Near-Network Distance Dynamic Monitoring and Early Warning Method Based on 3D Point Cloud Data

Abstract

The present invention discloses a method for dynamically monitoring and warning of the distance near a power grid based on three-dimensional point cloud data, which relates to the technical field of monitoring and warning. The present invention constructs power line point cloud data by collecting picture data and GPS data of a power line; obtains coarsely sampled data points and finely sampled data points by coarsely sampling and finely sampling the power line point cloud data; obtains three-dimensional positioning data of a crane by installing a real-time positioning device on the crane, calculates the precise shortest distance from the crane to the power line based on the coarsely sampled data points and the finely sampled data points, sets multiple levels of warning thresholds, and issues corresponding warnings and conducts safety path planning when the shortest distance triggers the warning threshold; the present invention realizes a near-electric warning method with high precision, high stability, and high real-time performance through the processing of the power line point cloud data and the processing of the three-dimensional positioning data of the crane.

Description

Technical Field

[0001] The present invention relates to the technical field of monitoring and early warning, and specifically relates to a near-net distance dynamic monitoring and early warning method based on three-dimensional point cloud data. Background Art

[0002] With the continuous expansion of the construction of power lines, the demand for special vehicles such as cranes to operate near power lines has gradually increased. However, when special vehicles such as cranes operate near power lines, near-electric accidents are extremely likely to occur, posing serious safety hazards to construction workers and equipment.

[0003] To avoid such accidents, a variety of near-electric warning systems have emerged in the current market. These systems usually rely on ultrasonic, infrared, laser ranging or electromagnetic induction technologies to provide warning information by real-time monitoring the distance between special vehicles such as cranes and power lines and the electromagnetic field intensity. However, there are still many deficiencies in these technical solutions in terms of accuracy, stability and real-time performance. Therefore, it is of great significance to design a monitoring and early warning system with high accuracy, strong stability and real-time performance. Summary of the Invention

[0004] The purpose of the present invention is to provide a near-net distance dynamic monitoring and early warning method based on three-dimensional point cloud data to solve the deficiencies in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A near-net distance dynamic monitoring and early warning method based on three-dimensional point cloud data, including:

[0006] Collecting image data and GPS data of the power line and its surrounding area by an unmanned aerial vehicle, and obtaining preliminary point cloud data of the power line and its surrounding area through image matching technology;

[0007] Checking the integrity of the preliminary point cloud data, and segmenting the point cloud data part of the power line to obtain power line point cloud data;

[0008] Performing coarse sampling and fine sampling on the power line point cloud data to obtain coarse sampling point cloud data points and fine sampling point cloud data points respectively;

[0009] Uploading the power line point cloud data for parsing to obtain longitude and latitude coordinates, and at the same time uploading the coarse sampling point cloud data points and fine sampling point cloud data points to the database, and respectively setting unique IDs;

[0010] Installing an RTK device on the crane to obtain three-dimensional positioning data of the crane, where the three-dimensional positioning data of the crane includes longitude, latitude and height coordinates, and calculating the exact shortest distance between the crane and the power line by using the three-dimensional positioning data, coarse sampling point cloud data points and fine sampling data point cloud data points;

[0011] Define a preset threshold and a warning level. When the precise shortest distance is less than the preset threshold, store and display the warning information. At the same time, provide safety warning prompts to the background and the driver through message prompts, text messages, or audible and visual warning devices, and plan a safe path for the crane operation.

[0012] In a new embodiment, the steps of collecting image data and GPS data of a power line and its surrounding area by a drone and obtaining preliminary point cloud data of the power line and its surrounding area through image matching technology are as follows:

[0013] Collect image data and GPS data of the power line and its surrounding area through a drone device;

[0014] Preprocess the image data to improve the quality, where the preprocessing includes correcting distortion, adjusting brightness, and enhancing contrast;

[0015] Use an image matching algorithm to extract feature points from the image data, and generate preliminary point cloud data through the feature points;

[0016] Use a filtering algorithm to remove noise and interference filtering in the preliminary point cloud data.

[0017] In a new embodiment, the steps of checking the integrity of the preliminary point cloud data and segmenting the point cloud data part of the power line to obtain the power line point cloud data are as follows:

[0018] Define a preset point density and a preset spatial range;

[0019] Import the preliminary point cloud data into point cloud processing software, calculate the point density and spatial range of the preliminary point cloud data. If both meet the preset point density and preset spatial range, it indicates that the preliminary point cloud data is complete;

[0020] Use a ground segmentation algorithm to extract the point cloud data of the ground part in the preliminary point cloud data, and calculate the point cloud data of the non-ground part in the preliminary point cloud data as the power line point cloud data;

[0021] Among them, the power line point cloud data includes the Mercator projection coordinates of the point cloud, point cloud density, classification information, and timestamp.

[0022] In a new embodiment, the steps of performing coarse sampling and fine sampling on the power line point cloud data to obtain coarse sampling point cloud data and fine sampling point cloud data respectively are as follows:

[0023] Define a preset coarse sampling accuracy and a preset fine sampling accuracy, and the preset fine sampling accuracy is higher than the preset coarse sampling accuracy;

[0024] Use the preset coarse sampling accuracy to perform coarse sampling on the power line point cloud data to obtain coarse sampling point cloud data points;

[0025] Perform fine sampling on the power line point cloud data using a preset fine sampling accuracy to obtain fine-sampled point cloud data points.

[0026] In a new embodiment, the steps of uploading the power line point cloud data, parsing to obtain longitude and latitude coordinates, and simultaneously uploading the coarsely sampled point cloud data points and the finely sampled point cloud data points to the database and respectively setting unique IDs are as follows:

[0027] Upload the power line point cloud data to the system, obtain the Mercator projection coordinates in the point cloud data and parse them into UTM coordinates;

[0028] First, convert the Mercator projection coordinates of each point cloud to UTM coordinates, and the calculation method is:

[0029]

[0030] Then use the projection conversion library to convert the UTM coordinates to longitude and latitude coordinates to obtain the longitude and latitude data of the power line point cloud data;

[0031] At the same time, upload the coarsely sampled point cloud data points to the database, generate a coarse sampling point ID for each coarsely sampled point cloud data point, upload the finely sampled point cloud data points to the database, generate a fine sampling point ID for each finely sampled point cloud data point, and associate the fine sampling point ID with the coarse sampling point ID.

[0032] In a new embodiment, the steps of calculating the exact shortest distance from the finely sampled data point cloud data to the crane are as follows:

[0033] Install RTK devices at the sling head, boom head, and boom tail positions to obtain the three-dimensional positioning data of the crane in real time, including longitude, latitude, and height coordinates, and upload the coordinate data of the three positions of the sling head, boom head, and boom tail to the background control system;

[0034] Calculate the shortest distances from the two line segments formed by the sling head and the boom head, and the boom head and the boom tail to the power line using the coarsely sampled point cloud data points. The calculation method is as follows:

[0035] First, convert the three-dimensional positioning data of the crane and the longitude, latitude, and height coordinates of the power line point cloud data to a spatial rectangular coordinate system. The conversion formula is as follows:

[0036]

[0037] Where, and are the radian values of latitude and longitude respectively, is the height of the power line, sling head, boom head, and boom tail, is the equatorial radius of the earth;

[0038] Then, the projection method is used to calculate the distances from the rough sampling point cloud data points to the two line segments formed by the sling head and the boom head, and the boom head and the boom tail as the distance between the crane and the power line;

[0039] Select the shortest distance between the crane and the power line as the rough shortest distance , obtain the rough sampling point ID of the rough sampling point cloud data point at the rough shortest distance, query the associated multiple fine sampling point IDs, and obtain the coordinates of the multiple fine sampling point cloud data points based on the multiple fine sampling point IDs;

[0040] Use the projection method and the coordinates of the multiple fine sampling point cloud data points to calculate and judge the exact shortest distance between the crane and the power line .

[0041] In a new embodiment, the steps of defining a preset threshold and a warning level, and storing and displaying the warning information when the exact shortest distance is less than the preset threshold are as follows:

[0042] Define the warning levels as red, orange, and yellow and define the corresponding thresholds as the first-level warning threshold , the second-level warning threshold , the third-level warning threshold ;

[0043] Based on the exact shortest distance Judge the warning level;

[0044] When , a red warning is triggered. When , an orange warning is triggered. When , a yellow warning is triggered;

[0045] When the exact shortest distance meets the warning conditions, store and display the warning information, and at the same time, warn the background and the driver through the message prompt and the sound and light warning device and plan the safe path of the crane operation, where the warning information includes the warning level, the three-dimensional positioning data of the crane, and the coordinates of the fine sampling point cloud data points.

[0046] In a new embodiment, the steps of planning the safe path of the crane operation are as follows:

[0047] Obtain the power line point cloud data and convert its longitude and latitude data into a spatial rectangular coordinate system;

[0048] Define a preset safety distance and construct a safety area in the shape of a cylinder with the preset safety distance as the radius based on the power line point cloud data;

[0049] Determine the initial position and target position of the crane. Connect the initial position and the target position in space to form an initial path, and mark the key points, where the key points include the turning points of the crane's operation and the intersection points of the power lines;

[0050] For the initial path, calculate the distance between the coarse sampled point cloud data points and the initial path to determine whether it intersects with the safe area;

[0051] If there is an intersection part, determine the intersection points and perform path offset. The offset direction is away from the safe area and the power line direction. The offset amount is based on the intersection distance between the fine sampled point cloud data points and the intersection points. The shortest distance between the offset intersection part and the fine sampled point cloud data points should be greater than the preset safe distance;

[0052] After the initial path completes the path offset, a secondary path is formed. Calculate the distance between the coarse sampled point cloud data points and the initial path for the secondary path to determine whether there is an intersection part. If there is, continue with the path offset. If not, output it as a safe path to the background control system;

[0053] If a new obstacle appears during the crane's operation based on the safe path and triggers an alarm, the crane will stop running, and the stop point will be used as the initial point to re-plan the path.

[0054] In the above technical solution, the technical effects and advantages provided by the present invention:

[0055] 1. The present invention realizes high-precision real-time monitoring and early warning by constructing the point cloud data of the power line and combining the RTK device on the crane. By installing the RTK high-precision device at three positions: the sling head, the boom head, and the boom tail, the positioning stability and accuracy are improved through multi-point positioning, the accurate measurement of the crane's position is realized, the positioning error of the early warning system is minimized, the accuracy of the early warning is improved, and at the same time, the position data of the crane can be obtained in real time. Combining with the lidar point cloud data, the high accuracy of positioning and ranging is ensured, and the high reliability of the system in various complex environments is guaranteed;

[0056] 2. The present invention sets different early warning levels and thresholds, conducts multi-level early warning according to different distances, provides detailed safety tips, and in a complex power line environment, after the crane operation triggers an early warning, a safe path planning will be carried out. A safe path is generated for the driver's reference through the three-dimensional positioning data of the crane and the point cloud data of the power line. The multi-level early warning and safe path planning enhance the flexibility and practicality of the early warning system. Description of the Drawings

[0057] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the accompanying drawings required for use in the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other accompanying drawings can also be obtained based on these drawings.

[0058] Figure 1 It is the flowchart of the method of the present invention. Specific embodiments

[0059] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0060] Embodiment 1. Please refer to Figure 1 As shown, the method for near-net distance dynamic monitoring and early warning based on three-dimensional point cloud data in this embodiment includes:

[0061] S1. Collect image data and GPS data of the power line and its surrounding area through a drone, and obtain preliminary point cloud data of the power line and its surrounding area through image matching technology;

[0062] S2. Check the integrity of the preliminary point cloud data, and segment the point cloud data part of the power line to obtain power line point cloud data;

[0063] S3. Coarsely sample and finely sample the power line point cloud data to obtain coarsely sampled point cloud data points and finely sampled point cloud data points respectively;

[0064] S4. Upload the power line point cloud data for analysis to obtain longitude and latitude coordinates. At the same time, upload the coarsely sampled point cloud data points and finely sampled point cloud data points to the database, and set unique IDs respectively;

[0065] S5. Install an RTK device on the crane to obtain three-dimensional positioning data of the crane. The three-dimensional positioning data of the crane includes longitude, latitude, and height coordinates. Use the three-dimensional positioning data, coarsely sampled point cloud data points, and finely sampled point cloud data points to calculate the exact shortest distance between the crane and the power line;

[0066] S6. Define a preset threshold and an early warning level. When the exact shortest distance is less than the preset threshold, store and display the early warning information. At the same time, provide a safety early warning prompt to the background and the driver through message prompts, text messages, or audible and visual warning devices, and plan a safe path for the operation of the crane;

[0067] As described in the above steps S1 - S6, with the continuous expansion of the construction of power lines, the demand for special vehicles such as cranes to operate near power lines has gradually increased. However, when special vehicles such as cranes operate near power lines, near - electricity accidents are extremely likely to occur, posing serious safety hazards to construction workers and equipment. To avoid such accidents, a variety of near - electricity warning systems have emerged in the current market. These systems usually rely on ultrasonic, infrared, laser ranging, or electromagnetic induction technologies to provide warning information by real - time monitoring the distance between special vehicles such as cranes and power lines and the electromagnetic field intensity. However, there are still many deficiencies in these technical solutions in terms of accuracy, stability, and real - time performance. Therefore, the present invention realizes high - precision real - time monitoring and warning by constructing the point - cloud data of power lines and combining RTK devices on the crane. By installing RTK high - precision devices at three positions: the sling head, the boom head, and the boom tail, the stability and accuracy of positioning are improved through multi - point positioning, achieving precise measurement of the crane's position, ensuring that the positioning error of the warning system is minimized, improving the accuracy of the warning, and at the same time being able to obtain the position data of the crane in real - time. Combining with the lidar point - cloud data, it ensures high accuracy in positioning and ranging, guarantees the high reliability of the system in various complex environments. At the same time, different warning levels and thresholds are set, and multi - level warnings are carried out according to different distances, providing detailed safety tips. And in a complex power - line environment, after the crane operation triggers a warning, a safe path planning will be carried out. A safe path is generated for the driver to refer to through the three - dimensional positioning data of the crane and the point - cloud data of the power line, enhancing the flexibility and practicality of the warning system.

[0068] In one embodiment, step S1 of collecting image data and GPS data of the power line and its surrounding area by the drone and obtaining the preliminary point - cloud data of the power line and its surrounding area through image matching technology includes:

[0069] S11. Collect image data and GPS data of the power line and its surrounding area by the drone device;

[0070] S12. Pre - process the image data to improve the quality, where the pre - processing includes correcting distortion, adjusting brightness, and enhancing contrast;

[0071] S13. Use the image matching algorithm to extract feature points in the image data and generate preliminary point - cloud data through the feature points;

[0072] S14. Use the filtering algorithm to remove noise and interference in the preliminary point - cloud data for filtering.

[0073] As described in the above steps S11 - S14, use a DJI drone to carry a lidar device to collect power line point cloud data images and GPS data. Import the collected image data and GPS information into the DJI Terra software, correct the distortion of the image data, adjust the brightness, and enhance the contrast. Use an image matching algorithm to extract feature points in the image data, generate preliminary point cloud data through the feature points, and optimize and process the preliminary point cloud data, including steps such as removing noise, filtering, and reconstruction. After completing the above steps, export the preliminary point cloud data for the next step of processing.

[0074] In one embodiment, the step S2 of checking the integrity of the preliminary point cloud data and segmenting the point cloud data part of the power line to obtain the power line point cloud data includes:

[0075] S21. Define a preset point density and a preset spatial range;

[0076] S22. Import the preliminary point cloud data into point cloud processing software, calculate the point density and spatial range of the preliminary point cloud data. If both meet the preset point density and preset spatial range, it indicates that the preliminary point cloud data is complete;

[0077] S23. Use a ground segmentation algorithm to extract the point cloud data of the ground part in the preliminary point cloud data, and calculate the point cloud data of the non - ground part in the preliminary point cloud data as the power line point cloud data;

[0078] S24. Among them, the power line point cloud data includes the Mercator projection coordinates of the point cloud, point cloud density, classification information, and timestamp;

[0079] As described in the above steps S21 - S24, check the integrity of the preliminary point cloud data, define a preset point density and a preset spatial range as inspection indicators, use software to check the point density and spatial range of the preliminary point cloud data, perform segmentation processing on the preliminary point cloud data that passes the inspection, and segment the power line point cloud data from the preliminary point cloud data. The power line point cloud data includes the Mercator projection coordinates of the point cloud, point cloud density, classification information, and timestamp. In the example, use CloudCompare to open the preliminary point cloud data and calculate its point density. At the same time, use browsing tools such as rotation, translation, and scaling to perform a preliminary inspection on the point cloud data to confirm that the preliminary point cloud data covers target areas such as power lines, the ground, and trees. After checking the integrity, select the Segment tool and use a rectangular or polygonal selection tool to circle the point cloud data of the power line part, and segment the selected power line part, and save the processed power line point cloud data for the next step of processing.

[0080] In one embodiment, the step S3 of performing coarse sampling and fine sampling on the power line point cloud data to obtain coarse - sampled point cloud data and fine - sampled point cloud data respectively includes:

[0081] S31. Define a preset coarse sampling accuracy and a preset fine sampling accuracy, where the preset fine sampling accuracy is higher than the preset coarse sampling accuracy;

[0082] S32. Coarsely sample the power line point cloud data using the preset coarse sampling accuracy to obtain coarsely sampled point cloud data points;

[0083] S33. Finely sample the power line point cloud data using the preset fine sampling accuracy to obtain finely sampled point cloud data points;

[0084] As described in the above steps S31 - S33, perform coarse sampling and fine sampling on the power line point cloud data. The preset coarse sampling accuracy and fine sampling accuracy are used to obtain the coarsely sampled point cloud data points and the finely sampled point cloud data points of the power line point cloud data. That is, in actual implementation, the coarse sampling process is to select the power line point cloud data, select the Subsample tool, set the sampling type to Spatial, and set the minimum point distance to 1 meter for coarse sampling to generate the coarsely sampled point cloud data. The coarsely sampled data is used to determine which power line the device is close to. The fine sampling process is for the power line point cloud data, select the Subsample tool, set the sampling type to Spatial, and set the minimum point distance to 0.1 meter for fine sampling to generate the finely sampled point cloud data. The finely sampled data is used to accurately calculate the distance between the crane and the power line and for 3D display.

[0085] In one embodiment, step S4 of uploading the power line point cloud data, parsing to obtain the longitude and latitude coordinates, and at the same time uploading the coarsely sampled point cloud data points and the finely sampled point cloud data points to the database and respectively setting unique IDs includes:

[0086] S41. Upload the power line point cloud data to the system, obtain the Mercator projection coordinates in the point cloud data and parse them into UTM coordinates;

[0087] S42. First, convert the Mercator projection coordinates of each point cloud into UTM coordinates. The calculation method is:

[0088]

[0089] S43. Then use the projection conversion library to convert the UTM coordinates into longitude and latitude coordinates to obtain the longitude and latitude data of the power line point cloud data;

[0090] S44. At the same time, upload the coarsely sampled point cloud data points to the database, generate a coarse sampling point ID for each coarsely sampled point cloud data point, upload the finely sampled point cloud data points to the database, generate a fine sampling point ID for each finely sampled point cloud data point, and associate the fine sampling point ID with the coarse sampling point ID;

[0091] As described in the above steps S41 - S44, upload the power line point cloud data to the system, and obtain the scaling factors and offset values of the Mercator projection coordinates X, Y, and Z coordinates in the power line point cloud data. Then, parse the power line point cloud data. First, convert the Mercator projection coordinates in the power line point cloud data into UTM coordinates. Taking the X coordinate as an example, the calculation method is , where represents the scaling factor of the X coordinate, represents the offset value of the X coordinate. Obtain the UTM coordinates of the power line point cloud data through calculation. Then, convert the UTM coordinates into the global coordinate system. The calculation method is to first obtain the UTM zone number. According to the division of the UTM coordinate system, every 6 degrees of longitude is a UTM zone. The UTM zone number corresponding to the longitude of the area where the point cloud data is located can be determined through the following calculation: , where UTMZone represents the UTM zone number and Longitude represents the longitude of the area where the point cloud data is located. Finally, based on the UTM zone number and the projection conversion library, convert the UTM coordinates into longitude and latitude coordinates. At the same time, upload the coarse - sampled point cloud data points and the fine - sampled point cloud data points to the database of the system, and assign an ID to each point cloud data point. Because there are differences in sampling accuracy, that is, the fine - sampling accuracy is higher than the coarse - sampling accuracy, the coarse - sampled point cloud data contains the fine - sampled point cloud data points. Therefore, in the system database, make the fine - sampling ID associated with the coarse - sampling ID.

[0092] In one embodiment, step S5 of calculating the exact shortest distance between the crane and the power line through the fine - sampled data point cloud data includes:

[0093] S51. Install RTK devices at the sling head, boom head, and boom tail positions to obtain the three - dimensional positioning data of the crane in real time, including longitude, latitude, and height coordinates, and upload the coordinate data of the three positions of the sling head, boom head, and boom tail to the background control system;

[0094] S52. Use the coarse - sampled point cloud data points to calculate the shortest distances from the two line segments formed by the sling head and the boom head, and the boom head and the boom tail to the power line. The calculation methods are as follows:

[0095] S53. First, convert the three - dimensional positioning data of the crane and the longitude, latitude, and height coordinates of the power line point cloud data into a space rectangular coordinate system. The conversion formulas are as follows:

[0096]

[0097] S54. Among them, and are the radian values of latitude and longitude respectively, is the height of the power line, sling head, boom head, and boom tail, is the equatorial radius of the Earth;

[0098] S55. Then, use the projection method to calculate the distances from the coarse-sampled point cloud data points to the two line segments formed by the sling head and the boom head, and the boom head and the boom tail as the distance between the crane and the power line;

[0099] S56. Select the shortest distance between the crane and the power line as the rough shortest distance , obtain the coarse-sampling point ID of the coarse-sampled point cloud data point at the rough shortest distance, query the associated multiple fine-sampling point IDs, and obtain the coordinates of the multiple fine-sampled point cloud data points based on the multiple fine-sampling point IDs;

[0100] S57. Use the projection method and the coordinates of the multiple fine-sampled point cloud data points to calculate and judge the exact shortest distance between the crane and the power line ;

[0101] As described in the above steps S51 - S57, install RTK devices at the sling head, boom head, and boom tail positions of the crane and ensure that the devices can work properly. Among them, the RTK device can be used for real-time dynamic high-precision positioning. Obtain the three-dimensional positioning data of the crane in real time through the RTK device, including longitude, latitude, and altitude coordinates, and transmit the coordinate data of the three positions of the sling head, boom head, and boom tail to the background control system after combined processing. First, use the coarse-sampled point cloud data to calculate the shortest distances from the two line segments formed by the sling head and the boom head, and the boom head and the boom tail to the power line. It is necessary to convert the coarse-sampled point cloud data and the three-dimensional positioning data of the crane into a space rectangular coordinate system. The XYZ axis coordinates of the power line point cloud data and the three-dimensional positioning data of the crane in the space rectangular coordinate system are all obtained from the formula in step S53. Then, calculate the distance from the coarse-sampled point cloud data point to the crane through the space rectangular coordinate system and the projection method, and judge the rough shortest distance. Because the accuracy of the coarse sampling is too low, there will be a large error in the calculated shortest distance. Therefore, the obtained shortest distance is the rough shortest distance. Obtain the information of the coarse-sampled point cloud data point for the rough shortest distance, search for the associated fine-sampled point cloud data points through the system database, use the fine-sampled point cloud data points and the projection method to solve the distance from the fine-sampled point cloud data point to the crane, and obtain the exact shortest distance through comparison. The calculation method of the projection method is as follows: Assume that in 3D space, calculate the shortest distance from the target point P (coarse-sampled point cloud data point or fine-sampled point cloud data point) to the line segment AB (the line segment composed of the sling head, boom head, and boom tail). First, calculate the parameter of the projection of the target point P onto the line segment AB :

[0102]

[0103] where A, B, and P are the coordinates of the starting point, ending point of the line segment AB, and the target point P respectively, and ;

[0104] Then calculate the projection point P1 of the target point on the line segment AB :

[0105]

[0106] Finally, calculate the distance from the target point P to the projection point P1:

[0107]

[0108] In one embodiment, the step S6 of defining a preset threshold and a warning level and storing and displaying a warning message when the precise shortest distance is less than the preset threshold includes:

[0109] S61. Define the warning levels as red, orange, and yellow and define the corresponding thresholds as the first-level warning threshold , the second-level warning threshold , and the third-level warning threshold ;

[0110] S62. Based on the precise shortest distance judge the warning level;

[0111] S63. When , trigger a red warning. When , trigger an orange warning. When , trigger a yellow warning;

[0112] S64. When the precise shortest distance meets the warning condition, store and display the warning message, and at the same time, warn the background and the driver through message prompts and sound and light warning devices and plan a safe path for the crane operation, where the warning message includes the warning level, the three-dimensional positioning data of the crane, and the coordinates of the fine sampling point cloud data points;

[0113] As described in the above steps S61 - S64, define the warning levels as red, orange, and yellow, divide the thresholds according to the severity of the warning, the red warning corresponds to the first-level warning threshold, the orange warning corresponds to the second-level warning threshold, and the yellow warning corresponds to the third-level warning threshold. When the precise shortest distance triggers the warning threshold, a corresponding warning will be issued, the warning message will be stored and displayed, and at the same time, the background and the driver will be warned through message prompts and sound and light warning devices. When the warning is triggered, the crane background control system will plan a safe path and display the safe path to the driver to provide a reference for the driver for the crane operation.

[0114] In one embodiment, the step S64 of planning a safe path for the crane operation includes:

[0115] S641. Obtain the point cloud data of the power line and convert its longitude and latitude data into a spatial rectangular coordinate system;

[0116] S642. Define a preset safety distance and construct a safety area in the shape of a cylinder with the preset safety distance as the radius based on the point cloud data of the power line;

[0117] S643. Determine the initial position and target position of the crane operation, connect the initial position and the target position in space to form an initial path, and mark the key points, where the key points include the turning points of the crane operation and the intersection points of the power lines;

[0118] S644. For the initial path, calculate the distance between the coarse-sampled point cloud data points and the initial path to determine whether there is an intersection with the safety area;

[0119] S645. If there is an intersection part, determine the intersection points and perform path offset. The offset direction is away from the safety area and the power line direction, and the offset amount is based on the intersection distance between the fine-sampled point cloud data points and the intersection points. The shortest distance between the offset intersection part and the fine-sampled point cloud data points should be greater than the preset safety distance;

[0120] S646. After the initial path completes the path offset, a secondary path is formed. Calculate the distance between the coarse-sampled point cloud data points and the initial path for the secondary path to determine whether there is an intersection part. If there is, continue to perform path offset. If not, output it as a safe path to the background control system;

[0121] S647. If a new obstacle appears during the crane operation based on the safe path and a warning is triggered, the crane will stop running, and the stop point will be used as the initial point to re-plan the path;

[0122] As described in the above steps SS641 - S647, in a complex power line environment, when a warning is triggered during the operation of the crane, the crane control system will give a reference for the safe path. First, the safe distance is defined. The selection of the safe distance depends on the complexity of the power line. The higher the complexity, the larger the safe distance is set. A cylindrical safety area is generated around the power line with the safe distance as the radius. Then, the initial position and the target position of the crane operation are determined. The line connecting the initial position and the target position is used as the initial path, and the distance between the coarse sampled point cloud data points and the initial path is calculated. It is judged whether the initial path intersects with the safety area through the distance. If there is an intersecting part, the initial path is offset. The offset direction is away from the safety area and the power line direction. At the same time, it should be ensured that after the offset, it will not enter the safety area of other power lines. The offset amount is determined by the distance between the intersection point and the fine sampled point cloud data points. Calculate the shortest distance between the intersection point and the fine sampled point cloud data points. It is necessary to ensure that the shortest distance is greater than the safe distance, then the offset amount corresponds to the corresponding difference. In actual operation, there will be redundancy, and the offset amount will be slightly larger than the corresponding difference. The initial path that realizes the path offset is defined as the secondary path, and the coarse sampled point cloud data points are repeatedly used to judge whether the secondary path has an intersecting part with the safety area. If so, repeat the path offset step. If not, it is output as the safe path for the driver's reference. When a new obstacle appears during the operation of the crane on the safe path and triggers a safety warning, the crane will stop running, and the point where it stops running is used as the initial position. Based on the original target position, the safe path planning process is restarted, and a new safe path is output to realize the real - time output of the safe path and ensure the safety of the crane working near electricity.

[0123] As mentioned above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art in the technical field disclosed by the present application can easily think of changes or substitutions, which should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

Claims

1. A near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data, characterized in that: The image data and GPS data of the power lines and their surroundings are collected by drones, and the preliminary point cloud data of the power lines and their surroundings are obtained by image matching technology; Check the integrity of the preliminary point cloud data, and segment the point cloud data portion of the power line to obtain the power line point cloud data; Performing coarse sampling and fine sampling on the power line point cloud data to obtain coarse sampling point cloud data points and fine sampling point cloud data points respectively; Upload the power line point cloud data for parsing to obtain the longitude and latitude coordinates, and upload the coarse sampling point cloud data points and the fine sampling point cloud data points to the database, and set unique IDs for each of them. Specifically, upload the coarse sampling point cloud data points to the database, generate a coarse sampling point ID for each coarse sampling point cloud data point, upload the fine sampling point cloud data points to the database, generate a fine sampling point ID for each fine sampling point cloud data point, and associate the fine sampling point ID with the coarse sampling point ID; Install RTK equipment on the crane to obtain the three-dimensional positioning data of the crane, where the three-dimensional positioning data of the crane includes latitude, longitude and altitude coordinates. Use the three-dimensional positioning data, coarse sampling point cloud data points and fine sampling point cloud data points to calculate the precise shortest distance between the crane and the power line. Use the coarse sampling point cloud data points to calculate the distance between the crane and the power line, and select the shortest distance between the crane and the power line as the rough shortest distance. , obtaining the coarse sampling point ID of the coarse sampling point cloud data point at the rough shortest distance, and querying the associated multiple fine sampling point IDs, and obtaining the coordinates of multiple fine sampling point cloud data points based on the multiple fine sampling point IDs; The precise shortest distance between the crane and the power line is calculated and determined using the projection method and the coordinates of multiple finely sampled point cloud data points. ; Define preset thresholds and warning levels. When the precise shortest distance is less than the preset threshold, the warning information will be stored and displayed. At the same time, safety warning prompts will be provided to the backend and the driver through message prompts, SMS or sound and light warning devices, and a safe path for the crane operation will be planned; When a new obstacle appears on the crane's safe path and triggers a safety warning, the crane will stop running and use the point where it stopped as the initial position. It will restart the safe path planning process based on the original target position and output a new safe path.

2. The near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data according to claim 1 is characterized by: The steps of collecting image data and GPS data of the power line and its surroundings by using a drone and obtaining preliminary point cloud data of the power line and its surroundings by using image matching technology are as follows: Collect image data and GPS data of power lines and their surroundings through drone equipment; Preprocess the image data to improve the quality, where the preprocessing includes correcting distortion, adjusting brightness and enhancing contrast; Use image matching algorithm to extract feature points in image data, and generate preliminary point cloud data through feature points; The filtering algorithm is used to remove noise and interference filtering in the preliminary point cloud data.

3. The near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data according to claim 1 is characterized by: The steps of checking the integrity of the preliminary point cloud data and segmenting the point cloud data portion of the power line to obtain the point cloud data of the power line are: Define preset point density and preset spatial range; Import the preliminary point cloud data into the point cloud processing software, calculate the point density and spatial range of the preliminary point cloud data, and if both meet the preset point density and preset spatial range, it means that the preliminary point cloud data is complete; The point cloud data of the ground part in the preliminary point cloud data is extracted by using a ground segmentation algorithm, and the point cloud data of the non-ground part in the preliminary point cloud data is calculated as the point cloud data of the power route; The power line point cloud data includes the Mercator projection coordinates of the point cloud, point cloud density, classification information and timestamp.

4. The near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data according to claim 1 is characterized by: The steps of performing coarse sampling and fine sampling on the power line point cloud data to obtain coarse sampling point cloud data and fine sampling point cloud data respectively are: Define a preset coarse sampling accuracy and a preset fine sampling accuracy, wherein the preset fine sampling accuracy is higher than the preset coarse sampling accuracy; The power line point cloud data is coarsely sampled using a preset coarse sampling accuracy to obtain coarse sampling point cloud data points; The power line point cloud data is finely sampled using the preset fine sampling accuracy to obtain finely sampled point cloud data points.

5. The near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data according to claim 1 is characterized by: The steps of uploading the power line point cloud data, parsing to obtain the longitude and latitude coordinates, uploading the coarse sampling point cloud data points and the fine sampling point cloud data points to the database, and setting the unique IDs respectively are as follows: Upload the power line point cloud data to the system, obtain the Mercator projection coordinates in the point cloud data and parse them into UTM coordinates; First, the Mercator projection coordinates of each point cloud Convert to UTM coordinates, calculated as: Then use the projection conversion library to convert the UTM coordinates into longitude and latitude coordinates to obtain the longitude and latitude data of the power line point cloud data.

6. The near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data according to claim 1 is characterized by: The step of calculating the precise shortest distance between the crane and the power line also includes: RTK equipment is installed at the cable head, boom head and boom tail to obtain the three-dimensional positioning data of the crane in real time, including longitude, latitude and altitude coordinates, and upload the coordinate data of the cable head, boom head and boom tail to the background control system; The shortest distances from the two line segments formed by the sling head and the boom head, and the boom head and the boom tail to the power line are calculated using the coarse sampling point cloud data points. The calculation method is as follows: First, the three-dimensional positioning data of the crane and the latitude, longitude and altitude coordinates of the power line point cloud data are converted into a spatial rectangular coordinate system. The conversion formula is as follows: in, and are the radians of latitude and longitude respectively, It is the height of the power line, sling head, boom head or boom tail, is the Earth's equatorial radius; Then, the projection method is used to calculate the distance from the coarse sampling point cloud data point to the two line segments formed by the sling head and the boom head, and the boom head and the boom tail as the distance between the crane and the power line.

7. The near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data according to claim 1 is characterized by: The steps of defining the preset threshold and warning level and storing and displaying the warning information when the precise shortest distance is less than the preset threshold are as follows: Define the warning levels as red, orange, and yellow and define the corresponding thresholds as the first-level warning threshold , Second level warning threshold , Level 3 warning threshold ; Based on exact shortest distance Determine the warning level; when , a red alert is triggered. , an orange warning is triggered. , a yellow warning is triggered; When the shortest distance When the warning conditions are met, the warning information will be stored and displayed. At the same time, the background and driver will be warned through message prompts and sound and light warning devices, and a safe path for the crane operation will be planned. The warning information includes the warning level, the crane's three-dimensional positioning data, and the coordinates of the fine sampling point cloud data points.

8. The near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data according to claim 7 is characterized by: The steps for planning a safe path for crane operation are: Obtain the point cloud data of the power line and convert its latitude and longitude data into a spatial rectangular coordinate system; Define a preset safety distance, and construct a cylindrical safety area with the preset safety distance as the radius based on the power line point cloud data; Determine the initial position and target position of the crane operation, connect the initial position and target position in space to form an initial path, and mark key points, including the turning point of the crane operation and the intersection of the power lines; For the initial path, calculate the distance between the coarse sampling point cloud data point and the initial path to determine whether it intersects with the safe area; If there is an intersection, the intersection point is determined and the path is offset. The offset direction is away from the safe area and the power line direction. The offset is based on the intersection distance between the fine sampling point cloud data point and the intersection point. The shortest distance between the offset intersection and the fine sampling point cloud data point should be greater than the preset safety distance. After the initial path is offset, a secondary path is formed. The distance between the coarse sampling point cloud data points and the initial path is calculated for the secondary path to determine whether there is an intersection. If so, the path offset is continued. If not, the path is output as a safe path to the background control system. If a new obstacle appears while the crane is running on a safe path and triggers an early warning, it will stop running and replan the path using the stopping point as the starting point.

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