Near network distance dynamic monitoring and early warning method based on three-dimensional point cloud data

Abstract

The invention discloses a near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data, and relates to the technical field of monitoring and early warning, and the method comprises the steps: building the point cloud data of a power line through collecting the image data and GPS data of the power line; coarsely sampling and finely sampling the point cloud data of the power line to obtain coarsely sampled data points and finely sampled data points; a real-time positioning device is installed on a crane to obtain three-dimensional positioning data of the crane, the accurate shortest distance from the crane to a power line is calculated based on rough sampling data points and fine sampling data points, multiple levels of early warning threshold values are set, the early warning threshold values are triggered in the shortest distance to give out corresponding early warning, and safe path planning is conducted. According to the near-electricity early warning method, the near-electricity early warning method with high precision, high stability and high real-time performance is realized by processing the point cloud data of the circuit and the three-dimensional positioning data of the crane.

Description

Technical Field

[0001] The present invention relates to the field of monitoring and early warning technology, and in particular to a close-range dynamic monitoring and early warning method based on three-dimensional point cloud data. Background Art

[0002] With the continuous expansion of power line construction, the demand for special vehicles such as cranes to operate near power lines is gradually increasing. However, when special vehicles such as cranes operate near power lines, they are prone to near-electricity accidents, which poses serious safety hazards to construction personnel and equipment. To avoid such accidents, a variety of near-electric warning systems have appeared on the market. These systems usually rely on ultrasonic, infrared, laser ranging or electromagnetic induction technology to provide warning information by real-time monitoring of the distance between special vehicles such as cranes and power lines and the intensity of the electromagnetic field. However, these technical solutions still have many shortcomings 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

[0003] The purpose of the present invention is to provide a near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data to solve the shortcomings of the background technology.

[0004] In order to achieve the above-mentioned purpose, the present invention provides the following technical solution: a near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data, comprising: 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 fine sampling point cloud data points to the database, and set unique IDs for each; 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, and 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; 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 background and the driver through message prompts, text messages or sound and light warning devices, and a safe path for the crane operation will be planned.

[0005] In a new embodiment, 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.

[0006] In a new embodiment, 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.

[0007] 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: 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.

[0008] In a new embodiment, 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; At the same time, the coarse sampling point cloud data points are uploaded to the database, a coarse sampling point ID is generated for each coarse sampling point cloud data point, the fine sampling point cloud data points are uploaded to the database, a fine sampling point ID is generated for each fine sampling point cloud data point, and the fine sampling point ID is associated with the coarse sampling point ID.

[0009] In a new embodiment, the step of calculating the precise shortest distance to the crane through fine sampling data point cloud data is: 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 and boom tail. is the Earth's equatorial radius; Then, 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 is calculated by the projection method as the distance between the crane and the power line; 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. .

[0010] In a new embodiment, the preset threshold and warning level are defined, and when the precise shortest distance is less than the preset threshold, the steps of storing and displaying the warning information are: 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.

[0011] In a new embodiment, the steps of 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.

[0012] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention realizes high-precision real-time monitoring and early warning by constructing point cloud data of power lines combined with RTK equipment on cranes. By installing RTK high-precision equipment at three positions: the cable head, boom head, and boom tail, the stability and accuracy of positioning are improved through multi-point positioning, and accurate measurement of the crane position is achieved, ensuring that the positioning error of the early warning system is reduced to a minimum, improving the accuracy of early warning, and at the same time being able to obtain the position data of the crane in real time, combined with the laser radar point cloud data, ensuring the high accuracy of positioning and ranging, and ensuring the high reliability of the system in various complex environments; 2. The present invention sets different warning levels and thresholds, performs multi-level warnings according to different distances, provides detailed safety tips, and in complex power line environments, performs safe path planning after the crane operation triggers an early warning. A safe path is generated through the crane's three-dimensional positioning data and the power line point cloud data for the driver's reference. Multi-level warnings and safe path planning enhance the flexibility and practicality of the warning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0014] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0015] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0016] Example 1, please refer to Figure 1 As shown, the near-network distance dynamic monitoring and early warning method based on three-dimensional point cloud data described in this embodiment includes: S1. Collect image data and GPS data of power lines and their surroundings through drones, and obtain preliminary point cloud data of power lines and their surroundings through image matching technology; S2. 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; S3, 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; S4, uploading the power line point cloud data for parsing to obtain longitude and latitude coordinates, and uploading the coarse sampling point cloud data points and the fine sampling point cloud data points to the database, and setting unique IDs for each; S5. Install an RTK device on the crane to obtain the three-dimensional positioning data of the crane, wherein the three-dimensional positioning data of the crane includes latitude, longitude and altitude coordinates, and 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; S6. Define preset thresholds and warning levels. 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 sound and light warning devices, and plan a safe path for the crane operation; As described in the above steps S1-S6, with the continuous expansion of power line construction, the demand for special vehicles such as cranes to operate near power lines is gradually increasing. However, when special vehicles such as cranes are operating near power lines, they are very prone to near-electricity accidents, which poses a serious safety hazard to construction personnel and equipment. In order to avoid such accidents, a variety of near-electricity warning systems have appeared on the market. These systems usually rely on ultrasonic, infrared, laser ranging or electromagnetic induction technology to provide warning information by real-time monitoring of the distance between special vehicles such as cranes and power lines and the electromagnetic field strength. However, these technical solutions still have many shortcomings in terms of accuracy, stability and real-time performance. Therefore, the present invention realizes high-precision real-time monitoring and warning by constructing point cloud data of power lines in combination with RTK equipment on the crane. TK high-precision equipment is installed at three positions: the sling head, boom head, and boom tail. Multi-point positioning improves the stability and accuracy of positioning, realizes accurate measurement of the crane position, ensures that the positioning error of the early warning system is minimized, and improves the accuracy of the early warning. At the same time, it can obtain the position data of the crane in real time. Combined with the lidar point cloud data, it ensures the high accuracy of positioning and ranging to ensure 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. Detailed safety prompts are provided. In complex power line environments, after the crane operation triggers an early warning, a safe path planning will be carried out. The three-dimensional positioning data of the crane and the point cloud data of the power line are used to generate a safe path for the driver's reference, thereby enhancing the flexibility and practicality of the early warning system.

[0017] In one embodiment, the step S1 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 includes: S11. Collect image data and GPS data of power lines and their surroundings through drone equipment; S12, preprocessing the image data to improve quality, wherein the preprocessing includes correcting distortion, adjusting brightness and enhancing contrast; S13, extracting feature points from the image data using an image matching algorithm, and generating preliminary point cloud data using the feature points; S14. Use a filtering algorithm to remove noise and interference filtering in the preliminary point cloud data.

[0018] As described in the above steps S11-S14, a DJI drone carrying a laser radar device is used to collect point cloud data images and GPS data of power lines, and the collected image data and GPS information are imported into the DJI Terra software. The image data is rectified for distortion, brightness is adjusted, and contrast is enhanced. The image matching algorithm is used to extract feature points in the image data, and preliminary point cloud data is generated through the feature points. The preliminary point cloud data is optimized and processed, including steps such as noise removal, filtering, and reconstruction. After completing the above steps, the preliminary point cloud data is exported for the next step of processing.

[0019] In one embodiment, the step S2 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 includes: S21, defining a preset point density and a preset spatial range; S22, importing the preliminary point cloud data into the point cloud processing software, calculating 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; S23, using a ground segmentation algorithm to extract the point cloud data of the ground part in the preliminary point cloud data, and calculating the point cloud data of the non-ground part in the preliminary point cloud data as the power route point cloud data; S24, wherein the power line point cloud data includes Mercator projection coordinates of the point cloud, point cloud density, classification information, and timestamp; As described in the above steps S21-S24, the preliminary point cloud data is checked for integrity, and a preset point density and a preset spatial range are defined as inspection indicators. The point density and spatial range of the preliminary point cloud data are checked using software, and the preliminary point cloud data that passes the inspection is segmented to 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, CloudCompare is used to open the preliminary point cloud data and calculate its point density. At the same time, browsing tools such as rotation, translation, and zoom are used to perform a preliminary check on the point cloud data to confirm that the preliminary point cloud data covers target areas such as power lines, ground, and trees. After checking the integrity, select the Segment tool and use the rectangle or polygon selection tool to circle the point cloud data of the power line part, segment the selected power line part, and save the processed power line point cloud data for the next step of processing.

[0020] In one embodiment, the step S3 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 comprises: S31, defining 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; S32, coarsely sampling the power line point cloud data using a preset coarse sampling accuracy to obtain coarse sampling point cloud data points; S33, fine sampling the power line point cloud data using a preset fine sampling accuracy to obtain fine sampling point cloud data points; As described in the above steps S31-S33, the power line point cloud data is coarsely sampled and finely sampled, and the coarse sampling accuracy and fine sampling accuracy are preset to obtain the coarse sampling point cloud data points and fine sampling point cloud data points of the power line point cloud data. That is, in the actual implementation, the coarse sampling process is to select the power line point cloud data, select the Subsample tool to set the sampling type to Spatial, and set the minimum point distance to 1 meter for coarse sampling, and 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 to set the sampling type to Spatial, and set the minimum point distance to 0.1 meter for fine sampling, and 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 three-dimensional display.

[0021] In one embodiment, the step S4 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 includes: S41, uploading the power line point cloud data to the system, obtaining the Mercator projection coordinates in the point cloud data and parsing them into UTM coordinates; S42, firstly, the Mercator projection coordinates of each point cloud Convert to UTM coordinates, calculated as: S43, then using the projection conversion library, converting the UTM coordinates into longitude and latitude coordinates to obtain longitude and latitude data of the power line point cloud data; S44, simultaneously uploading the coarse sampling point cloud data points to the database, generating a coarse sampling point ID for each coarse sampling point cloud data point, uploading the fine sampling point cloud data points to the database, generating a fine sampling point ID for each fine sampling point cloud data point, and associating the fine sampling point ID with the coarse sampling point ID; As described in the above steps S41-S44, the power line point cloud data is uploaded to the system, and the scaling factors and offset values ​​of the Mercator projection coordinates X, Y, and Z coordinates in the power line point cloud data are obtained. Then, the power line point cloud data is parsed. First, the Mercator projection coordinates in the power line point cloud data are converted into UTM coordinates. Taking the X coordinate as an example, the calculation method is: ,in Indicates the scaling factor of the X coordinate, Indicates the offset value of the X coordinate. The UTM coordinates of the power line point cloud data are obtained by calculation, and then the UTM coordinates are converted to the global coordinate system. The calculation method is to first obtain the UTM area code. 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 by the following calculation: , where UTMZone represents the UTM zone code, and Longitude represents the longitude of the area where the point cloud data is located. Finally, based on the UTM zone code and the projection conversion library, the UTM coordinates are converted into longitude and latitude coordinates. At the same time, the coarse sampling point cloud data points and the fine sampling point cloud data points are uploaded to the system database, and an ID is assigned to each point cloud data point. Because there is a difference in sampling accuracy, that is, the fine sampling accuracy is higher than the coarse sampling accuracy, the coarse sampling point cloud data contains the fine sampling point cloud data points. Therefore, the fine sampling ID is associated with the coarse sampling ID in the system database.

[0022] In one embodiment, the step S5 of calculating the precise shortest distance to the crane by using the finely sampled data point cloud data comprises: S51, installing RTK equipment at the cable head, boom head and boom tail to obtain the three-dimensional positioning data of the crane in real time, including latitude, longitude and altitude coordinates, and uploading the coordinate data of the cable head, boom head and boom tail to the background control system; S52, using the coarse sampling 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 method is as follows: S53, first convert the three-dimensional positioning data of the crane and the latitude, longitude and altitude coordinates of the power line point cloud data into a spatial rectangular coordinate system, and the conversion formula is as follows: S54, among which, and are the radians of latitude and longitude respectively, It is the height of the power line, sling head, boom head and boom tail. is the Earth's equatorial radius; S55, then using the projection method 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; S56. 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; S57, using the projection method and the coordinates of multiple fine sampling point cloud data points to calculate and determine the precise shortest distance between the crane and the power line ; As described in the above steps S51-S57, RTK equipment is installed at the cable head, boom head and boom tail of the crane and ensure that the equipment can work normally. The RTK equipment can be used for real-time dynamic high-precision positioning. The three-dimensional positioning data of the crane including the latitude, longitude and altitude coordinates are obtained in real time through the RTK equipment, and the coordinate data of the three positions of the cable head, boom head and boom tail are merged and processed and transmitted to the background control system together. First, the coarse sampling point cloud data is used to calculate the shortest distance from the two line segments formed by the cable head and boom head, and the boom head and boom tail to the power line. The coarse sampling point cloud data and the three-dimensional positioning data of the crane need to be converted into a spatial rectangular coordinate system. The XYZ axis coordinates of the power line point cloud data and the spatial rectangular coordinate system of the crane's three-dimensional positioning data are obtained by the formula of step S53, and then through the space The distance from the coarse sampling point cloud data point to the crane is calculated by the rectangular coordinate system and the projection method, and the rough shortest distance is determined. Because the accuracy of the coarse sampling is too low, the calculated shortest distance will have a large error, so the shortest distance solved is the rough shortest distance. The information of the coarse sampling point cloud data point is obtained for the rough shortest distance, and the fine sampling point cloud data point associated with it is searched through the system database. The distance from the fine sampling point cloud data point to the crane is solved by using the fine sampling point cloud data point and the projection method, and the precise shortest distance is obtained by comparison. The calculation method of the projection method is as follows: assuming that in 3D space, the shortest distance from the target point P (coarse sampling point cloud data point or fine sampling point cloud data point) to the line segment AB (the line segment composed of the sling head, boom head and boom tail) is calculated. First, the parameters of the target point P projected onto the line segment AB are calculated. : Where A, B, and P are the coordinates of the starting point, end point, and target point P of line segment AB, respectively, and ; Then calculate the target point Coordinates of the projection point P1 on line segment AB : Finally, calculate the distance from the target point P to the projection point P1: In one embodiment, the step S6 of defining a preset threshold and a warning level and storing and displaying the warning information when the precise shortest distance is less than the preset threshold comprises: S61. 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 ; S62, based on the shortest distance Determine the warning level; S63, when , a red alert is triggered. , an orange warning is triggered. , a yellow warning is triggered; S64, when the shortest distance When the warning conditions are met, the warning information is stored and displayed. At the same time, the background and the driver are warned through message prompts and sound and light warning devices, and a safe path for the crane operation is 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; As described in the above steps S61-S64, the warning levels are defined as red, orange, and yellow, and the thresholds are divided 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, and 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. When the warning is triggered, the crane background control system will plan a safe path and display the safe path to the driver, providing the driver with a reference for crane operation.

[0023] In one embodiment, the step S64 of planning a safe path for the crane operation includes: S641, obtaining power line point cloud data, and converting its latitude and longitude data into a spatial rectangular coordinate system; S642, defining a preset safety distance, and constructing a cylindrical safety area with the preset safety distance as a radius based on the power line point cloud data; S643, determining the initial position and target position of the crane operation, connecting the initial position and the target position in space to form an initial path, and marking key points, wherein the key points include turning points of the crane operation and intersections of power lines; S644, for the initial path, calculating the distance between the coarse sampling point cloud data point and the initial path to determine whether it intersects with the safety area; S645, if there is an intersection, determine the intersection point 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 sampling point cloud data point and the intersection point, and the shortest distance between the intersection part and the fine sampling point cloud data point after offset should be greater than the preset safety distance; S646, after the initial path is shifted, a secondary path is formed, and the distance between the coarse sampling point cloud data point and the initial path is calculated for the secondary path to determine whether there is an intersection. If so, the path shift is continued. If not, the path is output as a safe path to the background control system; S647, if a new obstacle appears while the crane is running on the safe path and triggers an early warning, the crane will stop running and re-plan the path using the stop point as the initial point; As described in the above steps SS641-S647, in a complex power line environment, when the crane operation process triggers an early warning, the crane control system will give a safe path reference. First, a safe distance is defined. The selection of the safe distance is based on the complexity of the power line. The higher the complexity, the larger the safety distance is set. A cylindrical safety area is generated around the power line with the safety distance as the radius. Then, the initial position and target position of the crane operation are determined. The initial position and target position are connected as the initial path, and the distance between the coarse sampling point cloud data point and the initial path is calculated. The distance is used to determine whether the initial path intersects with the safety area. If there is an intersection, 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 the offset will not enter the safety area of ​​other power lines. The offset is determined by the intersection. The distance between the intersection point and the fine sampling point cloud data point is determined, and the shortest distance between the intersection point and the fine sampling point cloud data point is calculated. To ensure that the shortest distance is greater than the safety distance, the offset corresponds to the corresponding difference. In actual operation, redundancy will be maintained, and the offset will be slightly greater than the corresponding difference. The initial path for path offset is defined as the secondary path, and the coarse sampling point cloud data points are reused to determine whether the secondary path has an intersection with the safety area. If so, the path offset steps are repeated. If not, it is output as a safe path for the driver's reference. When a new obstacle appears during the crane's safe path and triggers a safety warning, the crane will stop running and use the point where it stops running as the initial position. The safe path planning process is restarted based on the original target position, and a new safe path is output to achieve real-time safe path output to ensure the safety of the crane's near-electric work.

[0024] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

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 fine sampling point cloud data points to the database, and set unique IDs for each; 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, and 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; 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 background and the driver through message prompts, text messages or sound and light warning devices, and a safe path for the crane operation will be planned.

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; At the same time, the coarse sampling point cloud data points are uploaded to the database, a coarse sampling point ID is generated for each coarse sampling point cloud data point, the fine sampling point cloud data points are uploaded to the database, a fine sampling point ID is generated for each fine sampling point cloud data point, and the fine sampling point ID is associated with the coarse sampling point ID.

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 steps of calculating the precise shortest distance to the crane through fine sampling data point cloud data are: 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 and boom tail. is the Earth's equatorial radius; Then, 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 is calculated by the projection method as the distance between the crane and the power line; The calculation method of the projection method is as follows: Assuming that in 3D space, the shortest distance from the target point P to the line segment AB is calculated. First, the parameters of the projection of the target point P onto the line segment AB are calculated. : Where A, B, and P are the coordinates of the starting point, end point, and target point P of line segment AB, respectively, and ; Then calculate the target point Coordinates of the projection point P1 on line segment AB : Finally, calculate the distance from the target point P to the projection point P1: 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. .

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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