Unmanned aerial vehicle pseudo-random electromagnetic underwater pipeline leakage detection method and device

Abstract

The invention relates to an unmanned aerial vehicle pseudo-random electromagnetic underwater pipeline leakage detection method and device, and the method employs the magnetic field response difference between a leakage region and a normal region, and recognizes the leakage position and range of a pipeline through the change of a magnetic field. Multi-sequence variable-frequency-density pseudo-random electromagnetic signals are transmitted through a host, an unmanned aerial vehicle carries a signal receiver to carry out signal measurement and feedback in a near-water-surface area, the detection requirements of underwater pipelines with different depths are met, the longitudinal resolution of pipeline leakage is improved, and meanwhile, underwater electromagnetic interference is effectively reduced by enhancing the strength of a main frequency signal, so that the detection accuracy is improved. Therefore, the water pipe leakage detection precision is obviously improved. According to the method, the unmanned aerial vehicle is adopted to carry the three-component magnetic field receiver, the three-component probe can capture magnetic field components in three orthogonal directions at the same time, and the characteristic helps to reduce the influence on data acquisition caused by the attitude change of the receiver when the unmanned aerial vehicle flies.

Description

Technical Field

[0001] The present application relates to the technical field of underwater water pipeline leakage monitoring, and in particular to a method and device for detecting underwater pipeline leakage using a pseudo-random electromagnetic drone. Background Art

[0002] There are many methods for underwater water pipeline leakage detection, including manual inspection, hardware monitoring (such as electrical, acoustic, gas detection, pressure monitor, etc.), distributed fiber optic sensing technology (Distributed Fiber Optic Sensing, DFOS), far-infrared thermal imaging detection, audio-visual monitoring, correlation analysis detection, tracer gas detection, closed-circuit television pipeline endoscopic detection (CCTV), and ground penetrating radar (GPR) method, etc. These methods have different advantages, but in practical applications, they are often subject to terrain conditions, pipeline burial depth and environmental interference, and therefore have certain limitations.

[0003] At present, the leakage problem of water pipelines has become one of the important reasons for the widespread losses of domestic water pipeline companies. Especially in the detection of underwater water pipelines, traditional monitoring methods are difficult to achieve ideal results, which is specifically manifested in the following aspects: (1) It is impossible to accurately locate the leakage location and provide leakage range information: In the complex underwater environment, the existing technology is difficult to obtain accurate leakage points and leakage ranges, which often leads to missed detection or misdetection. (2) The pipeline is located in the underwater stratum, which is difficult to construct and costly: The underwater operating environment is complex and requires the use of diving equipment or special underwater construction tools, which significantly increases the construction difficulty and maintenance cost. (3) The construction efficiency is low and the leakage develops further: Due to the slow speed of underwater pipeline construction and detection, the pipeline leakage problem often worsens between detection and repair, expanding the scope of pipeline damage. Summary of the invention

[0004] Based on this, it is necessary to provide a method and device for detecting underwater pipeline leakage using a pseudo-random electromagnetic drone in response to the above technical problems. This method uses a host to transmit pseudo-random electromagnetic signals with multiple sequences and variable frequency density, and a drone equipped with a signal receiver performs signal measurement and feedback in the near-water surface area. Its working principle is to use the difference in magnetic field response between the leakage area and the normal area to identify the location and range of pipeline leakage through changes in the magnetic field.

[0005] A pseudo-random electromagnetic underwater pipeline leakage detection method using an unmanned aerial vehicle, the method comprising: A field source electrode is arranged at the inlet and outlet of the underwater water pipeline to be detected, and a signal controller is used to send pseudo-random electromagnetic signals of different frequencies to the field source electrode; A drone equipped with a three-component magnetic field sensor and a receiver is used to synchronously collect magnetic field components in three directions with different frequencies near the water surface.

[0006] After data collection is completed, the total magnetic field intensity is calculated from the magnetic field components in three directions, and then the data is cross-verified to ensure the consistency and relevance of the data.

[0007] The magnetic field components in three directions, the total magnetic field intensity and the gradient are imaged and processed respectively to obtain the abnormal plane distribution maps of different frequencies.

[0008] Identify leakage areas based on abnormal plane distribution maps and pipeline distribution.

[0009] In one embodiment, after data collection is completed, after the total magnetic field strength is calculated from the magnetic field components in three directions, data cross-verification is performed to ensure the consistency and relevance of the data, including: The magnetic field components in three directions are fitted by linear fitting, and the fitting models of the three magnetic field components are obtained as follows:

[0010]

[0011]

[0012] in, , , are the magnetic field components in the x, y, and z directions, respectively, , , are the offset terms of the magnetic field components in the x, y, and z directions, respectively, , , are the fitting coefficients of the magnetic field components in the x, y, and z directions, respectively, p is the position, , , are the error terms of the magnetic field components in the x, y, and z directions respectively.

[0013] The fitted model of one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check whether there is consistency and correlation.

[0014] In one embodiment, a magnetic field component fitting model is compared with actual data of magnetic field components in other two directions to check whether there is consistency and correlation, including: Comparing the fitting model of one magnetic field component with the actual data of the magnetic field components in the other two directions, the correlation of the magnetic field components is obtained as follows: 100% In the formula, For relevance, is the fitting model of a certain magnetic field component, is the actual value of the other magnetic field component.

[0015] when 0~5% is used as the consistency and correlation to meet the data verification standard. If it exceeds 5%, it means that the single three-directional magnetic field component data cannot correctly display the underwater pipeline leakage location. In this case, the magnetic field intensity and gradient changes are used to reveal the underwater pipeline leakage location or the data is collected again.

[0016] In one embodiment, a three-component magnetic field sensor is mounted on an unmanned aerial vehicle and is used to synchronously collect magnetic field components in three directions of different frequencies using a regular survey line collection method or an irregular collection method near the water surface.

[0017] In one embodiment, the magnetic field components in three directions, the total magnetic field intensity and the gradient are imaged and processed respectively to obtain abnormal plane distribution maps of different frequencies, including: The anomaly analysis of the leakage area is performed on the three-directional magnetic field components at different frequencies, and the anomaly plane distribution diagram of the magnetic field components in each direction at different frequencies is generated.

[0018] The total magnetic field intensity is calculated based on the magnetic field component data in three directions at different frequencies, and the anomaly analysis of the leakage area is performed on the total magnetic field intensity to generate the anomaly plane distribution map of the total magnetic field at different frequencies.

[0019] The gradient changes of the magnetic field components in three directions at different frequencies are calculated, and the abnormal analysis of the leakage area is performed on the changes in the magnetic field gradient to generate the abnormal plane distribution map of the magnetic field gradient changes at different frequencies.

[0020] A pseudo-random electromagnetic underwater pipeline leakage detection device for an unmanned aerial vehicle comprises: a transmitting system and a receiving system: a transmitting system and a receiving system.

[0021] The transmitting system includes: a signal controller, an excitation power supply, a field source electrode A and a field source electrode B; one end of the field source electrode A and the field source electrode B are connected to the signal controller, and the other ends of the field source electrode A and the field source electrode B are respectively arranged at the inlet and outlet of the underwater water pipeline to be detected; the excitation power supply is used to provide power for the signal controller; after the transmitting system is deployed, pseudo-random electromagnetic signals of different frequencies are sent through the signal controller.

[0022] The receiving system includes a three-component magnetic sensor, a receiver and a drone; the drone is equipped with a three-component magnetic sensor and a receiver to synchronously collect pseudo-random electromagnetic signals on the water inlet surface, and the drone records the movement trajectory in real time.

[0023] The receiver is used to calculate the total magnetic field intensity from the three-directional magnetic field components after data collection is completed, and then perform interactive verification of the data to ensure the consistency and relevance of the data; perform imaging processing on the three-directional magnetic field components, the total magnetic field intensity and the gradient respectively to obtain abnormal plane distribution maps of different frequencies; and identify the leakage area according to the abnormal plane distribution map and pipeline distribution.

[0024] In one embodiment, after data collection is completed, after the total magnetic field strength is calculated from the magnetic field components in three directions, data cross-verification is performed to ensure the consistency and relevance of the data, including: The magnetic field components in three directions are fitted by linear fitting, and the fitting models of the three magnetic field components are obtained as follows:

[0025]

[0026]

[0027] in, , , are the magnetic field components in the x, y, and z directions, respectively, , , are the offset terms of the magnetic field components in the x, y, and z directions, respectively, , , are the fitting coefficients of the magnetic field components in the x, y, and z directions, respectively, p is the position, , , are the error terms of the magnetic field components in the x, y, and z directions respectively.

[0028] The fitted model of one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check whether there is consistency and correlation.

[0029] In one embodiment, a magnetic field component fitting model is compared with actual data of magnetic field components in other two directions to check whether there is consistency and correlation, including: Comparing the fitting model of one magnetic field component with the actual data of the magnetic field components in the other two directions, the correlation of the magnetic field components is obtained as follows: 100% In the formula, For relevance, is the fitting model of a certain magnetic field component, is the actual value of the other magnetic field component.

[0030] when 0~5% is used as the consistency and correlation to meet the data verification standard. If it exceeds 5%, it means that the single three-directional magnetic field component data cannot correctly display the underwater pipeline leakage location. In this case, the magnetic field intensity and gradient changes are used to reveal the underwater pipeline leakage location or the data is collected again.

[0031] In one embodiment, a three-component magnetic field sensor is mounted on an unmanned aerial vehicle and is used to synchronously collect magnetic field components in three directions of different frequencies using a regular survey line collection method or an irregular collection method near the water surface.

[0032] In one embodiment, the magnetic field components in three directions, the total magnetic field intensity and the gradient are imaged and processed respectively to obtain abnormal plane distribution maps of different frequencies, including: The anomaly analysis of the leakage area is performed on the three-directional magnetic field components at different frequencies, and the anomaly plane distribution diagram of the magnetic field components in each direction at different frequencies is generated.

[0033] The total magnetic field intensity is calculated based on the magnetic field component data in three directions at different frequencies, and the anomaly analysis of the leakage area is performed on the total magnetic field intensity to generate the anomaly plane distribution map of the total magnetic field at different frequencies.

[0034] The gradient changes of the magnetic field components in three directions at different frequencies are calculated, and the abnormal analysis of the leakage area is performed on the changes in the magnetic field gradient to generate the abnormal plane distribution map of the magnetic field gradient changes at different frequencies.

[0035] The above-mentioned pseudo-random electromagnetic underwater pipeline leakage detection method and device for unmanned aerial vehicles utilizes the difference in magnetic field response between the leakage area and the normal area to identify the location and range of pipeline leakage through changes in the magnetic field. The host transmits a multi-sequence pseudo-random electromagnetic signal with variable frequency density, and the signal receiver carried by the unmanned aerial vehicle performs signal measurement and feedback in the near-water surface area, which meets the detection requirements of underwater pipelines at different depths and improves the longitudinal resolution of pipeline leakage. At the same time, by enhancing the strength of the main frequency signal, underwater electromagnetic interference is effectively reduced, thereby significantly improving the accuracy of water pipeline leakage detection; this method uses a three-component magnetic field receiver carried by an unmanned aerial vehicle. The three-component probe can simultaneously capture magnetic field components in three orthogonal directions. This feature helps to reduce the impact of changes in the receiver's posture on data collection when the unmanned aerial vehicle is flying. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 A schematic diagram of a flow chart of a pseudo-random electromagnetic underwater pipeline leakage detection method using a drone in one embodiment; Figure 2 A schematic plan view of pseudo-random electromagnetic underwater pipeline leakage detection by a drone in one embodiment; Figure 3A cross-sectional schematic diagram of pseudo-random electromagnetic underwater pipeline leakage detection by a drone in one embodiment; Figure 4 Schematic diagram of the acquisition method in another embodiment, wherein (a) is a schematic diagram of a "z" type back and forth measurement method, (b) is a schematic diagram of a measurement method in the same direction of the survey line, and (c) is an irregular acquisition method; Figure 5 The figure is a comparison diagram of the field value amplitudes of the measurement line at the leakage and non-leakage positions in one embodiment. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0038] In one embodiment, Figure 1 As shown, a pseudo-random electromagnetic underwater pipeline leakage detection method using an unmanned aerial vehicle is provided, and the method comprises the following steps: Step 100: a field source electrode is arranged at each inlet and outlet of the underwater water pipeline to be detected, and a signal controller is used to send pseudo-random electromagnetic signals of different frequencies to the field source electrode.

[0039] Specifically, one end of the field source electrodes A and B is connected to the signal controller, and the other ends of the field source electrodes A and B are not at the inlet and outlet of the underwater water pipeline to be detected. After the arrangement is completed, pseudo-random electromagnetic signals of different frequencies are sent through the signal controller. Preferably, the frequency range of the pseudo-random electromagnetic signal is 1Hz~10kHz. The schematic diagram of the pseudo-random electromagnetic underwater pipeline leakage detection by drone is shown in the figure. Figure 2 shown.

[0040] Step 102: Use a drone equipped with a three-component magnetic field sensor and a receiver to synchronously collect magnetic field components in three directions with different frequencies near the water surface.

[0041] Specifically, a three-component magnetic field sensor and a receiver are used to synchronously collect data near the water surface (0.2m-1m range) using a mounted tool. The three-component magnetic sensor is located at the bottom of the entire system, close to the water surface. The drone-mounted tool (or other mounted tools) needs a positioning system that records the trajectory and records the movement trajectory in real time to facilitate later data processing. The cross-sectional diagram of the pseudo-random electromagnetic underwater pipeline leakage detection by drone is shown in the figure. Figure 3 shown.

[0042] Step 104: After data collection is completed, after the total magnetic field strength is calculated from the magnetic field components in three directions, data cross-verification is performed to ensure data consistency and relevance.

[0043] Specifically, by comparing and interactively verifying the data of different components, errors can be reduced and the accuracy of the analysis can be ensured.

[0044] Step 106: Perform imaging processing on the magnetic field components in three directions, the total magnetic field intensity and the gradient respectively to obtain abnormal plane distribution maps of different frequencies.

[0045] Specifically, the three-directional magnetic field components (x, y, and z directions) at different frequencies are analyzed for anomalies in the leakage area, and finally an anomaly plane distribution map of each magnetic field component at different frequencies is generated to intuitively display the abnormal characteristics of each component in the leakage area.

[0046] The total magnetic field intensity is calculated using the magnetic field component data in three directions. The calculated total field data is used for anomaly analysis of the leakage area to generate anomaly plane distribution diagrams of the total magnetic field at different frequencies to more comprehensively reflect the magnetic field anomaly in the leakage area.

[0047] By calculating the gradient change of the three-component magnetic field, the change of the magnetic field gradient is analyzed. Then, the change of the magnetic field gradient is analyzed for anomalies in the leakage area, and an abnormal plane distribution map of the magnetic field gradient change at different frequencies is generated, and the leakage anomaly is further identified from the perspective of gradient change.

[0048] Step 108: Identify the leakage area according to the abnormal plane distribution map and pipeline distribution.

[0049] Specifically, the specific steps for identifying the leakage area based on the abnormal plane distribution map and pipeline distribution are as follows: 1) Ensure that the coordinates of the anomaly map and the pipeline distribution map correspond based on the trajectory recorded by the drone.

[0050] 2) Based on the relative anomaly of the magnetic field, identify candidate areas of suspected leakage points.

[0051] 3) Compare the abnormal magnetic field area with the distribution location of the pipeline, find the overlapping or close areas, and mark them as possible leakage areas.

[0052] 4) For overlapping areas, the suspected leakage areas are identified and marked through various magnetic field attributes (horizontal and vertical components, gradient changes, magnetic field abnormality intensity, etc.).

[0053] In the above-mentioned pseudo-random electromagnetic underwater pipeline leakage detection method using drones, the method uses the difference in magnetic field response between the leakage area and the normal area to identify the location and range of pipeline leakage through changes in the magnetic field. The host transmits a multi-sequence pseudo-random electromagnetic signal with variable frequency density, and the drone carries a signal receiver to measure and feedback the signal in the near-water surface area, which meets the detection needs of underwater pipelines at different depths and improves the longitudinal resolution of pipeline leakage. At the same time, by enhancing the strength of the main frequency signal, it effectively reduces underwater electromagnetic interference, thereby significantly improving the accuracy of water pipeline leakage detection; this method uses a drone equipped with a three-component magnetic field receiver, and the three-component probe can simultaneously capture magnetic field components in three orthogonal directions. This feature helps to reduce the impact of changes in the receiver's attitude on data collection when the drone is flying.

[0054] In one embodiment, step 104 includes: fitting the magnetic field components in three directions by linear fitting, and obtaining the fitting models of the three magnetic field components as follows:

[0055]

[0056]

[0057] in, , , are the magnetic field components in the x, y, and z directions, respectively, , , are the offset terms of the magnetic field components in the x, y, and z directions, respectively, , , are the fitting coefficients of the magnetic field components in the x, y, and z directions, respectively, p is the position, , , are the error terms of the magnetic field components in the x, y, and z directions respectively.

[0058] The fitted model of one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check whether there is consistency and correlation.

[0059] In one embodiment, a magnetic field component fitting model is compared with actual data of magnetic field components in other two directions to check whether there is consistency and correlation, including: Comparing the fitting model of one magnetic field component with the actual data of the magnetic field components in the other two directions, the correlation of the magnetic field components is obtained as follows: 100% In the formula, For relevance, is the fitting model of a certain magnetic field component, is the actual value of the other magnetic field component.

[0060] when 0~5% is used as the consistency and correlation to meet the data verification standard. If it exceeds 5%, it means that the single three-directional magnetic field component data cannot correctly display the underwater pipeline leakage location. In this case, the magnetic field intensity and gradient changes are used to reveal the underwater pipeline leakage location or the data is collected again.

[0061] Specifically, the same fitting and cross-validation can be performed with the total magnetic field intensity. Through this cross-validation analysis, errors can be reduced and the accuracy of magnetic field data can be improved.

[0062] In one embodiment, a three-component magnetic field sensor is mounted on an unmanned aerial vehicle and is used to synchronously collect magnetic field components in three directions of different frequencies using a regular survey line collection method or an irregular collection method near the water surface.

[0063] Specifically, there are two ways to collect data: one is regular line collection, where the line direction is perpendicular to the direction of the water pipeline, and the other is a "z" type back and forth measurement ( Figure 4 (a)) or the same direction of the survey line ( Figure 4 (b)); the second is the irregular way ( Figure 4 In (c), the receiving system moves randomly and disorderly in the area to be tested (above the water pipeline) and completes the collection.

[0064] In one of the embodiments, step 106 includes: performing abnormal analysis of the leakage area on the three-directional magnetic field components at different frequencies, and generating abnormal plane distribution diagrams of the magnetic field components in each direction of the magnetic field at different frequencies; calculating the total magnetic field intensity based on the three-directional magnetic field component data at different frequencies, performing abnormal analysis of the leakage area on the total magnetic field intensity, and generating abnormal plane distribution diagrams of the total magnetic field at different frequencies; calculating the gradient changes of the magnetic field components in the three directions at different frequencies, performing abnormal analysis of the leakage area on the changes in the magnetic field gradient, and generating abnormal plane distribution diagrams of the magnetic field gradient changes at different frequencies.

[0065] Specifically, the three-component magnetic field data collected at each frequency are matched with the spatial position of the UAV flight trajectory. After data processing, the three-component magnetic field plane distribution diagrams of different frequencies are generated to analyze the response characteristics of different frequencies to each magnetic field component.

[0066] The total magnetic field strength is calculated as follows: , for each measuring point and frequency, the total magnetic field intensity is calculated, and the deep and shallow buried areas are marked in combination with the pipeline distribution information to generate the total magnetic field intensity distribution map under different frequencies and burial depths, providing a basis for the identification of abnormal areas.

[0067] The spatial gradient of the magnetic field data at each frequency is calculated, and the gradients in the X and Y directions are calculated according to the burial depth. The magnetic field gradient change diagram at different frequencies is generated, and the gradient difference between deep and shallow buried areas is marked. By comparing the gradient distribution diagrams under different frequencies and burial depths, the areas with obvious magnetic field gradient mutations are identified, so as to accurately locate possible leakage points.

[0068] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0069] In one embodiment, a pseudo-random electromagnetic underwater pipeline leakage detection device for an unmanned aerial vehicle is provided, and the device includes: a transmitting system and a receiving system: a transmitting system and a receiving system.

[0070] The transmitting system includes: a signal controller, an excitation power supply, a field source electrode A and a field source electrode B; one end of the field source electrode A and the field source electrode B are connected to the signal controller, and the other ends of the field source electrode A and the field source electrode B are respectively arranged at the inlet and outlet of the underwater water pipeline to be detected; the excitation power supply is used to provide power for the signal controller; after the transmitting system is deployed, pseudo-random electromagnetic signals of different frequencies are sent through the signal controller.

[0071] Specifically, the transmitting system transmits a multi-sequence wideband pseudo-random electromagnetic signal with a frequency range of 1Hz to 10kHz. This setting is designed to meet the detection needs of underwater pipelines at different depths, improve the longitudinal resolution of pipeline leakage, and effectively reduce underwater electromagnetic interference by enhancing the strength of the main frequency signal, thereby significantly improving the accuracy of water pipeline leakage detection.

[0072] The receiving system includes a three-component magnetic sensor, a receiver and a drone; the drone is equipped with a three-component magnetic sensor and a receiver to synchronously collect pseudo-random electromagnetic signals on the water inlet surface, and the drone records the movement trajectory in real time.

[0073] Specifically, the carrying platform is integrated with the magnetic field receiver. The three-component magnetic sensor is located at the bottom of the entire system, close to the water surface.

[0074] The drone is equipped with a three-component magnetic field receiver. The three-component probe can capture magnetic field components in three orthogonal directions at the same time. This feature helps to reduce the impact of changes in the receiver's attitude on data collection when the drone is flying.

[0075] Using drones equipped with signal receivers to conduct water surface measurements can effectively improve the efficiency of pipeline leakage detection and has advantages in cost control.

[0076] The real-time recording of the movement trajectory of the drone is to correspond to the location of the continuous signal collection. It is a conventional data processing method in data processing, so it is not expressed in the data processing flow.

[0077] The receiver is used to calculate the total magnetic field intensity from the three-directional magnetic field components after data collection is completed, and then perform interactive verification of the data to ensure the consistency and relevance of the data; perform imaging processing on the three-directional magnetic field components, the total magnetic field intensity and the gradient respectively to obtain abnormal plane distribution maps of different frequencies; and identify the leakage area according to the abnormal plane distribution map and pipeline distribution.

[0078] The comparison of the field value amplitude of the measurement line at the leakage and non-leakage positions is shown in the figure below: Figure 5 shown.

[0079] In one embodiment, after data collection is completed, after the total magnetic field strength is calculated from the magnetic field components in three directions, data cross-verification is performed to ensure the consistency and relevance of the data, including: The magnetic field components in three directions are fitted by linear fitting, and the fitting models of the three magnetic field components are obtained as follows:

[0080]

[0081]

[0082] in, , , are the magnetic field components in the x, y, and z directions, respectively, , , are the offset terms of the magnetic field components in the x, y, and z directions, respectively, , , are the fitting coefficients of the magnetic field components in the x, y, and z directions, respectively, p is the position, , , are the error terms of the magnetic field components in the x, y, and z directions respectively.

[0083] The fitted model of one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check whether there is consistency and correlation.

[0084] In one embodiment, a magnetic field component fitting model is compared with actual data of magnetic field components in other two directions to check whether there is consistency and correlation, including: Comparing the fitting model of one magnetic field component with the actual data of the magnetic field components in the other two directions, the correlation of the magnetic field components is obtained as follows: 100% In the formula, For relevance, is the fitting model of a certain magnetic field component, is the actual value of the other magnetic field component.

[0085] when 0~5% is used as the consistency and correlation to meet the data verification standard. If it exceeds 5%, it means that the single three-directional magnetic field component data cannot correctly display the underwater pipeline leakage location. In this case, the magnetic field intensity and gradient changes are used to reveal the underwater pipeline leakage location or the data is collected again.

[0086] In one embodiment, a three-component magnetic field sensor is mounted on an unmanned aerial vehicle and is used to synchronously collect magnetic field components in three directions of different frequencies using a regular survey line collection method or an irregular collection method near the water surface.

[0087] In one embodiment, the magnetic field components in three directions, the total magnetic field intensity and the gradient are imaged and processed respectively to obtain abnormal plane distribution maps of different frequencies, including: The anomaly analysis of the leakage area is performed on the three-directional magnetic field components at different frequencies, and the anomaly plane distribution diagram of the magnetic field components in each direction at different frequencies is generated.

[0088] The total magnetic field intensity is calculated based on the magnetic field component data in three directions at different frequencies, and the anomaly analysis of the leakage area is performed on the total magnetic field intensity to generate the anomaly plane distribution map of the total magnetic field at different frequencies.

[0089] The gradient changes of the magnetic field components in three directions at different frequencies are calculated, and the abnormal analysis of the leakage area is performed on the changes in the magnetic field gradient to generate the abnormal plane distribution map of the magnetic field gradient changes at different frequencies.

[0090] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0091] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A pseudo-random electromagnetic underwater pipeline leakage detection method using an unmanned aerial vehicle, characterized in that: The method comprises: A field source electrode is arranged at the inlet and outlet of the underwater water pipeline to be detected, and a signal controller is used to send pseudo-random electromagnetic signals of different frequencies to the field source electrode; A drone equipped with a three-component magnetic field sensor and a receiver is used to synchronously collect magnetic field components in three directions with different frequencies near the water surface; After data collection is completed, the total magnetic field strength is calculated from the magnetic field components in three directions, and then the data is cross-verified to ensure the consistency and relevance of the data; The magnetic field components in three directions, the total magnetic field intensity and the gradient are imaged and processed respectively to obtain the abnormal plane distribution maps of different frequencies; The leakage area is identified based on the abnormal plane distribution map and the pipeline distribution.

2. The UAV pseudo-random electromagnetic underwater pipeline leakage detection method according to claim 1 is characterized in that: After data collection is completed, the total magnetic field strength is calculated from the magnetic field components in three directions, and then the data is cross-verified to ensure the consistency and relevance of the data, including: The magnetic field components in three directions are fitted by linear fitting, and the fitting models of the three magnetic field components are obtained as follows: in, , , are the magnetic field components in the x, y, and z directions, respectively, , , are the offset terms of the magnetic field components in the x, y, and z directions, respectively, , , are the fitting coefficients of the magnetic field components in the x, y, and z directions, respectively, p is the position, , , are the error terms of the magnetic field components in the x, y, and z directions respectively; The fitted model of one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check whether there is consistency and correlation.

3. The pseudo-random electromagnetic underwater pipeline leakage detection method using an unmanned aerial vehicle according to claim 2 is characterized in that: Compare the fitted model of one magnetic field component with the actual data of the other two magnetic field components to check for consistency and correlation, including: Comparing the fitting model of one magnetic field component with the actual data of the magnetic field components in the other two directions, the correlation of the magnetic field components is obtained as follows: ·100% In the formula, For relevance, is the fitting model of a certain magnetic field component, is the actual value of the other magnetic field component; when 0~5% is used as the consistency and correlation to meet the data verification standard. If it exceeds 5%, it means that the single three-directional magnetic field component data cannot correctly display the underwater pipeline leakage location. In this case, the magnetic field intensity and gradient changes are used to reveal the underwater pipeline leakage location or the data is collected again.

4. The method for detecting underwater pipeline leakage by pseudo-random electromagnetic using an unmanned aerial vehicle according to claim 1, characterized in that: A three-component magnetic field sensor is mounted on a drone to synchronously collect magnetic field components in three directions of different frequencies using a regular survey line collection method or an irregular collection method near the water surface.

5. The method for detecting underwater pipeline leakage by pseudo-random electromagnetic using an unmanned aerial vehicle according to claim 1, characterized in that: The three-directional magnetic field components, total magnetic field intensity and gradient are imaged and processed respectively to obtain the abnormal plane distribution maps of different frequencies, including: The three-directional magnetic field components at different frequencies are analyzed for anomalies in the leakage area, and an abnormal plane distribution diagram of the magnetic field components in each direction at different frequencies is generated; According to the magnetic field component data in three directions at different frequencies, the total magnetic field intensity is calculated, and the anomaly analysis of the leakage area is performed on the total magnetic field intensity to generate the anomaly plane distribution map of the total magnetic field at different frequencies; The gradient changes of the magnetic field components in three directions at different frequencies are calculated, and the abnormal analysis of the leakage area is performed on the changes in the magnetic field gradient to generate the abnormal plane distribution map of the magnetic field gradient changes at different frequencies.

6. A pseudo-random electromagnetic underwater pipeline leakage detection device using an unmanned aerial vehicle, characterized in that: The device comprises: a transmitting system and a receiving system; The transmitting system comprises: a signal controller, an excitation power supply, a field source electrode A and a field source electrode B; one end of the field source electrode A and the field source electrode B are connected to the signal controller, and the other ends of the field source electrode A and the field source electrode B are respectively arranged at the inlet and outlet of the underwater water pipeline to be detected; the excitation power supply is used to provide power for the signal controller; after the transmitting system is arranged, pseudo-random electromagnetic signals of different frequencies are sent through the signal controller; The receiving system includes a three-component magnetic sensor, a receiver and a drone; the drone is equipped with the three-component magnetic sensor and the receiver to synchronously collect pseudo-random electromagnetic signals on the water inlet surface, and the drone records the movement trajectory in real time; The receiver is used to perform interactive verification of data after data acquisition is completed, after calculating the total magnetic field intensity from the three-directional magnetic field components, to ensure the consistency and relevance of the data; perform imaging processing on the three-directional magnetic field components, the total magnetic field intensity and the gradient respectively to obtain abnormal plane distribution maps of different frequencies; and identify the leakage area according to the abnormal plane distribution map and the pipeline distribution.

7. The UAV pseudo-random electromagnetic underwater pipeline leakage detection device according to claim 6 is characterized in that: After data collection is completed, the total magnetic field strength is calculated from the magnetic field components in three directions, and then the data is cross-verified to ensure the consistency and relevance of the data, including: The magnetic field components in three directions are fitted by linear fitting, and the fitting models of the three magnetic field components are obtained as follows: in, , , are the magnetic field components in the x, y, and z directions, respectively, , , are the offset terms of the magnetic field components in the x, y, and z directions, respectively, , , are the fitting coefficients of the magnetic field components in the x, y, and z directions, respectively, p is the position, , , are the error terms of the magnetic field components in the x, y, and z directions respectively; The fitted model of one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check whether there is consistency and correlation.

8. The UAV pseudo-random electromagnetic underwater pipeline leakage detection device according to claim 7 is characterized in that: Compare the fitted model of one magnetic field component with the actual data of the other two magnetic field components to check for consistency and correlation, including: Comparing the fitting model of one magnetic field component with the actual data of the magnetic field components in the other two directions, the correlation of the magnetic field components is obtained as follows: ·100% In the formula, For relevance, is the fitting model of a certain magnetic field component, is the actual value of the other magnetic field component; when 0~5% is used as the consistency and correlation to meet the data verification standard. If it exceeds 5%, it means that the single three-directional magnetic field component data cannot correctly display the underwater pipeline leakage location. In this case, the magnetic field intensity and gradient changes are used to reveal the underwater pipeline leakage location or the data is collected again.

9. The UAV pseudo-random electromagnetic underwater pipeline leakage detection device according to claim 6 is characterized in that: A three-component magnetic field sensor is mounted on a drone to synchronously collect magnetic field components in three directions of different frequencies using a regular survey line collection method or an irregular collection method near the water surface.

10. The UAV pseudo-random electromagnetic underwater pipeline leakage detection device according to claim 6, characterized in that: The three-directional magnetic field components, total magnetic field intensity and gradient are imaged and processed respectively to obtain the abnormal plane distribution maps of different frequencies, including: The three-directional magnetic field components at different frequencies are analyzed for anomalies in the leakage area, and an abnormal plane distribution diagram of the magnetic field components in each direction at different frequencies is generated; According to the magnetic field component data in three directions at different frequencies, the total magnetic field intensity is calculated, and the anomaly analysis of the leakage area is performed on the total magnetic field intensity to generate the anomaly plane distribution map of the total magnetic field at different frequencies; The gradient changes of the magnetic field components in three directions at different frequencies are calculated, and the abnormal analysis of the leakage area is performed on the changes in the magnetic field gradient to generate the abnormal plane distribution map of the magnetic field gradient changes at different frequencies.

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