Dam face disease detection method, device and system
By acquiring and integrating dam surface meteorological and topographic data and using extended Kalman filtering and environmental risk assessment models for drone path planning, the drone control problem caused by the large span, width and height difference of the arch dam body was solved, and efficient and automated dam surface defect detection was achieved.
Patent Information
- Application Number
- CN202510638753.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-05-19
- Publication Date
- 2025-09-23
AI Technical Summary
When the span width and height difference of the arch dam body are large, it is difficult to accurately control the drone swarm. Traditional detection methods are time-consuming, have low resource utilization, and low detection efficiency.
By acquiring meteorological and topographic data of the target dam surface, using the extended Kalman filter algorithm for data fusion, and combining the environmental risk assessment model and three-dimensional obstacle model to plan the UAV inspection path, the UAV is controlled to automatically perform inspection tasks and identify dam surface defects.
The full process of drone inspection of dam surface defects has been automated, which has improved inspection efficiency and timing, and reduced resource waste.
Smart Images
Figure CN120686855A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the technical field of dam surface disease detection, and in particular to a dam surface disease detection method, device and system. Background Art
[0002] In related technologies, when the span width and height difference of an arch dam is large, it is difficult to accurately control a swarm of drones due to the diversity of sensors. In traditional control methods, the start and stop of drones and data collection require manual intervention. The single detection process of dam surface defects is time-consuming, resulting in low detection efficiency and low resource utilization. Summary of the Invention
[0003] In order to overcome the problems existing in the related art, the present disclosure provides a dam surface disease detection method, device and system.
[0004] According to a first aspect of an embodiment of the present disclosure, a dam surface disease detection method is provided, comprising:
[0005] Obtain meteorological and topographic data of the target dam surface;
[0006] Using an extended Kalman filter algorithm to fuse the meteorological data and the terrain data to obtain environmental fusion data;
[0007] Inputting the environmental fusion data into a trained environmental risk assessment model to obtain an environmental risk index value obtained after the environmental risk assessment model evaluates the environmental fusion data;
[0008] When the environmental risk index value satisfies a preset condition, updating a three-dimensional obstacle model corresponding to the target dam surface based on the terrain data;
[0009] Performing UAV inspection path planning based on the terrain data mixed with the three-dimensional obstacle model to obtain a planned path;
[0010] The UAV is controlled to perform an inspection task according to the planned path, dam surface information collected by the UAV is obtained, and defects of the target dam surface are identified using the dam surface information to obtain a defect identification result.
[0011] In some embodiments of the present disclosure, before fusing the meteorological data and the terrain data using the extended Kalman filter algorithm to obtain environmental fusion data, the method further includes:
[0012] Unifying the time bases of the meteorological and terrain data using GNSS and MEMS;
[0013] The center point of the upstream dam surface of the target arch dam is determined as the origin, the direction of the downstream symmetry axis of the target arch dam is determined as the y-axis direction, and the direction perpendicular to the dam axis of the target arch dam is determined as the x-axis direction to obtain a unified coordinate system. The spatial references of the meteorological data and terrain data are unified based on the unified coordinate system.
[0014] In some embodiments of the present disclosure, the environmental risk assessment model is trained using historical disease data of the target dam surface; the environmental risk assessment model is a Bayesian network model.
[0015] In some embodiments of the present disclosure, controlling the drone to perform the inspection task according to the planned path includes:
[0016] Controlling the UAV to perform the inspection task according to the planned path;
[0017] In response to identifying an obstacle, determining a distance between the drone and the obstacle;
[0018] When the distance is less than or equal to a first threshold, or when the obstacle is a dynamic obstacle and the obstacle's approach speed is greater than or equal to a preset speed, controlling the drone to immediately hover and outputting an alarm signal;
[0019] When the distance is greater than the first threshold, an A* algorithm and the planned path are used to generate a detour path for the obstacle, and the drone is controlled to perform the inspection task according to the detour path.
[0020] According to a second aspect of an embodiment of the present disclosure, a dam surface defect detection device is provided, comprising:
[0021] An acquisition unit, used for acquiring meteorological data and topographic data of the target dam surface;
[0022] a fusion unit, configured to fuse the meteorological data and the terrain data using an extended Kalman filter algorithm to obtain environmental fusion data;
[0023] An evaluation unit, configured to input the environmental fusion data into a trained environmental risk assessment model, and obtain an environmental risk index value obtained after the environmental risk assessment model evaluates the environmental fusion data;
[0024] an updating unit, configured to update the three-dimensional obstacle model corresponding to the target dam surface based on the terrain data when the environmental risk index value meets a preset condition;
[0025] A planning unit, configured to plan a UAV inspection path based on the terrain data mixed with the three-dimensional obstacle model to obtain a planned path;
[0026] The identification unit is used to control the UAV to perform the inspection task according to the planned path, obtain the dam surface information collected by the UAV, use the dam surface information to identify the defects of the target dam surface, and obtain a defect identification result.
[0027] In some embodiments of the present disclosure, the apparatus further comprises:
[0028] a unification unit for unifying the time references of the meteorological data and the terrain data using GNSS and MEMS;
[0029] The unification unit is further used to determine the center point of the upstream dam surface of the target arch dam as the origin, determine the direction of the downstream symmetry axis of the target arch dam as the y-axis direction, and determine the direction perpendicular to the dam axis of the target arch dam as the x-axis direction to obtain a unified coordinate system, and unify the spatial references of the meteorological data and terrain data based on the unified coordinate system.
[0030] In some embodiments of the present disclosure, the environmental risk assessment model is trained using historical disease data of the target dam surface; the environmental risk assessment model is a Bayesian network model.
[0031] According to a third aspect of an embodiment of the present disclosure, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any one of the first aspects is implemented.
[0032] According to a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method according to any one of the first aspects is implemented.
[0033] According to a fifth aspect of an embodiment of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program implements the method as described in any one of the first aspects when executed by a processor.
[0034] The technical solution provided by the embodiments of the present disclosure can provide the following beneficial effects: obtaining meteorological and topographic data of a target dam surface; fusing the meteorological and topographic data using an extended Kalman filter algorithm to obtain fused environmental data; inputting the fused environmental data into a trained environmental risk assessment model to obtain an environmental risk index value obtained by evaluating the fused environmental data by the environmental risk assessment model; updating a three-dimensional obstacle model corresponding to the target dam surface based on the topographic data when the environmental risk index value meets preset conditions; planning a UAV inspection path based on the terrain data and the three-dimensional obstacle model to obtain a planned path; controlling the UAV to perform the inspection task according to the planned path, obtaining dam surface information collected by the UAV, and identifying defects on the target dam surface using the dam surface information to obtain a defect identification result. This allows for automatic determination of whether meteorological and topographic data pose a risk to the UAV's flight. If not, the UAV swarm is automatically controlled to perform the dam surface defect inspection task according to the planned path, thereby improving the automation of the entire UAV inspection process for dam surface defects, rationalizing inspection timing, and improving inspection efficiency, thereby reducing resource waste caused by risks.
[0035] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.
[0037] Figure 1 The figure is a flow chart of a dam surface defect detection method according to an exemplary embodiment.
[0038] Figure 2 The figure is a block diagram of a dam surface defect detection device according to an exemplary embodiment.
[0039] Figure 3 The present invention is a block diagram showing a device for detecting dam surface defects according to an exemplary embodiment. DETAILED DESCRIPTION
[0040] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.
[0041] The terms used in the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure. The singular forms "a", "an" and "the" used in the present disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.
[0042] It should be understood that although the terms first, second, third, etc. may be used to describe various information in the embodiments of the present disclosure, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of the embodiments of the present disclosure, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".
[0043] Furthermore, the various forms of processes shown in the embodiments of this disclosure may be used to reorder, add, or delete steps. For example, the steps described in this application may be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.
[0044] In related technologies, when the span width and height difference of an arch dam is large, it is difficult to accurately control a swarm of drones due to the diversity of sensors. In traditional control methods, the start and stop of drones and data collection require manual intervention. The single detection process of dam surface defects is time-consuming, resulting in low detection efficiency and low resource utilization.
[0045] To address the aforementioned issues, the present disclosure provides a dam surface defect detection method, device, and system. These methods involve acquiring meteorological and topographic data of a target dam surface; fusing the meteorological and topographic data using an extended Kalman filter algorithm to generate fused environmental data; inputting the fused environmental data into a trained environmental risk assessment model to obtain an environmental risk index value derived from the environmental risk assessment model's evaluation of the fused environmental data; updating a three-dimensional obstacle model corresponding to the target dam surface based on the topographic data when the environmental risk index value meets preset conditions; planning an unmanned aerial vehicle (UAV) inspection path based on the terrain data and the three-dimensional obstacle model to obtain a planned path; controlling the UAV to perform the inspection task along the planned path, obtaining dam surface information collected by the UAV; and identifying defects on the target dam surface using the dam surface information to obtain a defect identification result. This method automatically determines whether the meteorological and topographic data pose a risk to the UAV's flight. If not, the UAV swarm is automatically controlled to perform the dam surface defect inspection task along the planned path, thereby improving the automation of the entire UAV inspection process for dam surface defects, optimizing inspection timing, and improving inspection efficiency, while reducing resource waste caused by risks.
[0046] Figure 1 FIG. 1 is a flow chart showing a method for detecting dam surface defects according to an exemplary embodiment. Figure 1 As shown, it should be noted that the dam surface disease detection method of the embodiment of the present disclosure is applied to a dam surface disease detection device. Figure 1 As shown, the method may include the following steps:
[0047] Step 101: Acquire meteorological data and terrain data of the target dam surface.
[0048] In one embodiment, the meteorological data may include precipitation, wind speed, and wind direction data in the target dam surface area, which may be collected using corresponding monitoring equipment or sensors.
[0049] In one embodiment, the terrain data can be collected by using a laser radar and an inertial navigation measurement unit (IMU) to collect the instantaneous posture of the terrain. These position and posture data can be further combined and solved to achieve the effect of mutual correction and improved accuracy.
[0050] In some embodiments of the present application, before step 102, the method may further include the following steps:
[0051] Unify the time base of meteorological and terrain data using GNSS and MEMS;
[0052] The center point of the upstream dam surface of the target arch dam is determined as the origin, the direction of the downstream symmetry axis of the target arch dam is determined as the y-axis direction, and the direction perpendicular to the dam axis of the target arch dam is determined as the x-axis direction to obtain a unified coordinate system. The spatial reference of meteorological data and terrain data is unified based on the unified coordinate system.
[0053] In one embodiment, a GNSS satellite timing module is used to output a PPS (pulse per second) signal, and a MEMS oscillator is used as a redundant backup when the GNSS signal is lost.
[0054] In one embodiment, the unified coordinate system mentioned above may be used to perform coordinate system conversion processing on all meteorological data and terrain data.
[0055] Step 102: Using the extended Kalman filter algorithm, the meteorological data and the terrain data are fused to obtain environmental fusion data.
[0056] In one embodiment, the key to multi-sensor integrated control is to coordinate and control various sensors with different temporal and spatial resolutions to operate under a unified time and space reference. However, a single sensor has its own data acquisition frequency and its own spatial coordinate reference. Therefore, the integration and synchronous control of multiple sensors must establish a unified temporal and spatial reference to solve the calibration and synchronous control of the multi-sensor integrated system.
[0057] In step 103 , the environmental fusion data is input into the trained environmental risk assessment model to obtain an environmental risk index value obtained after the environmental risk assessment model evaluates the environmental fusion data.
[0058] In some embodiments of the present application, the environmental risk assessment model is trained using historical disease data of the target dam surface; the environmental risk assessment model is a Bayesian network model.
[0059] In one embodiment, a Bayesian network model can be pre-built. The model's input parameters may include wind speed, rainfall, temperature, LiDAR point cloud density, and IMU attitude angle. The conditional probability table (CPT) is trained using historical accident data and can define risk levels as low, medium, or high. Dynamic thresholds can also be used for risk assessment. For example, a drone takeoff prohibition command is triggered when wind speeds ≥ 10 m / s or continuous rainfall ≥ 50 mm / h.
[0060] Step 104 : When the environmental risk index value meets a preset condition, the three-dimensional obstacle model corresponding to the target dam surface is updated based on the terrain data.
[0061] In one embodiment, the LiDAR point cloud and IMU attitude data can be combined and solved, and the iterative closest point algorithm (ICP) can be used for registration to generate a three-dimensional mesh model of the dam surface and a three-dimensional obstacle model. Obstacles such as protrusions and crack edges can be identified in real time, and risk areas (such as crack width ≥ 1 cm) can be marked.
[0062] Step 105 , performing UAV inspection path planning based on the terrain data mixed with the three-dimensional obstacle model to obtain a planned path.
[0063] In one embodiment, an improved A* algorithm can be used, introducing a cost function C = α·D + β·R (D is the path length, R is the risk factor, and weights α = 0.7, β = 0.3). Environmental model data can be received in real time, and the path can be dynamically adjusted to avoid high-risk areas.
[0064] Step 106 , controlling the UAV to perform the inspection task according to the planned path, obtaining the dam surface information collected by the UAV, and using the dam surface information to identify defects on the target dam surface to obtain defect identification results.
[0065] In one embodiment, the defect identification result may include the defect type and the defect location.
[0066] As an example, once takeoff conditions meet the requirements (i.e., environmental risk indicators meet pre-set conditions), the UAV observation station hatch opens. Once fully opened, the three serially connected UAV takeoff and landing platforms automatically roll out along guide rails to the designated location. The UAVs then use their self-test modules to perform battery checks and RTK signal searches, completing the RTK system connection.
[0067] In one embodiment, the drone arrives at a designated starting location according to a planned route. The aerial camera, lidar, GNSS receiver, and inertial measurement sensor equipment work in sync to collect dam surface data. Once the drone completes the dam surface data collection along the pre-set route and reaches the final location, the return control module activates, the drone automatically returns to land, and the drone's landing platform automatically retracts along the guide rails, closing the hatch.
[0068] In one embodiment, multiple sensors such as wind and rain measuring meteorological environment sensors, cabin door motors, apron rail motors, drone swarms, high-resolution CCD cameras, three-dimensional laser scanners, high-precision MEMS, and data transmission sensors are integrated into a system, breaking through the different time granularities of "microseconds-seconds-minutes" to implement spatiotemporal synchronization control technology for multiple sensors, thereby realizing automatic detection of meteorological conditions, automatic opening and closing of cabin doors, and automatic extension and retraction of apron rails for drone swarms. Moreover, the drone system works under dynamic conditions. In order to achieve higher time accuracy requirements for data fusion and registration, its sensor control signals are generally based on a unified time system, and the onboard flight control system is used to coordinate the triggering and control of data acquisition to achieve "instant" work.
[0069] In some embodiments of the present application, controlling the drone to perform the inspection task according to the planned path proposed in step 106 may specifically include the following steps:
[0070] Control the drone to perform inspection tasks according to the planned path;
[0071] In response to identifying the obstacle, determining a distance between the drone and the obstacle;
[0072] When the distance is less than or equal to the first threshold, or the obstacle is a dynamic obstacle and the obstacle's approach speed is greater than or equal to a preset speed, the drone is controlled to hover immediately and an alarm signal is output;
[0073] When the distance is greater than the first threshold, the A* algorithm and the planned path are used to generate a detour path for the obstacle, and the drone is controlled to perform the inspection task according to the detour path.
[0074] In one embodiment, six-directional obstacle avoidance sensors can be used for data collection. For example, time-of-flight (ToF) sensors are deployed in the front, back, left, right, up, and down directions of the drone, with a detection range of 0.1 to 30 meters, an accuracy of ±1 cm, and an update frequency of 50 Hz. Another example is a binocular vision module: configured forward with a resolution of 1280×720, used to assist in identifying dynamic obstacles (such as flying birds and mobile devices). All sensor data is timestamped using a unified time reference (GNSS / MEMS) to ensure spatiotemporal alignment of multi-source data.
[0075] In one embodiment, for dynamic obstacles, the motion characteristics of the obstacles can be extracted based on the optical flow method, and the distance between the UAV and the dynamic obstacle can be determined based on the running characteristics. In addition, the obstacle avoidance priority of dynamic obstacles is higher than that of static obstacles. When selecting the UAV flight platform, factors such as load capacity, flight time, and flight safety protection level are taken into consideration, and the requirements for the hovering positioning accuracy and obstacle avoidance function of the UAV are focused on, so as to meet the accuracy requirements of the UAV shooting operation task in the dam area and ensure the safety of flight operations. The optional equipment has six-way positioning obstacle avoidance, pause flight tasks, and hovering accuracy of centimeters. At the same time, the code obstacle avoidance function is modified according to the task of automatic take-off of the UAV group at the same time.
[0076] In addition, a dual-channel redundancy protocol is adopted: 5G main channel (bandwidth 100MHz, latency ≤10ms) and LRa backup channel (sensitivity -148dBm), with a switching time ≤50ms.
[0077] In one embodiment, the present disclosure also performs integrity check on the collected data using a CRC-64 algorithm with a bit error rate of ≤10-12.
[0078] In some embodiments of the present application, the present disclosure also adopts a high-precision wireless charging system with a magnetic resonance coupling design: the transmitting and receiving coils are wound with Litz wire, the Q value is ≥200, and the coupling coefficient k is ≥0.7; battery health prediction is adopted: an equivalent circuit model (Randles model) is established based on the electrochemical impedance spectroscopy EIS, and the parameter fitting error is ≤2%.
[0079] According to the dam surface defect detection method proposed in the embodiments of the present disclosure, meteorological and topographic data of a target dam surface are acquired; the meteorological and topographic data are fused using an extended Kalman filter algorithm to obtain fused environmental data; the fused environmental data is input into a trained environmental risk assessment model to obtain an environmental risk index value obtained by evaluating the fused environmental data by the environmental risk assessment model; when the environmental risk index value meets a preset condition, a three-dimensional obstacle model corresponding to the target dam surface is updated based on the topographic data; a UAV inspection path is planned based on the terrain data and the three-dimensional obstacle model to obtain a planned path; the UAV is controlled to perform the inspection task according to the planned path, dam surface information collected by the UAV is obtained, and defects on the target dam surface are identified using the dam surface information to obtain a defect identification result. This method can automatically determine whether the meteorological and topographic data pose a risk to the UAV flight. If not, the UAV swarm is automatically controlled to perform the dam surface defect inspection task according to the planned path, thereby improving the automation of the entire UAV inspection process for dam surface defects, rationalizing the inspection timing, and improving inspection efficiency, thereby reducing resource waste caused by risks.
[0080] Figure 2 This is a block diagram of a dam surface disease detection device according to an exemplary embodiment. Figure 2 The device includes an acquisition unit 201, a fusion unit 202, an evaluation unit 203, an update unit 204, a planning unit 205 and an identification unit 206.
[0081] The acquisition unit 201 is used to acquire meteorological data and topographic data of the target dam surface;
[0082] A fusion unit 202 is configured to fuse the meteorological data and the terrain data using an extended Kalman filter algorithm to obtain environmental fusion data;
[0083] An evaluation unit 203 is configured to input the environmental fusion data into the trained environmental risk assessment model, and obtain an environmental risk index value obtained after the environmental risk assessment model evaluates the environmental fusion data;
[0084] An updating unit 204 is configured to update the three-dimensional obstacle model corresponding to the target dam surface based on the terrain data when the environmental risk index value meets a preset condition;
[0085] The planning unit 205 is used to plan the inspection path of the UAV based on the terrain data mixed with the three-dimensional obstacle model to obtain a planned path;
[0086] The identification unit 206 is used to control the UAV to perform the inspection task according to the planned path, obtain the dam surface information collected by the UAV, use the dam surface information to identify defects on the target dam surface, and obtain defect identification results.
[0087] In some embodiments of the present application, the apparatus further comprises:
[0088] Unification unit for unifying the time base of meteorological and terrain data using GNSS and MEMS;
[0089] The unified unit is also used to determine the center point of the upstream dam surface of the target arch dam as the origin, the direction of the downstream symmetry axis of the target arch dam as the y-axis direction, and the direction perpendicular to the dam axis of the target arch dam as the x-axis direction, so as to obtain a unified coordinate system, and unify the spatial references of meteorological data and terrain data based on the unified coordinate system.
[0090] In some embodiments of the present application, the environmental risk assessment model is trained using historical disease data of the target dam surface; the environmental risk assessment model is a Bayesian network model.
[0091] In some embodiments of the present application, the identification unit 206 may be specifically configured to:
[0092] Control the drone to perform inspection tasks according to the planned path;
[0093] In response to identifying the obstacle, determining a distance between the drone and the obstacle;
[0094] When the distance is less than or equal to the first threshold, or the obstacle is a dynamic obstacle and the obstacle's approach speed is greater than or equal to a preset speed, the drone is controlled to hover immediately and an alarm signal is output;
[0095] When the distance is greater than the first threshold, the A* algorithm and the planned path are used to generate a detour path for the obstacle, and the UAV is controlled to perform the inspection task according to the detour path.
[0096] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.
[0097] According to the dam surface defect detection device proposed in the embodiments of the present disclosure, meteorological and topographic data of a target dam surface are acquired; the meteorological and topographic data are fused using an extended Kalman filter algorithm to obtain fused environmental data; the fused environmental data is input into a trained environmental risk assessment model to obtain an environmental risk index value obtained by evaluating the fused environmental data by the environmental risk assessment model; when the environmental risk index value meets a preset condition, a three-dimensional obstacle model corresponding to the target dam surface is updated based on the topographic data; a UAV inspection path is planned based on the terrain data and the three-dimensional obstacle model to obtain a planned path; the UAV is controlled to perform the inspection task according to the planned path, dam surface information collected by the UAV is obtained, and defects on the target dam surface are identified using the dam surface information to obtain a defect identification result. This system can automatically determine whether the meteorological and topographic data pose a risk to the UAV flight. If not, the UAV swarm is automatically controlled to perform the dam surface defect inspection task according to the planned path, thereby improving the automation of the entire UAV inspection process for dam surface defects, rationalizing the inspection timing, and improving inspection efficiency, thereby reducing resource waste caused by risks.
[0098] Figure 3 This is a block diagram illustrating an apparatus for detecting dam surface defects according to an exemplary embodiment. For example, apparatus 300 may be an electronic device, such as a mobile phone, computer, digital broadcast terminal, messaging device, game console, tablet device, medical device, fitness equipment, personal digital assistant, etc.
[0099] Reference Figure 3 , apparatus 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .
[0100] The processing component 302 generally controls the overall operation of the device 300, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 302 may include one or more processors 320 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 302 may include one or more modules to facilitate interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate interaction between the multimedia component 308 and the processing component 302.
[0101] The memory 304 is configured to store various types of data to support operations on the device 300. Examples of such data include instructions for any application or method operating on the device 300, contact data, phone book data, messages, pictures, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.
[0102] The power component 306 provides power to the various components of the device 300. The power component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 300.
[0103] The multimedia component 308 includes a screen that provides an output interface between the device 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of a touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 includes a front camera and / or a rear camera. When the device 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.
[0104] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC) that is configured to receive external audio signals when the device 300 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 304 or transmitted via the communication component 316. In some embodiments, the audio component 310 further includes a speaker for outputting audio signals.
[0105] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.
[0106] The sensor assembly 314 includes one or more sensors for providing various aspects of the status assessment of the device 300. For example, the sensor assembly 314 can detect the open / closed state of the device 300, the relative positioning of components, such as the display and keypad of the device 300. The sensor assembly 314 can also detect changes in the position of the device 300 or a component of the device 300, the presence or absence of user contact with the device 300, the orientation or acceleration / deceleration of the device 300, and temperature changes of the device 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
[0107] The communication component 316 is configured to facilitate wired or wireless communication between the device 300 and other devices. The device 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
[0108] In an exemplary embodiment, the apparatus 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above-described method.
[0109] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 304 including instructions, which can be executed by the processor 320 of the apparatus 300 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.
[0110] In an exemplary embodiment, a computer program product is also provided, comprising a computer program, which implements the above method when executed by the processor 320 of the apparatus 300 .
[0111] Other embodiments of the present invention will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This disclosure is intended to cover any variations, uses, or adaptations of the invention that follow from the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the invention being indicated by the following claims.
[0112] It should be understood that the present invention is not limited to the exact construction described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.
Claims
1. A dam surface disease detection method, characterized in that: include: Obtain meteorological and topographic data of the target dam surface; Using an extended Kalman filter algorithm to fuse the meteorological data and the terrain data to obtain environmental fusion data; Inputting the environmental fusion data into a trained environmental risk assessment model to obtain an environmental risk index value obtained after the environmental risk assessment model evaluates the environmental fusion data; When the environmental risk index value satisfies a preset condition, updating a three-dimensional obstacle model corresponding to the target dam surface based on the terrain data; Performing UAV inspection path planning based on the terrain data mixed with the three-dimensional obstacle model to obtain a planned path; The UAV is controlled to perform an inspection task according to the planned path, dam surface information collected by the UAV is obtained, and defects of the target dam surface are identified using the dam surface information to obtain a defect identification result.
2. The dam surface disease detection method according to claim 1, characterized in that: Before fusing the meteorological data and the terrain data using the extended Kalman filter algorithm to obtain environmental fusion data, the method further includes: Unifying the time bases of the meteorological and terrain data using GNSS and MEMS; The center point of the upstream dam surface of the target arch dam is determined as the origin, the direction of the downstream symmetry axis of the target arch dam is determined as the y-axis direction, and the direction perpendicular to the dam axis of the target arch dam is determined as the x-axis direction to obtain a unified coordinate system. The spatial references of the meteorological data and terrain data are unified based on the unified coordinate system.
3. The dam surface disease detection method according to claim 1, characterized in that: The environmental risk assessment model is obtained by training using the historical disease data of the target dam surface; the environmental risk assessment model is a Bayesian network model.
4. The dam surface disease detection method according to claim 3, characterized in that: The controlling the UAV to perform the inspection task according to the planned path includes: Controlling the UAV to perform the inspection task according to the planned path; In response to identifying an obstacle, determining a distance between the drone and the obstacle; When the distance is less than or equal to a first threshold, or when the obstacle is a dynamic obstacle and the obstacle's approach speed is greater than or equal to a preset speed, controlling the drone to immediately hover and outputting an alarm signal; When the distance is greater than the first threshold, an A* algorithm and the planned path are used to generate a detour path for the obstacle, and the drone is controlled to perform the inspection task according to the detour path.
5. A dam surface disease detection device, characterized in that: include: An acquisition unit, used for acquiring meteorological data and topographic data of the target dam surface; a fusion unit, configured to fuse the meteorological data and the terrain data using an extended Kalman filter algorithm to obtain environmental fusion data; An evaluation unit, configured to input the environmental fusion data into a trained environmental risk assessment model, and obtain an environmental risk index value obtained after the environmental risk assessment model evaluates the environmental fusion data; an updating unit, configured to update the three-dimensional obstacle model corresponding to the target dam surface based on the terrain data when the environmental risk index value meets a preset condition; A planning unit, configured to plan a UAV inspection path based on the terrain data mixed with the three-dimensional obstacle model to obtain a planned path; The identification unit is used to control the UAV to perform the inspection task according to the planned path, obtain the dam surface information collected by the UAV, use the dam surface information to identify the defects of the target dam surface, and obtain a defect identification result.
6. The dam surface disease detection device according to claim 5, characterized in that: The device also includes: a unification unit for unifying the time references of the meteorological data and the terrain data using GNSS and MEMS; The unification unit is further used to determine the center point of the upstream dam surface of the target arch dam as the origin, determine the direction of the downstream symmetry axis of the target arch dam as the y-axis direction, and determine the direction perpendicular to the dam axis of the target arch dam as the x-axis direction to obtain a unified coordinate system, and unify the spatial references of the meteorological data and terrain data based on the unified coordinate system.
7. The dam surface disease detection device according to claim 5, characterized in that: The environmental risk assessment model is obtained by training using the historical disease data of the target dam surface; the environmental risk assessment model is a Bayesian network model.
8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.
9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.
10. A computer program product comprising a computer program, characterized in that The computer program implements the method according to any one of claims 1 to 4 when executed by a processor.