A loess region oil pipeline ground subsidence dynamic monitoring management system

Abstract

The application discloses a loess area oil pipeline ground subsidence dynamic monitoring management system, which comprises a basic perception module, an intelligent analysis module and a decision execution module; the basic perception module is used for monitoring the ground surface deformation, soil humidity and pipeline surrounding environment parameters along the pipeline in a collapsible loess area in real time; the intelligent analysis module analyzes the data collected by the basic perception module, judges whether ground subsidence occurs or not, calculates the subsidence amount and further calculates the pipeline suspension length and analyzes the pipeline stress condition, and calculates a comprehensive risk index R; the decision execution module implements automatic risk assessment and emergency response according to the grading early warning strategy based on the result output by the intelligent analysis module. The management system realizes all-weather dynamic monitoring of loess collapsible deformation along the oil pipeline, and timely issues early warning and executes emergency management when potential hazards occur.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of oil and gas pipeline monitoring, and particularly relates to a loess area oil pipeline ground subsidence dynamic monitoring management system. BACKGROUND

[0002] Collapsible loess refers to soil that has significant additional deformation due to the destruction of soil structure after being soaked under the action of overburden stress or the combined action of overburden stress and additional stress, and belongs to special soil. Some miscellaneous fill also has collapsibility. Loess widely distributed in northeast, northwest, central China and part of east China has collapsibility. Loess has many internal pores, and is easy to collapse, subsidence or produce cavities after being soaked, which leads to uneven deformation of the foundation, excessive local deflection of the pipeline, stress concentration and then fracture or leakage accidents, causing serious environmental and economic losses.

[0003] The existing pipeline inspection and monitoring in the loess area mainly relies on manual or a small number of sensor arrangements, and it is difficult to timely and comprehensively grasp the settlement and cavities caused by loess collapsibility. Moreover, there is a lack of comprehensive analysis means for multiple parameters such as water content, ground subsidence and pipeline stress, and it is difficult to identify and prevent potential hazards caused by collapsibility in time. SUMMARY

[0004] In order to strengthen the safety monitoring of oil pipelines in the loess area, the present application provides a loess area oil pipeline ground subsidence dynamic monitoring management system. The system can realize all-weather dynamic monitoring of loess collapsibility deformation along the oil pipeline, accurately monitor and evaluate the foundation settlement and pipeline suspension along the pipeline, and realize automatic early warning and timely emergency disposal.

[0005] The loess area oil pipeline ground subsidence dynamic monitoring management system provided by the present application comprises a basic perception module, an intelligent analysis module and a decision execution module. The output end of the basic perception module is connected with the input end of the intelligent analysis module, and the output end of the intelligent analysis module is connected with the input end of the decision execution module.

[0006] The basic perception module is used for real-time monitoring of the ground deformation, soil humidity and pipeline surrounding environment parameters along the pipeline in the collapsible loess area.

[0007] The basic perception module comprises a fixed photography monitoring unit, a sensor integration unit and an unmanned aerial vehicle oblique photography unit. The perception layer of ground subsidence monitoring is constructed through multi-source data acquisition.

[0008] The fixed photography monitoring unit is fixedly installed at a high-risk pipe section along the pipeline, periodically shoots ground images, and compares ground subsidence by using time series images.

[0009] The sensor integration unit comprises a soil pressure sensor, a soil humidity sensor and a corresponding data acquisition module, and is used for real-time detection of soil pressure and water content and composite monitoring.

[0010] The unmanned aerial vehicle tilt photography unit is equipped with a lens camera and a high-precision RTK positioning module, performs multi-angle aerial photography along the pipeline, and generates centimeter-level three-dimensional point cloud data.

[0011] The intelligent analysis module analyzes the data collected by the basic perception module to determine whether ground subsidence occurs.

[0012] Specifically, in the intelligent analysis module, whether ground subsidence occurs is determined based on the data collected by the fixed photography monitoring unit and the sensor integration unit. When ground subsidence occurs, the unmanned aerial vehicle tilt photography unit is started to obtain three-dimensional point cloud data, and then the subsidence amount and the ground deformation distribution are accurately calculated based on the three-dimensional point cloud data. Then, the improved Burgers creep model and the soil-pipe coupling analysis model are used to calculate the pipeline suspension length and analyze the stress condition of the pipeline. Further, the comprehensive risk index R is calculated by combining the subsidence amount and the stress condition of the pipeline.

[0013] The improved Burgers creep model is used to dynamically quantify the viscoelastic properties of collapsible loess.

[0014] The improved Burgers creep model introduces variable viscosity coefficients f1(h) and f2(h) and humidity correction coefficients α1 and α2 based on the traditional Burgers creep model, and the formula is as follows:

[0015]

[0016] η1(h)=η 10 f1(h)

[0017] η2(h)=η 20 f2(h)

[0018] Further, the improved Burgers creep model formula is as follows:

[0019]

[0020] wherein: h is the mass moisture content, dimensionless; t is the creep time, in h; σ0 is the constant external load, in MPa; E1 is the instantaneous elastic modulus, in MPa; E2 is the delayed elastic modulus, in MPa; η1(h) is the Maxwell viscosity, describing viscous flow (permanent deformation), in MPa·s; η2(h) is the Kelvin viscosity, describing viscoelastic delayed behavior, in MPa·s; η0 10 is the reference viscosity (value of η1 at the reference moisture content h0), in MPa·s; η0 20 is the reference viscosity (value of η2 at the reference moisture content h0), in MPa·s; α1 and α2 are humidity correction coefficients, dimensionless; ε(t, h) is the total creep strain, dimensionless.

[0021] In this model, the total creep strain ε(t, h) of the soil is coupled by the mass moisture content h and σ0, which better reflects the dynamic characteristics of collapsible loess.

[0022] The calculation formula of the comprehensive risk index R is as follows:

[0023]

[0024] In the formula, σ pipc is the maximum equivalent stress of the pipe body obtained by finite element analysis, in MPa; σ allow is the allowable stress of the pipe material, in MPa; S current is the current settlement obtained by monitoring, in m; S threshold is the allowable settlement threshold, in m; α is the weight coefficient of the settlement component, dimensionless; β is the weight coefficient of the stress component, dimensionless; R is the comprehensive risk index, dimensionless.

[0025] The decision execution module implements automatic risk assessment and emergency response according to the hierarchical early warning strategy based on the result output by the intelligent analysis module.

[0026] Specifically, the decision execution module divides the risk level into four levels: normal, slight early warning, moderate early warning and severe early warning, each level corresponding to a different emergency response mechanism, as follows:

[0027] When no ground subsidence is detected, the normal condition response mechanism is started, the system maintains the normal monitoring frequency, and the monitoring data is uploaded regularly to continue monitoring.

[0028] When ground subsidence is detected, the slight early warning response mechanism is started, early warning information is sent to the operation and maintenance personnel, and the unmanned aerial vehicle oblique photography unit is started to take multi-angle aerial photographs of the pipe section where subsidence occurs.

[0029] When 0.5≤R<1, a moderate early warning response mechanism is started, prompting the operation and maintenance personnel to strengthen the inspection or moderate pressure reduction.

[0030] When R≥1, a serious early warning response mechanism is started, and the system immediately triggers an alarm and enters the maintenance repair process.

[0031] If R<0.5, the system response mechanism remains a mild early warning response mechanism, and the system slightly warns the operation and maintenance personnel and increases the image acquisition and data upload frequency of the monitoring device.

[0032] The control center server has a high-performance CPU / GPU, 128GB or more memory, and a high-speed solid state disk, and can perform parallel computing or distributed processing on the three-dimensional point cloud of the unmanned aerial vehicle, multi-source sensor data and mechanical model, and support docking with an external database to obtain geological, meteorological and pipeline state information.

[0033] The loess area oil pipeline ground subsidence dynamic monitoring management system of the application is also equipped with a multi-mode communication unit, including a LoRa and 5G dual-mode communication module, to realize long-distance and high-speed data transmission; in remote or no ground network signal coverage areas, satellite communication modules can be further integrated to ensure real-time data transmission and effective issuance of emergency instructions.

[0034] Compared with the prior art, the application has the following advantages:

[0035] The application can realize accurate monitoring and dynamic management of oil pipeline ground subsidence in collapsible loess environment by integrating multi-source sensors, unmanned aerial vehicle remote sensing, geological mechanics model and other technical means, and timely issue early warning and automatically start emergency disposal process when the risk exceeds the critical threshold. With fixed photographic monitoring devices, unmanned aerial vehicle tilt photography units and improved mechanical model analysis, the pipeline safety operation level can be significantly improved, and major economic losses and environmental disasters caused by uneven subsidence of the foundation can be avoided. The system has wide application value in loess areas and can also be popularized to pipeline line monitoring and management fields in similar soil environments or other collapsible areas.

[0036] Other advantages, objects and features of the application will be partially embodied by the following description, and partially understood by those skilled in the art through research and practice of the application. DETAILED DESCRIPTION

[0037] Figure 1 The loess area oil pipeline ground subsidence dynamic monitoring management system structure composition diagram of the application.

[0038] Figure 2 The loess area oil pipeline ground subsidence dynamic monitoring management system work flow chart of the application. DETAILED DESCRIPTION

[0039] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings, it should be understood that the preferred embodiments described herein are only used to explain and illustrate the present application, and are not used to limit the present application.

[0040] As shown in Figure 1 The loess region oil pipeline ground subsidence dynamic monitoring management system provided by the present application comprises a basic perception module, an intelligent analysis module and a decision execution module.

[0041] The basic perception module is used to construct a multi-dimensional data acquisition system, and through the cooperative operation of the fixed photography monitoring unit, the sensor integrated unit and the unmanned aerial vehicle tilt photography unit, the real-time monitoring of the ground deformation and the soil state is realized. The module adopts a combination of high-resolution cameras and micro-electromechanical sensors (including three-axis gyroscopes, pressure sensors, temperature and humidity sensors, micro-inclinometers, etc.), periodically captures ground images and collects soil inclination and environmental parameters (including monitoring soil micro-vibration, displacement, pressure, temperature, humidity, etc.), and forms a dynamic monitoring baseline. The unmanned aerial vehicle tilt photography unit is equipped with a lens camera and a high-precision RTK positioning module, and multi-angle aerial photography is performed along the pipeline to form centimeter-level three-dimensional point cloud data to provide a spatial reference for ground deformation. Laser radar and intelligent terrain following functions can also be selected to adapt to complex environments. The built-in energy solution of the module ensures continuous and stable operation in remote areas and harsh conditions, providing high-precision, multi-source data support for subsequent analysis.

[0042] The fixed photography monitoring unit is fixedly installed at a high-risk pipe section along the pipeline, periodically shoots ground images, and uses time series photos to roughly detect and compare the ground subsidence area; the resolution of the camera can be preferably between 8 million pixels and 20 million pixels, and the gimbal has automatic horizontal calibration and angle adjustment functions. Preferably, a laser ranging sensor can be added outside the camera to realize the synchronization of photographing and rapid measurement of surface height. Further, the support gimbal can be integrated with a solar panel and a battery to provide continuous power for the camera and the data transmission module.

[0043] The sensor integrated unit mainly comprises soil pressure sensors, soil moisture sensors and corresponding data acquisition modules. The soil pressure sensor can preferably adopt a high-precision sensor with a range of 0-5 MPa. The sensor integrated unit integrates the soil moisture sensor and the calibration circuit, and realizes the combined monitoring of moisture content and pressure for collapsible loess.

[0044] Further, the sensor integrated unit is arranged below the fixed photography monitoring unit around the pipeline, and real-time collection of soil pressure and soil moisture content is realized, and the sensor integrated unit is connected with the fixed photography monitoring unit through a wired mode to obtain power supply and unified data transmission.

[0045] The unmanned aerial vehicle oblique photography unit comprises a multi-rotor or fixed-wing unmanned aerial vehicle with a lens camera, and is equipped with a high-precision RTK positioning module to ensure that the geographic information collection accuracy can reach the centimeter level. The lens camera can be preferably in the 24-35mm focal length, and the pixel can be between 12 million and 20 million. The RTK positioning module can be compatible with Beidou satellite. The unmanned aerial vehicle performs oblique photography flight according to the task instruction, takes pictures of the ground along the pipeline from multiple angles, and obtains high-resolution three-dimensional point cloud data to provide accurate spatial information for subsequent settlement analysis. The output end of the unmanned aerial vehicle oblique photography unit is connected to the input end of the intelligent analysis module through a 5G network.

[0046] If the working environment is more complex, a laser radar module can be installed on the unmanned aerial vehicle for supplementation, especially in the case of night or poor visible light conditions to improve the surveying and mapping accuracy. An intelligent terrain following function can also be provided on the unmanned aerial vehicle to ensure shooting stability and reduce the interference of complex terrain on flight height.

[0047] In the intelligent analysis module, whether ground subsidence occurs is determined based on the data collected by the fixed photography monitoring unit and the sensor integration unit. When ground subsidence occurs, the unmanned aerial vehicle oblique photography unit is started to obtain three-dimensional point cloud data, and then three-dimensional point cloud analysis is performed to accurately calculate the settlement amount and ground deformation distribution. The three-dimensional point cloud analysis generates high-density point clouds from the multi-view photos obtained by the unmanned aerial vehicle oblique photography unit, and performs registration and differential analysis with historical point cloud data to obtain the ground subsidence amount. Point cloud stitching, coordinate transformation and differential calculation are performed on the graphics processing unit of the control center server to monitor the local protrusion or subsidence of the ground with millimeter-level accuracy. Then, the improved Burgers creep model and the soil-pipe coupling analysis model are used to calculate the pipeline suspension length and analyze the stress state of the pipeline; further combined with the settlement amount and the stress state of the pipeline, the comprehensive risk index R is calculated.

[0048] The improved Burgers creep model is based on the traditional Burgers model, and introduces variable viscosity coefficients f1(h) and f2(h), as well as humidity correction coefficients α1 and α2, in view of the characteristics of loess area, such as easy subsidence and uneven stress state.

[0049] The values of humidity correction coefficients α1 and α2 are different for different types of loess. The calculation method of humidity correction coefficients α1 and α2 is as follows:

[0050] Take multiple original loess columns, diameter 50 mm, height 80 mm. Immediately after sampling, store with plastic film and paraffin, keep 20±1℃, avoid moisture evaporation. First, test the initial water content of the loess column, then according to the size of the initial water content, dry or wet treatment is carried out to obtain samples with different water contents, and the following four kinds of water content samples are prepared: dry state (water content about 2-4%), natural water content (water content about 8-12%), wet state (water content about 15-20%), near saturation (water content more than 25%). Prepare 3 parallel samples for each level. When preparing the sample, the loess column can be sprayed with water in stages to achieve the target water content, then the sample is sealed with plastic film and weighed, and the near saturation sample can also be saturated with vacuum for 24h, then sealed with plastic film and weighed. The prepared sample is placed in a constant temperature box to keep the temperature at 20±1℃ throughout the process.

[0051] Take a sample from the constant temperature box, detect the constant weight, and confirm whether the water content of the sample is lost. If not, proceed to the next step, select a GDS dynamic triaxial test system or a creep meter with a displacement sensor (precision about 2μm) to obtain the ε-t evolution curve of η1, η2. The data recording frequency is set to once per second for the first 30 minutes, and then gradually reduced.

[0052] Single-stage constant stress loading is adopted, and the load σ0 is 0.2-0.4 times the dry state yield strength. The stable uniform load σ0 separates the humidity effect from the stress softening stage. The test time of the dry sample is not less than 10 3 seconds, and the wet and saturated samples can reach 10 5 seconds, until the steady-state strain rate is less than 1×10 -6 s -1 .

[0053] For each water content level, the ε-t curve is fitted with the traditional Burgers model by the least squares method to obtain the corresponding η1(h) and η2(h).

[0054] Take the natural logarithm of the viscosity

[0055] lnη1(h)=lnη 10 +α1h

[0056] lnη2(h)=lnη 20 +α2h

[0057] Linear regression of (h, lnη1) and (h, lnη2) can obtain the slope α1, α2, and when judging the goodness of fit, the correlation coefficient R 2 should be greater than or equal to 0.95, and the significance coefficient P value should be less than 0.01.

[0058] In this embodiment, the humidity correction coefficients α1 and α2 of three typical loess types are shown in Table 1.

[0059] Table 1, the humidity correction coefficients α1 and α2 of three typical loess types

[0060] Sample type Water content interval % a1 (Maxwell) % -1 ]] Alpha 2 (Kelvin) % -1 ]] Yan'an undisturbed loess 5-22 -0.09 -0.07 Saturated Q3 loess 8-26 -0.045 -0.035 Saturated Q2 loess ＞24 -0.02 -0.018

[0061] In the improved Burgers creep model, the total creep strain ε(t, h) of the soil is coupled by the mass moisture content h and σ0, so as to more accurately describe the creep behavior of the soil under different water chemical environments, and better reflect the dynamic characteristics of the collapsible loess.

[0062] Further, the improved Burgers creep model can also be combined with soil-pipe coupling analysis to simulate and compare the deformation conditions of soil bodies at different buried depths, optimize the model parameters f1(h) and f2(h) through simulation data, and improve the accuracy of the model results. The model parameters can also be dynamically corrected according to the observed real settlement rate, so that the model has an online updating capability.

[0063] In specific implementation, a regional terrain evolution database can be constructed in combination with a geographic information system (GIS) platform to realize overall settlement trend analysis in a larger range.

[0064] The decision execution module is used for automatic risk assessment and emergency response, and realizes closed-loop management and control through hierarchical early warning and multi-modal communication of the control center server. Based on the preset threshold and the dynamic rule library, the module triggers a four-level early warning signal in real time, and links the LoRa / 5G dual-mode communication, unmanned aerial vehicle emergency patrol and visual report generation function to ensure accurate delivery of instructions.

[0065] The output end of the basic perception module and the input end of the intelligent analysis module are connected through a multi-modal communication network; the output end of the intelligent analysis module and the input end of the decision execution module are interconnected through a control center server. The output end of the decision execution module sends review instructions to the unmanned aerial vehicle unit of the basic perception module through 5G, and simultaneously pushes emergency instructions to the operation and maintenance system, forming a "perception-analysis-decision-execution" closed-loop link. The entire system realizes long-distance data transmission through the LoRa and 5G dual-mode emergency communication module, so that efficient data exchange and instruction interaction are realized between the field monitoring and the control center.

[0066] In the basic perception module, the output end of the fixed photography monitoring unit and the input end of the intelligent analysis module are connected through a wired or wireless network. The output end of the sensor integration unit and the input end of the intelligent analysis module are connected through a 5G network.

[0067] The control center server is equipped with a high-performance CPU / GPU for point cloud processing, image recognition and mechanical model solving. The memory can be preferably configured at 128 GB or more to support large-scale three-dimensional data analysis; the storage module can preferably use a high-speed solid state disk. Preferably, it supports interfacing with external databases to obtain more rich geological, meteorological and pipeline state information.

[0068] Further, the multi-modal communication unit integrates LoRa and 5G dual-mode wireless communication modules, which can use LoRa long-distance communication in remote areas and automatically switch to high-speed 5G communication in areas covered by 5G network, realizing real-time data backhaul.

[0069] The working process of the loess area oil pipeline ground subsidence dynamic monitoring management system of the application is as shown in Figure 2 The figure shows the complete process from monitoring to early warning to emergency disposal. Overall, the application focuses on monitoring the collapsible section along the pipeline, using fixed photography monitoring units and multi-source sensors as the basis for data collection, and cooperating with intelligent analysis models (such as the improved Burgers creep model and soil-pipe coupling analysis) for real-time evaluation. When ground subsidence or pipeline suspension risk exceeds the threshold, the system automatically triggers unmanned aerial vehicle patrol instructions and early warning disposal, greatly improving the prevention and response capability of pipeline subsidence accidents.

[0070] First, during the pipeline construction planning or operation process, the control center conducts a geological risk assessment of the loess area and identifies key pipeline sections prone to collapsible disasters. This identification can be based on existing geological data, historical monitoring records and on-site drilling results. After determining the areas with high risk, fixed photography monitoring devices and sensor integrated units are preferentially arranged in these sections to achieve key monitoring of ground subsidence.

[0071] As shown in Figure 2 In the collapsible disaster-prone section, the system periodically schedules the fixed photography monitoring units and sensor integrated units set, using high-resolution cameras and sensors (such as soil pressure, humidity, temperature, etc.) to collect multi-source information from the ground. These devices also have real-time data backhaul functions, sending the collected image and sensor information data to the intelligent analysis module of the control center through wired / wireless or LoRa / 5G dual-mode communication.

[0072] After the control center server receives the data uploaded by the fixed photography monitoring unit and the sensor integrated unit, it first performs preliminary comparison and analysis to compare the current image and sensor data with the reference image or sensor data to detect whether there is ground subsidence. If no obvious subsidence is detected, it is a normal situation response mechanism, and the fixed photography monitoring unit and the sensor integrated unit enter the cycle of uploading data at regular intervals and continuous monitoring. In the "upload data at regular intervals and continue monitoring" link, the system still maintains the regular monitoring frequency, and only returns the data to the control center within the set monitoring period. If subsidence is detected in subsequent monitoring, the corresponding subsequent process is entered.

[0073] If the system detects that the ground has a subsidence trend (such as Figure 2 As shown in the judgment block "ground subsidence" in the middle, the intelligent analysis module records and compares the current subsidence amount and subsidence rate, and starts a slight early warning response mechanism. The control center automatically issues an unmanned aerial vehicle oblique photography instruction. At this time, the unmanned aerial vehicle oblique photography unit receives the instruction and takes off to take multi-angle aerial photographs of the pipe section where subsidence occurs. The unmanned aerial vehicle oblique photography not only obtains high-resolution images, but also uses the RTK positioning module to obtain accurate coordinates, so as to generate a high-density three-dimensional point cloud in the control center server, which is used for further accurate calculation of the subsidence amount and the ground surface deformation distribution. If there is a laser radar module, it can be used to supplement the surveying and mapping data at night or in poor light conditions.

[0074] After the unmanned aerial vehicle completes the oblique photography, the data is transmitted back to the control center server in real time through the 5G or LoRa network. The server performs splicing, coordinate transformation and difference analysis on the received images and positioning information to generate a three-dimensional model or distribution graph of the current ground surface deformation.

[0075] At the same time, the server matches the three-dimensional model data with the pipe burial depth information to calculate the potential length of the suspended pipe and assess whether the pipe forms a suspended section under the influence of subsidence or cavity.

[0076] After the generation of the three-dimensional point cloud of the ground surface and the calculation of the subsidence amount, the intelligent analysis module compares the results with the pipe design data and burial depth data, maps the difference value to the area where the pipe may be suspended. According to the improved Burgers creep model and the soil-pipe coupling analysis model, the system judges the relationship between the soil support strength and the subsidence amount of the pipe at this burial depth, calculates the actual length of the suspended pipe section that may be generated, simulates the stress situation, and obtains a comprehensive risk index R by comprehensively considering the subsidence amount and the stress situation of the pipe.

[0077] According to the comparison of the system preset comprehensive risk index limit: if R < 0.5, the system response mechanism remains a mild early warning response mechanism, and the system slightly early warning prompts the operation and maintenance personnel and increases the image acquisition and data upload frequency of the monitoring device; if 0.5 <= R < 1, the system response mechanism is upgraded to a moderate early warning response mechanism. The system will prompt the operation and maintenance personnel to strengthen the inspection or moderately reduce the pressure in a moderate early warning mode; if the comprehensive risk index R >= 1 has exceeded the limit value, the system response mechanism is upgraded to a serious early warning response mechanism, and an alarm is triggered immediately and the "emergency repair" process is entered.

[0078] The comprehensive risk index is established by combining the aforementioned settlement degree and pipeline stress, which can more accurately reflect the risk of the pipeline. When the system determines that the influence of the suspended length on the pipeline exceeds the pre-set limit or the ground settlement level reaches a serious early warning, the system automatically sends an alarm information to the operation and maintenance center and the related repair team, and suggests that the pipeline section be repaired and reinforced urgently. At the same time, the control center can issue a shutdown or pressure reduction order according to the severity to prevent the pipeline from breaking, leaking and other major safety accidents under high internal pressure.

[0079] When the on-site repair team arrives, the three-dimensional analysis data transmitted by the control center in real time can be viewed using a portable device to evaluate the ground settlement and pipeline suspension. If necessary, the unmanned aerial vehicle can be rescheduled for real-time review photography. After the repair is completed, the system still maintains high-frequency monitoring in the section to ensure that the repair scheme is effective and to prevent the problem of settlement or cavity expansion from occurring again.

[0080] In the whole monitoring and analysis process, the control center server and the basic sensing device (fixed photography monitoring device, unmanned aerial vehicle tilt photography unit, sensor integrated unit) realize stable data return and command issuance through LoRa / 5G dual-mode emergency communication.

[0081] In necessary cases, a satellite communication module can also be integrated to ensure that key data transmission can still be completed in extreme environments or without ground network coverage, ensuring the reliability and maintainability of the system.

[0082] Through the above implementation, the present application can realize accurate monitoring and dynamic management of ground settlement of the oil pipeline in the collapsible loess environment, and automatically start the emergency disposal process when the risk exceeds the critical threshold. With the fixed photography monitoring device, the unmanned aerial vehicle tilt photography unit and the improved mechanical model analysis, the safety operation level of the pipeline can be significantly improved, and major economic losses and environmental disasters caused by uneven subsidence of the foundation can be avoided. The system has wide application value in the loess area, and can also be popularized to the pipeline line monitoring and management field in similar soil environment or other collapsible areas.

Claims

1. A loess region oil pipeline ground subsidence dynamic monitoring management system, characterized in that, The system comprises a basic perception module, an intelligent analysis module and a decision execution module; the output end of the basic perception module is connected with the input end of the intelligent analysis module, and the output end of the intelligent analysis module is connected with the input end of the decision execution module; The basic perception module is used for monitoring the ground surface deformation, soil humidity and pipeline surrounding environment parameters along the pipeline in a collapsible loess area in real time. The intelligent analysis module analyzes the data collected by the basic perception module to determine whether ground subsidence occurs; if ground subsidence occurs, the subsidence amount is calculated, and the pipeline suspension length and the pipeline stress condition are further calculated according to the improved Burgers creep model and the soil-pipeline coupling analysis model; the comprehensive risk index R is calculated by combining the subsidence amount and the pipeline stress condition. The improved Burgers creep model is introduced on the basis of the traditional Burgers creep model variable viscosity coefficient and , and the humidity correction coefficient and , the formula is as follows: The improved Burgers creep model formula is as follows: wherein h is the mass moisture content, dimensionless; t is the creep elapsed time, in h; is the constant external load, in MPa; is the instantaneous elastic modulus, in MPa; is the retarded elastic modulus, in MPa; is the Maxwell viscosity, in MPa s; is the Kelvin viscosity, in MPa s; is the reference viscosity, at the reference moisture content , in MPa s; is the reference viscosity, at the reference moisture content , in MPa s; and is the humidity correction factor, dimensionless; is the total creep strain, dimensionless; The calculation formula of the comprehensive risk index R is as follows: In the formula: is the maximum equivalent stress of the pipe body obtained by finite element analysis, unit: MPa; is the allowable stress of the pipe material, unit: MPa; is the current settlement obtained by monitoring, unit: m; is the allowable settlement threshold, unit: m; is the settlement component weight coefficient, dimensionless; is the stress component weight coefficient, dimensionless; is the comprehensive risk index, dimensionless; The decision execution module implements automatic risk assessment and emergency response according to the results output by the intelligent analysis module based on the grading early warning strategy.

2. The loess area oil pipeline ground subsidence dynamic monitoring management system of claim 1, wherein, The basic perception module comprises: A fixed photography monitoring unit which is fixedly installed at a high-risk pipeline section along the pipeline, periodically shoots ground images, and compares the ground subsidence by using time series images; A sensor integrated unit comprising soil pressure sensors, soil humidity sensors and corresponding data acquisition modules, which are used for real-time detection of soil pressure and water content and composite monitoring; A UAV oblique photography unit which carries a lens camera and a high-precision RTK positioning module, performs multi-angle aerial photography along the pipeline, and generates centimeter-level three-dimensional point cloud data.

3. The loess area oil pipeline ground subsidence dynamic monitoring management system of claim 2, wherein, In the intelligent analysis module, first, whether ground subsidence occurs is determined based on the data collected by the fixed photography monitoring unit and the sensor integrated unit; when ground subsidence occurs, the UAV oblique photography unit is started to obtain three-dimensional point cloud data, and then the subsidence amount is calculated based on the three-dimensional point cloud data.

4. The loess area oil pipeline ground settlement dynamic monitoring management system of claim 3, wherein, The decision execution module divides the risk level into four levels of normal, slight early warning, moderate early warning and severe early warning, and each level corresponds to a different emergency response mechanism.

5. The loess area oil pipeline ground subsidence dynamic monitoring management system of claim 4, wherein, The emergency response mechanisms corresponding to different risk levels are as follows: When no ground subsidence is detected, the normal condition response mechanism is started, the system maintains the normal monitoring frequency, and the monitoring data is uploaded regularly to continue monitoring; When ground subsidence is detected, the slight early warning response mechanism is started, early warning information is sent to the operation and maintenance personnel, and the UAV oblique photography unit is started to perform multi-angle aerial photography on the pipeline section where subsidence occurs; When 0.5≤R<1, the moderate early warning response mechanism is started, the operation and maintenance personnel are prompted to strengthen the inspection or to appropriately reduce the pressure; When R≥1, the severe early warning response mechanism is started, the system immediately triggers an alarm and enters the maintenance and repair process.

6. The loess area oil pipeline ground settlement dynamic monitoring management system of claim 5, wherein, The system further comprises a control center server which has a high-performance CPU / GPU, more than 128 GB of memory and a high-speed solid state disk, can perform parallel computing or distributed processing on the UAV three-dimensional point cloud, sensor multi-source data and mechanical model, and supports connection with an external database to obtain geological, meteorological and pipeline state information.

7. The loess area oil pipeline ground subsidence dynamic monitoring management system of claim 1, wherein, The system is also equipped with a multi-modal communication unit, including a LoRa and 5G dual-mode communication module, and further integrates a satellite communication module in remote or ground network signal coverage-free areas, ensuring real-time data transmission and effective emergency command delivery.

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