Deeply buried pipeline positioning system based on unmanned aerial vehicle integrated pipeline detector

Abstract

The application relates to the technical field of pipeline positioning, in particular to a large-buried-depth pipeline positioning system based on a pipeline detector integrated with a UAV, which comprises a task generation module, a magnetic field control module, a data acquisition module and a data analysis module. The task generation module is used to determine a plurality of target collection points and generate target flight tasks. The magnetic field control module is used to determine magnetic field application points of a target area based on the target collection points, and control the magnetic field parameters of the electromagnetic field applied to the target area. The data acquisition module comprises a UAV component, a pipeline detection component and an environment monitoring component. The data analysis module is used to construct a magnetic field distribution model of the target area, determine initial pipeline parameters of the large-buried-depth pipeline based on the magnetic field distribution model and the magnetic field parameters of the electromagnetic field applied to the target area, and determine a key influence coefficient based on environment data and initial flight parameters. The parameter adjustment module is used to adjust the pipeline parameters based on the key influence coefficient to obtain target pipeline parameters. The application can improve the detection and positioning efficiency.

Description

Technical Field

[0001] This invention relates to the field of pipeline positioning technology, and in particular to a deep-buried pipeline positioning system based on an unmanned aerial vehicle (UAV) integrated pipeline detector. Background Technology

[0002] With the continuous and rapid development of the national economy and society, infrastructure construction projects such as highways, railways, and pipelines are springing up everywhere. The construction of pipelines intersecting and running parallel with existing pipelines is becoming increasingly common. Ensuring the safe operation of existing pipelines has become the top priority for new projects. In order to ensure the safety of existing pipelines, it is necessary to regularly inspect and locate existing deep-buried pipelines.

[0003] Currently, the location of deep-buried pipelines is mainly achieved by manually placing pipeline measuring instruments at the measuring points. However, this manual measurement method is limited by factors such as terrain and low efficiency, making it difficult to achieve comprehensive, orderly, and efficient location of deep-buried pipelines.

[0004] Chinese Patent Publication No. CN119535615A discloses a method and system for locating deep-buried metal pipelines using electromagnetic non-destructive testing, comprising the following steps: S1. Data acquisition and model construction: S1.1. Acquiring magnetic field data when the pipeline leaks, summarizing the data, and then analyzing the data to construct a detection model; S1.2. Acquiring magnetic field data at the pipeline location, summarizing the data, and then analyzing the data to construct a calculation model; S2. Test preparation: S2.1. Setting up test stakes and laying test cables between two test cables to form an energized circuit; S2.2. Energizing the test circuit, and then using a magnetic field receiving sensor to walk along the pipeline path to obtain magnetic signals at various locations on the pipeline; S3. Data analysis and calculation: S3.1. Analyzing the raw data and setting a boundary condition based on the analysis results; S3.2. Transmitting the analyzed data to the detection model and the calculation model, and then the detection model and the calculation model will calculate the magnetic field data. Based on the calculation results, the location information and leakage information of the pipeline can be obtained.

[0005] The existing technology has the following problems: it requires manual walking of the magnetic field receiving sensor along the pipeline path to obtain the magnetic signals at various locations in the pipeline, which is relatively inefficient for locating pipelines with large burial depths. Summary of the Invention

[0006] Therefore, the present invention provides a deep-buried pipeline positioning system based on an unmanned aerial vehicle (UAV) integrated pipeline detector, which overcomes the problem of the efficiency of manual measurement methods in positioning deep-buried pipelines in the prior art.

[0007] To achieve the above objectives, the present invention provides a deep-buried pipeline positioning system based on an unmanned aerial vehicle (UAV) integrated pipeline detector, comprising:

[0008] The mission generation module is used to determine several target acquisition points based on the regional information of the target area, and to generate target flight missions based on each target acquisition point.

[0009] A magnetic field control module, which is connected to the task generation module, is used to determine the magnetic field application point of the target area based on each of the target acquisition points, and to control the magnetic field parameters of the electromagnetic field applied to the target area, including the application time, magnetic field strength and magnetic field frequency.

[0010] The data acquisition module includes a drone component, a pipeline detection component mounted on the drone component, and an environmental monitoring component. The drone component is connected to the task generation module and the magnetic field control module respectively, and is used to execute a target flight mission based on initial flight parameters. The pipeline detection component is used to collect electromagnetic signals of the target area at each of the target acquisition points, and the environmental monitoring component is used to collect environmental data of the target area.

[0011] The data analysis module is connected to the magnetic field control module and the data acquisition module respectively. It is used to construct a magnetic field distribution model of the target area based on the electromagnetic signals collected by the pipeline detection component at each of the target acquisition points, and to determine the initial pipeline parameters of the deep-buried pipeline based on the magnetic field distribution model and the magnetic field parameters of the electromagnetic field applied to the target area. It also determines the key influence coefficients based on the environmental data and the initial flight parameters. The pipeline parameters include pipeline direction, pitch angle and azimuth angle.

[0012] The parameter adjustment module, which is connected to the data analysis module, is used to adjust the pipeline parameters based on the key influence coefficients to obtain the target pipeline parameters.

[0013] Furthermore, the task generation module includes:

[0014] The regional analysis submodule is used to obtain regional information of the target area and divide the target area into regions based on the regional information to determine several key sub-regions. The regional information includes geological parameters and topographic parameters.

[0015] The data collection point determination submodule is connected to the region analysis submodule and is used to determine several target data collection points based on the positional relationship of each key subregion.

[0016] The task generation submodule, which is connected to the collection point determination submodule, is used to generate target flight tasks based on each target collection point and a preset flight task generation model.

[0017] Furthermore, the magnetic field control module includes:

[0018] The acquisition point analysis submodule, which is connected to the acquisition point determination submodule, is used to determine the magnetic field application point of the target area based on each of the target acquisition points;

[0019] The magnetic field application control submodule is connected to the region analysis submodule, the mission generation submodule, and the acquisition point analysis submodule, respectively. It is used to determine the magnetic field parameters based on the target flight mission, the magnetic field application point, and the regional information of the target region, and to apply an electromagnetic field at the magnetic field application point in the target region based on the magnetic field parameters.

[0020] Furthermore, the data analysis module includes:

[0021] An electromagnetic signal analysis submodule, which is connected to the data acquisition module, is used to construct a magnetic field distribution model of the target area based on the electromagnetic signals acquired by the pipeline detection component at each of the target acquisition points.

[0022] The magnetic field parameter analysis submodule is connected to the electromagnetic signal analysis submodule and the magnetic field application control submodule, respectively. It is used to reverse simulate the electromagnetic induction process of the deep-buried pipeline based on the magnetic field distribution model of the target area and the magnetic field parameters of the electromagnetic field applied to the target area, so as to obtain the pipeline parameters of the deep-buried pipeline.

[0023] Furthermore, the data analysis module also includes:

[0024] An adjustment analysis submodule, which is connected to the data acquisition module, is used to determine the environmental impact coefficient based on the comparison results of the environmental data and the preset environmental data, and to determine the flight impact coefficient based on the comparison results of the initial flight parameters and the preset flight parameters, and to determine the key impact coefficient based on the environmental impact coefficient and the flight impact coefficient.

[0025] Furthermore, under the first judgment condition, the parameter adjustment module determines the target pipeline parameters based on the key influence coefficient and the initial pipeline parameters;

[0026] The first determination condition is that the key influence coefficient is less than the preset influence coefficient.

[0027] Furthermore, under the second determination condition, the parameter adjustment module determines the target magnetic field parameters based on the key influence coefficient and the magnetic field parameters, and reapplies an electromagnetic field to the target area based on the target magnetic field parameters to redetermine the pipe parameters, thereby obtaining the target pipe parameters;

[0028] The second determination condition is that the key influence coefficient is greater than or equal to the preset influence coefficient.

[0029] Furthermore, the acquisition point analysis submodule performs correlation analysis on each of the target acquisition points to determine the correlation between each target acquisition point, and determines the magnetic field application point of the target area based on the correlation between each target acquisition point.

[0030] Furthermore, the data acquisition module also includes:

[0031] An image acquisition component, mounted on the UAV component, is used to periodically acquire ground images;

[0032] A flight analysis component, connected to the image acquisition component, is used to determine the flight stability of the UAV component based on ground images within a target time period, and to determine whether the initial flight parameters need to be adjusted based on the flight stability.

[0033] Furthermore, based on the flight stability of the UAV component, the flight analysis component adjusts the initial flight parameters according to the first determination result;

[0034] The first determination result is that the initial flight parameters need to be adjusted.

[0035] Compared with existing technologies, the beneficial effects of this invention are as follows: By setting up a task generation module, this invention accurately determines several target acquisition points based on the regional information of the target area, and rationally generates target flight missions, optimizing flight routes and parameters to improve detection efficiency. It can flexibly adjust target acquisition points and flight mission planning according to the characteristics of different target areas and mission requirements, adapting to various complex detection scenarios. By setting up a magnetic field control module, it determines the magnetic field application points in the target area and accurately controls the magnetic field parameters of the applied electromagnetic field, providing a stable electromagnetic field source for deep-buried pipeline detection, ensuring the stability of the detection signal, effectively suppressing external electromagnetic interference, and improving the accuracy of detection and positioning. By setting up a data acquisition module, it integrates pipeline detection components with UAVs to simultaneously acquire electromagnetic signals and environmental data of the target area, improving detection and positioning efficiency and accuracy, and providing data support for subsequent data analysis. By setting up a data analysis module, it can accurately determine the initial pipeline parameters of deep-buried pipelines, comprehensively consider environmental data and flight parameters to determine key influence coefficients, and comprehensively evaluate the impact of environmental factors and flight processes on the detection results. By setting up a parameter adjustment module, it can adjust pipeline parameters based on key influence coefficients, improving the accuracy of detection and positioning.

[0036] Furthermore, the task generation module of this invention performs in-depth analysis of the target area's regional information through a regional analysis submodule, rationally dividing the target area into several key sub-regions. This makes subsequent detection work more targeted, improving detection efficiency and accuracy. The data collection point determination submodule determines several target data collection points based on the positional relationships of each key sub-region, making the distribution of target data collection points more rational, reducing unnecessary flight paths and redundant detection, and improving the detection efficiency of the UAV. The task generation submodule, based on each target data collection point and a preset flight task generation model, can automatically generate optimal target flight tasks, improving detection and positioning efficiency.

[0037] Furthermore, the magnetic field control module of this invention, by setting up a data acquisition point analysis submodule, determines the magnetic field application points in the target area based on each target data acquisition point, ensuring that the magnetic field application points can effectively cover the target area, and optimizes the detection layout, which helps to quickly determine pipeline parameters and improve detection and positioning efficiency. By setting up a magnetic field application control submodule that comprehensively considers the target flight mission, the magnetic field application points, and the regional information of the target area, the magnetic field parameters are accurately determined, and the electromagnetic field is accurately applied at the magnetic field application points in the target area, improving the coverage effect of the electromagnetic field and the strength of the detection signal, and enhancing the detection capability of deep-buried pipelines.

[0038] Furthermore, the data analysis module of this invention, by setting up an electromagnetic signal analysis submodule, constructs a magnetic field distribution model of the target area based on the electromagnetic signals collected from each target acquisition point, and deeply analyzes the characteristics of the electromagnetic signals, providing reliable basic data for subsequent pipeline parameter inversion and improving the accuracy of detection and positioning. By setting up a magnetic field parameter analysis submodule to simulate the electromagnetic induction process of a deep-buried pipeline based on the magnetic field distribution model of the target area and the magnetic field parameters of the applied electromagnetic field in the target area, the pipeline parameters of the deep-buried pipeline can be obtained, thereby improving detection efficiency and accuracy.

[0039] Furthermore, when the key influence coefficient is less than the preset influence coefficient, the parameter adjustment module of the present invention determines the target pipeline parameters based on the key influence coefficient and the initial pipeline parameters. When the key influence coefficient is less than the preset influence coefficient, it indicates that environmental factors and other factors have little impact on the initial pipeline parameters. Fine-tuning the initial pipeline parameters based on the key influence coefficient can improve the accuracy of detection and positioning.

[0040] Furthermore, when the key influence coefficient is greater than or equal to the preset influence coefficient, the parameter adjustment module of the present invention re-determines the magnetic field parameters to reapply the electromagnetic field to the target area. When the key influence coefficient is greater than or equal to the preset influence coefficient, it indicates that environmental factors have a significant impact on the initial pipeline parameters. By reapplying the electromagnetic field, the interference of environmental factors on the detection results can be effectively reduced, and the accuracy of the detection signal can be improved. By adjusting the magnetic field parameters in a timely manner and reapplying the electromagnetic field, inaccurate detection results caused by environmental factors can be avoided, the number of repeated detections can be reduced, and the overall detection and positioning efficiency can be improved.

[0041] Furthermore, the data acquisition module of this invention periodically acquires ground images by setting up an image acquisition component to assist in flight mission planning and adjustment, and sets up a flight analysis component to determine the flight stability of the UAV component based on the ground images acquired within the target time period, accurately assesses the flight attitude and motion trend, and thereby determines whether the initial flight parameters need to be adjusted. This enables timely optimization of flight parameters and improves flight efficiency and data acquisition quality. Attached Figure Description

[0042] Figure 1 This is a structural block diagram of a deep-buried pipeline positioning system based on an unmanned aerial vehicle (UAV) integrated pipeline detector, according to an embodiment of the present invention.

[0043] Figure 2 This is a structural block diagram of the task generation module in an embodiment of the present invention;

[0044] Figure 3 This is a structural block diagram of the task generation module in an embodiment of the present invention;

[0045] Figure 4 This is a schematic diagram of the bottom structure of the drone according to an embodiment of the present invention;

[0046] Figure 5 This is a schematic diagram of the top structure of the drone according to an embodiment of the present invention;

[0047] In the diagram: 1. UAV component; 2. Pipeline detection component; 3. Image acquisition component; 4. Communication mechanism; 5. Auxiliary positioning mechanism; 6. Radar mechanism. Detailed Implementation

[0048] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0049] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0050] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0051] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0052] Please see Figure 1 The diagram shown is a structural block diagram of a deep-buried pipeline positioning system based on an integrated UAV pipeline detector according to an embodiment of the present invention; in the diagram: UAV component 1; pipeline detection component 2; image acquisition component 3; communication mechanism 4; auxiliary positioning mechanism 5; radar mechanism 6; the present invention provides a deep-buried pipeline positioning system based on an integrated UAV pipeline detector, including:

[0053] The mission generation module is used to determine several target acquisition points based on the regional information of the target area, and to generate target flight missions based on each target acquisition point.

[0054] Please see Figure 2 The diagram shown is a structural block diagram of the task generation module according to an embodiment of the present invention; specifically, the task generation module includes:

[0055] The regional analysis submodule is used to obtain regional information of the target area and divide the target area into regions based on the regional information to determine several key sub-regions. The regional information includes geological parameters and topographic parameters.

[0056] In implementation, the target area is divided into several grids. Based on the geological and topographic parameters of the target area, the region type and interference level of each grid are determined. Based on the geological and topographic parameters of the target area, each grid is divided into region types such as strong impact level, general impact level, and weak impact level. According to the distribution of ground features in the target area and the known locations of electromagnetic interference sources, each grid is divided into interference levels such as unobstructed level, slight obstruction level, and severe obstruction level. Each grid can be quantitatively scored based on preset evaluation criteria to determine the detection difficulty of each grid. The average detection difficulty of each grid and its neighborhood is calculated to obtain the comprehensive detection difficulty of several grid neighborhoods. The comprehensive detection difficulties are then sorted in descending order, and the top 1 / 8 to 1 / 10 of the grid neighborhoods in each grid are identified as key sub-regions. Alternatively, based on the determined geological and topographic parameters of each grid, each grid is clustered to obtain several cluster sets, and the cluster centers are identified as key sub-regions.

[0057] The data collection point determination submodule is connected to the region analysis submodule and is used to determine several target data collection points based on the positional relationship of each key subregion.

[0058] In implementation, if the sum of the distances between the center of any key sub-region and the centers of other key sub-regions is greater than the preset distance, then the center of that key sub-region is taken as a target acquisition point. The preset distance can be set based on 1 / 3 to 1 / 5 of the sum of the distances between the centers of all key sub-regions.

[0059] The task generation submodule, which is connected to the collection point determination submodule, is used to generate target flight tasks based on each target collection point and a preset flight task generation model.

[0060] In practice, those skilled in the art will understand that any existing model capable of generating a target flight mission can serve as a preset flight mission generation model, such as an Actor-Critic neural network model, or a preset flight mission generation model based on a path planning algorithm, such as A... * Algorithms, Dijkstra's algorithm, genetic algorithms, and ant colony algorithms, etc.

[0061] The task generation module of this invention performs in-depth analysis of the target area's regional information through a regional analysis submodule, rationally dividing the target area into several key sub-regions. This makes subsequent detection work more targeted, improving detection efficiency and accuracy. The data collection point determination submodule determines several target data collection points based on the positional relationships of each key sub-region, making the distribution of target data collection points more rational, reducing unnecessary flight paths and repeated detections, and improving the detection efficiency of the UAV. The task generation submodule automatically generates optimal target flight tasks based on each target data collection point and a preset flight task generation model, improving detection and positioning efficiency.

[0062] A magnetic field control module, which is connected to the task generation module, is used to determine the magnetic field application point of the target area based on each of the target acquisition points, and to control the magnetic field parameters of the electromagnetic field applied to the target area, including the application time, magnetic field strength and magnetic field frequency.

[0063] Please see Figure 3 The diagram shown is a structural block diagram of the task generation module according to an embodiment of the present invention; specifically, the magnetic field control module includes:

[0064] The acquisition point analysis submodule, which is connected to the acquisition point determination submodule, is used to determine the magnetic field application point of the target area based on each of the target acquisition points;

[0065] Specifically, the acquisition point analysis submodule performs correlation analysis on each of the target acquisition points to determine the correlation between each target acquisition point, and determines the magnetic field application point of the target area based on the correlation between each target acquisition point.

[0066] In practice, the correlation of electromagnetic signal characteristics between acquisition points can be quantified based on Pearson correlation coefficient or Spearman rank correlation coefficient to determine the association relationship of each target acquisition point. Based on the correlation coefficient matrix, hierarchical clustering or K-means clustering algorithms can be used to cluster the acquisition points, so that acquisition points with high correlation are grouped into the same class, and the ground coordinate position corresponding to the center position of the class with the most acquisition points is used as the magnetic field application point.

[0067] The magnetic field application control submodule is connected to the region analysis submodule, the mission generation submodule, and the acquisition point analysis submodule, respectively. It is used to determine the magnetic field parameters based on the target flight mission, the magnetic field application point, and the regional information of the target region, and to apply an electromagnetic field at the magnetic field application point in the target region based on the magnetic field parameters.

[0068] In implementation, a training sample set can be determined based on the regional information of each area in historical data, flight missions, and corresponding secondary magnetic field application points. The magnetic field parameters corresponding to each magnetic field application point are used as sample labels to train the initial neural network model to obtain a magnetic field parameter generation model. The target flight mission, magnetic field application points, and regional information of the target area are input into the magnetic field parameter generation model to obtain the magnetic field parameters output by the magnetic field parameter generation model.

[0069] The magnetic field control module of this invention, through the setting of a data acquisition point analysis submodule, determines the magnetic field application points in the target area based on each target data acquisition point, ensuring that the magnetic field application points can effectively cover the target area and optimizing the detection layout. This facilitates the rapid determination of pipeline parameters and improves detection and positioning efficiency. By setting up a magnetic field application control submodule that comprehensively considers the target flight mission, the magnetic field application points, and the regional information of the target area, the magnetic field parameters are accurately determined, and the electromagnetic field is accurately applied at the magnetic field application points in the target area, improving the coverage effect of the electromagnetic field and the strength of the detection signal, thereby enhancing the detection capability of pipelines buried at great depths.

[0070] The data acquisition module includes a drone component 1, a pipeline detection component 2 mounted on the drone component 1, and an environmental monitoring component. The drone component 1 is connected to the task generation module and the magnetic field control module respectively, and is used to execute a target flight mission based on initial flight parameters. The pipeline detection component 2 is used to collect electromagnetic signals of the target area at each of the target acquisition points. The environmental monitoring component is used to collect environmental data of the target area.

[0071] Specifically, the data acquisition module further includes:

[0072] Image acquisition component 3, which is mounted on the UAV component 1, is used to periodically acquire ground images;

[0073] The flight analysis component, which is connected to the image acquisition component 3, is used to determine the flight stability of the UAV component 1 based on ground images within the target time period, and to determine whether the initial flight parameters need to be adjusted based on the flight stability.

[0074] Specifically, the flight analysis component adjusts the initial flight parameters based on the flight stability of the UAV component 1 under the first determination result;

[0075] The first determination result is that the initial flight parameters need to be adjusted.

[0076] In implementation, key feature points are extracted from the ground images corresponding to each acquisition time point. Position change curves are plotted based on the positional changes of these key feature points in the ground images within the target time period. Flight curves are also plotted based on the UAV's flight path within the target time period. The similarity between the position change curves and the flight curves is calculated and used as the flight stability. If the flight stability is less than a preset stability, the initial flight parameters are determined to need adjustment; if the flight stability is greater than or equal to the preset stability, the initial flight parameters are determined not to need adjustment.

[0077] It is understandable that implementers can set a preset stability value based on the actual situation. Preferably, the preset stability value is set to a range of 0.8 to 0.9.

[0078] In implementation, the UAV component includes the UAV, communication mechanism 4, auxiliary positioning mechanism 5, and radar mechanism 6. The pipeline detection component 2 includes a pipeline detector and a pipeline positioning data acquisition device. The pipeline positioning data acquisition device provides power and communication channels for the pipeline detector. Its internal circuit first amplifies, filters, compares, shapes, and demodulates the voltage signal sent by the pipeline detector, and then enters the corresponding receiving interface of the microcontroller MCU. Then, the MCU parses the received data, repackages it, and finally transmits it to the host computer through the communication interface (4G or RF wireless).

[0079] Understandably, the UAV receives initial flight parameters from the backend control software via communication mechanism 4. These initial flight parameters mainly include the starting point, coordinates of each target acquisition point, UAV data return type, UAV flight speed, and UAV flight altitude. After the UAV completes parameter setting, it sends a self-test command to the pipeline detection component 2 via the CAN bus. Once both the UAV and the pipeline detection component 2 have completed their self-tests, the UAV uploads its current status information to the backend control software via communication mechanism 4. This status information includes the UAV's current coordinates, remaining battery power, and the operating status of the pipeline detection component 2, among other field equipment data. The backend control software then issues a start inspection flight command via communication mechanism 4. Upon receiving the start command, the UAV first controls the pipeline positioning data acquisition unit to power the pipeline detector via the CAN bus. The pipeline positioning data acquisition unit immediately begins receiving data returned by the pipeline detector. Next, the UAV executes the target flight mission based on the initial flight parameters. The UAV uploads images of the site via communication mechanism 4, while the pipeline positioning data acquisition unit uploads the collected electromagnetic signal data to the backend in real time every second. The radar mechanism 6 and image acquisition component 3 on the drone can identify obstacles. When an obstacle is encountered on the inspection route, the drone sends abnormal data to the back-end control software and reminds on-site personnel to use remote control to assist the drone's flight.

[0080] The data acquisition module of this invention periodically acquires ground images by setting up an image acquisition component to assist in flight mission planning and adjustment, and sets up a flight analysis component to determine the flight stability of the UAV component based on the ground images acquired within the target time period, accurately assesses the flight attitude and motion trend, and determines whether the initial flight parameters need to be adjusted. This enables timely optimization of flight parameters, improving flight efficiency and data acquisition quality.

[0081] The data analysis module is connected to the magnetic field control module and the data acquisition module respectively. It is used to construct a magnetic field distribution model of the target area based on the electromagnetic signals collected by the pipeline detection component at each of the target acquisition points, and to determine the initial pipeline parameters of the deep-buried pipeline based on the magnetic field distribution model and the magnetic field parameters of the electromagnetic field applied to the target area. It also determines the key influence coefficients based on the environmental data and the initial flight parameters. The pipeline parameters include pipeline direction, pitch angle and azimuth angle.

[0082] Specifically, the data analysis module includes:

[0083] An electromagnetic signal analysis submodule, which is connected to the data acquisition module, is used to construct a magnetic field distribution model of the target area based on the electromagnetic signals acquired by the pipeline detection component at each of the target acquisition points.

[0084] In practice, the electromagnetic signals collected at each target acquisition point are numerically simulated, and the electromagnetic signals corresponding to the repeatedly acquired coverage area are averaged to obtain a magnetic field distribution model. The magnetic field distribution model is used to reflect the magnetic field distribution at each location in the target area.

[0085] The magnetic field parameter analysis submodule is connected to the electromagnetic signal analysis submodule and the magnetic field application control submodule, respectively. It is used to reverse simulate the electromagnetic induction process of the deep-buried pipeline based on the magnetic field distribution model of the target area and the magnetic field parameters of the electromagnetic field applied to the target area, so as to obtain the pipeline parameters of the deep-buried pipeline.

[0086] The data analysis module of this invention, by setting up an electromagnetic signal analysis submodule, constructs a magnetic field distribution model of the target area based on the electromagnetic signals collected from each target acquisition point. It then deeply analyzes the characteristics of the electromagnetic signals, providing reliable basic data for subsequent pipeline parameter inversion and improving the accuracy of detection and positioning. Furthermore, by setting up a magnetic field parameter analysis submodule, it simulates the electromagnetic induction process of a deep-buried pipeline based on the magnetic field distribution model of the target area and the magnetic field parameters of the applied electromagnetic field, thereby obtaining the pipeline parameters and improving detection efficiency and accuracy.

[0087] Specifically, the data analysis module also includes:

[0088] An adjustment analysis submodule, which is connected to the data acquisition module, is used to determine the environmental impact coefficient based on the comparison results of the environmental data and the preset environmental data, and to determine the flight impact coefficient based on the comparison results of the initial flight parameters and the preset flight parameters, and to determine the key impact coefficient based on the environmental impact coefficient and the flight impact coefficient.

[0089] During implementation, environmental data includes at least temperature, humidity, and wind speed, while flight parameters include flight speed and altitude. Based on environmental data YB1, YB2, ..., YB... j , ..., YB m With preset environmental data EB1, EB2, ..., EB j , ..., EB m Determine the environmental impact coefficient HY, where j = 1, 2, ..., m, and m is the number of environmental parameters. The environmental impact coefficient HY = sqrt(∑ m j＝1 (YB j -EB j ) 2 ); sqrt() is a preset square root determination function, based on the initial flight parameters YF1, YF2, ..., YF j , ..., YF m With preset flight parameters EF1, EF2, ..., EF j , ..., EF m Determine the flight influence coefficient HY, where j = 1, 2, ..., m, m is the number of flight parameters, and the flight influence coefficient FY = sqrt(∑ m j＝1 (YF j -EF j ) 2 ).

[0090] It is understandable that the average of the environmental impact coefficient and the flight impact coefficient is determined as the key impact coefficient. Practitioners can set preset environmental data based on actual conditions or the average of environmental data that passed compliance testing from historical data, and can set preset flight parameters based on actual conditions or the average of flight parameters that passed compliance testing from historical data.

[0091] The parameter adjustment module, which is connected to the data analysis module, is used to adjust the pipeline parameters based on the key influence coefficients to obtain the target pipeline parameters.

[0092] Specifically, under the first judgment condition, the parameter adjustment module determines the target pipeline parameters based on the key influence coefficient and the initial pipeline parameters;

[0093] The first determination condition is that the key influence coefficient is less than the preset influence coefficient.

[0094] In practice, implementers can set key impact coefficients based on the actual situation. Preferably, the key impact coefficient is set to a range of 0.5 to 0.9.

[0095] Understandably, the product of the key influence coefficient and the initial pipeline parameters is used to determine the target pipeline parameters.

[0096] When the critical influence coefficient is less than the preset influence coefficient, the parameter adjustment module of this invention determines the target pipeline parameters based on the critical influence coefficient and the initial pipeline parameters. When the critical influence coefficient is less than the preset influence coefficient, it indicates that environmental factors and other factors have little impact on the initial pipeline parameters. Fine-tuning the initial pipeline parameters based on the critical influence coefficient can improve the accuracy of detection and positioning.

[0097] Specifically, under the second determination condition, the parameter adjustment module determines the target magnetic field parameters based on the key influence coefficient and the magnetic field parameters, and reapplies an electromagnetic field to the target area based on the target magnetic field parameters to redetermine the pipe parameters, so as to obtain the target pipe parameters.

[0098] The second determination condition is that the key influence coefficient is greater than or equal to the preset influence coefficient.

[0099] In practice, the target magnetic field parameter is determined by the ratio of the magnetic field parameter to the key influence coefficient.

[0100] When the key influence coefficient is greater than or equal to the preset influence coefficient, the parameter adjustment module of this invention re-determines the magnetic field parameters to reapply the electromagnetic field to the target area. When the key influence coefficient is greater than or equal to the preset influence coefficient, it indicates that environmental factors have a significant impact on the initial pipeline parameters. By reapplying the electromagnetic field, the interference of environmental factors on the detection results can be effectively reduced, and the accuracy of the detection signal can be improved. By adjusting the magnetic field parameters in a timely manner and reapplying the electromagnetic field, inaccurate detection results caused by environmental factors can be avoided, the number of repeated detections can be reduced, and the overall detection and positioning efficiency can be improved.

[0101] This invention, through a task generation module, accurately determines several target acquisition points based on the regional information of the target area, and rationally generates target flight missions, optimizing flight routes and parameters to improve detection efficiency. It can flexibly adjust target acquisition points and flight mission planning according to the characteristics of different target areas and mission requirements, adapting to various complex detection scenarios. By setting up a magnetic field control module, it determines the magnetic field application points in the target area and accurately controls the magnetic field parameters of the applied electromagnetic field, providing a stable electromagnetic field source for deep-buried pipeline detection, ensuring the stability of the detection signal, effectively suppressing external electromagnetic interference, and improving the accuracy of detection and positioning. By setting up a data acquisition module, it integrates pipeline detection components with UAVs to simultaneously collect electromagnetic signals and environmental data of the target area, improving detection and positioning efficiency and accuracy, and providing data support for subsequent data analysis. By setting up a data analysis module, it can accurately determine the initial pipeline parameters of deep-buried pipelines, comprehensively consider environmental data and flight parameters to determine key influence coefficients, and comprehensively evaluate the impact of environmental factors and the flight process on the detection results. By setting up a parameter adjustment module, it can adjust pipeline parameters based on key influence coefficients, further improving the accuracy of detection and positioning. This application is able to adapt to complex environments and perform positioning in complex and ever-changing environments. Combined with the long-duration flight capability and efficient data processing capability of UAVs, it can complete the positioning task of deep buried pipelines in a large area in a short time. UAVs can fly in deep buried pipeline areas regularly or in real time. This real-time monitoring capability enables UAVs to detect problems in a timely manner and take corresponding measures during the positioning of deep buried pipelines.

[0102] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A deep-buried pipeline positioning system based on an unmanned aerial vehicle (UAV) integrated pipeline detector, characterized in that, include: The mission generation module is used to determine several target acquisition points based on the regional information of the target area, and to generate target flight missions based on each target acquisition point. A magnetic field control module, which is connected to the task generation module, is used to determine the magnetic field application point of the target area based on each of the target acquisition points, and to control the magnetic field parameters of the electromagnetic field applied to the target area, including the application time, magnetic field strength and magnetic field frequency. The data acquisition module includes a drone component, a pipeline detection component mounted on the drone component, and an environmental monitoring component. The drone component is connected to the task generation module and the magnetic field control module respectively, and is used to execute a target flight mission based on initial flight parameters. The pipeline detection component is used to collect electromagnetic signals of the target area at each of the target acquisition points, and the environmental monitoring component is used to collect environmental data of the target area. The data analysis module, connected to both the magnetic field control module and the data acquisition module, is used to construct a magnetic field distribution model of the target area based on the electromagnetic signals collected by the pipeline detection component at each target acquisition point. It then determines the initial pipeline parameters for the deep-buried pipeline based on the magnetic field distribution model and the magnetic field parameters of the applied electromagnetic field in the target area. Furthermore, it determines key influence coefficients based on the environmental data and the initial flight parameters. The pipeline parameters include the pipeline direction, pitch angle, and azimuth angle. The average of the environmental influence coefficient and the flight influence coefficient is determined as the key influence coefficient. The environmental influence coefficient is determined based on the environmental data, and the flight influence coefficient is determined based on the initial flight parameters. A parameter adjustment module, which is connected to the data analysis module, is used to adjust the pipeline parameters based on the key influence coefficient to obtain the target pipeline parameters; Under the first determination condition, the product of the key influence coefficient and the initial pipeline parameter is determined as the target pipeline parameter; Under the second determination condition, the ratio of the magnetic field parameter to the key influence coefficient is determined as the target magnetic field parameter, and an electromagnetic field is reapplied to the target area based on the target magnetic field parameter to redetermine the pipe parameters, so as to obtain the target pipe parameters. The first determination condition is that the key influence coefficient is less than the preset influence coefficient; the second determination condition is that the key influence coefficient is greater than or equal to the preset influence coefficient.

2. The deep-buried pipeline positioning system based on an unmanned aerial vehicle integrated pipeline detector according to claim 1, characterized in that, The task generation module includes: The regional analysis submodule is used to obtain regional information of the target area and divide the target area into regions based on the regional information to determine several key sub-regions. The regional information includes geological parameters and topographic parameters. The data collection point determination submodule is connected to the region analysis submodule and is used to determine several target data collection points based on the positional relationship of each key subregion. The task generation submodule, which is connected to the collection point determination submodule, is used to generate target flight tasks based on each target collection point and a preset flight task generation model.

3. The deep-buried pipeline positioning system based on an unmanned aerial vehicle integrated pipeline detector according to claim 2, characterized in that, The magnetic field control module includes: The acquisition point analysis submodule, which is connected to the acquisition point determination submodule, is used to determine the magnetic field application point of the target area based on each of the target acquisition points; The magnetic field application control submodule is connected to the region analysis submodule, the mission generation submodule, and the acquisition point analysis submodule, respectively. It is used to determine the magnetic field parameters based on the target flight mission, the magnetic field application point, and the regional information of the target region, and to apply an electromagnetic field at the magnetic field application point in the target region based on the magnetic field parameters.

4. The deep-buried pipeline positioning system based on an unmanned aerial vehicle integrated pipeline detector according to claim 3, characterized in that, The data analysis module includes: An electromagnetic signal analysis submodule, which is connected to the data acquisition module, is used to construct a magnetic field distribution model of the target area based on the electromagnetic signals acquired by the pipeline detection component at each of the target acquisition points. The magnetic field parameter analysis submodule is connected to the electromagnetic signal analysis submodule and the magnetic field application control submodule, respectively. It is used to reverse simulate the electromagnetic induction process of the deep-buried pipeline based on the magnetic field distribution model of the target area and the magnetic field parameters of the electromagnetic field applied to the target area, so as to obtain the pipeline parameters of the deep-buried pipeline.

5. The deep-buried pipeline positioning system based on an unmanned aerial vehicle integrated pipeline detector according to claim 4, characterized in that, The data analysis module also includes: An adjustment analysis submodule, which is connected to the data acquisition module, is used to determine the environmental impact coefficient based on the comparison results of the environmental data and the preset environmental data, and to determine the flight impact coefficient based on the comparison results of the initial flight parameters and the preset flight parameters, and to determine the key impact coefficient based on the environmental impact coefficient and the flight impact coefficient.

6. The deep-buried pipeline positioning system based on an unmanned aerial vehicle integrated pipeline detector according to claim 5, characterized in that, The acquisition point analysis submodule performs correlation analysis on each target acquisition point to determine the correlation between each target acquisition point, and determines the magnetic field application point of the target area based on the correlation between each target acquisition point.

7. The deep-buried pipeline positioning system based on an unmanned aerial vehicle integrated pipeline detector according to claim 6, characterized in that, The data acquisition module also includes: An image acquisition component, mounted on the UAV component, is used to periodically acquire ground images; A flight analysis component, connected to the image acquisition component, is used to determine the flight stability of the UAV component based on ground images within a target time period, and to determine whether the initial flight parameters need to be adjusted based on the flight stability.

8. The deep-buried pipeline positioning system based on an unmanned aerial vehicle integrated pipeline detector according to claim 7, characterized in that, The flight analysis component adjusts the initial flight parameters based on the flight stability of the UAV component under the first determination result; The first determination result is that the initial flight parameters need to be adjusted.

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