Crane anti-collision early warning method and system based on image analysis

Abstract

The invention discloses a crane anti-collision early warning method and system based on image analysis, and relates to the technical field of image analysis processing, and the method comprises the steps: obtaining a to-be-analyzed image of the surrounding environment of a crane, carrying out the feature analysis processing of the to-be-analyzed image of the surrounding environment of the crane based on an intelligent analysis terminal, and determining the contour information of an obstacle; and performing feature analysis processing on the to-be-analyzed image of the surrounding environment of the crane to determine crane contour information. According to the method, the obstacle contour information and the crane contour information are determined by performing feature analysis on the to-be-analyzed image of the surrounding environment of the crane, and then the obstacle contour information and the crane contour information are compared and analyzed to determine whether a coincident part exists between the obstacle contour information and the crane contour information; and if the coincident part exists, the obstacle can collide with the crane, so that collision information is sent to an operator, and the operator adjusts the crane according to the collision information, so that the collision between the crane and the obstacle is avoided, and the risk that the crane is damaged is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of image analysis and processing, and in particular to a crane anti-collision warning method and system based on image analysis. Background Art

[0002] A crane is a type of lifting and handling machinery widely used in ports, workshops, power plants, construction sites, and other areas. Cranes are a common name for hoists, a type of lifting machinery. Commonly referred to as cranes are truck cranes, crawler cranes, and tire cranes. Cranes are used for lifting equipment, emergency rescue, lifting machinery, and rescue operations.

[0003] Cranes can be used in different environments, but some environments may have obstacles that affect the use of the crane, and may even collide with the crane, causing a certain degree of damage to the crane. Summary of the Invention

[0004] In order to solve the above technical problems, a crane anti-collision warning method and system based on image analysis is provided. This technical solution solves the problem that the crane proposed in the above background technology can be used in different environments, but some environments may have obstacles that affect the use of the crane, and may even collide with the crane, causing a certain degree of damage to the crane.

[0005] In order to achieve the above objects, the technical solution adopted by the present invention is: A crane anti-collision warning method based on image analysis, comprising: Obtain the image of the crane's surrounding environment to be analyzed, and perform feature analysis on the image based on the intelligent analysis terminal to determine the obstacle outline information; Based on the intelligent analysis terminal, the image of the crane's surrounding environment to be analyzed is analyzed and processed to determine the crane's outline information; Based on the intelligent analysis terminal, the obstacle outline information and the crane outline information are calculated and processed to determine whether a collision will occur between the crane and the obstacle.

[0006] Preferably, the step of obtaining an image of the crane's surrounding environment to be analyzed, performing feature analysis on the image of the crane's surrounding environment to be analyzed based on an intelligent analysis terminal, and determining obstacle contour information specifically comprises the following steps: Based on the intelligent analysis terminal, the image capture device is controlled to collect and process images of the crane's surrounding environment to obtain images of the crane's surrounding environment to be analyzed; Based on the intelligent analysis terminal, image preprocessing is performed on the image to be analyzed of the crane's surrounding environment; wherein the image preprocessing includes signal enhancement, signal denoising, signal restoration and image normalization processing; Based on the intelligent analysis terminal, the image of the crane's surrounding environment to be analyzed is processed for contour recognition to determine the obstacle contour information.

[0007] Preferably, the intelligent analysis terminal is used to perform contour recognition processing on the image to be analyzed of the crane's surrounding environment to determine the obstacle contour information, specifically comprising the following steps: Based on the intelligent analysis terminal, RGB modeling is performed on the image to be analyzed of the crane's surrounding environment to obtain a model map of the crane's surrounding environment; Based on the intelligent analysis terminal, the model map of the crane's surrounding environment is processed for feature recognition to determine the approximate range of obstacles; Based on the intelligent analysis terminal, a marking point is selected within the approximate range of the obstacle, and at least one first ray is drawn through the marking point, with the angles between adjacent first rays being a preset angle; Based on the intelligent analysis terminal, a gradient formula is used along the first ray, and starting from the identification point, the gradient of the adjacent pixel points on the first ray is calculated. If the gradient does not exceed the second preset value, no processing is performed. If the gradient is greater than the second preset value, the corresponding pixel point is used as a contour point; Based on the intelligent analysis terminal, the contour points corresponding to each first ray are summarized and processed to obtain a contour point set, and the contour points in the contour point set are fitted to determine the fitting function of the obstacle.

[0008] Preferably, the intelligent analysis terminal is used to perform feature analysis on the image to be analyzed of the crane's surrounding environment to determine the crane's contour information, specifically comprising the following steps: Based on the intelligent analysis terminal, the model image of the crane's surrounding environment is processed for feature recognition to determine the approximate range of the crane; Based on the intelligent analysis terminal, a reference point is selected within the approximate range of the crane, and at least one second ray is drawn from the reference point, with the angles between adjacent second rays being preset angles; Based on the intelligent analysis terminal, a gradient formula is used along the second ray, and starting from the reference point, the gradient of the adjacent pixel points on the second ray is calculated. If the gradient does not exceed the second preset value, no processing is performed; if the gradient is greater than the second preset value, the corresponding pixel point is regarded as an edge point; Based on the intelligent analysis terminal, the edge points corresponding to each second ray are summarized and processed to obtain an edge point set, and the edge points in the edge point set are fitted to determine the crane contour fitting function.

[0009] Preferably, the intelligent analysis terminal is used to calculate and process the obstacle contour information and the crane contour information to determine whether a collision will occur between the crane and the obstacle, specifically comprising the following steps: Based on the intelligent analysis terminal, the approximate range of obstacles and the approximate range of the crane are judged and processed; If there is an overlap between the approximate range of the obstacle and the approximate range of the crane, the shortest distance between the crane and the obstacle is zero, and the crane will collide with the obstacle. Based on the intelligent analysis terminal, a collision warning information is sent to the operator's electronic device; If there is no overlap between the approximate range of the obstacle and the approximate range of the crane, the intelligent analysis terminal will compare and analyze the crane contour fitting function and the obstacle fitting function to determine whether a collision will occur between the crane and the obstacle.

[0010] Preferably, the comparative analysis of the crane profile fitting function and the obstacle fitting function based on the intelligent analysis terminal to determine whether a collision will occur between the crane and the obstacle specifically includes the following steps: Construct a rectangular coordinate system based on the intelligent analysis terminal; Based on the intelligent analysis terminal, curves are drawn in the rectangular coordinate system according to the crane profile fitting function and the obstacle fitting function to obtain the crane profile curve and the obstacle profile curve; Based on the intelligent analysis terminal, the crane contour curve and the obstacle contour curve are analyzed to obtain the curve analysis results; Based on the intelligent analysis terminal, the curve analysis results are analyzed and processed to determine whether a collision will occur between the crane and the obstacle.

[0011] Preferably, the analyzing and processing of the curve analysis results based on the intelligent analysis terminal to determine whether a collision will occur between the crane and the obstacle specifically includes the following steps: If the curve analysis result shows that there is an intersection between the crane contour curve and the obstacle contour curve, a collision between the crane and the obstacle will occur. Based on the intelligent analysis terminal, a collision warning information is sent to the operator's electronic device; If the curve analysis result shows that there is no intersection between the crane contour curve and the obstacle contour curve, there will be no collision between the crane and the obstacle, and the operator can operate the crane normally.

[0012] Furthermore, a crane anti-collision warning system based on image analysis is proposed, which is used to implement the above-mentioned crane anti-collision warning method based on image analysis, including: An intelligent analysis terminal is used to perform feature analysis on an image of the crane's surroundings to determine obstacle profile information and crane profile information; the intelligent analysis terminal is used to compare and analyze the obstacle profile information and crane profile information to determine whether a collision will occur between the crane and the obstacle; the intelligent analysis terminal is used to control information exchange and data transmission between various modules; An image capturing device is used to capture and process images of the surrounding environment of the crane to obtain images of the surrounding environment of the crane to be analyzed; An image preprocessing module, the image preprocessing module being used to perform image preprocessing on the image to be analyzed of the crane's surrounding environment; An image analysis module, the image analysis module is used to perform feature analysis on the image to be analyzed of the crane's surrounding environment to determine obstacle outline information and crane outline information; a curve drawing module, wherein the curve drawing module draws curves in a rectangular coordinate system according to the crane profile fitting function and the obstacle fitting function, respectively, to obtain a crane profile curve and an obstacle profile curve; The curve analysis module is used to perform curve analysis on the crane contour curve and the obstacle contour curve to determine whether a collision will occur between the crane and the obstacle.

[0013] Compared with the existing technology, the present invention provides a crane anti-collision warning method and system based on image analysis, which has the following beneficial effects: The present invention determines the outline information of the obstacle and the outline information of the crane by performing feature analysis on the image to be analyzed of the surrounding environment of the crane. Then, the obstacle outline information and the crane outline information are compared and analyzed to determine whether there is any overlapping part between the obstacle outline information and the crane outline information. If there is an overlapping part, it means that the obstacle will collide with the crane. Therefore, the collision information is sent to the operator, and the operator adjusts the crane according to the collision information to avoid collision between the crane and the obstacle, thereby reducing the risk of damage to the crane. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of steps S100-S300 in a crane anti-collision warning method based on image analysis proposed by the present invention; Figure 2 This is a structural block diagram of a crane anti-collision warning system based on image analysis proposed by the present invention. DETAILED DESCRIPTION

[0015] The following description is intended to disclose the present invention so that those skilled in the art can implement the present invention. The preferred embodiments described below are merely examples, and those skilled in the art may conceive of other obvious variations.

[0016] Reference Figure 1 As shown, a crane anti-collision warning method based on image analysis includes: S100, obtaining an image of the crane's surrounding environment to be analyzed, and performing feature analysis on the image of the crane's surrounding environment to be analyzed based on the intelligent analysis terminal to determine obstacle contour information; S200, based on the intelligent analysis terminal, performing feature analysis on the image to be analyzed of the crane's surrounding environment to determine the crane's contour information; S300: Calculate and process the obstacle profile information and the crane profile information based on the intelligent analysis terminal to determine whether a collision will occur between the crane and the obstacle; It will be understood by those skilled in the art that cranes can be used in different environments, but there may be obstacles in some environments. The crane may collide with the obstacles during use, causing a certain degree of damage to the crane. Therefore, feature analysis is performed on the image to be analyzed of the environment around the crane to determine whether the obstacles in the environment around the crane will cause damage to the crane. If the obstacles in the environment around the crane will cause damage to the crane, collision information is sent to the operator, and the operator adjusts the crane to avoid collision between the crane and the obstacles, thereby reducing the risk of damage to the crane. Example

[0017] S100, obtaining an image of the crane's surrounding environment to be analyzed, and performing feature analysis on the image of the crane's surrounding environment to be analyzed based on the intelligent analysis terminal to determine obstacle contour information, specifically including the following steps: S101. Based on the intelligent analysis terminal, control the image capturing device to collect and process images of the environment surrounding the crane to obtain images of the environment surrounding the crane to be analyzed; S102. Preprocessing the image of the crane surrounding environment to be analyzed based on the intelligent analysis terminal; wherein the image preprocessing includes signal enhancement, signal denoising, signal restoration, and image normalization processing; S103: Based on the intelligent analysis terminal, perform contour recognition processing on the image to be analyzed of the crane's surrounding environment to determine obstacle contour information.

[0018] Among them, S103, based on the intelligent analysis terminal, performs contour recognition processing on the image to be analyzed of the crane's surrounding environment to determine the obstacle contour information specifically includes the following steps: S1031. Based on the intelligent analysis terminal, perform RGB modeling processing on the image to be analyzed of the crane surrounding environment to obtain a model image of the crane surrounding environment; S1032. Based on the intelligent analysis terminal, perform feature recognition processing on the model image of the crane's surrounding environment to determine the approximate range of obstacles; S1033. Based on the intelligent analysis terminal, select a marker point within the approximate range of the obstacle, and draw at least one first ray from the marker point, where the angles between adjacent first rays are all preset angles; S1034. Based on the intelligent analysis terminal, using a gradient formula along the first ray and starting from the identification point, calculate the gradient of the adjacent pixel points on the first ray. If the gradient does not exceed a second preset value, no processing is performed. If the gradient is greater than the second preset value, the corresponding pixel point is used as a contour point. S1035. Based on the intelligent analysis terminal, the contour points corresponding to each first ray are aggregated to obtain a contour point set, and the contour points in the contour point set are fitted to determine a fitting function for the obstacle. It is understandable that in order to reduce the subsequent amount of calculation, there is no need to analyze the entire obstacle, only the outline of the obstacle needs to be determined. Subsequently, only the outline of the obstacle and the outline of the crane need to be compared and analyzed to determine whether a collision will occur between the crane and the obstacle. This shortens the collision analysis time and can give the operator more time to adjust the crane, further reducing the risk of damage to the crane. Example

[0019] S200, based on the intelligent analysis terminal, performing feature analysis on the image to be analyzed of the crane's surrounding environment to determine the crane's contour information, specifically including the following steps: S201. Based on the intelligent analysis terminal, perform feature recognition processing on the model image of the crane's surrounding environment to determine the approximate range of the crane; S202: Based on the intelligent analysis terminal, select a reference point within the approximate range of the crane, and draw at least one second ray with the reference point, wherein the angles between adjacent second rays are preset angles; S203. Based on the intelligent analysis terminal, using a gradient formula along the second ray and starting from the reference point, calculate the gradient of the adjacent pixel points on the second ray. If the gradient does not exceed a second preset value, no processing is performed. If the gradient is greater than the second preset value, the corresponding pixel point is regarded as an edge point. S204: Based on the intelligent analysis terminal, the edge points corresponding to each second ray are aggregated to obtain an edge point set, and the edge points in the edge point set are fitted to determine a crane profile fitting function; It is understandable that the first part where the crane collides with the obstacle should be the edge of the crane and the outline of the crane. Therefore, in order to determine whether a collision will occur between the crane and the obstacle, it is only necessary to analyze the outlines of the crane and the obstacle. If there will be no collision between the outlines of the crane and the obstacle, there will be no collision between the crane and the obstacle. Analyzing the outlines of the crane and the obstacle can effectively reduce the amount of calculation and the calculation time of the collision analysis process. Example

[0020] S300, calculating and processing the obstacle contour information and the crane contour information based on the intelligent analysis terminal to determine whether a collision will occur between the crane and the obstacle, specifically includes the following steps: S301. Based on the intelligent analysis terminal, the approximate range of the obstacle and the approximate range of the crane are judged and processed; S302: If there is an overlap between the approximate range of the obstacle and the approximate range of the crane, and the shortest distance between the crane and the obstacle is zero, the crane will collide with the obstacle, and a collision warning message is sent to the operator's electronic device based on the intelligent analysis terminal; S303. If there is no overlap between the approximate range of the obstacle and the approximate range of the crane, a comparative analysis is performed on the crane profile fitting function and the obstacle fitting function based on the intelligent analysis terminal to determine whether a collision will occur between the crane and the obstacle.

[0021] S303, based on the intelligent analysis terminal, compares and analyzes the crane profile fitting function and the obstacle fitting function to determine whether a collision will occur between the crane and the obstacle, specifically includes the following steps: S3031. Construct a rectangular coordinate system based on the intelligent analysis terminal; S3032. Based on the intelligent analysis terminal, curves are drawn in a rectangular coordinate system according to the crane profile fitting function and the obstacle profile fitting function to obtain a crane profile curve and an obstacle profile curve. S3033. Based on the intelligent analysis terminal, perform curve analysis on the crane contour curve and the obstacle contour curve to obtain curve analysis results; S3034. Based on the intelligent analysis terminal, the curve analysis results are analyzed and processed to determine whether a collision will occur between the crane and the obstacle.

[0022] S3034, analyzing the curve analysis results based on the intelligent analysis terminal to determine whether a collision will occur between the crane and the obstacle, specifically includes the following steps: S30341. If the curve analysis result indicates that there is an intersection between the crane contour curve and the obstacle contour curve, a collision between the crane and the obstacle will occur, and a collision warning message is sent to the operator's electronic device based on the intelligent analysis terminal; S30342. If the curve analysis result shows that there is no intersection between the crane contour curve and the obstacle contour curve, there will be no collision between the crane and the obstacle, and the operator operates the crane normally. It is understandable that when there is no overlapping part between the outline of the crane and the outline of the obstacle, it does not mean that there will be no collision between the obstacle and the crane. It is also necessary to analyze the fitting function of the crane outline and the fitting function of the obstacle. When the curves corresponding to the crane outline fitting function and the obstacle fitting function have no intersection, it means that there will be no collision between the crane and the obstacle. If the curves corresponding to the crane outline fitting function and the obstacle fitting function have an intersection, it means that there will be a collision between the crane and the obstacle.

[0023] Reference Figure 2 As shown, a crane anti-collision warning system based on image analysis is used to implement the above-mentioned crane anti-collision warning method based on image analysis, including: An intelligent analysis terminal is used to perform feature analysis on an image of the crane's surroundings to determine obstacle profile information and crane profile information; the intelligent analysis terminal is used to compare and analyze the obstacle profile information and crane profile information to determine whether a collision will occur between the crane and the obstacle; the intelligent analysis terminal is used to control information exchange and data transmission between various modules; An image capturing device is used to capture and process images of the surrounding environment of the crane to obtain images of the surrounding environment of the crane to be analyzed; An image preprocessing module, the image preprocessing module being used to perform image preprocessing on the image to be analyzed of the crane's surrounding environment; An image analysis module, the image analysis module is used to perform feature analysis on the image to be analyzed of the crane's surrounding environment to determine obstacle outline information and crane outline information; a curve drawing module, wherein the curve drawing module draws curves in a rectangular coordinate system according to the crane profile fitting function and the obstacle fitting function, respectively, to obtain a crane profile curve and an obstacle profile curve; The curve analysis module is used to perform curve analysis on the crane contour curve and the obstacle contour curve to determine whether a collision will occur between the crane and the obstacle.

[0024] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions merely illustrate the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and modifications are intended to fall within the scope of the present invention. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A crane anti-collision warning method based on image analysis, characterized in that: include: Obtain the image of the crane's surrounding environment to be analyzed, and perform feature analysis on the image based on the intelligent analysis terminal to determine the obstacle outline information; Based on the intelligent analysis terminal, the image of the crane's surrounding environment to be analyzed is analyzed and processed to determine the crane's outline information; Based on the intelligent analysis terminal, the obstacle outline information and the crane outline information are calculated and processed to determine whether a collision will occur between the crane and the obstacle.

2. The crane anti-collision warning method based on image analysis according to claim 1 is characterized in that: The steps of obtaining an image of the crane's surrounding environment to be analyzed, performing feature analysis on the image of the crane's surrounding environment to be analyzed based on the intelligent analysis terminal, and determining obstacle contour information specifically include the following steps: Based on the intelligent analysis terminal, the image capture device is controlled to collect and process images of the crane's surrounding environment to obtain images of the crane's surrounding environment to be analyzed; Based on the intelligent analysis terminal, image preprocessing is performed on the image to be analyzed of the crane's surrounding environment; wherein the image preprocessing includes signal enhancement, signal denoising, signal restoration and image normalization processing; Based on the intelligent analysis terminal, the image of the crane's surrounding environment to be analyzed is processed for contour recognition to determine the obstacle contour information.

3. The crane anti-collision warning method based on image analysis according to claim 2 is characterized in that: The intelligent analysis terminal performs contour recognition processing on the image to be analyzed of the crane's surrounding environment to determine the obstacle contour information, specifically including the following steps: Based on the intelligent analysis terminal, RGB modeling is performed on the image to be analyzed of the crane's surrounding environment to obtain a model map of the crane's surrounding environment; Based on the intelligent analysis terminal, the model map of the crane's surrounding environment is processed for feature recognition to determine the approximate range of obstacles; Based on the intelligent analysis terminal, a marking point is selected within the approximate range of the obstacle, and at least one first ray is drawn through the marking point, with the angles between adjacent first rays being a preset angle; Based on the intelligent analysis terminal, a gradient formula is used along the first ray, and starting from the identification point, the gradient of the adjacent pixel points on the first ray is calculated. If the gradient does not exceed the second preset value, no processing is performed. If the gradient is greater than the second preset value, the corresponding pixel point is used as a contour point; Based on the intelligent analysis terminal, the contour points corresponding to each first ray are summarized and processed to obtain a contour point set, and the contour points in the contour point set are fitted to determine the fitting function of the obstacle.

4. The crane anti-collision warning method based on image analysis according to claim 1 is characterized in that: The method of performing feature analysis on the image to be analyzed of the crane's surrounding environment based on the intelligent analysis terminal to determine the crane's contour information specifically includes the following steps: Based on the intelligent analysis terminal, the model image of the crane's surrounding environment is processed for feature recognition to determine the approximate range of the crane; Based on the intelligent analysis terminal, a reference point is selected within the approximate range of the crane, and at least one second ray is drawn from the reference point, with the angles between adjacent second rays being preset angles; Based on the intelligent analysis terminal, a gradient formula is used along the second ray, and starting from the reference point, the gradient of the adjacent pixel points on the second ray is calculated. If the gradient does not exceed the second preset value, no processing is performed; if the gradient is greater than the second preset value, the corresponding pixel point is regarded as an edge point; Based on the intelligent analysis terminal, the edge points corresponding to each second ray are summarized and processed to obtain an edge point set, and the edge points in the edge point set are fitted to determine the crane contour fitting function.

5. The crane anti-collision warning method based on image analysis according to claim 1 is characterized in that: The intelligent analysis terminal is used to calculate and process the obstacle contour information and the crane contour information to determine whether a collision will occur between the crane and the obstacle. Specifically, the steps include: Based on the intelligent analysis terminal, the approximate range of obstacles and the approximate range of the crane are judged and processed; If there is an overlap between the approximate range of the obstacle and the approximate range of the crane, the shortest distance between the crane and the obstacle is zero, and the crane will collide with the obstacle. Based on the intelligent analysis terminal, a collision warning information is sent to the operator's electronic device; If there is no overlap between the approximate range of the obstacle and the approximate range of the crane, the intelligent analysis terminal will compare and analyze the crane contour fitting function and the obstacle fitting function to determine whether a collision will occur between the crane and the obstacle.

6. The crane anti-collision warning method based on image analysis according to claim 5 is characterized in that: The method of comparing and analyzing the crane profile fitting function and the obstacle fitting function based on the intelligent analysis terminal to determine whether a collision will occur between the crane and the obstacle specifically includes the following steps: Construct a rectangular coordinate system based on the intelligent analysis terminal; Based on the intelligent analysis terminal, curves are drawn in the rectangular coordinate system according to the crane profile fitting function and the obstacle fitting function to obtain the crane profile curve and the obstacle profile curve; Based on the intelligent analysis terminal, the crane contour curve and the obstacle contour curve are analyzed to obtain the curve analysis results; Based on the intelligent analysis terminal, the curve analysis results are analyzed and processed to determine whether a collision will occur between the crane and the obstacle.

7. The crane anti-collision warning method based on image analysis according to claim 6 is characterized in that: The intelligent analysis terminal is used to analyze the curve analysis results to determine whether a collision will occur between the crane and the obstacle, specifically including the following steps: If the curve analysis result shows that there is an intersection between the crane contour curve and the obstacle contour curve, a collision between the crane and the obstacle will occur. Based on the intelligent analysis terminal, a collision warning information is sent to the operator's electronic device; If the curve analysis result shows that there is no intersection between the crane contour curve and the obstacle contour curve, there will be no collision between the crane and the obstacle, and the operator can operate the crane normally.

8. A crane anti-collision warning system based on image analysis, used to implement the crane anti-collision warning method based on image analysis according to any one of claims 1 to 7, characterized in that: include: An intelligent analysis terminal, the intelligent analysis terminal being used to perform feature analysis on an image to be analyzed of the crane's surrounding environment to determine obstacle contour information and crane contour information; The intelligent analysis terminal is used to compare and analyze the obstacle outline information and the crane outline information to determine whether a collision will occur between the crane and the obstacle; the intelligent analysis terminal is used to control information interaction and data transmission between various modules; An image capturing device is used to capture and process images of the surrounding environment of the crane to obtain images of the surrounding environment of the crane to be analyzed; An image preprocessing module, the image preprocessing module being used to perform image preprocessing on the image to be analyzed of the crane's surrounding environment; An image analysis module, the image analysis module is used to perform feature analysis on the image to be analyzed of the crane's surrounding environment to determine obstacle outline information and crane outline information; a curve drawing module, wherein the curve drawing module draws curves in a rectangular coordinate system according to the crane profile fitting function and the obstacle fitting function, respectively, to obtain a crane profile curve and an obstacle profile curve; The curve analysis module is used to perform curve analysis on the crane contour curve and the obstacle contour curve to determine whether a collision will occur between the crane and the obstacle.

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