Foreign matter detection method, device, and apparatus for power conductor, storage medium, and program product

Abstract

The application relates to a foreign matter detection method, device and equipment of a power conductor, a storage medium and a program product. The method comprises the following steps: acquiring load data of the power conductor on a preset inspection path; loading the load data to an initial conductor model to obtain a three-dimensional dynamic model; and finally, detecting foreign matters on the power conductor on the preset inspection path according to the load data and the three-dimensional dynamic model to obtain a detection result. The detection result comprises a foreign matter object and an attachment state of the foreign matter object on the power conductor. The load data comprises at least one of image data, thermal imaging data, positioning data and laser radar data. The initial conductor model is obtained by three-dimensional modeling based on the power conductor and the surrounding environment of the power conductor. The above method realizes real-time and accurate detection and rapid response of foreign matters on the power conductor, and improves the safe operation and maintenance level of the power grid.

Description

Technical Field

[0001] This application relates to the field of power system safety monitoring technology, and in particular to a method, apparatus, equipment, storage medium, and program product for detecting foreign objects in power conductors. Background Technology

[0002] In the power system sector, power lines are a critical component of the transmission system, and the safe operation of the transmission system directly affects the stability of the power grid. However, in the natural environment, power lines are often threatened by foreign objects such as birds nesting, falling branches, and kites getting tangled, which may lead to short circuits, tripping, or even large-scale power outages.

[0003] Currently, methods for detecting foreign objects in power lines mainly rely on manual inspections or fixed camera monitoring, which suffers from inaccurate detection. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, equipment, storage medium, and program product for detecting foreign objects in power conductors that can improve the accuracy of detection, addressing the aforementioned technical problems.

[0005] In a first aspect, this application provides a method for detecting foreign objects in power conductors, the method comprising:

[0006] Acquire load data of power conductors along a preset inspection path; the load data includes at least one of image data, thermal imaging data, positioning data, and lidar data;

[0007] Load data is loaded into the initial conductor model to obtain a three-dimensional dynamic model; the initial conductor model is obtained by three-dimensional modeling based on the power conductor and its surrounding environment.

[0008] Foreign object detection is performed on power lines along a preset inspection path based on load data and a three-dimensional dynamic model, and the detection results are obtained. The detection results include the foreign object and its attachment status on the power lines.

[0009] In some embodiments, load data is loaded onto an initial conductor model to obtain a three-dimensional dynamic model, including:

[0010] Based on image data and thermal imaging data, determine the visual data of the power conductors;

[0011] Based on the positioning data and lidar data, determine the movement data of the foreign object;

[0012] Visual data of the duct and motion data of the foreign object are added to the initial duct model to obtain a three-dimensional dynamic model.

[0013] In some embodiments, visual data of the power conductor is determined based on image data and thermal imaging data, including:

[0014] Feature extraction is performed on image data and thermal imaging data respectively to obtain image feature data and thermal imaging feature data; the image features and thermal imaging features are feature data of the same foreign object at the same location;

[0015] Based on the pixel deviation of image feature data and thermal imaging feature data, sensor offset correction is performed on thermal imaging data and image data to obtain conductor visual data.

[0016] In some embodiments, sensor offset correction is performed on the thermal imaging data and image data based on the pixel deviation of the image feature data and thermal imaging feature data to obtain conductor visual data, including:

[0017] Determine the marker positions for image feature data and thermal imaging feature data; the marker positions are the feature point positions of the same physical entity.

[0018] Based on the marked positions, the image feature data and thermal imaging feature data are projected onto the reference plane to obtain the first projection position and the second projection position.

[0019] The pixel coordinates of the first projection position and the second projection position are determined respectively to obtain the first pixel coordinates and the second pixel coordinates;

[0020] Using the coordinates of the first pixel as a reference point, determine the direction and distance of the deviation between the coordinates of the second pixel and the coordinates of the first pixel;

[0021] The second pixel coordinates are corrected based on the deviation direction and deviation distance to make the first pixel coordinates and the second pixel coordinates coincide, thus obtaining the visual data of the conductor.

[0022] In some embodiments, foreign object detection is performed on power conductors along a preset inspection path based on load data and a three-dimensional dynamic model to obtain detection results, including:

[0023] Based on the abnormal temperature areas in the thermal imaging data, identify the overlapping areas in the image data that coincide with the abnormal temperature areas.

[0024] Identify overlapping areas to determine if there are foreign objects on the power lines, and if so, determine the position of the foreign objects in the 3D dynamic model.

[0025] The attachment status of the foreign object is determined based on its position, location data, and lidar data in the 3D dynamic model.

[0026] In some embodiments, the attachment state of the foreign object is determined based on its position in the 3D dynamic model, positioning data, and LiDAR data, including:

[0027] The positioning data is analyzed to determine the first trend of change of the foreign object, and the lidar data is analyzed to determine the second trend of change of the foreign object.

[0028] The dynamic parameters of the foreign object are determined based on the first and second trends of change; the dynamic parameters include at least one of the swing amplitude, rotational angular velocity, and displacement acceleration.

[0029] The attachment state of the foreign object is determined based on its dynamic parameters; the attachment state includes any one of the following: stable attachment state, oscillating state, or near-detachment state.

[0030] In some embodiments, the method further includes:

[0031] When the foreign object is in a state of imminent detachment, determine whether the emergency treatment equipment meets the emergency energy requirements based on the energy status of the emergency treatment equipment.

[0032] If the emergency response equipment meets the emergency energy requirements, a control signal is generated based on the attachment location of the foreign object and sent to the emergency response equipment to instruct it to perform a clearing operation.

[0033] If the emergency response equipment does not meet the emergency energy requirements, the trajectory of the foreign object is determined, and a warning area is determined based on the trajectory. A warning signal is generated based on the warning area and sent to the emergency response equipment to instruct the equipment to issue a warning.

[0034] Secondly, this application also provides a foreign object detection device for power conductors, the device comprising:

[0035] The acquisition module is used to acquire load data of power conductors along a preset inspection path; the load data includes at least one of image data, thermal imaging data, positioning data, and lidar data;

[0036] The loading module is used to load load data into the initial conductor model to obtain a three-dimensional dynamic model; the initial conductor model is obtained by three-dimensional modeling based on the power conductor and its surrounding environment.

[0037] The detection module is used to detect foreign objects on power lines along a preset inspection path based on load data and a three-dimensional dynamic model, and obtain the detection results. The detection results include the foreign object and its attachment status on the power line.

[0038] Thirdly, this application also provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to perform the following steps:

[0039] Acquire load data of power conductors along a preset inspection path; the load data includes at least one of image data, thermal imaging data, positioning data, and lidar data;

[0040] Load data is loaded into the initial conductor model to obtain a three-dimensional dynamic model; the initial conductor model is obtained by three-dimensional modeling based on the power conductor and its surrounding environment.

[0041] Foreign object detection is performed on power lines along a preset inspection path based on load data and a three-dimensional dynamic model, and the detection results are obtained. The detection results include the foreign object and its attachment status on the power lines.

[0042] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the following steps:

[0043] Acquire load data of power conductors along a preset inspection path; the load data includes at least one of image data, thermal imaging data, positioning data, and lidar data;

[0044] Load data is loaded into the initial conductor model to obtain a three-dimensional dynamic model; the initial conductor model is obtained by three-dimensional modeling based on the power conductor and its surrounding environment.

[0045] Foreign object detection is performed on power lines along a preset inspection path based on load data and a three-dimensional dynamic model, and the detection results are obtained. The detection results include the foreign object and its attachment status on the power lines.

[0046] Fifthly, this application also provides a computer program product, which includes a computer program that, when executed by a processor, performs the following steps:

[0047] Acquire load data of power conductors along a preset inspection path; the load data includes at least one of image data, thermal imaging data, positioning data, and lidar data;

[0048] Load data is loaded into the initial conductor model to obtain a three-dimensional dynamic model; the initial conductor model is obtained by three-dimensional modeling based on the power conductor and its surrounding environment.

[0049] Foreign object detection is performed on power lines along a preset inspection path based on load data and a three-dimensional dynamic model, and the detection results are obtained. The detection results include the foreign object and its attachment status on the power lines.

[0050] The aforementioned method, apparatus, equipment, storage medium, and program product for foreign object detection on power conductors involve acquiring load data of the power conductor along a preset inspection path, loading the load data into an initial conductor model to obtain a three-dimensional dynamic model, and finally performing foreign object detection on the power conductor along the preset inspection path based on the load data and the three-dimensional dynamic model to obtain the detection results. The detection results include the foreign object and its attachment status on the power conductor. The load data includes at least one of image data, thermal imaging data, positioning data, and lidar data. The initial conductor model is obtained through three-dimensional modeling of the power conductor and its surrounding environment. In this method, since foreign objects on power conductors have different physical characteristics and risk features, by fusing visible light image data, infrared thermal imaging, and lidar point cloud data, the morphological characteristics, temperature distribution, and spatial movement trajectory of the foreign object can be captured simultaneously, thereby comprehensively judging whether it is in a stable attachment, wind-induced swaying, or on the verge of detachment. This multi-dimensional comprehensive analysis fully considers the differences in characteristics of various foreign objects, avoids misjudgments that may be caused by a single detection method, and greatly improves the detection accuracy of foreign objects. Moreover, by loading the load data into the initial conductor model, a three-dimensional dynamic model is obtained. This model can reflect the actual state of the power conductor in real time and dynamically. By comparing the three-dimensional dynamic models at different times, the occurrence and changes of foreign objects can be more clearly discovered, realizing real-time accurate detection and rapid response to foreign objects in power conductors, and improving the level of safe operation and maintenance of the power grid. Attached Figure Description

[0051] Figure 1 These are internal structural diagrams of the computer device in some embodiments;

[0052] Figure 2 This is one of the flowcharts illustrating a foreign object detection method for power conductors in some embodiments;

[0053] Figure 3 This is a second schematic flowchart of a foreign object detection method for power conductors in some embodiments;

[0054] Figure 4 This is the third flowchart illustrating the foreign object detection method for power conductors in some embodiments;

[0055] Figure 5 This is a fourth flowchart illustrating a foreign object detection method for power conductors in some embodiments;

[0056] Figure 6This is the fifth flowchart illustrating a foreign object detection method for power conductors in some embodiments;

[0057] Figure 7 This is a flowchart of a foreign object detection method for power conductors in some embodiments, number six.

[0058] Figure 8 This is the seventh flowchart illustrating a foreign object detection method for power conductors in some embodiments;

[0059] Figure 9 This is a structural block diagram of a foreign object detection device for power lines in some embodiments. Detailed Implementation

[0060] In the embodiments of this application, the term "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following associated objects have an "or" relationship.

[0061] In the embodiments of this application, the term "multiple" refers to two or more, and other quantifiers are similar.

[0062] In the embodiments of this application, the term "at least one" means one or more. For example, at least one of A, B and C can represent six situations: A exists alone, B exists alone, C exists alone, A and B exist simultaneously, A and C exist simultaneously, B and C exist simultaneously, and A, B and C exist simultaneously.

[0063] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0064] In the power system sector, power conductors are a critical component of the transmission system, and the safe operation of the transmission system directly affects the stability of the power grid. However, in the natural environment, power conductors are often threatened by foreign objects such as bird nests, falling branches, and kite entanglements, which can lead to short circuits, tripping, or even large-scale power outages. Currently, foreign object detection methods for power conductors mainly rely on manual inspections or fixed camera monitoring, which suffers from low efficiency, slow response, and numerous blind spots, resulting in inaccurate detection.

[0065] In view of this, embodiments of this application propose a method, apparatus, device, storage medium, and program product for foreign object detection on power conductors. This method integrates image data, infrared thermal imaging data, positioning data, and lidar data to construct a three-dimensional dynamic model of the power conductor and its surrounding environment, enabling accurate identification and attachment status analysis of the conductor and foreign objects. Simultaneously, based on the attachment status, it adaptively switches between inspection and emergency tasks, dynamically generating warning zones and warning signals to drive emergency response equipment to perform handling tasks. The system further introduces a communication code and hash mapping mechanism to achieve secure communication between devices and the establishment of manual control channels, constructing an intelligent closed loop from detection and warning to response, significantly improving the real-time performance, accuracy, and security of foreign object detection on power conductors.

[0066] It should be noted that the beneficial effects or technical problems solved by the embodiments of this application are not limited to this one, but may also be other implicit or related problems. For details, please refer to the description of the embodiments below.

[0067] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0068] In some embodiments, the foreign object detection method for power conductors provided in this application can be applied to, for example... Figure 1 The computer device shown can be a terminal or a server, and its internal structure diagram can be as follows: Figure 1As shown, the computer device includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The input / output interface is used for exchanging information between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When executed by the processor, the computer program implements a method for detecting foreign objects in power lines. The display unit is used to form a visually visible image and can be a display screen, projection device, or virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads set on the casing of the computer device, or external keyboards, touchpads, or mice, etc.

[0069] Those skilled in the art will understand that Figure 1 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0070] In some embodiments, such as Figure 2 As shown, a method for detecting foreign objects in power conductors is provided, which can be applied to... Figure 1 Taking a computer device as an example, the explanation includes the following steps:

[0071] S201, Obtain load data of power conductors on the preset inspection path.

[0072] Payload data refers to the set of multimodal raw data collected by monitoring equipment during inspections using its airborne or fixedly installed sensors. Payload data includes at least one of image data, thermal imaging data, positioning data, and lidar data. The preset inspection path refers to a three-dimensional spatial flight or scanning route pre-planned based on the coordinates of power poles, conductor routes, and safety regulations, ensuring full coverage and blind-spot-free monitoring of the target line.

[0073] In this embodiment, monitoring equipment such as drones or fixed intelligent monitoring terminals can be pre-deployed around power lines to perform power line inspection tasks. This monitoring equipment is equipped with various sensors to collect image data, thermal imaging data, positioning data, and lidar data. During flight or scanning along a preset path, the monitoring equipment simultaneously activates a visible light camera to collect high-resolution image data to identify the morphological characteristics of the power line and attached foreign objects, activates an infrared thermal imager to acquire thermal imaging data to detect abnormal temperature zones caused by foreign object attachment or discharge, and uses GNSS (Global Navigation Satellite System) and IMU (Inertial Measurement Unit) fusion calculations to obtain centimeter-level precision positioning data to provide a spatial reference. It also utilizes lidar to actively scan and acquire high-precision point cloud data to construct a three-dimensional geometric model of the power line and its surrounding environment. After time synchronization and preliminary encapsulation by an airborne or edge computing unit, the aforementioned multi-source heterogeneous data is transmitted in real-time or near real-time to a backend computer device via a wireless communication link for data processing. The computer device can then obtain the load data of the power line along the preset inspection path.

[0074] Optionally, before acquiring the load data of the power conductor on the preset inspection path, the computer equipment may also include: determining the line partition and airspace partition of the power conductor, generating an electronic fence based on the no-fly zones of the airspace partition and line partition; determining the high-risk zone in the line partition, generating an initial inspection path based on the energy priority principle and the monitoring points and high-risk zones; and constraining the initial inspection path according to the electronic fence to obtain the preset inspection path.

[0075] Line zoning refers to the management unit that divides power lines into several logical segments based on voltage levels, tower coordinates, and terrain. Airspace zoning refers to the three-dimensional flight airspace designated for drones according to air traffic control requirements and inspection safety needs. No-fly zones refer to airspace areas that prohibit or restrict drone flights, including military zones, airport airspace, and densely populated areas. Electronic fences are virtual boundaries set up in the airspace using geographic coordinates and altitude information to restrict the flight range of drones. High-risk areas refer to line segments identified based on historical fault data, meteorological conditions, and environmental characteristics that are prone to foreign object intrusion. The energy priority principle refers to an optimization strategy that prioritizes extending drone endurance during path planning, taking into account factors such as total path length, flight speed, and wind resistance energy consumption.

[0076] Specifically, the system automatically divides the route into zones based on GIS maps and route ledger data, generates airspace zones with height constraints by combining them with the airspace management database, and converts the coordinates of no-fly zones into electronic fence boundaries through spatial overlay analysis. At the same time, by analyzing historical fault records, meteorological data, and on-site image samples, machine learning algorithms are used to identify high-risk sections such as bird habitats and densely vegetated areas. The path planning engine uses monitoring point locations as nodes and high-risk areas as key coverage areas. Using algorithms such as Dijkstra or A*, it generates an initial inspection path with the shortest total flight distance and the most comprehensive coverage while meeting electronic fence constraints. The path is then smoothed and optimized using an energy consumption model, and finally outputs a preset inspection path that meets safety regulations and endurance requirements.

[0077] S202, load the load data into the initial conductor model to obtain a three-dimensional dynamic model; the initial conductor model is obtained by three-dimensional modeling based on the power conductor and its surrounding environment.

[0078] The three-dimensional dynamic model refers to a four-dimensional (three-dimensional space + time) digital representation that integrates geometric structure, visual texture, temperature distribution, and spatial location information. It can reflect the precise spatial configuration and state changes of power conductors, attached foreign objects, and the surrounding environment in real time. Loading load data onto the initial conductor model specifically refers to the technical process of registering, associating, and integrating multi-source heterogeneous data from different sensors under a unified spatiotemporal reference to generate a comprehensive data product that surpasses the information value of a single data source.

[0079] In this embodiment, the computer device can pre-create an initial conductor model based on a 3D model of the power conductor and its surrounding environment. Then, after obtaining load data based on the above steps, the load data can be loaded onto the initial conductor model to obtain a three-dimensional dynamic model. Specifically, firstly, the data streams from each sensor are aligned using a time synchronization module. Then, calibration parameters are used to project the image and thermal imaging data onto a 3D mesh constructed from the lidar point cloud, and pixel-level fusion is achieved through feature matching. The positioning data provides absolute geographic coordinates for each fused data point, serving as a spatial reference. Finally, SLAM (Simultaneous Localization and Mapping) technology, combined with real-time data streams, dynamically updates changes such as conductor sway and foreign object displacement, forming a high-precision, interactive 3D model that can accurately recreate the spatial orientation of the conductor and monitor the dynamics of foreign objects.

[0080] S203, based on load data and a three-dimensional dynamic model, foreign object detection is performed on the power conductors along the preset inspection path to obtain the detection results; the detection results include the foreign object and its attachment status on the power conductor.

[0081] Foreign objects refer to non-inherent objects attached to or suspended from power lines, including but not limited to plastic film, kites, bird nests, and tree branches. Attachment status is a comprehensive evaluation indicator used to describe the connection between the foreign object and the power line, its stability, and the level of risk. Specifically, it includes stable attachment, swaying, slipping, or imminent detachment.

[0082] In this embodiment, a computer device can locate and classify foreign objects in fused visual data using a trained deep learning object detection model. Subsequently, by analyzing the changes in the three-dimensional spatial coordinates of the foreign object in a continuous time series (provided by lidar point clouds and positioning data), and combining the changes in its morphological features in the image sequence, the device comprehensively calculates its dynamic parameters such as swing amplitude, displacement velocity, and rotational attitude. This allows for an accurate determination of whether the object is in a stable attached state, swinging due to wind, or in a dynamic state with a risk of detachment, providing a basis for subsequent early warning and handling decisions.

[0083] The foreign object detection method for power lines provided in this application involves acquiring load data of the power line along a preset inspection path, loading the load data into an initial power line model to obtain a three-dimensional dynamic model, and finally performing foreign object detection on the power line along the preset inspection path based on the load data and the three-dimensional dynamic model to obtain the detection result. The detection result includes the foreign object and its attachment state on the power line. The load data includes at least one of image data, thermal imaging data, positioning data, and lidar data. The initial power line model is obtained by three-dimensional modeling based on the power line and its surrounding environment. In this method, since foreign objects on power lines have different physical characteristics and risk features, by fusing visible light image data, infrared thermal imaging, and lidar point cloud data, the morphological characteristics, temperature distribution, and spatial movement trajectory of the foreign object can be captured simultaneously, thereby comprehensively judging whether it is in a stable attachment, wind-induced swaying, or on the verge of detachment. This multi-dimensional comprehensive analysis fully considers the differences in characteristics of various foreign objects, avoids misjudgments that may be caused by a single detection method, and greatly improves the detection accuracy of foreign objects. Moreover, by loading the load data into the initial conductor model, a three-dimensional dynamic model is obtained. This model can reflect the actual state of the power conductor in real time and dynamically. By comparing the three-dimensional dynamic models at different times, the occurrence and changes of foreign objects can be more clearly discovered, realizing real-time accurate detection and rapid response to foreign objects in power conductors, and improving the level of safe operation and maintenance of the power grid.

[0084] In some embodiments, a specific implementation method for loading load data into the initial conductor model is also provided, such as... Figure 3 As shown, the "loading load data onto the initial traverse model to obtain a three-dimensional dynamic model" in S202 above includes:

[0085] S301, Based on image data and thermal imaging data, determine the visual data of the power conductor.

[0086] Among them, the line visual data is a comprehensive visual dataset that has been corrected and integrated with visible light texture and thermal radiation information.

[0087] In this embodiment of the application, the computer device can correct the thermal imaging data to align it with the visible light image data at the pixel level, and then fuse the corrected visible light texture and thermal radiation information to obtain a comprehensive visual dataset, namely, the wire visual data.

[0088] Optional, such as Figure 4 As shown, S301 above includes:

[0089] S3011, extract features from image data and thermal imaging data respectively to obtain image feature data and thermal imaging feature data; the image features and thermal imaging features are feature data of the same foreign object at the same location.

[0090] S3012, based on the pixel deviation of image feature data and thermal imaging feature data, performs sensor offset correction on thermal imaging data and image data to obtain conductor visual data.

[0091] Image feature data and thermal imaging feature data refer to identifiable feature points or regions extracted from visible light image data and infrared thermal images, representing the same foreign object at the same spatial location. Pixel deviation refers to the difference in pixel coordinates of the same object in the two images due to differences in the physical installation positions and optical parameters of the camera and infrared sensor. Sensor offset correction is a technique that uses calculated deviation values ​​to spatially transform the thermal imaging data, aligning it with the visible light image data at the pixel level.

[0092] In this embodiment, the computer device first identifies feature point pairs representing the same foreign object in the image and thermal imaging data using a feature matching algorithm (such as SIFT or ORB), and calculates the positional deviation vector of these feature points in the two image coordinate systems. Then, it uses an affine transformation or perspective transformation matrix to resample and correct the thermal imaging data, achieving pixel-level alignment with the visible light image data, and forming a fused visual-thermal radiation conductor visual data.

[0093] S302, based on positioning data and lidar data, determine the movement data of the foreign object.

[0094] The foreign object motion data includes dynamic attributes such as spatial coordinates, velocity, and trajectory of the foreign object, derived from positioning and lidar data.

[0095] In this embodiment, the computer device can determine the spatial and motion attributes of a foreign object based on positioning data and lidar data, thereby obtaining foreign object motion data. Specifically, the computer device fuses GNSS / IMU positioning data with lidar point clouds, uses a target tracking algorithm to calculate the three-dimensional coordinate changes of the foreign object, and thus extracts its speed and trajectory to generate foreign object motion data.

[0096] S303, add the visual data of the guide wire and the motion data of the foreign object to the initial guide wire model to obtain a three-dimensional dynamic model.

[0097] In this embodiment, the computer device adds visual data of the conductor and motion data of the foreign object to the initial conductor model to obtain a three-dimensional dynamic model. Specifically, the computer device can map these multimodal data onto a pre-constructed three-dimensional mesh model of the conductor to form a three-dimensional dynamic model with physical properties, visual features, and motion states.

[0098] The method described in this application involves adding visual data of the power conductor and motion data of foreign objects to an initial power conductor model to obtain a three-dimensional dynamic model. This model integrates the visual features of the power conductor and the motion information of foreign objects, enabling a more comprehensive and realistic reflection of the actual situation of the power conductor and its surrounding environment. Compared with models built from single data, the three-dimensional dynamic model provides more dimensional information, including not only the motion data of foreign objects but also the visual data of the power conductor, allowing for more accurate identification of foreign objects and their attachment status.

[0099] In some embodiments, a specific implementation method for sensor offset correction of thermal imaging data and image data is also provided, such as... Figure 5 As shown, the step S3012 above, "based on the pixel deviation of image feature data and thermal imaging feature data, performing sensor offset correction on thermal imaging data and image data to obtain conductor visual data," includes:

[0100] S401, determine the marking positions of image feature data and thermal imaging feature data; the marking positions are the feature point positions of the same physical entity.

[0101] S402, based on the marked position, the image feature data and thermal imaging feature data are projected onto the reference plane to obtain the first projection position and the second projection position.

[0102] S403, determine the pixel coordinates of the first projection position and the second projection position respectively, and obtain the first pixel coordinates and the second pixel coordinates.

[0103] S404, using the first pixel coordinates as a reference point, determine the direction and distance of the deviation between the second pixel coordinates and the first pixel coordinates.

[0104] S405, the position of the second pixel coordinate is corrected according to the deviation direction and deviation distance so that the first pixel coordinate and the second pixel coordinate coincide, and the visual data of the conductor is obtained.

[0105] In this context, the marked location refers to the position of a feature point representing the same physical entity, manually marked or automatically identified in visible light image data and infrared thermal imaging. The reference plane is a projection plane established to unify the coordinate reference system, typically a geographically corrected two-dimensional plane or a three-dimensional model surface. The first projection position and the second projection position refer to the coordinate positions of the feature point after it has been projected onto the reference plane in the visible light image data and thermal imaging, respectively. The first pixel coordinates and the second pixel coordinates are the digital representations of these two projection positions in the image pixel coordinate system. The deviation direction and deviation distance together describe the offset vector of the thermal imaging feature point relative to the visible light feature point. The correction data are spatial transformation parameters calculated based on this offset vector.

[0106] In this embodiment, the computer device identifies at least three pairs of non-collinear matching feature points in two images as marker positions using a feature extraction algorithm. These feature points are then back-projected onto a unified reference coordinate system using camera calibration parameters to obtain their precise projected coordinates on the reference plane. Next, the positional differences between these corresponding points are calculated, and a complete offset transformation model is fitted using the least squares method. Finally, this model is applied to resample and geometrically correct the entire thermal image, ensuring complete pixel-level registration with the visible light image data, thereby generating comprehensive visual data that integrates visible light texture and thermal radiation characteristics.

[0107] The method described in this application addresses the spatial discrepancies often present in data collected by different sensors. By marking feature points of the same physical entity, image data and thermal imaging data can be accurately correlated spatially. Based on the marked positions, both types of data are projected onto a reference plane, and the pixel coordinates of the projected positions are determined. The deviation direction and distance are then calculated and corrected, effectively eliminating offsets caused by installation errors and viewing angle differences between different sensors. The resulting wire vision data, after position correction, fully combines the advantages of image data and thermal imaging data. During foreign object detection, the algorithm can analyze the data based on accurately aligned image and thermal imaging data, avoiding misjudgments or omissions due to data deviations, thereby improving the accuracy of foreign object identification.

[0108] In some embodiments, a specific implementation method for detecting foreign objects in power conductors along a preset inspection path is also provided, such as... Figure 6 As shown, the "foreign object detection is performed on the power conductors along the preset inspection path based on load data and a three-dimensional dynamic model, and the detection results are obtained" in S203 above includes:

[0109] S501, based on the abnormal temperature area in the thermal imaging data, determines the overlapping area in the image data that coincides with the abnormal temperature area.

[0110] Among them, the abnormal temperature zone refers to the area in the infrared thermal imaging data that is significantly different from the normal temperature distribution of the surrounding conductors. It is usually caused by local heat dissipation changes or discharge heat generation due to the attachment of foreign objects.

[0111] In this embodiment, the computer device can filter abnormal temperature areas in infrared thermal imaging data and determine the overlapping areas between these abnormal temperature areas and the image data. Specifically, a suitable temperature threshold can be set based on the normal operating temperature range of power lines, combined with actual conditions and experience. This threshold is used to distinguish between normal and abnormal temperature areas. Then, each pixel in the infrared thermal imaging data is traversed, and its temperature value is compared with the set temperature threshold. If the temperature value of a pixel exceeds the threshold range, the pixel is marked as part of an abnormal temperature area. Connectivity analysis is performed on the marked abnormal temperature area pixels to merge adjacent abnormal pixels into a complete abnormal temperature area. Finally, after image registration is completed, the filtered abnormal temperature areas are mapped onto the visible light image data. Specifically, based on the transformation matrix obtained from registration, the pixel coordinates of the abnormal temperature areas are converted into corresponding coordinates in the visible light image data to determine the overlapping position of the abnormal temperature areas in the visible light image data, i.e., finding the region corresponding to the abnormal temperature areas in the visible light image data, i.e., the overlapping region. This region may contain the target object causing the temperature anomaly.

[0112] S502, identify overlapping areas, determine whether there are foreign objects on the power lines, and if it is determined that there are foreign objects on the power lines, determine the position of the foreign objects in the three-dimensional dynamic model.

[0113] In this embodiment of the application, after the computer device determines the overlapping area based on the above steps, it can use a pre-trained Faster R-CNN deep learning model to identify the type of foreign object in the area, and determine the position of the foreign object in the three-dimensional dynamic model if it is determined that there is a foreign object on the power line.

[0114] S503, based on the position, positioning data and lidar data of the foreign object in the three-dimensional dynamic model, determine the attachment state of the foreign object.

[0115] In this embodiment, after the computer device determines the position of the foreign object in the three-dimensional dynamic model, it can analyze the motion trend of the foreign object and then determine its attachment state based on the motion trend. Specifically, the computer device can continuously track the precise position of the foreign object in three-dimensional space using the ICP point cloud registration algorithm, and combine it with the GNSS / IMU positioning data of the monitoring device itself to establish time series models of position and morphology respectively; finally, it uses the Kalman filter algorithm to fuse these two trend data to calculate the dynamic parameters of the foreign object, such as the swing amplitude, rotational angular velocity, and displacement acceleration, thereby comprehensively judging whether it is in a stable attachment, wind-induced swing, or on the verge of detachment.

[0116] Optional, such as Figure 7 As shown, the above S503 includes:

[0117] S5031, Analyze the positioning data to determine the first trend of change of the foreign object, and analyze the lidar data to determine the second trend of change of the foreign object.

[0118] The first trend refers to the spatial location change pattern of the foreign object calculated from continuous time-series monitoring equipment location data. The second trend refers to the change pattern of the foreign object's geometric shape and spatial attitude obtained from lidar point cloud sequence analysis.

[0119] In this embodiment, the computer device can extract the position coordinates of a foreign object at different times from the positioning data, and construct a time series data by arranging the position coordinates of the foreign object in chronological order. Then, a difference operation is performed on the time series data to calculate the change in the position of the foreign object at adjacent times, obtaining a first-order difference sequence of positions. Finally, methods such as polynomial fitting and linear regression can be used to fit the change in position to obtain the trend of position change over time, i.e., the first trend of change. For example, linear regression can be used to obtain a linear relationship between the change in position and time, and its slope can represent the average rate of position change.

[0120] Computer equipment can extract the distance and relative angle information between foreign objects and the lidar from lidar point cloud data, and construct a time series in chronological order. Then, clustering algorithms are used to cluster the point cloud of the foreign objects, extracting their shape and contour features, and analyzing how these features change over time. Finally, similar to the positioning data, difference calculations and fitting analyses are performed on features such as distance, angle, and shape to obtain the trends of these features over time, i.e., the second trend. For example, analyzing the trend of the distance between the foreign object and the lidar can determine whether it is approaching or moving away.

[0121] S5032, determine the dynamic parameters of the foreign object based on the first and second changing trends.

[0122] The dynamic parameters include at least one of the swing amplitude, rotational angular velocity, and displacement acceleration.

[0123] In this embodiment, the data of the first and second trends are synchronized in time to establish a correlation between them. Timestamp matching can be used to ensure that positioning data and LiDAR data at the same time can correspond.

[0124] By combining the positional changes of the foreign object in the positioning data and the angular changes of the foreign object in the lidar data, the maximum offset angle or displacement range of the foreign object within a certain time period is calculated as the swing amplitude. For example, by analyzing the difference between the maximum and minimum vertical displacement of the foreign object in the positioning data, the vertical swing amplitude can be obtained.

[0125] The rotational angular velocity is calculated based on the rate of change of the angle of the foreign object in the lidar data. The derivative of the angle change sequence over time is taken to obtain the time series of angular velocity, and the average value or the value within a specific time period is taken as the rotational angular velocity.

[0126] By performing a second-order difference on the positional changes of the foreign object in the positioning data, a second-order difference sequence of displacement is obtained, which is the time series of displacement acceleration. The average value or the value within a specific time period can be taken as the displacement acceleration.

[0127] S5033, determine the attachment state of the foreign object based on the dynamic parameters of the foreign object.

[0128] The attachment state includes any one of the following: stable attachment state, swinging state, and near-detachment state.

[0129] In this embodiment, based on previous experimental data and practical experience, threshold ranges corresponding to different adhesion states are set for dynamic parameters such as swing amplitude, rotational angular velocity, and displacement acceleration. The calculated dynamic parameters are then compared with the set threshold ranges, and the adhesion state of the foreign object is determined based on the comparison results. If all dynamic parameters meet the threshold range for a stable adhesion state, the foreign object is determined to be in a stable adhesion state; if some or all parameters meet the threshold range for a swing state, it is determined to be in a swing state; if the parameters exceed the threshold range for a swing state and approach or reach the threshold for a near-detachment state—for example, if the swing amplitude exceeds the threshold, there is continuous slippage, or an abnormal temperature increase—it is determined to be in a near-detachment state.

[0130] The method described in this application utilizes thermal imaging data to reflect the temperature distribution on the surface of an object, with abnormal temperature zones often indicating potential problems. Image data, on the other hand, clearly displays the object's appearance. By identifying overlapping areas in the image data that coincide with abnormal temperature zones, and fusing the thermal imaging and image data, the advantages of both types of data are fully utilized. This allows for a more accurate determination of the presence of foreign objects, avoiding the limitations of single-data detection and significantly improving the accuracy of foreign object detection. Furthermore, after determining the presence of a foreign object on the power line, a 3D dynamic model is used to determine the object's location. This model integrates three-dimensional information about the power line and its surrounding environment, providing more accurate spatial positioning. Additionally, positioning data provides information on the object's positional changes in macroscopic space, reflecting its approximate movement trajectory and trend, while lidar data can precisely measure subtle changes in the object's three-dimensional shape, distance, and angle. By combining these two types of data, the dynamic changes of the foreign object can be comprehensively captured from different angles, avoiding the limitations of a single data source and providing rich and reliable evidence for subsequent accurate determination of the object's attachment status.

[0131] In some embodiments, such as Figure 8 As shown, in Figure 2 Based on this, the foreign object detection method for power conductors also includes:

[0132] S601, when the foreign object is in a state of imminent detachment, determine whether the emergency treatment equipment meets the emergency energy requirements based on the energy status of the emergency treatment equipment.

[0133] The highest warning level is triggered when the foreign object attachment state, which is on the verge of detachment, reaches a critical risk level that may cause power grid faults such as short circuits, flashovers, or line breaks. Emergency handling equipment includes, but is not limited to, automated handling devices such as flame-spraying obstacle-clearing drones, insulated rod-operated robots, or nearby dispatched maintenance vehicles. The energy status is remaining energy.

[0134] In this embodiment, when the computer device determines that the foreign object is in a state of imminent detachment based on the aforementioned steps, it can switch the current inspection task to an emergency handling task. Then, the energy management module acquires the remaining energy of the emergency equipment in real time and predicts the minimum emergency energy value based on an energy consumption model established from historical operational data. The remaining energy is then compared with the minimum emergency energy value. If the remaining energy is less than the minimum emergency energy value, it is determined that the emergency handling equipment does not meet the emergency energy requirements; if the remaining energy is greater than or equal to the minimum emergency energy value, it is determined that the emergency handling equipment meets the emergency energy requirements. The minimum emergency energy value refers to the minimum energy threshold required for the emergency handling equipment to complete its journey from its current location to the disposal point, perform standard obstacle removal operations, and return safely. This value is calculated comprehensively based on factors such as equipment performance parameters, operating distance, and ambient wind speed.

[0135] S602, if the emergency handling equipment meets the emergency energy requirements, a control signal is generated based on the attachment position of the foreign object, and the control signal is sent to the emergency handling equipment to instruct the emergency handling equipment to perform a clearing operation.

[0136] In this embodiment, if the emergency handling equipment meets the emergency energy requirements, the computer equipment can generate a control signal based on the attachment location of the foreign object and send the control signal to the emergency handling equipment to instruct it to perform a clearing operation. Specifically, the computer equipment can simulate the movement path of the foreign object over the next few minutes using a trajectory prediction algorithm, and combine this with power grid topology data to generate a columnar or spherical warning area centered on the predicted path and considering safety margins; simultaneously, it generates standardized warning messages that conform to standards and sends instructions to designated clearing drones or robot terminals through a message queue. After receiving the instructions, these devices can autonomously or assistedly complete the emergency operation process of approaching, identifying, and removing the foreign object.

[0137] S603 If the emergency handling equipment does not meet the emergency energy requirements, the trajectory of the foreign object is determined, the warning area is determined based on the trajectory, and a warning signal is generated based on the warning area and sent to the emergency handling equipment to instruct the emergency handling equipment to issue a warning.

[0138] The detachment trajectory is a three-dimensional path of the foreign object after it detaches from the conductor, predicted based on its current motion state, mechanical model, and environmental parameters. This includes possible swaying, free fall, and wind drift. The warning area is a three-dimensional geographic warning zone defined based on the foreign object's current location, predicted trajectory, and impact range analysis. The warning area is a cylindrical or spherical spatial warning zone determined according to the predicted trajectory and impact range, with its boundaries taking into account prediction errors and safety margins. The warning signal is a structured alarm message containing the foreign object type, coordinates, risk level, and disposal recommendations.

[0139] In this embodiment, if the emergency response equipment does not meet the emergency energy requirements, the trajectory of the foreign object is determined, a warning area is identified based on the trajectory, and a warning signal is generated and sent to the emergency response equipment to instruct it to issue a warning. Specifically, the computer equipment can simulate the trajectory of the foreign object under various stress conditions using an extended Kalman filter algorithm combined with a rigid body kinematics model, based on currently collected data on the foreign object's attitude, wind speed, and wind direction. Subsequently, a three-dimensional warning area covering all possible trajectories is generated through spatial buffer analysis, and a structured warning signal containing area coordinates, risk level, and disposal priority is generated to drive the backup emergency equipment or adjust the existing equipment's operating strategy to perform the disposal task.

[0140] The method described in this application addresses the issue of foreign objects nearing detachment. These objects could potentially fall onto power lines, causing short circuits, tripping, and other faults, severely threatening the safe and stable operation of the power grid. By determining whether the emergency handling equipment meets the emergency energy requirements, and if so, generating a control signal to instruct the equipment to clear the obstruction, the foreign object can be removed before it falls, preventing a fault. Even if the emergency handling equipment does not meet the emergency energy requirements, by determining the trajectory of the foreign object and delineating a warning zone, potential affected areas can be warned in advance. This helps relevant personnel take preventative measures, such as evacuating people and strengthening monitoring of equipment in relevant areas, thereby reducing the scope of the fault caused by the falling foreign object and minimizing the impact on surrounding residents and businesses.

[0141] In some embodiments, the method further includes: determining the communication address of the target monitoring device and generating a communication code based on the communication address; generating an image feedback signal based on the communication code and the image feedback instruction; sending the image feedback signal to the monitoring device and receiving a real-time view image fed back by the monitoring device based on the image feedback signal; and projecting the real-time view image onto the large screen of the power monitoring center.

[0142] The communication address is a unique identifier for the monitoring device within the network, which can be an IP address, device ID, or SIM card number. The communication code is a time-sensitive and unique encrypted identifier generated based on the communication address to ensure communication security. The image transmission signal is a control signal containing the communication code and image transmission instructions. Real-time viewpoint images refer to real-time video streams or high-frame-rate image sequences acquired by the monitoring device at its current spatial location and orientation. The power monitoring center large screen refers to a large display screen system deployed in the power dispatch center with multi-screen display capabilities.

[0143] In this embodiment, the network address of the target monitoring device is first obtained through the device management module, and a dynamic communication code is generated by combining the SHA-256 hash algorithm with a timestamp. Then, the communication code and the preset image transmission command are encapsulated into a data packet that conforms to the communication protocol and sent to the monitoring device through a 4G / 5G private network. After the monitoring device verifies the validity of the communication code, it starts the video encoder to compress the real-time image in H.265 format and transmits it back to the monitoring center through the RTP / UDP protocol. Finally, the video decoder and the screen splitter send multiple video streams to the large screen display system to realize panoramic monitoring visualization.

[0144] In some embodiments, the method further includes: determining the communication address of the target monitoring device and generating a communication code based on the communication address; sending a manual intervention signal to the target monitoring device based on the communication code and receiving a response message from the target monitoring device after successful verification of the manual intervention signal; establishing a control channel based on the communication code after receiving the response message; transmitting data with the target monitoring device based on the control channel and manually controlling the target monitoring device.

[0145] The manual intervention signal is a command signal sent by the system to the monitoring equipment requesting manual control authority. The response information is the confirmation reply returned by the monitoring equipment after verifying the legitimacy of the manual intervention signal. The control channel is a dedicated encrypted communication link established between the monitoring equipment and the control system, used to transmit control commands and equipment status data. Manual control refers to authorized operators remotely controlling the flight attitude, sensor parameters, or operational tasks of the monitoring equipment in real time through the control channel.

[0146] In this embodiment, a unique communication code is first generated based on the MAC address or SIM card number of the target device. This communication code is then encrypted with a timestamp to form a manual intervention signal, which is sent to the device. After receiving the signal, the monitoring device verifies the validity and timeliness of the communication code using its built-in security chip. Once the verification is successful, it immediately returns a response message containing a session key. The system then establishes a TLS-encrypted control channel based on the session key and transmits digitally signed control commands through this channel to achieve real-time remote control of the monitoring device's flight control, gimbal rotation, sensor switching, and other functions.

[0147] In some embodiments, the step of establishing a control channel based on a communication code after receiving a response message includes: performing a hash mapping on the communication code after receiving the response message to obtain communication verification information; sending the communication verification information to a target monitoring device to enable the target monitoring device to listen to the communication verification information; appending and compiling the communication verification information with the transmitted data to obtain dedicated communication information; and establishing a control channel based on the dedicated communication information.

[0148] Hash mapping refers to the process of converting a communication code into a fixed-length digital fingerprint using hash algorithms such as SHA-256 or MD5. Communication verification information is a unique and irreversible verification identifier generated through hash mapping. Listening refers to the behavior of a monitoring device continuously listening to a specific port or frequency band, waiting to receive verification information. Additional compilation refers to the technical means of encoding communication verification information as a data packet header or checksum along with the transmitted data. Specific communication information is a complete communication message containing both verification information and transmitted data.

[0149] In this embodiment, upon receiving the response information, the original communication code is immediately hashed to generate 256-bit communication verification information. This verification information is then sent to the monitoring device through an independent verification channel. The device starts a dedicated thread to listen on a designated port. When establishing formal communication, the system uses the TLS protocol to encode the verification information as part of the session key, along with the control command, to form a dedicated communication information packet with authentication functionality. Finally, an end-to-end encrypted control channel is established based on these dual-verified data packets.

[0150] In summary, based on all the above embodiments, a method for detecting foreign objects in power conductors is also provided, the method comprising:

[0151] S701, acquire load data of power lines along a preset inspection path. The load data includes at least one of image data, thermal imaging data, positioning data, and lidar data.

[0152] S702, feature extraction is performed on the image data and thermal imaging data respectively to obtain image feature data and thermal imaging feature data. Among them, the image features and thermal imaging features are feature data of the same foreign object at the same location.

[0153] S703, determine the marker positions for image feature data and thermal imaging feature data. The marker positions are the feature point positions of the same physical entity.

[0154] S704, based on the marked position, the image feature data and thermal imaging feature data are projected onto the reference plane to obtain the first projection position and the second projection position.

[0155] S705, determine the pixel coordinates of the first projection position and the second projection position respectively, and obtain the first pixel coordinates and the second pixel coordinates.

[0156] S706, using the first pixel coordinates as a reference point, determine the direction and distance of the deviation between the second pixel coordinates and the first pixel coordinates.

[0157] S707, the position of the second pixel coordinate is corrected according to the deviation direction and deviation distance so that the first pixel coordinate and the second pixel coordinate coincide, thereby obtaining the visual data of the conductor.

[0158] S708 determines the movement data of the foreign object based on positioning data and lidar data.

[0159] S709, add the conductor visual data and foreign object motion data to the initial conductor model to obtain a three-dimensional dynamic model. The initial conductor model is obtained by three-dimensional modeling based on the power conductor and its surrounding environment.

[0160] S710 identifies overlapping regions in image data that coincide with abnormal temperature zones based on the abnormal temperature zones in the thermal imaging data.

[0161] S711 identifies overlapping areas, determines whether there are foreign objects on the power lines, and, if foreign objects are found on the power lines, determines the position of the foreign objects in the three-dimensional dynamic model.

[0162] S712 analyzes the positioning data to determine the first trend of change of the foreign object, and analyzes the lidar data to determine the second trend of change of the foreign object.

[0163] S713, determine the dynamic parameters of the foreign object based on the first and second changing trends. The dynamic parameters include at least one of the following: swing amplitude, rotational angular velocity, and displacement acceleration.

[0164] S714, determine the attachment state of the foreign object based on its dynamic parameters. The attachment state includes any one of the following: stable attachment, oscillating attachment, or near-detachment attachment. The detection results include the foreign object itself and its attachment state on the power line.

[0165] S715, when the foreign object is in a state of imminent detachment, determine whether the emergency treatment equipment meets the emergency energy requirements based on the energy status of the emergency treatment equipment.

[0166] S716 If the emergency handling equipment meets the emergency energy requirements, a control signal is generated based on the attachment position of the foreign object, and the control signal is sent to the emergency handling equipment to instruct the emergency handling equipment to perform a clearing operation.

[0167] S717 If the emergency response equipment does not meet the emergency energy requirements, the trajectory of the foreign object is determined, and a warning area is determined based on the trajectory. A warning signal is generated based on the warning area and sent to the emergency response equipment to instruct the emergency response equipment to issue a warning.

[0168] The methods described in each of the above steps have been described in the foregoing embodiments. For details, please refer to the foregoing descriptions. They will not be repeated here.

[0169] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0170] Based on the same inventive concept, this application also provides a foreign object detection device for power conductors to implement the aforementioned foreign object detection method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more embodiments of the foreign object detection device for power conductors provided below can be found in the limitations of the foreign object detection method for power conductors described above, and will not be repeated here.

[0171] In some embodiments, such as Figure 9 As shown, a foreign object detection device for power conductors is provided, comprising:

[0172] The acquisition module 11 is used to acquire load data of power conductors on a preset inspection path; the load data includes at least one of image data, thermal imaging data, positioning data and lidar data.

[0173] Loading module 12 is used to load load data into the initial conductor model to obtain a three-dimensional dynamic model; the initial conductor model is obtained by three-dimensional modeling based on the power conductor and its surrounding environment.

[0174] The detection module 13 is used to detect foreign objects on the power conductors along the preset inspection path based on load data and a three-dimensional dynamic model, and obtain the detection results. The detection results include the foreign object and the attachment status of the foreign object on the power conductor.

[0175] In some embodiments, the loading module described above includes:

[0176] The first determining unit is used to determine the visual data of the power conductor based on the image data and thermal imaging data.

[0177] The second determining unit is used to determine the foreign object's motion data based on the positioning data and the lidar data.

[0178] The loading unit is used to add the visual data of the guide wire and the motion data of the foreign object to the initial guide wire model to obtain a three-dimensional dynamic model.

[0179] In some embodiments, the first determination described above includes:

[0180] The extraction subunit is used to extract features from image data and thermal imaging data respectively, to obtain image feature data and thermal imaging feature data; the image features and thermal imaging features are feature data of the same foreign object at the same location.

[0181] The correction subunit is used to perform sensor offset correction on thermal imaging data and image data based on the pixel deviation of image feature data and thermal imaging feature data to obtain conductor visual data.

[0182] In some embodiments, the above-mentioned correction subunit is specifically used to determine the marking positions of image feature data and thermal imaging feature data; the marking positions are the feature point positions of the same physical entity; based on the marking positions, the image feature data and thermal imaging feature data are projected onto a reference plane to obtain a first projection position and a second projection position; the pixel coordinates of the first projection position and the second projection position are determined to obtain a first pixel coordinate and a second pixel coordinate; using the first pixel coordinate as a reference point, the deviation direction and deviation distance between the second pixel coordinate and the first pixel coordinate are determined; the position of the second pixel coordinate is corrected according to the deviation direction and deviation distance so that the first pixel coordinate and the second pixel coordinate coincide to obtain the wire visual data.

[0183] In some embodiments, the detection module described above includes:

[0184] The third determining unit is used to determine the overlapping area in the image data that coincides with the abnormal temperature area in the thermal imaging data.

[0185] The fourth determining unit is used to identify overlapping areas, determine whether there are foreign objects on the power lines, and, if it is determined that there are foreign objects on the power lines, determine the position of the foreign objects in the three-dimensional dynamic model.

[0186] The fifth determining unit is used to determine the attachment state of the foreign object based on its position, positioning data, and lidar data in the three-dimensional dynamic model.

[0187] In some embodiments, the fifth determining unit includes:

[0188] The first determining subunit is used to analyze the positioning data to determine the first trend of change of the foreign object, and to analyze the lidar data to determine the second trend of change of the foreign object.

[0189] The second determining subunit is used to determine the dynamic parameters of the foreign object based on the first and second changing trends; the dynamic parameters include at least one of swing amplitude, rotational angular velocity and displacement acceleration.

[0190] The third determining subunit is used to determine the attachment state of the foreign object based on its dynamic parameters; the attachment state includes any one of the following: stable attachment state, oscillating state, or near-detachment state.

[0191] In some embodiments, the foreign object detection device for the power conductor further includes:

[0192] The emergency treatment module is used to determine whether the emergency treatment equipment meets the emergency energy requirements when the foreign object is in a state of imminent detachment, based on the energy status of the emergency treatment equipment.

[0193] The obstacle removal module is used to generate a control signal based on the attachment location of the foreign object if the emergency handling equipment meets the emergency energy requirements, and then send the control signal to the emergency handling equipment to instruct the emergency handling equipment to perform obstacle removal operations.

[0194] The early warning module is used to determine the trajectory of the foreign object if the emergency handling equipment does not meet the emergency energy requirements, determine the early warning area based on the trajectory, and generate an early warning signal based on the early warning area to send to the emergency handling equipment to instruct the emergency handling equipment to issue an early warning.

[0195] Each module in the aforementioned foreign object detection device for power conductors can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of a computer device in hardware form or independent of it, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0196] In some embodiments, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for detecting foreign objects in power conductors as described in any of the above embodiments.

[0197] The computer device provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0198] In some embodiments, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps of the foreign object detection method for obtaining power conductors as described in any of the above embodiments.

[0199] The computer-readable storage medium provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0200] In some embodiments, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps of the foreign object detection method for obtaining power conductors as described in any of the above embodiments.

[0201] The computer program product provided in the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be described again here.

[0202] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0203] 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.

[0204] 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 patent 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 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 foreign matter detection method of a power conductor, characterized by, The method includes: The load data of the power conductors along the preset inspection path is acquired; the load data includes at least one of image data, thermal imaging data, positioning data, and lidar data. The load data is loaded into the initial conductor model to obtain a three-dimensional dynamic model; the initial conductor model is obtained by three-dimensional modeling based on the power conductor and its surrounding environment. Based on the load data and the three-dimensional dynamic model, foreign object detection is performed on the power conductors along the preset inspection path to obtain detection results; the detection results include the foreign object and the attachment status of the foreign object on the power conductor. The step of loading the load data into the initial conductor model to obtain a three-dimensional dynamic model includes: determining the conductor visual data of the power conductor based on the image data and the thermal imaging data; determining the foreign object motion data based on the positioning data and the lidar data; and adding the conductor visual data and the foreign object motion data into the initial conductor model to obtain the three-dimensional dynamic model. The step of detecting foreign objects on the power lines along the preset inspection path based on the load data and the three-dimensional dynamic model to obtain detection results includes: determining an overlapping area in the image data that coincides with the abnormal temperature area in the thermal imaging data; identifying the overlapping area to determine whether the foreign object exists on the power line; and, if the presence of the foreign object is confirmed, determining the position of the foreign object in the three-dimensional dynamic model; and determining the attachment state of the foreign object based on its position in the three-dimensional dynamic model, the positioning data, and the lidar data.

2. The method of claim 1, wherein, The step of determining the conductor visual data of the power conductor based on the image data and the thermal imaging data includes: Feature extraction is performed on the image data and the thermal imaging data respectively to obtain image feature data and thermal imaging feature data; the image feature and the thermal imaging feature are feature data of the same foreign object at the same location; Based on the pixel deviation of the image feature data and the thermal imaging feature data, sensor offset correction is performed on the thermal imaging data and the image data to obtain the visual data of the conductor.

3. The method of claim 2, wherein, The step of performing sensor offset correction on the thermal imaging data and the image data based on the pixel deviation of the image feature data and the thermal imaging feature data to obtain the conductor visual data includes: Determine the marker positions of the image feature data and the thermal imaging feature data; the marker positions are the feature point positions of the same physical entity. Based on the marked position, the image feature data and the thermal imaging feature data are projected onto the reference plane to obtain the first projection position and the second projection position; The pixel coordinates of the first projection position and the second projection position are determined respectively to obtain the first pixel coordinates and the second pixel coordinates; Using the first pixel coordinates as a reference point, determine the direction and distance of the deviation between the second pixel coordinates and the first pixel coordinates; The second pixel coordinates are corrected according to the deviation direction and the deviation distance so that the first pixel coordinates and the second pixel coordinates coincide, thereby obtaining the visual data of the conductor.

4. The method of claim 1, wherein, The step of determining the attachment state of the foreign object based on its position in the 3D dynamic model, the positioning data, and the lidar data includes: The positioning data is analyzed to determine the first trend of change of the foreign object, and the lidar data is analyzed to determine the second trend of change of the foreign object. The dynamic parameters of the foreign object are determined based on the first and second trends of change; the dynamic parameters include at least one of swing amplitude, rotational angular velocity, and displacement acceleration. The attachment state of the foreign object is determined based on its dynamic parameters; the attachment state includes any one of a stable attachment state, a swinging state, or a state on the verge of detachment.

5. The method of claim 4, wherein, The method further includes: When the foreign object is in a state of near detachment, it is determined whether the emergency treatment equipment meets the emergency energy requirements based on the energy status of the emergency treatment equipment. If the emergency handling equipment meets the emergency energy requirements, a control signal is generated based on the attachment position of the foreign object, and the control signal is sent to the emergency handling equipment to instruct the emergency handling equipment to perform a clearing operation; If the emergency response equipment does not meet the emergency energy requirements, the trajectory of the foreign object is determined, a warning area is determined based on the trajectory, and a warning signal is generated based on the warning area and sent to the emergency response equipment to instruct the emergency response equipment to issue a warning.

6. A foreign object detection device for a power conductor, characterized by For implementing the foreign object detection method for power conductors as described in claim 1, the apparatus comprises: The acquisition module is used to acquire load data of power conductors along a preset inspection path; the load data includes at least one of image data, thermal imaging data, positioning data, and lidar data; The loading module is used to load the load data into the initial conductor model to obtain a three-dimensional dynamic model; the initial conductor model is obtained by three-dimensional modeling based on the power conductor and its surrounding environment. The detection module is used to perform foreign object detection on the power conductors along the preset inspection path based on the load data and the three-dimensional dynamic model, and obtain the detection results; the detection results include the foreign object and the attachment status of the foreign object on the power conductor. 7.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-6 when the computer program is executed by the processor. When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 5.

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