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

Abstract

The application relates to a kind of unmanned aerial vehicle pseudo-random electromagnetic underwater pipeline leakage detection method and device, the method utilizes the magnetic field response difference of leakage area and normal area, and the change of magnetic field is used to identify pipeline leakage position and range.By host transmitting multiple sequence variable frequency density pseudo-random electromagnetic signal, signal receiver is carried by unmanned aerial vehicle, signal measurement and feedback are carried out in near water surface area, meet the detection needs of underwater pipeline of different depth, improve the longitudinal resolution of pipeline leakage, at the same time, by enhancing the intensity of main frequency signal, effectively reduce underwater electromagnetic interference, thereby significantly improve the precision of water pipeline leakage detection;The method uses unmanned aerial vehicle to carry three-component magnetic field receiver, three-component probe can simultaneously capture three orthogonal direction magnetic field components, this characteristic helps to reduce the influence on data acquisition caused by receiver posture change when unmanned aerial vehicle flies.

Description

Technical Field

[0001] This application relates to the field of underwater water pipeline leakage monitoring technology, and in particular to a method and device for detecting underwater pipeline leakage using pseudo-random electromagnetic methods from unmanned aerial vehicles. Background Technology

[0002] There are numerous methods for detecting leaks in underwater water pipelines, including manual inspection, hardware monitoring (such as electrical, acoustic, gas detection, and pressure monitors), Distributed Fiber Optic Sensing (DFOS), far-infrared thermal imaging, acoustic monitoring, correlation analysis, tracer gas detection, closed-circuit television (CCTV) pipeline inspection, and ground penetrating radar (GPR). Each method has its own advantages, but in practical applications, they are often limited by terrain conditions, pipeline depth, and environmental interference, thus exhibiting certain limitations.

[0003] Currently, leakage in water pipelines has become one of the major reasons for the widespread losses of domestic water pipeline companies. Especially in the inspection of underwater water pipelines, traditional monitoring methods are difficult to achieve ideal results, specifically in the following aspects: (1) Inability to accurately locate the leakage location and provide information on the leakage range: Existing technologies are difficult to obtain accurate leakage points and leakage ranges in complex underwater environments, often leading to missed or false detections. (2) Pipelines are located in underwater strata, making construction difficult and costly: The underwater working environment is complex, requiring the use of diving equipment or specialized underwater construction tools, which significantly increases the difficulty of construction and maintenance costs. (3) Low construction efficiency, leading to further leakage: Due to the slow speed of underwater pipeline construction and inspection, pipeline leakage problems often worsen between inspection and repair, expanding the scope of pipeline damage. Summary of the Invention

[0004] Therefore, it is necessary to provide a method and device for detecting underwater pipeline leaks using pseudo-random electromagnetic signals from a UAV, addressing the aforementioned technical problems. This method involves the host computer emitting multiple sequences of pseudo-random electromagnetic signals with variable frequency density, which are then measured and fed back by a signal receiver mounted on a UAV in the near-water surface area. Its working principle is based on utilizing the difference in magnetic field response between the leaking area and the normal area; changes in the magnetic field are used to identify the location and extent of the pipeline leak.

[0005] A method for detecting underwater pipeline leaks using pseudo-random electromagnetic signals from unmanned aerial vehicles (UAVs), the method comprising: A field source electrode is placed at the inlet and outlet of the underwater water pipeline to be tested, and a signal controller sends pseudo-random electromagnetic signals of different frequencies to the field source electrode. A drone equipped with a three-component magnetic field sensor and receiver was used to simultaneously collect magnetic field components of three directions at different frequencies near the water surface.

[0006] After data acquisition, the total magnetic field strength is calculated from the three directional magnetic field components, and then the data is interactively verified to ensure data consistency and relevance.

[0007] Imaging was performed on the magnetic field components in three directions, the total magnetic field strength, and the gradient to obtain anomalous planar distribution maps at different frequencies.

[0008] Identify leakage areas based on the abnormal planar distribution map and pipeline distribution.

[0009] In one embodiment, after data acquisition is completed, the total magnetic field strength is calculated from the three directional magnetic field components, and then the data is interactively verified to ensure data consistency and correlation, including: The magnetic field components in the three directions are fitted using a linear fitting method, resulting in the following fitting model for the three magnetic field components:

[0010]

[0011]

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

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

[0014] In one embodiment, the fitting model for one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check for consistency and correlation, including: By comparing the fitted model for one magnetic field component with the actual data for the other two magnetic field components, the correlation between the magnetic field components is obtained as follows: 100% In the formula, For correlation, This is a fitting model for a certain magnetic field component. This is the actual value of another magnetic field component.

[0015] when A consistency and correlation ratio of 0-5% is considered to meet the data validation standard. If the ratio exceeds 5%, it indicates that the data of the three magnetic field components in a single direction cannot correctly show the location of the underwater pipeline leakage. In this case, the magnetic field strength and gradient changes are used to reveal the location of the underwater pipeline leakage, or the data is collected again.

[0016] In one embodiment, a drone equipped with a three-component magnetic field sensor is used to simultaneously collect three magnetic field components of different frequencies in the near-water surface using either a regular survey line acquisition method or an irregular acquisition method.

[0017] In one embodiment, imaging processing is performed on the three directional magnetic field components, the total magnetic field strength, and the gradient to obtain anomaly plane distribution maps of different frequencies, including: Anomaly analysis of the leakage area was performed on the three-directional magnetic field components at different frequencies, generating anomaly planar distribution maps of the magnetic field components in each direction at different frequencies.

[0018] Based on the magnetic field component data in three directions at different frequencies, the total magnetic field strength is calculated, and anomaly analysis of the leakage area is performed on the total magnetic field strength to generate anomaly planar distribution maps of the total magnetic field at different frequencies.

[0019] The gradient changes of the three-directional magnetic field components at different frequencies are calculated, and anomaly analysis of the leakage area is performed on the changes in the magnetic field gradient to generate anomaly planar distribution maps of the magnetic field gradient changes at different frequencies.

[0020] A pseudo-random electromagnetic underwater pipeline leakage detection device for unmanned aerial vehicles (UAVs) includes 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 both field source electrode A and field source electrode B is connected to the signal controller, and the other ends of field source electrode A and field source electrode B are respectively arranged at the inlet and outlet of the underwater water transmission pipeline to be tested; the excitation power supply is used to provide power to 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, equipped with the three-component magnetic sensor and receiver, synchronously collects pseudo-random electromagnetic signals at the water inlet surface, and records the movement trajectory in real time.

[0023] The receiver is used to perform interactive verification of the data after the data acquisition is completed, by calculating the total magnetic field strength from the three magnetic field components, to ensure the consistency and correlation of the data; it performs imaging processing on the three magnetic field components, the total magnetic field strength and the gradient respectively to obtain anomaly plane distribution maps of different frequencies; and it identifies the leakage area based on the anomaly plane distribution map and the pipeline distribution.

[0024] In one embodiment, after data acquisition is completed, the total magnetic field strength is calculated from the three directional magnetic field components, and then the data is interactively verified to ensure data consistency and correlation, including: The magnetic field components in the three directions are fitted using a linear fitting method, resulting in the following fitting model for the three magnetic field components:

[0025]

[0026]

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

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

[0029] In one embodiment, the fitting model for one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check for consistency and correlation, including: By comparing the fitted model for one magnetic field component with the actual data for the other two magnetic field components, the correlation between the magnetic field components is obtained as follows: 100% In the formula, For correlation, This is a fitting model for a certain magnetic field component. This is the actual value of another magnetic field component.

[0030] when A consistency and correlation ratio of 0-5% is considered to meet the data validation standard. If the ratio exceeds 5%, it indicates that the data of the three magnetic field components in a single direction cannot correctly show the location of the underwater pipeline leakage. In this case, the magnetic field strength and gradient changes are used to reveal the location of the underwater pipeline leakage, or the data is collected again.

[0031] In one embodiment, a drone equipped with a three-component magnetic field sensor is used to simultaneously collect three magnetic field components of different frequencies in the near-water surface using either a regular survey line acquisition method or an irregular acquisition method.

[0032] In one embodiment, imaging processing is performed on the three directional magnetic field components, the total magnetic field strength, and the gradient to obtain anomaly plane distribution maps of different frequencies, including: Anomaly analysis of the leakage area was performed on the three-directional magnetic field components at different frequencies, generating anomaly planar distribution maps of the magnetic field components in each direction at different frequencies.

[0033] Based on the magnetic field component data in three directions at different frequencies, the total magnetic field strength is calculated, and anomaly analysis of the leakage area is performed on the total magnetic field strength to generate anomaly planar distribution maps of the total magnetic field at different frequencies.

[0034] The gradient changes of the three-directional magnetic field components at different frequencies are calculated, and anomaly analysis of the leakage area is performed on the changes in the magnetic field gradient to generate anomaly planar distribution maps of the magnetic field gradient changes at different frequencies.

[0035] The aforementioned UAV-based pseudo-random electromagnetic underwater pipeline leakage detection method and device utilizes the difference in magnetic field response between the leakage area and the normal area to identify the location and extent of pipeline leakage through changes in the magnetic field. The method involves the host emitting a multi-sequence, variable-frequency-density pseudo-random electromagnetic signal, which is then measured and fed back by a signal receiver mounted on the UAV near the water surface. This meets the detection needs of underwater pipelines at different depths, improves the longitudinal resolution of pipeline leakage, and effectively reduces underwater electromagnetic interference by enhancing the strength of the dominant frequency signal, thereby significantly improving the accuracy of water pipeline leakage detection. Furthermore, this method employs a UAV equipped with a three-component magnetic field receiver. The three-component probe can simultaneously capture magnetic field components in three orthogonal directions. This characteristic helps reduce the impact of receiver attitude changes on data acquisition during UAV flight. Attached Figure Description

[0036] Figure 1 This is a flowchart illustrating a pseudo-random electromagnetic underwater pipeline leakage detection method using a drone in one embodiment. Figure 2 This is a plan view of a drone-based pseudo-random electromagnetic underwater pipeline leakage detection method in one embodiment. Figure 3This is a cross-sectional schematic diagram of a drone-based pseudo-random electromagnetic underwater pipeline leakage detection method in one embodiment. Figure 4 The following is a schematic diagram of the acquisition method in another embodiment, where (a) is a schematic diagram of the "z"-shaped back-and-forth measurement method, (b) is a schematic diagram of the measurement method in the same direction of the measurement line, and (c) is a random acquisition method; Figure 5 This is a comparison chart of the field value amplitudes at the leakage and non-leakage locations in one embodiment. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0038] In one embodiment, such as Figure 1 As shown, a pseudo-random electromagnetic underwater pipeline leakage detection method using an unmanned aerial vehicle (UAV) is provided. This method includes the following steps: Step 100: Place a field source electrode at the inlet and outlet of the underwater water pipeline to be tested, and use a signal controller to send pseudo-random electromagnetic signals of different frequencies to the field source electrodes.

[0039] Specifically, one end of the source electrodes A and B is connected to the signal controller, while the other ends of the source electrodes A and B are respectively positioned 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 signals is 1Hz~10kHz. A schematic diagram of the UAV pseudo-random electromagnetic underwater pipeline leakage detection is shown below. Figure 2 As shown.

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

[0041] Specifically, a payload is used to simultaneously collect data from a three-component magnetic field sensor and receiver near the water surface (0.2m-1m range). The three-component magnetic sensor is located at the bottom of the entire system, close to the water surface. The UAV payload (or other payloads) needs a trajectory-recording positioning system to record the movement trajectory in real time for easy data processing later. A cross-sectional schematic diagram of UAV pseudo-random electromagnetic underwater pipeline leakage detection is shown below. Figure 3 As shown.

[0042] Step 104: After data acquisition is completed, the total magnetic field strength is calculated from the three directional magnetic field components, and then the data is interactively verified to ensure the consistency and relevance of the data.

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

[0044] Step 106: Imaging the magnetic field components, total magnetic field strength, and gradient in the three directions respectively to obtain anomaly plane distribution maps of different frequencies.

[0045] Specifically, anomaly analysis of the leakage area is performed on the three-directional magnetic field components (x, y, and z directions) at different frequencies, and finally anomaly planar distribution maps of each magnetic field component at different frequencies are generated to intuitively show the abnormal characteristics of each component in the leakage area.

[0046] The total magnetic field strength is calculated using the magnetic field component data in three directions. Anomaly analysis of the leakage area is then performed on the calculated total field data to generate anomaly planar distribution maps of the total magnetic field at different frequencies, which can more comprehensively reflect the magnetic field anomalies in the leakage area.

[0047] By calculating the gradient changes of the three components of the magnetic field, the variation of the magnetic field gradient is analyzed. Then, anomaly analysis of the leakage area is performed on the changes in the magnetic field gradient, generating anomaly planar distribution maps of magnetic field gradient changes at different frequencies, and further identifying leakage anomalies from the perspective of gradient changes.

[0048] Step 108: Identify the leakage area based on the abnormal planar distribution map and pipeline distribution.

[0049] Specifically, the steps for identifying leakage areas based on the abnormal planar distribution map and pipe distribution are as follows: 1) Ensure that the coordinates of the anomaly plan 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, delineate the candidate areas for suspected leakage points.

[0051] 3) Compare the locations of the magnetic field anomalies with the distribution of the pipelines to find overlapping or close areas, and mark them as possible leakage areas.

[0052] 4) For overlapping areas, suspected leakage areas are identified and marked by using various magnetic field properties (horizontal and vertical components, gradient changes, magnetic field anomaly intensity, etc.).

[0053] In the aforementioned UAV pseudo-random electromagnetic underwater pipeline leakage detection method, the method utilizes the difference in magnetic field response between the leakage area and the normal area to identify the location and extent of pipeline leakage through changes in the magnetic field. The host computer emits pseudo-random electromagnetic signals with multiple sequences of variable frequency density, and the UAV, equipped with a signal receiver, performs signal measurement and feedback in the near-water surface area. This meets the detection needs of underwater pipelines at different depths, improves the longitudinal resolution of pipeline leakage, and effectively reduces underwater electromagnetic interference by enhancing the strength of the dominant frequency signal, thereby significantly improving the accuracy of water pipeline leakage detection. This method uses a UAV equipped with a three-component magnetic field receiver. The three-component probe can simultaneously capture magnetic field components in three orthogonal directions. This characteristic helps reduce the impact of receiver attitude changes on data acquisition during UAV flight.

[0054] In one embodiment, step 104 includes: fitting the three directional magnetic field components using a linear fitting method to obtain the fitting model for the three magnetic field components as follows:

[0055]

[0056]

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

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

[0059] In one embodiment, the fitting model for one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check for consistency and correlation, including: By comparing the fitted model for one magnetic field component with the actual data for the other two magnetic field components, the correlation between the magnetic field components is obtained as follows: 100% In the formula, For correlation, This is a fitting model for a certain magnetic field component. This is the actual value of another magnetic field component.

[0060] when A consistency and correlation ratio of 0-5% is considered to meet the data validation standard. If the ratio exceeds 5%, it indicates that the data of the three magnetic field components in a single direction cannot correctly show the location of the underwater pipeline leakage. In this case, the magnetic field strength and gradient changes are used to reveal the location of the underwater pipeline leakage, or the data is collected again.

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

[0062] In one embodiment, a drone equipped with a three-component magnetic field sensor is used to simultaneously collect three magnetic field components of different frequencies in the near-water surface using either a regular survey line acquisition method or an irregular acquisition method.

[0063] Specifically, there are two data collection methods: one is regular survey line collection, where the survey line direction is perpendicular to the direction of the water pipeline, and the measurement is performed back and forth in a "Z" pattern. Figure 4 (a) or the same direction of the survey line (measurement in the same direction) Figure 4 (b)); the second is the irregular method ( Figure 4 (c) means that the receiving system can move arbitrarily and randomly in the area to be measured (above the water pipeline) and directly complete the data acquisition.

[0064] In one embodiment, step 106 includes: performing anomaly analysis on the leakage area for the three-directional magnetic field components at different frequencies, generating anomaly planar distribution maps of the magnetic field components in each direction at different frequencies; calculating the total magnetic field strength based on the three-directional magnetic field component data at different frequencies, performing anomaly analysis on the leakage area for the total magnetic field strength, generating anomaly planar distribution maps of the total magnetic field at different frequencies; calculating the gradient changes of the three-directional magnetic field components at different frequencies, performing anomaly analysis on the changes in the magnetic field gradient for the leakage area, generating anomaly planar distribution maps of the changes in the magnetic field gradient at different frequencies.

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

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

[0067] Spatial gradients are calculated for magnetic field data at various frequencies, with gradients in the X and Y directions calculated separately for burial depth. This generates magnetic field gradient variation maps for different frequencies, and the gradient differences between deep and shallow burial areas are marked. By comparing gradient distribution maps under different frequency and burial depth conditions, areas with significant abrupt changes in magnetic field gradients are identified, thereby accurately locating potential leakage points.

[0068] It should be understood that, although Figure 1 The steps in the flowchart are shown sequentially as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order in which these steps are executed, and they can be performed in other orders. Figure 1 At least some of the steps in the process may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed 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 some of the sub-steps or stages of other steps.

[0069] In one embodiment, a drone pseudo-random electromagnetic underwater pipeline leakage detection device is provided, the device comprising: 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 both field source electrode A and field source electrode B is connected to the signal controller, and the other ends of field source electrode A and field source electrode B are respectively arranged at the inlet and outlet of the underwater water transmission pipeline to be tested; the excitation power supply is used to provide power to 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 emits multi-sequence broadband pseudo-random electromagnetic signals with a frequency range of 1Hz to 10kHz. This setup aims to meet the detection needs of underwater pipelines at different depths, improve the longitudinal resolution of pipeline leaks, and effectively reduce underwater electromagnetic interference by enhancing the strength of the main frequency signal, thereby significantly improving the accuracy of water pipeline leak detection.

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

[0073] Specifically, the 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 simultaneously capture magnetic field components in three orthogonal directions. This feature helps to reduce the impact of receiver attitude changes on data acquisition during drone flight.

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

[0076] The drone records its movement trajectory in real time to correspond to the location of continuously collected signals. This is a conventional data processing method and therefore is not described in the data processing flow.

[0077] The receiver is used to perform interactive verification of the data after the data acquisition is completed, by calculating the total magnetic field strength from the three magnetic field components, to ensure the consistency and correlation of the data; it performs imaging processing on the three magnetic field components, the total magnetic field strength and the gradient respectively to obtain anomaly plane distribution maps of different frequencies; and it identifies the leakage area based on the anomaly plane distribution map and the pipeline distribution.

[0078] The comparison chart of the field value amplitudes of the measuring lines at the locations of leakage and non-leakage is shown below. Figure 5 As shown.

[0079] In one embodiment, after data acquisition is completed, the total magnetic field strength is calculated from the three directional magnetic field components, and then the data is interactively verified to ensure data consistency and correlation, including: The magnetic field components in the three directions are fitted using a linear fitting method, resulting in the following fitting model for the three magnetic field components:

[0080]

[0081]

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

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

[0084] In one embodiment, the fitting model for one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check for consistency and correlation, including: By comparing the fitted model for one magnetic field component with the actual data for the other two magnetic field components, the correlation between the magnetic field components is obtained as follows: 100% In the formula, For correlation, This is a fitting model for a certain magnetic field component. This is the actual value of another magnetic field component.

[0085] when A consistency and correlation ratio of 0-5% is considered to meet the data validation standard. If the ratio exceeds 5%, it indicates that the data of the three magnetic field components in a single direction cannot correctly show the location of the underwater pipeline leakage. In this case, the magnetic field strength and gradient changes are used to reveal the location of the underwater pipeline leakage, or the data is collected again.

[0086] In one embodiment, a drone equipped with a three-component magnetic field sensor is used to simultaneously collect three magnetic field components of different frequencies in the near-water surface using either a regular survey line acquisition method or an irregular acquisition method.

[0087] In one embodiment, imaging processing is performed on the three directional magnetic field components, the total magnetic field strength, and the gradient to obtain anomaly plane distribution maps of different frequencies, including: Anomaly analysis of the leakage area was performed on the three-directional magnetic field components at different frequencies, generating anomaly planar distribution maps of the magnetic field components in each direction at different frequencies.

[0088] Based on the magnetic field component data in three directions at different frequencies, the total magnetic field strength is calculated, and anomaly analysis of the leakage area is performed on the total magnetic field strength to generate anomaly planar distribution maps of the total magnetic field at different frequencies.

[0089] The gradient changes of the three-directional magnetic field components at different frequencies are calculated, and anomaly analysis of the leakage area is performed on the changes in the magnetic field gradient to generate anomaly planar distribution maps of the magnetic field gradient changes at different frequencies.

[0090] The technical features of the above embodiments can be combined in any way. For the sake of brevity, 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 embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these modifications and improvements all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for detecting underwater pipeline leakage using pseudo-random electromagnetic signals from unmanned aerial vehicles (UAVs), characterized in that, The method includes: A field source electrode is arranged at the inlet and outlet of the underwater water pipeline to be tested, 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 receiver was used to simultaneously collect magnetic field components of three directions at different frequencies near the water surface. After data acquisition is completed, the total magnetic field strength is calculated from the three directional magnetic field components, and then the data is interactively verified to ensure the consistency and relevance of the data. Imaging processing is performed on the three directional magnetic field components, the total magnetic field strength, and the gradient to obtain anomaly planar distribution maps at different frequencies. Specifically, this includes: performing anomaly analysis on the leakage areas of the three directional magnetic field components at different frequencies to generate anomaly planar distribution maps of the magnetic field components in each direction at different frequencies; calculating the total magnetic field strength based on the three directional magnetic field component data at different frequencies, performing anomaly analysis on the leakage areas of the total magnetic field strength to generate anomaly planar distribution maps of the total magnetic field at different frequencies; calculating the gradient changes of the three directional magnetic field components at different frequencies, performing anomaly analysis on the changes in the magnetic field gradient to generate anomaly planar distribution maps of the magnetic field gradient changes at different frequencies. The leakage areas were identified based on the abnormal planar distribution map and the pipeline distribution.

2. The UAV pseudo-random electromagnetic underwater pipeline leakage detection method according to claim 1, characterized in that, After data acquisition, the total magnetic field strength is calculated from the three directional magnetic field components. Then, cross-validation of the data is performed to ensure consistency and relevance, including: The magnetic field components in the three directions are fitted using a linear fitting method, resulting in the following fitting model for the three magnetic field components: in, , , These represent the magnetic field components in the x, y, and z directions, respectively. , , These are the offset terms for the magnetic field components in the x, y, and z directions, respectively. , , These are the fitting coefficients for the magnetic field components in the x, y, and z directions, respectively. p For location, , , These are the error terms for the magnetic field components in the x, y, and z directions, respectively. The fitted model for one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check for consistency and correlation.

3. The UAV pseudo-random electromagnetic underwater pipeline leakage detection method according to claim 2, characterized in that, The fitted model for one magnetic field component is compared with the actual data for the other two magnetic field components to check for consistency and correlation, including: By comparing the fitted model for one magnetic field component with the actual data for the other two magnetic field components, the correlation between the magnetic field components is obtained as follows: ·100% In the formula, For correlation, This is a fitting model for a certain magnetic field component. This is the actual value of another magnetic field component; when A consistency and correlation ratio of 0-5% is considered to meet the data validation standard. If the ratio exceeds 5%, it indicates that the data of the three magnetic field components in a single direction cannot correctly show the location of the underwater pipeline leakage. In this case, the magnetic field strength and gradient changes are used to reveal the location of the underwater pipeline leakage, or the data is collected again.

4. The UAV pseudo-random electromagnetic underwater pipeline leakage detection method according to claim 1, characterized in that, Using a drone equipped with a three-component magnetic field sensor, magnetic field components of three directions at different frequencies are simultaneously collected near the water surface using either a regular survey line or an irregular survey method.

5. A pseudo-random electromagnetic underwater pipeline leakage detection device for unmanned aerial vehicles, characterized in that, The device includes: a transmitting system and a receiving system; 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 each of the field source electrode A and the field source electrode B is 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 transmission pipeline to be detected; the excitation power supply is used to provide power to the signal controller; after the transmitting system is deployed, 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, equipped with the three-component magnetic sensor and the receiver, synchronously collects pseudo-random electromagnetic signals at the water inlet surface, and the drone records its movement trajectory in real time. The receiver is used to perform interactive verification of the data after the data acquisition is completed, after calculating the total magnetic field strength from the three directional magnetic field components, to ensure the consistency and correlation of the data; to perform imaging processing on the three directional magnetic field components, the total magnetic field strength and the gradient respectively to obtain anomaly plane distribution maps of different frequencies; and to identify the leakage area based on the anomaly plane distribution maps and the pipeline distribution. The imaging process involves analyzing the magnetic field components in three directions, the total magnetic field strength, and the gradient to obtain anomaly plane distribution maps at different frequencies, including: Anomaly analysis of the leakage area was performed on the three-directional magnetic field components at different frequencies, generating anomaly planar distribution maps of the magnetic field components in each direction at different frequencies. Based on the magnetic field component data in three directions at different frequencies, the total magnetic field strength is calculated, and anomaly analysis of the leakage area is performed on the total magnetic field strength to generate anomaly planar distribution maps of the total magnetic field at different frequencies. The gradient changes of the three-directional magnetic field components at different frequencies are calculated, and anomaly analysis of the leakage area is performed on the changes in the magnetic field gradient to generate anomaly planar distribution maps of the magnetic field gradient changes at different frequencies.

6. The UAV pseudo-random electromagnetic underwater pipeline leakage detection device according to claim 5, characterized in that, After data acquisition, the total magnetic field strength is calculated from the three directional magnetic field components. Then, cross-validation of the data is performed to ensure consistency and relevance, including: The magnetic field components in the three directions are fitted using a linear fitting method, resulting in the following fitting model for the three magnetic field components: in, , , These represent the magnetic field components in the x, y, and z directions, respectively. , , These are the offset terms for the magnetic field components in the x, y, and z directions, respectively. , , These are the fitting coefficients for the magnetic field components in the x, y, and z directions, respectively. p For location, , , These are the error terms for the magnetic field components in the x, y, and z directions, respectively. The fitted model for one magnetic field component is compared with the actual data of the magnetic field components in the other two directions to check for consistency and correlation.

7. The UAV pseudo-random electromagnetic underwater pipeline leakage detection device according to claim 6, characterized in that, The fitted model for one magnetic field component is compared with the actual data for the other two magnetic field components to check for consistency and correlation, including: By comparing the fitted model for one magnetic field component with the actual data for the other two magnetic field components, the correlation between the magnetic field components is obtained as follows: ·100% In the formula, For correlation, This is a fitting model for a certain magnetic field component. This is the actual value of another magnetic field component; when A consistency and correlation ratio of 0-5% is considered to meet the data validation standard. If the ratio exceeds 5%, it indicates that the data of the three magnetic field components in a single direction cannot correctly show the location of the underwater pipeline leakage. In this case, the magnetic field strength and gradient changes are used to reveal the location of the underwater pipeline leakage, or the data is collected again.

8. The UAV pseudo-random electromagnetic underwater pipeline leakage detection device according to claim 5, characterized in that, Using a drone equipped with a three-component magnetic field sensor, magnetic field components of three directions at different frequencies are simultaneously collected near the water surface using either a regular survey line or an irregular survey method.

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