Intelligent Monitoring and Warning Method, System and Storage Medium for Crane

Abstract

The present application relates to the technical field of intelligent monitoring of cranes, and discloses an intelligent monitoring and early warning method, system and storage medium for cranes. The method includes: periodically collecting monitoring data of the crane, generating an operation area image, and virtually setting a hook and a sling on the operation area image to generate a region annotation map for display on a display screen, identifying ground objects on the operation area image, if the ground object is a human body, setting a protection area, calculating the horizontal distance between the projection points of the human body and the hook on a reference plane, and sending a first early warning message when the human body is within the protection area, if the ground object is an object, calculating the horizontal distance and the vertical distance between the object and the load, and sending a second early warning message when the horizontal distance is less than or equal to a first preset value and the vertical distance is less than or equal to a second preset value. The present application improves the transparency and controllability of crane operations, and improves the accuracy and intelligence of monitoring.

Description

Technical Field

[0001] This application relates to the technical field of crane intelligent monitoring, and particularly to an intelligent monitoring and warning method, system and storage medium for cranes. Background Art

[0002] With the rapid development of industrial automation and intelligence, cranes, as important lifting and transportation equipment, play a crucial role in fields such as construction, ports, and logistics. However, there are many potential safety hazards during the operation of cranes, such as operation errors, equipment failures, environmental factors, etc., which may all lead to serious safety accidents. In order to improve the safety and reliability of cranes and reduce the accident rate, an intelligent monitoring and warning system has emerged.

[0003] Similar prior arts include a Chinese patent application with the publication number CN114314355A, which discloses a crane safety monitoring method, device, system and remote monitoring center. When it detects that the distance between an obstacle and the crane is less than a distance threshold, it sends a deceleration instruction to the controller of the crane to control the crane to decelerate; when it samples and detects that the obstacle is accelerating towards the crane and the number of sampling detections is greater than a preset detection number threshold, it sends a steering instruction to the controller of the crane to control the crane to steer in a direction away from the obstacle. There is also a Chinese patent application with the publication number CN102092640A, which discloses a safety monitoring device and method for cranes and a crane including this device. It detects the surrounding space of the crane and generates corresponding distance information according to the detected set target; reads the current operation parameter information of the crane, compares the distance information with the current operation parameter information to determine whether the target is within the operation range of the crane, and generates a corresponding output signal according to the comparison result; and executes a predetermined operation in response to the output signal.

[0004] When the above prior arts are performing monitoring, they only consider the distance between the target object and the crane, do not distinguish the target object, and cannot effectively prompt the driver to adjust the operation actions. Therefore, it is an urgent problem to provide an intelligent monitoring and warning method, system and storage medium for cranes to improve the transparency and controllability of operations and the accuracy and intelligence level of monitoring. Summary of the Invention

[0005] This application provides an intelligent monitoring and warning method, system and storage medium for cranes, which are used to improve the operation efficiency and control accuracy of cranes.

[0006] In a first aspect, this application provides an intelligent monitoring and warning method for cranes, and the intelligent monitoring and warning method for cranes includes:

[0007] Step 1: Collect the monitoring data of the crane at a preset period, where the monitoring data includes image data;

[0008] Step 2: Generate a first working area image based on the image data;

[0009] Step 3: Blur the hook and sling of the crane and set them on the first working area image to generate a second working area image. Subsequently, generate a regional annotation map of the working area based on the monitoring data and the second working area image, and display the regional annotation map on the crane display screen, where the regional annotation image includes the first relative distance from the lower surface of the load to the reference plane;

[0010] Step 4: Identify the ground objects on the first working area image. If the ground object is a human body, go to Step 5; if the ground object is an object, go to Step 6;

[0011] Step 5: Set a protection area based on the monitoring data, and at the same time calculate the first horizontal distance between the human body and the projection point of the hook on the reference plane. Determine whether the human body is within the protection area based on the first horizontal distance. If so, send a first warning message;

[0012] Step 6: Calculate the second horizontal distance between the object and the projection point, and the vertical distance between the load and the object. When the second horizontal distance is less than or equal to the first preset value and the vertical distance is less than or equal to the second preset value, send a second warning message.

[0013] Combined with the first aspect, in the first implementation manner of the first aspect of the present application, in Step 2, the method for generating the first working area image is as follows:

[0014] Step 21: Extract any image data, input the any image data into a preset model, and obtain the recognition results of all elements in the any image data;

[0015] Step 22: Based on the recognition results, extract the coordinates of the pixel points corresponding to the hook and sling, and define them as the first coordinate points. Based on the first coordinate points, intercept the image on one side that does not include the hook and sling from the any image data, and define it as the first image;

[0016] Step 23: After traversing all the image data, generate a first working area image based on all the first images.

[0017] Combined with the first aspect, in the second implementation manner of the first aspect of the present application, the monitoring data includes first three-dimensional point cloud data and geometric correction parameters. In Step 3, generating a regional annotation map of the working area based on the monitoring data and the second working area image includes:

[0018] Step 31: Adjust the coordinates of the first 3D point cloud based on the geometric correction parameters to generate a second 3D point cloud, and then calculate the second relative distance between the reference plane in the operation area and the 3D data acquisition device based on the second 3D point cloud;

[0019] Step 32: Based on the first relative distance and the second 3D point cloud, obtain the first relative distance, the third relative distance between the upper surface of the load and the reference plane, and the fourth relative distance between the upper surface of the ground object and the reference plane, and correspondingly mark the first relative distance, the third relative distance, and the fourth relative distance on the second operation area image;

[0020] Step 33: Obtain the device parameters of the image acquisition device, and calculate the data acquisition range of the image acquisition device based on the first relative distance and the device parameters;

[0021] Step 34: Obtain the length of the crane boom. Taking the fulcrum of the left - right movement of the crane boom as the first origin, generate the first trajectory line of the left - right movement of the crane boom based on the length of the crane boom, and draw the first trajectory line within the data acquisition range on the second operation area image. At the same time, obtain the vertical projection line from the hook to the reference plane based on the projection point, and generate the second trajectory line of the front - back movement of the crane boom based on the first origin and the vertical projection line, and draw the second trajectory line within the data acquisition range on the second operation area image.

[0022] Combined with the first aspect, in the third implementation manner of the first aspect of the present application, the calculation method of the second relative distance is as follows:

[0023] Step 311: Set a plurality of longitudinal first grids based on the area of the operation area, and divide the point cloud corresponding to any first grid into any first grid;

[0024] Step 312: Extract any first grid, calculate the first vertical distance between each scan point in any first grid and the 3D data acquisition device. After traversing all scan points, extract the scan point corresponding to the maximum value of the first vertical distance and define it as the first reference point;

[0025] Step 313: Calculate the vertical distance difference between any scan point and the first reference point. When the vertical distance difference is less than or equal to the third preset value, define any scan point as the second reference point;

[0026] Step 314: After traversing all scan points, generate a first reference point group based on the first reference point and all second reference points, and take the average value of the first vertical distances of all reference points in the first reference point group as the reference distance of any first grid;

[0027] Step 315: After traversing all first grids, take the minimum value of the reference distances as the second relative distance.

[0028] In combination with the first aspect, in the fourth implementation manner of the first aspect of the present application, step 32 includes:

[0029] Step 321: Set a plurality of horizontal second grids based on the second relative distance and a preset interval, and divide the point cloud corresponding to any second grid into any second grid;

[0030] Step 322: Extract any second grid, and extract any two scanning points. Calculate the point distance between any two scanning points, and determine whether the point distance is less than or equal to a fourth preset value. If not, re-obtain two scanning points and repeat the judgment in step 322. If so, define any two scanning points as a second reference point group, and then enter step 323;

[0031] Step 323: Calculate the geometric center of the second reference point group, and determine whether there is a first scanning point in any second grid whose point distance from the geometric center is less than or equal to the fourth preset value. If so, generate a new second reference point group based on the second reference point group and the first scanning point, and then return to step 323. If not, determine whether the point group division of all scanning points in any second grid has been completed. If so, enter step 324. If not, return to step 322;

[0032] Step 324: Cluster the scanning points in all second reference point groups respectively to obtain a plurality of point sets. Extract any point set, calculate the point-plane distance between any scanning point in any point set and the reference plane, and take the average value of all point-plane distances as the fifth relative distance of any point set;

[0033] Step 325: After traversing all second grids, extract any two adjacent point sets belonging to different second grids, calculate the inter-set distance between the two point sets based on the fifth relative distance, and determine whether the inter-set distance is less than or equal to a fifth preset value. If so, enter step 326. If not, return to step 325;

[0034] Step 326: Determine whether the two point sets overlap in the vertical direction. If so, determine that the two point sets belong to the same object. If not, return to step 325;

[0035] Step 327: After traversing all point sets, divide all point sets into different objects, assign an identifier to each object, and simultaneously identify the load and the ground object based on the division result;

[0036] Step 328: Extract the first identifier corresponding to the load, and extract all the point sets corresponding to the first identifier, which is defined as the first set. Take the maximum value of the fifth relative distance corresponding to the first set as the second relative distance, and the minimum value of the fifth relative distance as the first relative distance. At the same time, extract the second identifier corresponding to any ground object, and extract all the point sets corresponding to the second identifier, which is defined as the second set. Take the maximum value of the fifth relative distance corresponding to the second set as the fourth relative distance.

[0037] Combined with the first aspect, in the fifth implementation manner of the first aspect of the present application, a calibration device is provided on the hook fixing device. The first calibration device is on the same horizontal line as the hook fixing point. The second calibration device is fixed outside the first calibration device and on the same horizontal line as the first calibration device. The third calibration device is fixed below the first calibration device. A data acquisition device is provided on the crane. In step 34, the method for obtaining the length of the crane boom is as follows:

[0038] Step 341: Set up a three-dimensional coordinate system with the data acquisition device as the second origin, and respectively obtain the first relative coordinate, the second relative coordinate, and the third relative coordinate of the first calibration device, the second calibration device, and the third calibration device in the three-dimensional coordinate system;

[0039] Step 342: Generate a correction matrix based on the first relative coordinate, the second relative coordinate, and the third relative coordinate;

[0040] Step 343: Extract the fourth relative coordinate of the hook fixing point relative to the first calibration device, and generate a first vector based on the fourth relative coordinate;

[0041] Step 344: Define the product of the correction matrix and the first vector as the second vector, and obtain the fifth relative coordinate of the hook fixing point in the three-dimensional coordinate system based on the second vector;

[0042] Step 345: Obtain the sixth relative coordinate of the first origin relative to the second origin, and calculate the length of the crane boom based on the fifth relative coordinate and the sixth relative coordinate.

[0043] Combined with the first aspect, in the sixth implementation manner of the first aspect of the present application, the monitoring data includes load characteristic parameters and environmental parameters. In step 5, the method for setting the protection area is as follows: Calculate the protection range corresponding to the load based on the first relative distance, the environmental parameters, and the load characteristic parameters, and then set a protection area with the projection point as the center.

[0044] Combined with the first aspect, in the seventh implementation manner of the first aspect of the present application, the calculation method of the horizontal distance is as follows:

[0045] Define the pixel coordinates of the pixel point of any ground object closest to the load as the second coordinate point, and obtain the pixel spacing between the second coordinate point and the projection point;

[0046] Obtain the relative distance between any ground object and the reference plane, as well as the device parameters of the image acquisition device. Based on the device parameters, the first relative distance, and the fourth relative distance of any ground object, obtain the pixel size of a pixel, and calculate the horizontal distance based on the pixel pitch and the pixel size.

[0047] In a second aspect, the present application provides an intelligent monitoring and warning system for a crane. The intelligent monitoring and warning system for a crane includes: a data acquisition module, an image generation module, an image annotation module, a ground object judgment module, a first warning module, and a second warning module;

[0048] The data acquisition module is used to collect the monitoring data of the crane at a preset period, where the monitoring data includes image data;

[0049] The image generation module is used to generate a first operation area image according to the image data;

[0050] The image annotation module is used to blur the hook and sling of the crane on the first operation area image to generate a second operation area image, and then generate a regional annotation map of the operation area based on the monitoring data and the second operation area image, and display the regional annotation map on the crane display screen, where the regional annotation image includes the first relative distance from the lower surface of the load to the reference plane;

[0051] The ground object judgment module is used to identify the ground object on the first operation area image. If the ground object is a human body, it enters the first warning module; if the ground object is an object, it enters the second warning module;

[0052] The first warning module is used to set a protection area according to the monitoring data, and at the same time calculate the first horizontal distance between the human body and the projection point of the hook on the reference plane, and judge whether the human body is located in the protection area based on the first horizontal distance. If so, issue a first warning message;

[0053] The second warning module is used to calculate the second horizontal distance between the object and the projection point, as well as the vertical distance between the load and the object. When the second horizontal distance is less than or equal to the first preset value and the vertical distance is less than or equal to the second preset value, issue a second warning message.

[0054] In a third aspect of the present application, a computer-readable storage medium is provided. Instructions are stored in the computer-readable storage medium, and when it runs on a computer, it causes the computer to execute the above-mentioned intelligent monitoring and warning method for a crane.

[0055] Compared with the prior art, the beneficial effects of the technical solution of the present application are at least as follows:

[0056] 1. Automatically process the images captured from multiple angles to generate the working area image, avoiding shooting dead angles, improving the usability of the working area image, enabling the monitoring system to cover the working area more comprehensively, and enhancing the comprehensiveness and effectiveness of monitoring.

[0057] 2. Through image processing technology and 3D point cloud data, it can accurately identify and locate the loads and ground objects in the working area, calculate key parameters such as the relative distance between the load, ground object and the reference plane, and the horizontal distance between the ground object and the load, providing accurate data support for early warning judgment, improving the accuracy and reliability of early warning judgment, and reducing the risks of false alarms and missed alarms.

[0058] 3. The regional annotation map of the working area is displayed in real time through the display screen, enabling the operator to intuitively understand the real-time situation and early warning information of the working area, facilitating effective management and decision-making, and improving the transparency and controllability of the operation.

[0059] 4. Timely identify the human bodies and objects in the working area, accurately judge their relative positional relationship with the load. Once it is found that a person enters the protection area or the distance between an object and the load is too close, an early warning message is immediately sent, thus effectively avoiding the occurrence of accidents and improving the operation safety. Description of the Drawings

[0060] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0061] Figure 1 It is a schematic diagram of an embodiment of the intelligent monitoring and early warning method for a crane in an embodiment of the present application;

[0062] Figure 2 It is a schematic diagram of an embodiment of the intelligent monitoring and early warning system for a crane in an embodiment of the present application. Detailed Embodiments

[0063] The embodiments of the present application provide an intelligent monitoring and early warning method, system and storage medium for a crane. The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described here can be implemented in an order other than those illustrated or described here. In addition, the term "comprising" or "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0064] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 One embodiment of the intelligent monitoring and early warning method for a crane in the embodiments of the present application includes:

[0065] Step 1: Collect the monitoring data of the crane according to a preset period, where the monitoring data includes image data.

[0066] The preset period is set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this.

[0067] Specifically, the monitoring data at least includes environmental parameters, image data, three-dimensional point cloud data, etc.

[0068] Step 2: Generate a first working area image based on the image data.

[0069] In a specific embodiment, in step 2, the method for generating the first working area image is:

[0070] Step 21: Extract any image data, input the any image data into a preset model, and obtain the recognition results of all elements in the any image data.

[0071] Step 22: Based on the recognition results, extract the coordinates of the pixel points corresponding to the hook and the sling, and define them as the first coordinate points. Based on the first coordinate points, intercept the image on one side that does not include the hook and the sling from the any image data, and define it as the first image.

[0072] After traversing all the image data, generate a first working area image based on all the first images.

[0073] Specifically, there are multiple pieces of image data, and each piece of image data is captured by a different image acquisition device. The image acquisition devices are arranged on the hook fixing device at the front end of the jib. Since the image acquisition devices are located above the load, there will be shooting blind spots in the captured image data due to occlusion by slings and the like. To obtain images of the working area in all directions, multiple image acquisition devices are set up. Each image acquisition device shoots the working area from a different angle, and an image of the working area is generated based on the images taken from multiple angles, avoiding shooting blind spots and improving the comprehensiveness of the image of the working area.

[0074] The preset model is a neural network model that has been pre-trained to identify elements (such as loads, hooks, slings, human bodies, and objects) on the image data and identify the pixels belonging to any element.

[0075] Based on the recognition result, a first image that does not contain the hook and the sling is intercepted from the image data. Preferably, the shape of the first image can be determined according to the number of pieces of image data. Exemplarily, when there are two pieces of image data, the shape of the first image is rectangular. The side that does not contain sling pixels is defined as the first side, the pixel point coordinates of the hook closest to the first side are extracted, and the image area from the above pixel point coordinates to the edge of the first side is used as the first image. Subsequently, the two first images are merged to generate a first working area image.

[0076] By intercepting and merging multiple pieces of image data, an image of the working area with no or less occluded parts can be obtained, ensuring the accuracy and integrity of the image of the working area. Further, the load and ground objects can be more accurately identified from the image of the working area, and the distance between the load and the ground objects can be more accurately obtained, enhancing the comprehensiveness and effectiveness of the monitoring image.

[0077] Step 3: The hook and sling of the crane are blurred and set on the first working area image to generate a second working area image. Subsequently, a regional annotation map of the working area is generated based on the monitoring data and the second working area image, and the regional annotation map is displayed on the crane display screen. The regional annotation image includes the first relative distance from the lower surface of the load to the reference plane.

[0078] Blurring and setting the hook and sling of the crane on the first working area image enables the operator to intuitively understand the positions of the hook and sling in the working area, enhancing the visualization of the information in the working area. Generating a regional annotation map of the working area based on the monitoring data and the second working area image can accurately obtain the data parameters of the working area, facilitating the operator's operation planning and safety monitoring, and providing an accurate basis for subsequent early warning judgment, helping to improve the accuracy and reliability of the early warning system and effectively preventing accidents.

[0079] In a specific embodiment, the monitoring data includes first three-dimensional point cloud data and geometric correction parameters. In step 3, generating a regional annotation map of the working area based on the monitoring data and the second working area image includes:

[0080] Step 31: Adjust the coordinates of the first three-dimensional point cloud based on the geometric correction parameters to generate a second three-dimensional point cloud. Subsequently, calculate the second relative distance between the reference plane in the working area and the three-dimensional data acquisition device based on the second three-dimensional point cloud.

[0081] Step 32: Based on the first relative distance and the second three-dimensional point cloud, obtain the first relative distance, the third relative distance between the upper surface of the load and the reference plane, and the fourth relative distance between the upper surface of the ground object and the reference plane, and correspondingly mark the first relative distance, the third relative distance, and the fourth relative distance on the second working area image.

[0082] Step 33: Obtain the device parameters of the image acquisition device, and calculate the data acquisition range of the image acquisition device based on the first relative distance and the device parameters.

[0083] Step 34: Obtain the length of the crane boom. Taking the fulcrum of the left and right movement of the crane boom as the first origin, generate a first trajectory line for the left and right movement of the crane boom based on the length of the crane boom, and draw the first trajectory line within the data acquisition range on the second working area image. At the same time, obtain the vertical projection line of the hook to the reference plane based on the projection point, and generate a second trajectory line for the front and back movement of the crane boom based on the first origin and the vertical projection line, and draw the second trajectory line within the data acquisition range on the second working area image.

[0084] Specifically, the horizontal view angle, vertical view angle, acceleration, etc. of the image acquisition device and the three-dimensional data acquisition device when acquiring data are obtained through a measuring device. Adjust the coordinates of the first three-dimensional point cloud based on the geometric correction parameters to eliminate errors caused by scanning angles, etc., improve the accuracy and reliability of the three-dimensional point cloud data, and provide a reliable data basis for subsequent working area analysis and early warning judgment.

[0085] Specifically, the reference plane is the plane on which the load is placed, which can be the ground or a plane such as a roof.

[0086] The data acquisition range of the image acquisition device is the area that the operator can directly observe. By marking the relative distances of the lower surface of the load from the reference plane, the upper surface of the load from the reference plane, and the upper surface of the ground object from the reference plane within the data acquisition range of the image acquisition device, and at the same time marking the first trajectory line representing the left - right movement of the crane and the second trajectory line representing the front - back movement of the crane, the operator can intuitively understand the movement range of the crane and the potential path of the load during rotation. This intuitive visual information enhances the visualization effect of the warning system, helps the operator better plan and execute the hoisting operation, thus avoiding collisions with surrounding obstacles and ensuring the safety of the operation.

[0087] In a specific embodiment, the calculation method of the second relative distance is as follows:

[0088] Step 311: Set a plurality of longitudinal first grids based on the area of the operation area, and divide the point cloud corresponding to any first grid into any first grid.

[0089] Step 312: Extract any first grid, calculate the first vertical distance between each scan point in any first grid and the three - dimensional data acquisition device. After traversing all scan points, extract the scan point corresponding to the maximum value of the first vertical distance and define it as the first reference point.

[0090] Step 313: Calculate the vertical distance difference between any scan point and the first reference point. When the vertical distance difference is less than or equal to the third preset value, define any scan point as the second reference point.

[0091] Step 314: After traversing all scan points, generate a first reference point group based on the first reference point and all second reference points, and take the average value of the first vertical distances of all reference points in the first reference point group as the reference distance of any first grid.

[0092] Step 315: After traversing all first grids, take the minimum value of the reference distances as the second relative distance.

[0093] The third preset value is set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this.

[0094] Specifically, the three - dimensional point cloud data is vertically divided into a plurality of grid point clouds with the same shape and the same upper - surface area. The first reference point corresponding to the maximum value of the first vertical distance is the scan point closest to the ground in the first grid. The scan points with a vertical distance difference less than or equal to the third preset value from the first reference point are considered to be scan points on the same horizontal plane, that is, the scan points in the first reference point group are the scan points on the bottom horizontal plane of the first grid. Take the average value of the first vertical distances of the first reference point and the second reference points in the first reference point group as the reference distance of the bottom horizontal plane of the first grid.

[0095] By setting multiple vertical first grids and analyzing the point cloud in each grid in detail, through steps such as calculating the vertical distance between the scanned points and the three-dimensional data acquisition device and extracting reference points, the point cloud data can be divided and processed more precisely, thereby improving the accuracy of calculating the second relative distance, which helps to more accurately reflect the height changes and terrain features in the operation area. During the calculation process, by extracting the first reference point group and generating the second relative distance based on the first reference point group, the influence of individual scanned point errors on the overall calculation result can be effectively reduced, enhancing the robustness of the data and making the calculated relative distance more stable and reliable.

[0096] The technical solution of this application can adapt to terrain conditions with different degrees of complexity. Through grid processing and multi-point reference, it can better handle operation areas with large terrain undulations or irregularities, providing technical support for the application of the warning system in complex environments and improving the applicability and flexibility of the system.

[0097] In a specific embodiment, step 32 includes:

[0098] Step 321: Set multiple horizontal second grids based on the second relative distance and a preset interval, and divide the point cloud corresponding to any second grid into any second grid.

[0099] Step 322: Extract any second grid, and extract any two scanned points. Calculate the point distance between any two scanned points, and determine whether the point distance is less than or equal to a fourth preset value. If not, re-obtain two scanned points and repeat the judgment in step 322. If so, define any two scanned points as the second reference point group, and then enter step 323.

[0100] Step 323: Calculate the geometric center of the second reference point group, and determine whether there is a first scanned point in any second grid whose point distance from the geometric center is less than or equal to the fourth preset value. If so, generate a new second reference point group based on the second reference point group and the first scanned point, and then return to step 323. If not, determine whether the point group division of all scanned points in any second grid has been completed. If so, enter step 324. If not, return to step 322.

[0101] Step 324: Cluster the scanned points in all second reference point groups respectively to obtain multiple point sets. Extract any point set, calculate the point-plane distance between any scanned point in any point set and the reference plane, and take the average value of all point-plane distances as the fifth relative distance of any point set.

[0102] Step 325: After traversing all the second grids, extract any two adjacent point sets belonging to different second grids, calculate the inter-set distance between the two point sets based on the fifth relative distance, and determine whether the inter-set distance is less than or equal to the fifth preset value. If so, proceed to Step 326; if not, return to Step 325.

[0103] Step 326: Determine whether the two point sets overlap in the vertical direction. If so, determine that the two point sets belong to the same object; if not, return to Step 325.

[0104] Step 327: After traversing all the point sets, divide all the point sets into different objects, assign an identifier to each object, and simultaneously identify the load and the ground object based on the division result.

[0105] Step 328: Extract the first identifier corresponding to the load, and extract all the point sets corresponding to the first identifier, which are defined as the first set. Take the maximum value of the fifth relative distance corresponding to the first set as the second relative distance and the minimum value of the fifth relative distance as the first relative distance. At the same time, extract the second identifier corresponding to any ground object, and extract all the point sets corresponding to the second identifier, which are defined as the second set. Take the maximum value of the fifth relative distance corresponding to the second set as the fourth relative distance.

[0106] The preset interval, the fourth preset value, and the fifth preset value are set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this.

[0107] Specifically, the three-dimensional point cloud data is stratified at a preset interval and segmented into multiple horizontal grid point clouds.

[0108] Specifically, there may be multiple horizontal planes (i.e., horizontal planes belonging to different objects) in any grid point cloud. Extract any two scanning points according to spatial proximity. When the point distance between any two scanning points is less than the fourth preset value, it is considered that the two scanning points are on the same plane. Subsequently, the two scanning points are defined as the second reference point group, and scanning points belonging to the same plane are searched again based on the geometric center of the second reference point group. A single scanning point may not be sufficient to accurately represent the true position of the plane due to measurement errors or noise. Searching for scanning points belonging to the same plane through the geometric center can effectively reduce random errors, more stably reflect the center position of the plane, and can more accurately classify adjacent points into the correct plane, especially in the case of uneven point distribution or multiple plane intersections, improving the accuracy and robustness of plane recognition. By estimating the scanning points belonging to the same plane through the above method, the upper and lower surfaces of the load and the upper surface of the ground object can be quickly identified, reducing the calculation amount and improving the calculation efficiency.

[0109] When the distance between the sets of two adjacent point sets belonging to different second grids is less than or equal to the fifth preset value and the two point sets overlap in the vertical direction, it indicates that the two point sets belong to the same object. After traversing all the point sets, all the point sets belonging to the same object are identified. When the object does not contact the reference plane, the object is a load. When the object contacts the reference plane, the object is a ground object. Further, the relative distances between the load and the ground object and the reference plane are calculated based on the fifth relative distance of the point set corresponding to each object. Through the above method, the relative distance between the object and the reference plane can be statistically calculated without using the normal vector of the three-dimensional point cloud, with less computational effort and improved computational efficiency.

[0110] By setting multiple horizontal second grids and performing detailed clustering and analysis on the point cloud in each grid, different objects in the working area can be identified and divided more accurately, which helps to distinguish between loads and ground objects. At the same time, by calculating the geometric center and the point-plane distance of the point set, as well as the distance between point sets, the relative positional relationship between the object and the reference plane can be accurately determined. This technical solution can adapt to objects of different shapes and sizes, as well as working environments with different load characteristics. Through grid processing and clustering analysis, it can better handle working areas with large terrain undulations or irregularities, improving the adaptability, flexibility, and robustness of the system, and ensuring stable operation in complex environments.

[0111] In a specific embodiment, a calibration device is provided on the hook fixing device. The first calibration device is on the same horizontal line as the hook fixing point. The second calibration device is fixed outside the first calibration device and on the same horizontal line as the first calibration device. The third calibration device is fixed below the first calibration device. A data acquisition device is provided on the crane. In step 34, the method for obtaining the length of the crane boom is as follows:

[0112] Step 341: Set up a three-dimensional coordinate system with the data acquisition device as the second origin, and respectively obtain the first relative coordinate, the second relative coordinate, and the third relative coordinate of the first calibration device, the second calibration device, and the third calibration device in the three-dimensional coordinate system.

[0113] Step 342: Generate a correction matrix based on the first relative coordinate, the second relative coordinate, and the third relative coordinate.

[0114] Step 343: Extract the fourth relative coordinate of the hook fixing point relative to the first calibration device, and generate a first vector based on the fourth relative coordinate.

[0115] Step 344: Define the product of the correction matrix and the first vector as the second vector, and obtain the fifth relative coordinate of the hook fixing point in the three-dimensional coordinate system based on the second vector.

[0116] Step 345: Obtain the sixth relative coordinate of the first origin with respect to the second origin, and calculate the length of the crane boom based on the fifth relative coordinate and the sixth relative coordinate.

[0117] Specifically, the data acquisition device is set above the cockpit and can observe the calibration device without obstruction. The first calibration device, the hook fixing point, and the second calibration device are located on the same horizontal line parallel to the horizontal plane. The third calibration device and the first calibration device are located on the same vertical line extending downward vertically, and the horizontal line and the vertical line intersect perpendicularly. The data acquisition device has the function of measuring the distance and the included angles of directions (pitch angle, roll angle, rotation angle). Based on the measurement results, obtain the first relative coordinate, the second relative coordinate, and the third relative coordinate of the first calibration device, the second calibration device, and the third calibration device in the three-dimensional coordinate system. Preferably, the relative coordinate is the relative coordinate of the center point of the calibration device.

[0118] Respectively obtain the first vector, the second vector, and the third vector of the first calibration device, the second calibration device, and the third calibration device with respect to the second origin based on the first relative coordinate, the second relative coordinate, and the third relative coordinate. Calculate the first vector based on the first vector and the second vector. The calculation formula is: Calculate the first vector based on the first vector and the second vector. The calculation formula is: where VE1 is the first vector, VE2 is the second vector, V1 is the first vector, V2 is the second vector, and V3 is the third vector. is the modulus operation. Subsequently, perform the vector product operation on the first vector and the second vector to generate the third vector. Generate the above-mentioned correction matrix based on the first vector, the second vector, and the third vector. The column vectors of this correction matrix are composed of the first vector, the second vector, and the third vector.

[0119] The hook fixing point is located inside the hook fixing device, directly above the hook. It is impossible to directly obtain the relative coordinate of the hook fixing point in the three-dimensional coordinate system through the measurement of the data acquisition device. The fourth relative coordinate of the hook fixing point with respect to the first calibration device is a fixed value set in advance according to the design data. Convert the fourth relative coordinate into the fifth relative coordinate in the three-dimensional coordinate system through the correction matrix.

[0120] Specifically, the position of the data acquisition device is fixed, and the fulcrum for the left and right movement of the crane boom is fixed. The sixth relative coordinate of the first origin with respect to the second origin can be obtained according to the design data, and further calculate the length of the crane boom and the horizontal distance between the hook fixing point and the fulcrum for the left and right movement of the crane boom. Use this horizontal distance as the radius of the left and right movement range of the crane boom, that is, the radius of the first trajectory line.

[0121] According to the technical solution of the present application, positions that cannot be directly measured can be indirectly measured. When the boom is bent under the influence of factors such as load, self-weight, and wind, the position of the hook fixing point can be accurately determined, overcoming the limitations of direct line of sight or direct measurement, realizing precise monitoring of the crane boom length and the boom movement radius, and providing reliable data support for the safe use and operation of the crane.

[0122] Step 4: Identify the ground objects in the first working area image. If the ground object is a human body, go to Step 5; if the ground object is an object, go to Step 6.

[0123] Preferably, identify the ground objects within the preset range of the projection point, without having to identify and judge all the ground objects on the working area image, reducing the amount of calculation data.

[0124] By identifying the ground objects in the first working area image, two different types of ground objects, namely human bodies and objects, are distinguished, and the warning strategy is flexibly adjusted according to different types of ground objects, improving the accuracy and effectiveness of the warning system.

[0125] Step 5: Set a protection area based on the monitoring data. At the same time, calculate the first horizontal distance between the projection points of the human body and the hook on the reference plane, and judge whether the human body is within the protection area based on the first horizontal distance. If so, send the first warning message.

[0126] During the lifting and lowering of the load, due to the influence of the load itself or environmental factors, if the load falls, there will be different ranges of influence. Set a protection area based on the monitoring data and conduct real-time monitoring to judge whether the human body is within the protection area, timely detect the potential dangerous distance between the human body and the hook, send a warning before the dangerous situation occurs, remind the operator to take safety measures, thereby effectively avoiding the occurrence of collision accidents, enhancing the reliability and effectiveness of the monitoring, and reducing the possibility of false alarms and missed alarms.

[0127] Preferably, draw the protection area on the area annotation map, which helps to remind the operator to approach the users in the protection area in advance according to the observation.

[0128] Preferably, when sending the first warning message, send a safety locking signal or a deceleration signal to the controller of the crane to control the locking or deceleration of the crane.

[0129] In a specific embodiment, the monitoring data includes load characteristic parameters and environmental parameters. In Step 5, the method for setting the protection area is as follows: calculate the protection range corresponding to the load based on the first relative distance, environmental parameters, and load characteristic parameters, and then set the protection area with the projection point as the center.

[0130] Specifically, the load characteristic parameters include but are not limited to volume data and weight data, and the environmental parameters include but are not limited to wind speed and wind direction.

[0131] The higher the height and the greater the weight of the lifted load, the larger the scattered area or the range of dust when it falls, and a larger protection area needs to be set; the greater the wind speed, the larger the scattered area or the range of dust when it falls, and a larger protection area also needs to be set; moreover, the scattered area or the dust direction when the load falls will change with the wind direction. Therefore, the wind direction is considered when setting the protection area.

[0132] Flexibly adjusting the protection area according to the monitoring data can more accurately determine whether a person is in a dangerous area, timely send out warning information, remind the operator to take corresponding safety measures, thus effectively preventing potential safety accidents and improving the accuracy and effectiveness of early warning and the safety of the entire operation process.

[0133] In a specific embodiment, the calculation method of the horizontal distance is as follows:

[0134] Define the pixel coordinates of the closest feature to the load to the projection point as the second coordinate point, and obtain the pixel spacing between the second coordinate point and the projection point;

[0135] Obtain the relative distance between any feature and the reference plane, as well as the device parameters of the image acquisition device. Based on the device parameters, the first relative distance, and the fourth relative distance of any feature, obtain the pixel size of one pixel, and calculate the horizontal distance based on the pixel spacing and the pixel size.

[0136] The image of the operation area is formed by merging the image data taken from a top view. The pixel size of any coordinate point in the image is related to the parameters of the image acquisition device, the height of the image acquisition device from the reference plane, and the height of the feature relative to the reference plane. By defining the pixel coordinates of the closest feature to the load as the second coordinate point, and combining the pixel spacing, the relative distance between the feature and the reference plane, and the device parameters of the image acquisition device, the minimum horizontal distance between the feature and the projection point can be accurately calculated, and the relative relationship between the human body and the protection area, as well as the potential collision risk between the object and the load, can be accurately evaluated, thereby improving the reliability of the warning system and reducing the possibility of false alarms and missed alarms.

[0137] Step 6: Calculate the second horizontal distance between the object and the projection point, and the vertical distance between the load and the object. When the second horizontal distance is less than or equal to the first preset value and the vertical distance is less than or equal to the second preset value, send out the second warning information.

[0138] The first preset value and the second preset value are set according to the experience of those skilled in the art or according to the actual application scenario, and the embodiments of the present application do not limit this.

[0139] Specifically, obtain the fourth relative distance of the object relative to the reference plane and the first relative distance from the lower surface of the load to the reference plane, and take the difference between the first relative distance and the fourth relative distance as the vertical distance between the load and the object.

[0140] By calculating the horizontal distance between the object and the load and the vertical distance between the load and the object, the relative position relationship between the object and the load and the potential collision risk can be accurately evaluated, which helps the operator better plan and adjust the operation process, avoid operation interruption and equipment damage caused by collision, and improve the operation efficiency.

[0141] The intelligent monitoring and warning method for a crane in the embodiments of the present application has been described above. Next, the intelligent monitoring and warning system for a crane in the embodiments of the present application will be described. Please refer to Figure 2 An embodiment of the intelligent monitoring and warning system for a crane in the embodiments of the present application includes: a data acquisition module 101, an image generation module 102, an image annotation module 103, a ground object judgment module 104, a first warning module 105, and a second warning module 106.

[0142] The data acquisition module 101 is used to collect the monitoring data of the crane according to a preset period, where the monitoring data includes image data.

[0143] The image generation module 102 is used to generate a first operation area image according to the image data.

[0144] The image annotation module 103 is used to blur the hook and sling of the crane on the first operation area image to generate a second operation area image, and then generate a regional annotation map of the operation area based on the monitoring data and the second operation area image, and display the regional annotation map on the crane display screen 200, where the regional annotation image includes the first relative distance from the lower surface of the load to the reference plane.

[0145] The ground object judgment module 104 is used to identify the ground object on the first operation area image. If the ground object is a human body, it enters the first warning module; if the ground object is an object, it enters the second warning module.

[0146] The first warning module 105 is used to set a protection area according to the monitoring data, and at the same time calculate the first horizontal distance between the human body and the projection point of the hook on the reference plane, and judge whether the human body is located in the protection area based on the first horizontal distance. If so, a first warning message is issued.

[0147] The second warning module 106 is used to calculate the second horizontal distance between the object and the projection point and the vertical distance between the load and the object. When the second horizontal distance is less than or equal to the first preset value and the vertical distance is less than or equal to the second preset value, a second warning message is issued.

[0148] The present application also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions run on a computer, the computer is caused to execute the steps of the intelligent monitoring and early warning method for a crane.

[0149] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described system, system and unit can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0150] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0151] As described above, the above embodiments are only used to illustrate the technical solution of the present application and are not intended to limit it; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of various embodiments of the present application.

Claims

1. An intelligent monitoring and early warning method for a crane, characterized in that: The intelligent monitoring and early warning method for a crane comprises: Step 1: Collect monitoring data of the crane according to a preset period, wherein the monitoring data includes image data; Step 2: generating a first operation area image based on the image data; Step 3: setting the hook and sling of the crane in a blurred manner on the first working area image to generate a second working area image, then generating a region annotation map of the working area based on the monitoring data and the second working area image, and displaying the region annotation map on the crane display screen, wherein the region annotation image includes a first relative distance from the lower surface of the load to the reference plane; Step 4, identifying the ground feature on the first working area image, if the ground feature is a human body, proceed to step 5, if the ground feature is an object, proceed to step 6; Step 5: setting a protection area based on the monitoring data, calculating a first horizontal distance between the human body and the projection point of the hook on the reference plane, judging whether the human body is within the protection area based on the first horizontal distance, and if so, issuing a first warning message; Step 6: Calculate a second horizontal distance between the object and the projection point, and a vertical distance between the load and the object, and issue a second warning message when the second horizontal distance is less than or equal to a first preset value and the vertical distance is less than or equal to a second preset value; The monitoring data includes first three-dimensional point cloud data and geometric correction parameters. In step 3, generating an area annotation map of the working area based on the monitoring data and the second working area image includes: Step 31, adjusting the coordinates of the first three-dimensional point cloud based on the geometric correction parameters to generate a second three-dimensional point cloud, and then computing a second relative distance between the reference plane in the working area and the three-dimensional data acquisition device based on the second three-dimensional point cloud; Step 32: based on the first relative distance and the second three-dimensional point cloud, obtain the first relative distance, the third relative distance between the upper surface of the load and the reference plane, and the fourth relative distance between the upper surface of the object and the reference plane, and mark the first relative distance, the third relative distance and the fourth relative distance on the second working area image correspondingly; Step 33: Acquire device parameters of an image acquisition device, and calculate a data acquisition range of the image acquisition device based on the first relative distance and the device parameters; Step 34, obtain the length of the crane boom, take the fulcrum for the left and right movement of the crane boom as the first origin, generate a first trajectory line for the left and right movement of the crane boom based on the length of the crane boom, draw the first trajectory line within the data acquisition range on the second work area image, and at the same time, obtain the vertical projection line of the hook to the reference plane based on the projection point, and generate a second trajectory line for the forward and backward movement of the crane boom based on the first origin and the vertical projection line, and draw the second trajectory line within the data acquisition range on the second work area image.

2. The intelligent monitoring and early warning method for cranes according to claim 1 is characterized in that: In step 2, the method for generating the first working area image is: Step 21: extract any image data, input any of the image data into a preset model, and obtain recognition results of all elements in any of the image data; Step 22: based on the recognition result, extract the coordinates of the pixel points corresponding to the hook and the sling, and define them as first coordinate points; based on the first coordinate points, intercept a side image that does not contain the hook and the sling from any of the image data, and define it as the first image; Step 23: After traversing all the image data, generate the first working area image based on all the first images.

3. The intelligent monitoring and early warning method for cranes according to claim 1 is characterized in that: The calculation method of the second relative distance is: Step 311: setting a plurality of longitudinal first grids based on the area of ​​the operation area, and dividing a point cloud corresponding to any first grid into any first grid; Step 312: extract any of the first grids, calculate a first vertical distance between each scanning point in any of the first grids and the three-dimensional data acquisition device, and after traversing all scanning points, extract a scanning point corresponding to the maximum value of the first vertical distance and define it as a first reference point; Step 313, calculating a vertical distance difference between any scanning point and the first reference point, and when the vertical distance difference is less than or equal to a third preset value, defining any scanning point as a second reference point; Step 314: after traversing all the scanning points, generate a first reference point group based on the first reference point and all the second reference points, and use the average of the first vertical distances of all the reference points in the first reference point group as the reference distance of any of the first grids; Step 315: After traversing all first grids, the minimum reference distance is used as the second relative distance.

4. The intelligent monitoring and early warning method for cranes according to claim 1 is characterized in that: The step 32 comprises: Step 321: setting a plurality of second horizontal grids based on the second relative distance and a preset interval, and dividing a point cloud corresponding to any second grid into any second grid; Step 322: extract any second grid, and extract any two scanning points, calculate the point distance between any two scanning points, and determine whether the point distance is less than or equal to a fourth preset value. If not, reacquire two scanning points and repeat the determination of step 322. If yes, define any two scanning points as a second reference point group, and then proceed to step 323. Step 323, calculating the geometric center of the second reference point group, determining whether there is a first scanning point in any of the second grids whose point distance to the geometric center is less than or equal to the fourth preset value, if so, generating a new second reference point group based on the second reference point group and the first scanning point, and then returning to step 323, if not, determining whether point group division is completed for all scanning points in any of the second grids, if so, proceeding to step 324, if not, returning to step 322; Step 324: clustering all the scan points in the second reference point group respectively to obtain multiple point sets, extracting any point set, calculating the point-to-surface distance between any scan point in any of the point sets and the reference plane, and taking the average of all the point-to-surface distances as the fifth relative distance of any of the point sets; Step 325, after traversing all the second grids, extract any two adjacent point sets belonging to different second grids, calculate the inter-set distance of the two point sets based on the fifth relative distance, and determine whether the inter-set distance is less than or equal to the fifth preset value, if so, proceed to step 326, if not, return to step 325; Step 326: determine whether the two point sets overlap in the vertical direction. If so, determine that the two point sets belong to the same object. If not, return to step 325. Step 327: After traversing all point sets, divide all the point sets into different objects, assign an identifier to each object, and identify the load and ground objects based on the division results; Step 328, extract the first identifier corresponding to the load, and extract all point sets corresponding to the first identifier, define them as the first set, and use the maximum value of the fifth relative distance corresponding to the first set as the second relative distance, and the minimum value of the fifth relative distance as the first relative distance. At the same time, extract the second identifier corresponding to any ground feature, and extract all point sets corresponding to the second identifier, define them as the second set, and use the maximum value of the fifth relative distance corresponding to the second set as the fourth relative distance.

5. The intelligent monitoring and early warning method for cranes according to claim 1 is characterized in that: A calibration device is arranged on the hook fixing device, the first calibration device is on the same horizontal line as the hook fixing point, the second calibration device is fixed outside the first calibration device and on the same horizontal line as the first calibration device, the third calibration device is fixed below the first calibration device, and a data acquisition device is arranged on the crane. In the step 34, the method for obtaining the boom length of the crane is: Step 341: set a three-dimensional coordinate system with the data acquisition device as a second origin, and respectively obtain first relative coordinates, second relative coordinates, and third relative coordinates of the first calibration device, the second calibration device, and the third calibration device in the three-dimensional coordinate system; Step 342: Generate a correction matrix based on the first relative coordinate, the second relative coordinate and the third relative coordinate; Step 343: extract the fourth relative coordinate of the hook fixing point relative to the first calibration device, and generate a first vector based on the fourth relative coordinate; Step 344: define the product of the correction matrix and the first vector as a second vector, and obtain the fifth relative coordinate of the hook fixing point in the three-dimensional coordinate system based on the second vector; Step 345: Obtain a sixth relative coordinate of the first origin relative to the second origin, and calculate the crane boom length based on the fifth relative coordinate and the sixth relative coordinate.

6. The intelligent monitoring and early warning method for cranes according to claim 1 is characterized in that: The monitoring data includes load characteristic parameters and environmental parameters. In step 5, the method for setting the protection area is: based on the first relative distance, the environmental parameters and the load characteristic parameters, the protection range corresponding to the load is calculated, and then the protection area is set with the projection point as the center of the circle.

7. The intelligent monitoring and early warning method for cranes according to claim 1 is characterized in that: The calculation method of horizontal distance is: The pixel point coordinates of any ground feature closest to the load are defined as a second coordinate point, and the pixel distance between the second coordinate point and the projection point is obtained; Obtain the relative distance between any of the above-mentioned objects and the reference plane, and the device parameters of the image acquisition device, obtain the pixel size of one pixel based on the device parameters, the first relative distance and the fourth relative distance of any of the above-mentioned objects, and calculate the horizontal distance based on the pixel spacing and the pixel size.

8. An intelligent monitoring and early warning system for cranes, characterized in that: The intelligent monitoring and early warning system for cranes comprises: a data acquisition module, an image generation module, an image annotation module, a ground object judgment module, a first early warning module and a second early warning module; The data acquisition module is used to collect monitoring data of the crane according to a preset period, wherein the monitoring data includes image data; The image generation module is used to generate a first operation area image according to the image data; The image annotation module is used to set the hook and the sling of the crane in a blurred manner on the first working area image to generate a second working area image, then generate a region annotation map of the working area based on the monitoring data and the second working area image, and display the region annotation map on the crane display screen, wherein the region annotation image includes a first relative distance from the lower surface of the load to the reference plane; The ground object judgment module is used to identify the ground object on the first operation area image, and if the ground object is a human body, enter the first early warning module; if the ground object is an object, enter the second early warning module; The first warning module is used to set a protection area according to the monitoring data, and calculate a first horizontal distance between the human body and the projection point of the hook on the reference plane, and determine whether the human body is located in the protection area based on the first horizontal distance, and if so, issue a first warning message; The second warning module is used to calculate a second horizontal distance between the object and the projection point, and a vertical distance between the load and the object, and issue a second warning message when the second horizontal distance is less than or equal to a first preset value and the vertical distance is less than or equal to a second preset value; The monitoring data includes first three-dimensional point cloud data and geometric correction parameters. In the image annotation module, generating an area annotation map of the operating area based on the monitoring data and the second operating area image includes: Step 31, adjusting the coordinates of the first three-dimensional point cloud based on the geometric correction parameters to generate a second three-dimensional point cloud, and then computing a second relative distance between the reference plane in the working area and the three-dimensional data acquisition device based on the second three-dimensional point cloud; Step 32: based on the first relative distance and the second three-dimensional point cloud, obtain the first relative distance, the third relative distance between the upper surface of the load and the reference plane, and the fourth relative distance between the upper surface of the object and the reference plane, and mark the first relative distance, the third relative distance and the fourth relative distance on the second working area image correspondingly; Step 33: Acquire device parameters of an image acquisition device, and calculate a data acquisition range of the image acquisition device based on the first relative distance and the device parameters; Step 34, obtain the length of the crane boom, take the fulcrum for the left and right movement of the crane boom as the first origin, generate a first trajectory line for the left and right movement of the crane boom based on the length of the crane boom, draw the first trajectory line within the data acquisition range on the second work area image, and at the same time, obtain the vertical projection line of the hook to the reference plane based on the projection point, and generate a second trajectory line for the forward and backward movement of the crane boom based on the first origin and the vertical projection line, and draw the second trajectory line within the data acquisition range on the second work area image.

9. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the intelligent monitoring and early warning method for a crane as described in any one of claims 1-7 is implemented.

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