CN-122023756-A - Method for automatically searching station of crane

CN122023756ACN 122023756 ACN122023756 ACN 122023756ACN-122023756-A

Abstract

The invention discloses a method for automatically searching a crane station, which belongs to the technical field of cranes and is used for selecting the crane station, and comprises the steps of performing preliminary selection on vectorized site photogrammetry data to obtain a selectable set of the crane station, detecting feasibility of the selectable set of the filtered crane station based on site cloud data to obtain feasible crane stations, and performing collision check detection on each feasible crane station during secondary hoisting based on the site cloud data and crane parameters in the feasible crane stations to obtain a unique feasible crane station. The invention effectively reduces the dependence on personal experience by establishing a standardized decision model and a quantitative analysis system, improves the scientificity and consistency of the station position scheme, rapidly analyzes site parameters and equipment information by adopting an automatic data processing and intelligent algorithm, and greatly shortens the scheme making period.

Inventors

Zhang Qianran
Guo Yingxiu
WANG XIUKUN
KAN ZHUO
YANG FANGYAN
CHEN BOJIE

Assignees

山东科技大学

Dates

Publication Date: 20260512
Application Date: 20251224

Claims (10)

1. A method for automatically locating a crane station, comprising: s1, acquiring field photogrammetry data of unmanned aerial vehicle oblique photogrammetry, and synchronously scanning the field data to acquire field point cloud data; s2, modeling and vectorizing the field photogrammetry data, and preprocessing field point cloud data; s3, aiming at vectorized field photogrammetry data, performing preliminary selection of crane stations based on a crane station selection principle to obtain a selectable set of crane stations; S4, based on vectorized field photogrammetry data, carrying out collision check detection during hoisting on the cranes in the optional set of crane stations obtained in the step S3, if the collision check detection passes during hoisting, reserving corresponding crane parameters, and detecting one by one to finally obtain the optional set of filtered crane stations; S5, semantic segmentation is carried out on the preprocessed site cloud data based on the deep learning model; S6, detecting feasibility of the filtered selectable set of crane stations based on site cloud data to obtain feasible crane stations; S7, carrying out collision check detection on each feasible crane station when carrying out secondary hoisting on the basis of site cloud data and crane parameters in the feasible crane stations, and obtaining a unique feasible crane station; and S8, transmitting the unique feasible crane station to a processing system, and automatically outputting a crane start-stop station drawing under the unique feasible crane station by the processing system by using a graphical interface.
2. The method for automatically searching for a crane station according to claim 1, wherein S1 comprises integrating a laser radar scanner on an unmanned aerial vehicle platform carrying a multi-lens oblique photography camera, and ensuring time-space reference synchronization of the laser radar scanner and the multi-lens oblique photography camera, operating the unmanned aerial vehicle to simultaneously start the multi-lens oblique photography camera and the laser radar scanner for operation, continuously and synchronously acquiring information of a target site during flight, acquiring site cloud data by the laser radar scanner, and acquiring site photogrammetry data by the multi-lens oblique photography camera.
3. The method for automatically searching for the crane station according to claim 1, wherein the modeling process comprises performing space-three encryption calculation on the site photogrammetry data to construct a three-dimensional model; preprocessing includes data resolution and registration, filtering and denoising, and classification.
4. A method of automatically finding a crane position according to claim 1, wherein the preliminary selection of the crane position comprises: S3.1, scanning and identifying obstacles by taking the edge point of the hoisting position as the center of a circle and taking the sum of the operation radius and the super-lifting radius during hoisting operation as the radius, and bringing the direction of barrier-free distribution in the search area into a selectable set of crane stations to form a sector area with the sum of the operation radius and the super-lifting radius as the radius in the barrier-free distribution direction; S3.2, scanning and identifying obstacles by taking the edge point of the hoisting position as the center of a circle and taking the sum of the maximum length of the object to be hoisted and a preset safety margin as a radius, and grouping the directions of the distribution of the obstacles in the search area into a selectable set of the station positions of the crane to form a sector area taking the sum of the maximum length of the object to be hoisted and the preset safety margin as the radius in the direction of the distribution of the obstacles; S3.3, aiming at each barrier-free distribution direction in the selectable set of the crane station positions in the S3.2, scanning and identifying barriers by taking the edge point of the hoisting position as the center of a circle and the sum of the operation radius and the super-lifting radius during hoisting operation, and listing the directions of barrier-free distribution in the search area into the selectable set of the crane station positions to form a sector area with the sum of the operation radius and the super-lifting radius as the radius in the barrier-free distribution direction.
5. The method for automatically searching for a crane station according to claim 1, wherein the collision check detection during hoisting comprises calculating a safe distance between the crawler belt of the crane and a structure at a hoisting target position, a safe distance between the main arm of the crane and the edge of a hoisted object, and a safe distance between the main arm of the crane and a column of the hoisted object based on a selectable set of the crane station; On the premise of constraint of three safety distances, searching available cranes in all the stations of the selectable set of the crane stations, storing the parameter information of the cranes and the crane stations at the moment together if the available cranes are found, and eliminating the crane stations if the available cranes are not found.
6. The method for automatically locating a crane station according to claim 5, wherein the safe distance between the crawler belt of the crane and the structure at the hoisting target position is Comprising the following steps: ; In the formula, Is the operating radius of the crane during operation, Is the distance from the projection point of the hanging hook on the hung object to the edge of the hung object, Is the track radius of the crane; safety distance between main arm of crane and edge of suspended object Comprising the following steps: ; In the formula, Is the length of the main arm of the crane, Is the angle between the main arm of the crane and the horizontal direction, Is the height of the object to be suspended, Is the height of the hoisting position, Is the vertical height of the hung object from the hanging position, Is the height of the base of the crane, Is the radius of the main arm of the crane; safety distance between main arm of crane and upright post of suspended object Comprising the following steps: ; In the formula, Is the upright post height of the suspended object.
7. A method for automatically finding a crane station according to claim 1, wherein S6 comprises: S6.1, judging the range of all station points of the filtered selectable set of crane station points, filtering out station points which do not belong to the air-ground label, and constructing a crane operation domain cone model for each filtered candidate station point; S6.2, verifying whether the station position points in the optional list of the termination positions of the crane are qualified or not; s6.3, traversing candidate placement points of the hung object, and judging whether the hung object collides with the hanging position or other obstacles; S6.4, verifying whether the station position points in the crane starting position selectable list are qualified or not; s6.5, judging whether the station point is likely to collide in the hoisting path.
8. The method of automatically finding a crane site according to claim 7, wherein the crane domain cone model for each filtered candidate site satisfies a first condition and a second condition, condition one: ; Condition II: ; ; ; ; In the formula, Is the shortest extendable distance of the crane arm length, Is the actual length of the suspension arm, 、、 Is a three-dimensional relative displacement of the two, The longest extendable distance of the crane arm length, Is the minimum value of the pitch angle, Is the maximum value of the pitch angle, The station point points to the horizontal angle of the suspended object, Is that Is set to be a minimum value of (c), Is that Is set at the maximum value of (c), Is the operating radius of the crane, Is that Is set to be a minimum value of (c), Is that Is set at the maximum value of (c), Is the maximum lifting height of the crane, Is the weight of the object to be suspended, Is that The corresponding maximum amount of liftable mass is calculated, Is the coordinates of the station point of the crane, The space coordinate of the top end of the suspension arm when the object to be suspended is lifted; s6.2 includes condition three: ; In the formula, Is the horizontal plane of the hoisting position The minimum value of the coordinates is set to be, Is the horizontal plane of the hoisting position The maximum value of the coordinates, Is the horizontal plane of the hoisting position The minimum value of the coordinates is set to be, Is the horizontal plane of the hoisting position A maximum value of the coordinates; The station point elimination is executed, and if the station point does not meet the first condition, the second condition and the third condition, the station point is eliminated; taking the whole crane as a bounding box, and recording the minimum coordinate value of the plane on the bounding box at the moment And maximum coordinate value Calculating whether there is an obstacle plane coordinate at this time Collision with the crane bounding box occurs, and the station site is eliminated if the following conditions are met: ; after the station position point is eliminated, taking the rest station position points as the station position points of the termination position of the crane during hoisting operation; s6.3 includes recording the plane minimum coordinate value on the bounding box And maximum coordinate value Judging whether the object enclosure is at a point of the lifting position And (3) collision, namely, eliminating station sites when the following conditions are met: ; ; recording minimum coordinate values of planes on bounding box And maximum coordinate value Judging whether there is obstacle plane coordinate Collision with the suspended object bounding box occurs, and the station points are eliminated if the following conditions are met: ; s6.4, taking the whole crane and the suspended object as a bounding box, and recording the minimum coordinate value of the plane on the bounding box at the moment And maximum coordinate value Calculating whether the bounding box at the initial position of the crane is at a point of the hoisting position And (3) collision, namely, eliminating station sites when the following conditions are met: ; ; Calculating whether there is obstacle plane coordinates Collision with the surrounding box at the starting position of the crane occurs, and the station site is eliminated if the following conditions are met: ; S6.5, fusing the bounding box of the starting position of the crane and the bounding box of the ending position of the crane and the space between the bounding box and the bounding box as one bounding box, and recording the minimum coordinate value of the plane on the bounding box at the moment And maximum coordinate value Calculating whether there is an obstacle plane coordinate at this time Collision with the bounding box occurs, and the station site is eliminated if the following conditions are met: 。
9. the method of claim 8, wherein S7 includes detecting whether there is a point on the main arm of the crane that collides with the object or the hoisting location: ; ; In the formula, Is a point coordinate on the hoisting position, Is a point coordinate on the suspended object; construction of a comprehensive cost function And (3) optimizing the rest candidate stations, and outputting the minimum cost as the only feasible crane station: ; ; In the formula, 、、 Is a weight that controls the impact of each term on the total cost, Is the distance of the current station point to the target lifting point, Is the cost of the collision penalty, Is a hoisting infeasibility punishment and represents whether the current station can hoist the target; The hoisting condition comprises that the distance from the current station to the target point is smaller than or equal to the length of the suspension arm, the current hoisting weight is smaller than or equal to the hoisting capacity of the crane, and the hoisting point is in the angle range of the suspension arm.
10. The method for automatically searching for the crane station according to claim 1, wherein the step S8 comprises determining a crane start position station position coordinate, a crane end position station position coordinate, a suspended object placement point coordinate and crane parameters, drawing a crane start-stop position map on the base map based on vectorized field photogrammetry data and suspended object model data, taking a crane operation area cone model around a hoisting position in a field and a suspended object top view model as a base map of a crane start-stop position map, and drawing a crane start-stop position map on the base map based on a crane station position distribution state and a suspended object placement distribution state, and adding an identifier.

Description

Method for automatically searching station of crane Technical Field The invention discloses a method for automatically searching a crane station, and belongs to the technical field of cranes. Background The prior art is mainly developed based on the traditional manual mode aiming at the selection of the crane station, and mainly relies on engineers to determine the crane station through manual calculation and analysis according to the field plane drawing information, deck sheet parameters and crane performance data, and draw corresponding drawings accordingly. However, the traditional manual method is not only limited by strong subjectivity and low calculation efficiency of engineers experience, but also has the problem that the searching of feasible stations is difficult, so that the suitable stations meeting various constraint conditions are difficult to quickly determine under the condition of complex sites, and the dynamic optimization by comprehensively integrating multiple factors is difficult. At present, feasibility research of a partial automatic site selection scheme is developed based on the existing crane site selection method, theoretical discussion is conducted by combining site obstacle distribution, hoisting path planning and other factors, and feasibility under different verification conditions is analyzed. However, how to integrate the elements systematically and form a set of crane station automatic layout method and system suitable for complex construction scenes is still an unresolved key problem. Disclosure of Invention The invention aims to provide a method for automatically searching a crane station, which aims to solve the problem that the crane station is difficult to search in the prior art. A method of automatically finding a crane site, comprising: S1, acquiring field photogrammetry data of unmanned aerial vehicle oblique photogrammetry, and synchronously scanning the field data to acquire field point cloud data; S2, modeling and vectorizing the field photogrammetry data, and preprocessing field point cloud data; s3, aiming at vectorized field photogrammetry data, performing preliminary selection of crane stations based on a crane station selection principle to obtain a selectable set of crane stations; S4, based on vectorized field photogrammetry data, carrying out collision check detection during hoisting on the cranes in the optional set of crane stations obtained in the step S3, if the collision check detection passes during hoisting, reserving corresponding crane parameters, and detecting one by one to finally obtain the optional set of filtered crane stations; S5, semantic segmentation is carried out on the preprocessed site cloud data based on the deep learning model; S6, detecting feasibility of the filtered selectable set of crane stations based on site cloud data to obtain feasible crane stations; S7, carrying out collision check detection on each feasible crane station when carrying out secondary hoisting on the basis of site cloud data and crane parameters in the feasible crane stations, and obtaining a unique feasible crane station; and S8, transmitting the unique feasible crane station to a processing system, and automatically outputting a crane start-stop station drawing under the unique feasible crane station by the processing system by using a graphical interface. S1 comprises the step of integrating a laser radar scanner on an unmanned aerial vehicle platform carrying a multi-lens oblique photography camera, and ensuring that space-time reference synchronization of the laser radar scanner and the multi-lens oblique photography camera is ensured, controlling the unmanned aerial vehicle to simultaneously start the multi-lens oblique photography camera and the laser radar scanner to operate, continuously and synchronously acquiring information of a target site in a flight process, acquiring site cloud data by the laser radar scanner, and acquiring site photogrammetry data by the multi-lens oblique photography camera. The modeling processing comprises the steps of performing space three encryption calculation on field photogrammetry data to construct a three-dimensional model; preprocessing includes data resolution and registration, filtering and denoising, and classification. The preliminary selection of the crane station position comprises the following steps: S3.1, scanning and identifying obstacles by taking the edge point of the hoisting position as the center of a circle and taking the sum of the operation radius and the super-lifting radius during hoisting operation as the radius, and bringing the direction of barrier-free distribution in the search area into a selectable set of crane stations to form a sector area with the sum of the operation radius and the super-lifting radius as the radius in the barrier-free distribution direction; S3.2, scanning and identifying obstacles by taking the edge point of the hoisting position as the center of a circle and ta