to perform spatiotemporal alignment and reliable weighted fusion of the multi-source heterogeneous data to obtain the fused settlement amount. The fusion formula is: ; In the formula: : The amount of sedimentation measured by the i-th type of sensor at time t; The fusion weights of the i-th type of sensor at time t, satisfying... and ; The fusion weight The following calculations are performed using the dynamic weighted confidence method: ; In the formula: : The basic weighting coefficients for the i-th type of sensor; The confidence level of the i-th type of sensor at time t is inversely proportional to the current noise variance of the sensor and directly proportional to the environmental adaptation factor.
[0015] Furthermore, the projection coupling coefficient The range of values is The upper limit value is taken for soft soil areas and the lower limit value is taken for hard rock areas.
[0016] Furthermore, the simplified engineering calculation formula for the actual pipe settlement in step S3 is as follows: ; in The correction factor for engineering simplification is determined based on the regional geological conditions.
[0017] Furthermore, the engineering simplification correction coefficient The value is , of which 0.8 is taken from typical geological areas across the country.
[0018] Furthermore: The default values for the basic weighting coefficients of various sensors are: 0.4 for GNSS, 0.2 for InSAR, 0.3 for hydrostatic level, and 0.1 for ground-penetrating radar. The fusion weights are dynamically adjusted according to different monitoring scenarios, including normal sunny weather, rainy / cloudy weather, subway construction period, and sensor failure scenarios.
[0019] Furthermore, it also includes settlement risk assessment and visualization steps: triggering a multi-level early warning mechanism based on the actual settlement of the pipe body or the integrated settlement, and connecting the monitoring data and assessment results to the smart pipeline digital twin platform; The multi-level early warning mechanism adopts a three-level early warning system, and its trigger threshold is determined based on pipeline design parameters and geological conditions. The visualization content of the digital twin platform includes a settlement risk heat map, a crack propagation trend prediction map, a fatigue accumulation curve, a failure probability evolution path, and a dynamic stress hotspot tracking map.
[0020] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The problem of the coupling effect between surface displacement and burial depth variation was solved. By establishing a projection geometric correction model, the false settlement caused by surface soil compaction was removed from GNSS observations for the first time, eliminating systematic errors and reducing the error of true settlement inversion by more than 30%. The vertical positioning deviation was reduced from ±10mm to within ±3mm.
[0021] 2. Breaking the constraints of the static assumption of "constant burial depth", a dynamic burial depth evolution model is constructed, which comprehensively considers construction deviations, long-term soil compaction, seasonal disturbances and external engineering effects, and realizes real-time updates of physical parameters, so that the monitoring model truly conforms to the actual geological evolution process.
[0022] 3. Break down barriers to the fusion of multi-source heterogeneous data, develop an adaptive spatiotemporal fusion engine, and achieve spatiotemporal alignment and reliable weighted fusion of multi-source data such as GNSS, InSAR, hydrostatic level, and ground-penetrating radar. The overall reliability is improved by 40%, effectively reducing false alarms and missed alarms.
[0023] 4. Enables lightweight deployment and real-time response of the monitoring system. The algorithm can be embedded in the edge computing terminal to meet the real-time diagnostic needs on site. The system response latency is compressed to the second level, which can identify potential failure trends in advance.
[0024] 5. Construct a closed-loop system of "perception-modeling-deduction-early warning", and output advanced diagnostic information such as settlement risk heat map and crack propagation trend prediction through digital twin platform, so as to promote the transformation of pipeline network operation and maintenance from "passive emergency response" to "proactive prevention", and achieve a fundamental breakthrough in "accurately seeing, clearly judging and early reporting" underground structures. Attached Figure Description
[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0026] Figure 1 A technical roadmap for a GNSS-integrated gas pipeline settlement monitoring method combining burial depth and GNSS; Figure 2 This is a schematic diagram of the equivalent stress cloud map simulation in the bend area (including turning points SQ3 / SQ5); Figure 3 Radar chart of key risks for pipeline A-SQ5; Figure 4 Settlement trend diagram of 7 buried pipelines at Longche Gas Transmission Station; Detailed Implementation
[0027] The following is in conjunction with the appendix Figure 1-4 The present invention will be further described in detail with reference to specific embodiments. These embodiments are implemented based on the technical solution of the present invention, providing detailed implementation methods and specific operating procedures. However, the scope of protection of the present invention is not limited to the following embodiments.
[0028] The overall technical process of this invention is as follows: Figure 1 As shown.
[0029] Detailed instructions on the method and steps Step 1: GNSS Observation Network Deployment and Data Acquisition A GNSS observation network is deployed along the pipeline route within the monitoring area. The spacing between stations is determined based on geological conditions, generally ranging from 50 to 200 meters, with appropriate densification in high-risk areas (such as landslide-sensitive areas and fault-crossing sections). Each observation station is equipped with a dual-frequency GNSS receiver with a sampling frequency of 1 Hz, acquiring the three-dimensional coordinate sequence of each observation point in real time. The raw observation data is processed using GNSS processing software to obtain the three-dimensional displacement sequence of each observation point, and the vertical displacement component is extracted. This is used for subsequent settlement calculations.
[0030] Step 2: Construction and Calculation of Dynamic Burial Depth Evolution Model The dynamic burial depth evolution model comprehensively considers various factors affecting pipeline burial depth changes, and its mathematical expression is as follows: ; The methods for obtaining and calculating each parameter are as follows: Design burial depth Import the burial depth data from the pipeline construction design drawings from the BIM / GIS database as the initial values for the model.
[0031] Construction backfill deviation After the pipeline construction is completed, the difference between the actual burial depth and the design burial depth is obtained by ground-penetrating radar detection or manual measurement, and is included in the model as a constant term.
[0032] Long-term soil compaction settlement An exponential model is used to describe the long-term compaction process of soil, and the formula is as follows: .in For the maximum settlement, The attenuation rate can be obtained by least-squares fitting of the burial depth monitoring data during the initial stage of pipeline operation.
[0033] Variation in burial depth caused by seasonal disturbances A sinusoidal modulated exponential model is used to describe the impact of seasonal freeze-thaw cycles and rainwater infiltration on burial depth. The formula is as follows: .in For seasonal fluctuations, Annual angular frequency ( ), This is the initial phase. The attenuation coefficient can be obtained through correlation analysis between historical burial depth data and temperature and humidity data.
[0034] Changes in burial depth caused by external engineering disturbances The piecewise step function is used to describe the sudden impact of adjacent construction projects (such as subway excavation and foundation pit construction) on the burial depth. The formula is as follows: .in The settlement increment for the j-th disturbance can be obtained through InSAR monitoring data or on-site measurements. For unit step function, Let be the time when the j-th disturbance occurs.
[0035] The actual burial depth of the pipeline at time t is calculated using the above model. This allows us to obtain the dynamic change in burial depth. .
[0036] Step 3: Projected geometric correction model and actual settlement calculation The GNSS observation point is installed on the ground surface, and the measured vertical displacement includes two parts: the actual settlement of the pipe body caused by the overall settlement of the strata and the change in burial depth caused by the compaction of the surface backfill soil. To isolate the spurious settlement component from the GNSS observations, a projection geometric correction model is established: ; Where the projection attenuation function The influence of burial depth variation on GNSS vertical displacement observations is described, and its mathematical expression is as follows: ; The projection coupling coefficient reflects the proportional relationship between surface subsidence and depth changes caused by surface soil compaction, and its value ranges from [value range missing]. In soft soil areas, due to the high compressibility of the soil, Take a value close to the upper limit of 0.9; the soil in the hard rock area has low compressibility. Take a lower limit value close to 0.6. It can be determined through on-site calibration tests or finite element simulation.
[0037] To meet the lightweight deployment requirements of edge computing terminals, a simplified calculation formula can be used in most engineering scenarios: ; The coefficient 0.8 represents the average value for typical geological regions across the country and can be fine-tuned within ±0.1 based on regional geological conditions. This simplified formula requires only one multiplication and subtraction operation, resulting in low computational complexity. It is suitable for execution on embedded MCUs and can improve vertical accuracy by more than 30%.
[0038] Step 4: Multi-source heterogeneous data fusion To further improve the reliability and coverage of settlement monitoring, multi-source monitoring data such as InSAR, hydrostatic level, and ground-penetrating radar are collected, and spatiotemporal alignment and reliable weighted fusion are performed through an adaptive spatiotemporal fusion engine.
[0039] First, all sensor data are unified under the same spatiotemporal reference framework. Interpolation is performed on data with different sampling frequencies to achieve time alignment. Spatial interpolation is performed on data with different spatial resolutions to achieve spatial alignment.
[0040] Then, the fusion weight of each sensor data is calculated using the confidence-weighted dynamic method, with the following formula: ; in is the basic weighting coefficient for the i-th type of sensor, determined based on the sensor's accuracy and reliability. The default values are: 0.4 for GNSS, 0.2 for InSAR, 0.3 for hydrostatic level, and 0.1 for ground-penetrating radar. Let be the confidence level of the i-th type of sensor at time t, and be the variance of the sensor's current noise. Inversely proportional to environmental adaptation factors Proportional. Obtained by sliding window variance estimation of real-time sensor data; Used to correct for the impact of environmental factors on sensor accuracy, such as the decrease in coherence of InSAR during the rainy season. Take 0.3; when GNSS is obstructed by trees, The corresponding reduction.
[0041] Finally, the fusion settlement is calculated based on the fusion weight:
[0042] The fusion weights of the sensors can be dynamically adjusted according to different monitoring scenarios: Normal clear weather: GNSS weight 0.4, InSAR weight 0.3, hydrostatic level weight 0.3; Rainy season / cloudy weather: GNSS weight 0.6, InSAR weight 0.1, hydrostatic level weight 0.3; During the subway construction period: GNSS weight 0.5, InSAR weight 0.2, hydrostatic level weight 0.3; When the hydrostatic level fails: GNSS weight 0.7, InSAR weight 0.3, hydrostatic level weight 0.
[0043] The adaptive spatiotemporal fusion engine is deployed on edge computing terminals to achieve minute-level online fusion of multi-source data with minimal system response latency. s.
[0044] Step 5: Settlement Risk Assessment and Visualization Based on actual pipe settlement Or integrate settlement A settlement risk assessment was conducted, triggering a three-level early warning mechanism: Level 1 Warning: mm; Level 2 warning: mm mm; Level 3 Warning: mm mm.
[0045] The warning threshold can be adjusted according to pipeline design parameters, pipe material strength, working pressure and geological conditions.
[0046] Monitoring data and assessment results are integrated into the smart pipeline network digital twin platform to enable visualized simulations. The digital twin platform outputs the following: Settlement risk heat map: presents a spatially continuous risk distribution and identifies high-risk clusters; Crack propagation trend prediction diagram: Based on settlement data and pipeline mechanical model, predict the initiation and propagation trend of cracks; Fatigue accumulation curve: Calculates the cumulative fatigue damage of a pipeline under uneven settlement. Failure probability evolution path: assessing pipeline failure probability under different settlement levels; Dynamic stress hotspot tracking map: shows the migration trend of the location of the maximum stress point caused by uneven settlement.
[0047] Example: Settlement monitoring application at the Longche gas transmission station This embodiment takes the Longche gas transmission station as an example to explain in detail the specific application process and calculation method of the present invention.
[0048] The basic parameters are shown in Table 1 below: Table 1 Basic Parameters of Longche Gas Transmission Station
[0049] Monitoring data acquisition Eight GNSS observation stations (SQ1, SQ3, SQ5, SQ8, SQ11, SQ15, SQ17, SQ21) and two surface subsidence monitoring points (DB160, DB141) were set up at the Longche gas transmission station, and InSAR, hydrostatic level and ground-penetrating radar data were collected at the same time.
[0050] Core parameter calculation process Taking high-risk point SQ5 (pipeline A) as an example, the detailed calculation process is as follows: (1) Calculation of dynamic burial depth variation Based on BIM / GIS data, the pipeline design burial depth m; Construction backfill deviation The initial calibration was set to 0.1m. Long-term soil compaction settlement: commissioning time In that year, the fitting yielded... mm, Substitute into the exponential model:
[0051] Seasonal disturbances: Monitoring is conducted during winter. Therefore ; External engineering disturbance: No nearby construction was carried out during the monitoring period, therefore ; Therefore, the actual burial depth of the pipeline at time t is:
[0052] Dynamic changes in burial depth:
[0053] (2) Calculation of actual pipe settlement The geological conditions of this area are a transition zone between soft soil and hard rock, with a projected coupling coefficient. ; The vertical displacement of point SQ5 was measured by the GNSS observation station. m; Substitute the projection geometry correction model: ; The result is consistent with the calculated elevation of the top of the pipe as measured in the experiment.
[0054] (3) Calculation of additional stress Based on the calculation method for bending stress caused by uneven settlement in pipeline mechanics, combined with the difference in settlement between adjacent areas... m, span Given m and pipe material parameters, the additional stress is calculated as follows:
[0055] (4) Safety factor calculation The yield strength of L415 pipe is 415 MPa. Considering the axial stress caused by working pressure, the total stress is 197.6 MPa. Safety factor:
[0056] (5) Fatigue level calculation Based on Miner's cumulative fatigue damage theory, and combining stress amplitude and cycle number, the fatigue damage index is calculated as follows:
[0057] (6) Calculation of failure probability Based on reliability analysis methods, and combining the probability distributions of settlement, additional stress, and material strength, the failure probability is calculated as follows:
[0058] Summary of overall calculation results The complete calculation results for each monitoring point are shown in Table 2 below: Table 2 Complete Technical Results for Each Monitoring Point
[0059] Visualized results analysis The overall settlement trend of the seven buried pipelines at the Longche gas transmission station is as follows: Figure 4 As shown in the figure, the settlement curves of each pipeline, the ±3cm uncertainty zone, the baseline (0m), and the warning line (-0.1m) are displayed. It can be seen that pipeline A has the most severe settlement, with a settlement of nearly -0.5m at a distance of about 18m, far exceeding the warning line; the settlement of the other pipelines is relatively small, but all show a downward trend to varying degrees.
[0060] Section SQ3-SQ5 of pipeline A is a bend and turning area, and its equivalent stress distribution is as follows: Figure 2 As shown in the figure, the stress gradually increases from the inside to the outside of the elbow, reaching its maximum value at the end of the elbow (near SQ5), which is consistent with the calculated additional stress of 36.9 MPa, verifying the existence of severe stress concentration in this area.
[0061] A multidimensional risk assessment was conducted on points A-SQ5 of the pipeline, and the results are as follows: Figure 3 As shown, the risk values at this point are significantly higher than those for flow velocity, vibration, and corrosion in the three dimensions of settlement, additional stress, and fatigue. This indicates that the additional stress and fatigue damage caused by uneven settlement are the main sources of risk at this point, which is consistent with the calculated results of high fatigue index and low safety factor.
[0062] Results Analysis and Recommendations 1. The settlement of the maximum settlement point DB160 on the ground surface reached -0.337m, corresponding to a failure probability as high as 18.6%, triggering a level one warning. The surrounding soil is at high risk of instability. Geological surveys should be carried out immediately, settlement monitoring piles should be installed, and a warning area should be demarcated.
[0063] 2. Sections SQ3-SQ5 of pipeline A experienced continuous and significant settlement, with an adjacent settlement difference of -0.24m, resulting in an additional stress as high as 36.9MPa, accounting for approximately 9%-15% of the material's yield strength, and a fatigue index reaching [missing value]. The levels are far exceeding safety thresholds. Additional supports should be installed in this section, settlement monitoring frequency should be increased, and internal pipeline inspections should be arranged.
[0064] 3. A new stress hotspot, SC-1, was discovered at approximately 180m. Although this location is not the point of maximum settlement, it is considered the highest-risk area due to the severe settlement gradient in the vicinity. This indicates that the point of maximum stress can migrate as uneven foundation settlement develops, and traditional fixed-point detection may miss newly formed dangerous points. Therefore, a dynamic stress hotspot tracking mechanism must be established.
[0065] 4. All turning points (such as SQ2, SQ13, SQ17, etc.) are prone to stress concentration due to the superposition of settlement differences caused by the elbow effect. CFD simulation should be performed to optimize the flow pattern and anti-surge baffles should be installed.
[0066] 5. Although the venting pipelines (C / F / G) are operating without pressure, the settlement is still significant. It should be checked whether the overall displacement is caused by the loosening of the foundation.
[0067] This embodiment verifies the effectiveness of the method of the present invention, which can accurately invert the actual settlement of the pipe body, identify potential risk points, and intuitively display the risk distribution and evolution trend through multi-dimensional visualization, providing a scientific basis for pipeline safe operation and maintenance.
[0068] In summary, the GNSS and burial depth fusion method for monitoring gas pipeline settlement proposed in this invention combines GNSS observation data with a dynamic burial depth evolution model. After projection geometric correction, the actual settlement is obtained, and high-precision monitoring is achieved through adaptive fusion of multi-source data. This effectively solves the problem of large errors in traditional methods that rely solely on surface observations to estimate pipeline settlement. It can accurately identify stress concentration areas and migrating stress hotspots. Compared to single monitoring methods, it has higher accuracy and greater adaptability, meeting the real-time monitoring and risk assessment needs of gas pipeline settlement under different weather conditions and engineering scenarios. This provides reliable technical support for the safe operation and maintenance of long-distance buried pipelines. Future development can further optimize the risk assessment model by combining pipeline body inspection data to improve monitoring accuracy under small settlement amounts and expand the application of the method in monitoring gas pipelines in complex geological areas.
[0069] The above description discloses only one preferred embodiment of the present invention, and should not be construed as limiting the scope of the present invention. Those skilled in the art will understand that all or part of the processes of the above embodiments can be implemented, and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.
Claims
1. A method for monitoring the settlement of a gas pipeline by fusing GNSS and buried depth, characterized in that, Includes the following steps: S1. Deploy a GNSS observation station network, and obtain the GNSS three-dimensional displacement sequence of each observation point in the monitoring area in real time, and extract the vertical displacement component ; S2. Construct a dynamic buried depth evolution model (DDBM) to calculate the dynamic buried depth change of the pipeline by combining the initial buried depth data of BIM / GIS and the measured feedback mechanism ; S3. Establish a projection geometry correction model, according to the formula to calculate the real pipe body settlement : ； wherein is a projection attenuation function used to correct for Z-projection bias caused by changes in burial depth.
2. The GNSS and burial depth integrated gas pipeline settlement monitoring method according to claim 1, characterized in that, The dynamic burial depth change mentioned in step S2 Let t be the actual burial depth of the pipeline. With design burial depth The difference, the actual burial depth of the pipeline The mathematical expression is: ； In the formula: Pipeline design burial depth; Construction backfilling deviation; Long-term soil compaction settlement; : Changes in burial depth caused by seasonal disturbances; Changes in burial depth caused by external engineering disturbances.
3. The GNSS and burial depth integrated gas pipeline settlement monitoring method according to claim 2, characterized in that: The long-term soil compaction settlement Expressed using an exponential model: ； in For the maximum settlement, The decay rate can be obtained through experimental training. The change in burial depth caused by the seasonal disturbance Represented using a sinusoidal modulation exponential model: ； in For seasonal fluctuations, Angular frequency, This is the initial phase. The attenuation coefficient; The change in burial depth caused by external engineering disturbance Represented using piecewise step functions: ； in Let J be the settlement increment of the j-th disturbance. It is a unit step function. Let be the time when the j-th disturbance occurs.
4. The GNSS and burial depth integrated gas pipeline settlement monitoring method according to claim 1, characterized in that, The projection attenuation function described in step S3 The mathematical expression is: ； in The projection coupling coefficient is determined through on-site calibration or finite element simulation.
5. The GNSS and burial depth integrated gas pipeline settlement monitoring method according to claim 1, characterized in that, It also includes a multi-source heterogeneous data fusion step: collecting monitoring data from InSAR, hydrostatic level, and ground-penetrating radar, and using an adaptive spatiotemporal fusion engine to perform spatiotemporal alignment and reliable weighted fusion of the multi-source heterogeneous data to obtain the fused settlement amount. The fusion formula is: ； In the formula: : The amount of sedimentation measured by the i-th type of sensor at time t; The fusion weights of the i-th type of sensor at time t, satisfying... and ; The fusion weight The following calculations are performed using the dynamic weighted confidence method: ； In the formula: : The basic weighting coefficients for the i-th type of sensor; The confidence level of the i-th type of sensor at time t is inversely proportional to the current noise variance of the sensor and directly proportional to the environmental adaptation factor.
6. The GNSS and burial depth integrated gas pipeline settlement monitoring method according to claim 4, characterized in that, The projection coupling coefficient The range of values is The upper limit value is taken for soft soil areas and the lower limit value is taken for hard rock areas.
7. The GNSS and burial depth integrated gas pipeline settlement monitoring method according to claim 1, characterized in that, The simplified engineering calculation formula for the actual pipe settlement in step S3 is as follows: ； in The correction factor for engineering simplification is determined based on the regional geological conditions.
8. The GNSS and burial depth integrated gas pipeline settlement monitoring method according to claim 7, characterized in that, The engineering simplification correction coefficient The value is , of which 0.8 is taken from typical geological areas across the country.
9. The GNSS and burial depth integrated gas pipeline settlement monitoring method according to claim 5, characterized in that: The default values for the basic weighting coefficients of various sensors are: 0.4 for GNSS, 0.2 for InSAR, 0.3 for hydrostatic level, and 0.1 for ground-penetrating radar. The fusion weights are dynamically adjusted according to different monitoring scenarios, including normal sunny weather, rainy / cloudy weather, subway construction period, and sensor failure scenarios.
10. The GNSS and burial depth integrated gas pipeline settlement monitoring method according to claim 1, characterized in that, It also includes settlement risk assessment and visualization steps: triggering a multi-level early warning mechanism based on the actual settlement of the pipe body or the integrated settlement, and connecting the monitoring data and assessment results to the smart pipeline digital twin platform; The multi-level early warning mechanism adopts a three-level early warning system, and its trigger threshold is determined based on pipeline design parameters and geological conditions. The visualization content of the digital twin platform includes a settlement risk heat map, a crack propagation trend prediction map, a fatigue accumulation curve, a failure probability evolution path, and a dynamic stress hotspot tracking map.