Machine Vision-based Prefabricated Big Tree Transplanting Device and Transplanting Method

Abstract

This application relates to the technical field of large tree transplantation, and particularly to an assembled large tree transplantation device and transplantation method based on machine vision. This application uses an assembled cross-shaped beam to construct a support frame. By adjusting the assembled connection points on the cross-shaped beam, the size of the support frame can be conveniently changed to fit different-sized soil balls. First, a camera installed on the hanging beam platform is used to capture an image of the current support platform, and the safe load-bearing weight of the current support platform is calculated based on the image data and the preset cross-shaped beam parameters. Secondly, the volume of the large tree and its soil ball is estimated through the large tree image, and the overall weight is estimated according to the preset density. Then, the safe load-bearing weight is compared with the overall weight of the large tree. If the overall weight of the large tree exceeds the safe load-bearing weight, a new assembly mode is generated and the construction personnel are notified. Finally, the assembly mode is communicated to the construction personnel to guide them to reassemble the support frame and the support platform, thereby improving the success rate of large tree transplantation.

Description

Technical Field

[0001] This application relates to the technical field of large tree transplantation, and particularly to an assembled large tree transplantation device and transplantation method based on machine vision. Background Art

[0002] Large tree transplantation is a complex project. To ensure the survival rate of transplanted large trees, it is usually necessary to transplant the soil around the large tree together to form a soil ball. However, due to the different sizes and shapes of large trees, the sizes of the soil balls also vary, which poses higher requirements for the transplantation platform. Traditional transplantation platforms are often fixed and cannot adapt to soil balls of different sizes, easily resulting in damage to the large trees or transplantation failure during the transplantation process.

[0003] In the prior art, most large tree transplantation devices use support platforms with fixed sizes. Although they can meet the transplantation needs of small trees to a certain extent, for large trees, especially those with larger soil ball sizes, the support platforms with fixed sizes are not flexible enough to adapt to different transplantation requirements, reducing the transplantation efficiency. In addition, the fixed platforms may cause safety accidents due to insufficient load-bearing during actual use, thus affecting the success rate of large tree transplantation. This situation needs to be further improved. Summary of the Invention

[0004] To solve the problem of low success rate of existing large tree transplantation devices, this application provides an assembled large tree transplantation device and transplantation method based on machine vision, adopting the following technical solutions:

[0005] In the first aspect, this application provides an assembled large tree transplantation device based on machine vision, including:

[0006] An assembled cross-shaped beam, which can be assembled to form a support frame;

[0007] A plurality of support pipes, arranged on the support frame to form a support platform for carrying the transplanted large tree and soil ball;

[0008] A hanging beam platform, with the lower end connected to the support frame by a rope and the upper end used to connect a hoisting device;

[0009] Among them, a plurality of assembled connection points are arranged on the cross-shaped beam for changing the size of the support frame by adjusting the assembly position;

[0010] A processor, a camera and an alarm are arranged on the hanging beam platform, and the processor performs the following steps:

[0011] Obtain the current image of the support platform captured by the camera, and calculate the safe load-bearing weight of the current support platform according to the current support platform image, the preset cross-shaped beam parameter data, and the current support pipe parameter data;

[0012] Obtain the image of the big tree captured by the camera, estimate the volume of the big tree and the volume of the soil ball according to the big tree image, and estimate the overall weight of the big tree according to the volume of the big tree, the volume of the soil ball, and the corresponding preset density;

[0013] Compare the safe load-bearing weight with the overall weight of the big tree. If the overall weight of the big tree is greater than the safe load-bearing weight, generate an assembly mode that meets the overall weight of the big tree according to the comparison result, and send an alarm signal through the alarm. The assembly mode includes the assembly position of the cross-shaped beam, the size and quantity of the support pipes;

[0014] Convey the assembly mode to the construction personnel according to the preset mode, so that the construction personnel can reassemble the support frame and the support platform according to the assembly mode.

[0015] By adopting the above technical solution, due to the different sizes of big trees, the sizes of the soil balls also vary greatly. The existing transplant platforms with fixed sizes often cannot meet the load-bearing requirements of soil balls of different sizes in actual applications; for example, when transplanting a big tree with a large diameter and well-developed roots, due to the huge volume of the soil ball, the fixed-size platform may be structurally unstable due to insufficient bearing capacity, and even tilt or break during the hoisting process, seriously affecting the transplant effect; this application uses an assembled cross-shaped beam to construct the support frame. By adjusting the assembled connection points on the cross-shaped beam, the size of the support frame can be easily changed to suit soil balls of different sizes. First, use the camera installed on the hanging beam platform to capture the image of the current support platform, and the processor calculates the safe load-bearing weight of the current support platform according to the image data and the preset cross-shaped beam parameters; secondly, estimate the volume of the big tree and its soil ball by capturing the big tree image, and estimate the overall weight according to the preset density; then compare the safe load-bearing weight with the overall weight of the big tree. If the overall weight of the big tree exceeds the safe load-bearing weight, the processor generates a new assembly mode and notifies the construction personnel through the alarm; finally, convey the assembly mode to the construction personnel to guide them to reassemble the support frame and the support platform, thereby improving the success rate of big tree transplantation.

[0016] Optionally, obtaining the current image of the support platform captured by the camera and calculating the safe load-bearing weight of the current support platform according to the current support platform image, the preset cross-shaped beam parameter data, and the current support pipe parameter data specifically includes the following steps:

[0017] Obtain the current image of the support platform captured by the camera, input the current support platform image into a preset structure analysis model, and generate the structure data of the current support platform based on the current support platform image;

[0018] Based on the structure data, preset cross-shaped beam parameter data, and current support pipe parameter data, calculate the safe bearing weight of the current support platform through mechanical formulas.

[0019] By adopting the above technical solution, the present application uses a camera installed on the hanging beam platform to capture an image of the current support platform to obtain the actual configuration of the current support platform, inputs the captured current support platform image into a preset structure analysis model, and the model generates the structure data of the current support platform based on the image data, including structure layout information and structure size information. Then, based on the generated structure data, preset cross-shaped beam parameter data, and current support pipe parameter data, calculate the safe bearing weight of the current support platform through mechanical formulas. Therefore, the safe bearing weight of the current support platform can be accurately calculated, reducing human error and improving the reliability and efficiency of the transplantation process.

[0020] Optionally, calculating the safe bearing weight of the current support platform through mechanical formulas based on the structure data, preset cross-shaped beam parameter data, and current support pipe parameter data specifically includes the following steps:

[0021] Process the structure data, preset cross-shaped beam parameter data, and current support pipe parameter data based on a preset structure strength mapping rule to obtain the first relationship information between the structure strength and the assembly position of the cross-shaped beam, and the second relationship information between the structure strength and the support pipe size;

[0022] Calculate the safe bearing weight of the current support platform based on the first relationship information and the second relationship information.

[0023] By adopting the above technical solution, since the assembly position of the grid beam directly affects the structural strength distribution of the entire support platform, the assembly at different positions will affect the stress state of the entire platform, and thus affect the safe load-bearing capacity of the platform; on the other hand, the size of the support pipe directly affects the load-bearing capacity of the support platform, and support pipes of different sizes can withstand different loads. Therefore, it is necessary to adjust the size of the support pipe according to actual requirements to ensure the safe load-bearing capacity of the platform. In this application, by processing the structural data, the grid beam parameter data, and the support pipe parameter data based on the structural strength mapping rules, the first relationship information between the structural strength and the assembly position of the grid beam, and the second relationship information between the structural strength and the support pipe size are obtained. By obtaining the first relationship information, the contribution degree of the grid beam to the platform structural strength under different assembly positions can be determined. By obtaining the second relationship information, the contribution degree of the support pipe to the platform structural strength under different sizes can be determined, so as to find the optimal support pipe size. Finally, the safe load-bearing weight of the current support platform is calculated, which can ensure the safety and effectiveness of the transplantation process.

[0024] Optionally, the structural data includes structural layout information and structural size information, and the structural analysis model is generated in the following manner:

[0025] Collect images of the support platform under different configurations and the corresponding structural data, where the structural data includes structural layout information and structural size information;

[0026] Design and implement a series of structural tests, and record the structural performance data under different configurations;

[0027] Based on the images of the support platform under different configurations and the corresponding structural performance data, train a preset machine learning model to generate a structural analysis model.

[0028] By adopting the above technical solution, this application uses a camera to take images of the support platform under different configurations, and records the structural data under each configuration, including structural layout information and structural size information. Then, a series of structural tests are designed in advance to simulate the stress conditions of the support platform under different configuration conditions, and the structural performance data under different configurations, such as load-bearing capacity and deformation amount, are recorded. The collected images of the support platform and their corresponding structural data and structural performance data are input into a preset machine learning model for training to obtain a structural analysis model, so that the trained structural analysis model can generate the structural data of the current support platform according to the current support platform image.

[0029] Optionally, compare the safe load-bearing weight with the overall weight of the big tree. If the overall weight of the big tree is greater than the safe load-bearing weight, then generate an assembly mode that meets the overall weight of the big tree according to the comparison result, which specifically includes the following steps:

[0030] Compare the safe load-bearing weight with the overall weight of the big tree;

[0031] If the overall weight of the big tree is greater than the safe load-bearing weight, obtain the target load-bearing weight according to the overall weight of the big tree;

[0032] According to the target load-bearing weight, the preset cross-shaped beam parameter data, and all the support pipe parameter data in the alternative library, determine the set of assembly modes that meet the target load-bearing weight;

[0033] In the set of assembly modes, according to the preset sorting rules, finally generate the assembly mode that meets the overall weight of the big tree.

[0034] By adopting the above technical solution, during the actual transplantation process, if the overall weight of the big tree and its soil ball exceeds the safe load-bearing weight of the existing support platform, the existing platform cannot automatically adjust the assembly mode to adapt to different load-bearing requirements, which may lead to problems such as unstable structure or insufficient load-bearing during the transplantation process. In this application, the target load-bearing weight is obtained according to the overall weight of the big tree, combined with the preset cross-shaped beam parameter data and all the support pipe parameter data in the alternative library, the set of assembly modes that meet the target load-bearing weight is determined, and according to the preset sorting rules, finally the assembly mode that meets the overall weight of the big tree is generated, so as to be able to adjust the assembly mode in time and avoid the occurrence of unstable structure or accidents.

[0035] Optionally, in the set of assembly modes, according to the preset sorting rules, finally generating the assembly mode that meets the overall weight of the big tree specifically includes the following steps:

[0036] Evaluate each assembly mode in the set of assembly modes, where the evaluation factors include material utilization rate, required time, and structural stability;

[0037] Obtain the current spare materials, urgency level, and safety level, and determine the evaluation weights corresponding to each evaluation factor according to the current spare materials, urgency level, and safety level;

[0038] Perform weighted processing on each evaluation factor according to the evaluation weights corresponding to each evaluation factor to obtain the comprehensive score of each assembly mode;

[0039] Determine the assembly mode with the highest comprehensive score as the final assembly mode.

[0040] By adopting the above technical solution, the present application evaluates each assembly mode in the set of assembly modes, where the evaluation factors include material utilization rate, required time, and structural stability, obtains the current spare materials, urgency level, and safety level, determines the evaluation weights corresponding to each evaluation factor according to the current spare materials, urgency level, and safety level, performs weighted processing on each evaluation factor according to the evaluation weights corresponding to each evaluation factor to obtain the comprehensive score of each assembly mode, and determines the assembly mode with the highest comprehensive score as the final assembly mode, so that the assembly mode better matches the current situation of the big tree transplantation project.

[0041] In a second aspect, the present application provides a big tree transplantation method based on machine vision, which applies the above-mentioned prefabricated big tree transplantation device based on machine vision and includes the following steps:

[0042] Obtain the current support platform image captured by the camera, and calculate the safe bearing weight of the current support platform according to the current support platform image, the preset cross-shaped beam parameter data, and the current support pipe parameter data;

[0043] Obtain the big tree image captured by the camera, estimate the big tree volume and the soil ball volume according to the big tree image, and estimate the overall weight of the big tree according to the big tree volume, the soil ball volume, and the corresponding preset density;

[0044] Compare the safe bearing weight and the overall weight of the big tree. If the overall weight of the big tree is greater than the safe bearing weight, generate an assembly mode that meets the overall weight of the big tree according to the comparison result, and send an alarm signal through the alarm. The assembly mode includes the assembly position of the cross-shaped beam, the size and quantity of the support pipes;

[0045] Convey the assembly mode to the construction personnel according to the preset mode, so that the construction personnel can reassemble the support frame and the support platform according to the assembly mode.

[0046] Optionally, obtaining the current support platform image captured by the camera and calculating the safe bearing weight of the current support platform according to the current support platform image, the preset cross-shaped beam parameter data, and the current support pipe parameter data specifically includes the following steps:

[0047] Obtain the current support platform image captured by the camera, input the current support platform image into a preset structure analysis model, and generate the structure data of the current support platform based on the current support platform image;

[0048] Based on the structure data, the preset cross-shaped beam parameter data, and the current support pipe parameter data, calculate the safe bearing weight of the current support platform through mechanical formulas.

[0049] Optionally, based on the structural data, preset cross-shaped beam parameter data, and current support pipe parameter data, calculate the safe load-bearing weight of the current support platform through mechanical formulas, specifically including the following steps:

[0050] Process the structural data, preset cross-shaped beam parameter data, and current support pipe parameter data based on a preset structural strength mapping rule to obtain first relationship information between structural strength and the assembly position of the cross-shaped beam, and second relationship information between structural strength and the support pipe size;

[0051] Calculate the safe load-bearing weight of the current support platform based on the first relationship information and the second relationship information.

[0052] Optionally, the structural data includes structural layout information and structural size information, and the structural analysis model is generated in the following manner:

[0053] Collect images of the support platform and corresponding structural data under different configurations, where the structural data includes structural layout information and structural size information;

[0054] Design and implement a series of structural tests, and record the structural performance data under different configurations;

[0055] Train a preset machine learning model based on the images of the support platform under different configurations and the corresponding structural performance data to generate a structural analysis model.

[0056] In summary, the present application includes at least one of the following beneficial technical effects:

[0057] 1. The present application uses an assembled cross-shaped beam to construct a support frame. By adjusting the assembled connection points on the cross-shaped beam, the size of the support frame can be easily changed to fit different-sized soil balls. First, use a camera installed on the hanging beam platform to take an image of the current support platform, and the processor calculates the safe load-bearing weight of the current support platform based on the image data and the preset cross-shaped beam parameters; secondly, estimate the volume of the big tree and its soil ball by taking a picture of the big tree, and estimate the overall weight according to the preset density; then compare the safe load-bearing weight with the overall weight of the big tree. If the overall weight of the big tree exceeds the safe load-bearing weight, the processor generates a new assembly mode and notifies the construction personnel through an alarm; finally, convey the assembly mode to the construction personnel to guide them to reassemble the support frame and the support platform, thereby improving the success rate of big tree transplantation;

[0058] 2. This application uses a camera installed on the hanging beam platform to capture an image of the current support platform to obtain the actual configuration of the current support platform. The captured image of the current support platform is input into a preset structural analysis model. The model generates structural data of the current support platform based on the image data, including structural layout information and structural dimension information. Then, based on the generated structural data, the preset cross-shaped beam parameter data, and the current support pipe parameter data, the safe bearing weight of the current support platform is calculated through mechanical formulas. Therefore, the safe bearing weight of the current support platform can be accurately calculated, reducing human error and improving the reliability and efficiency of the transplantation process;

[0059] 3. During the actual transplantation process, if the overall weight of the big tree and its soil ball exceeds the safe bearing weight of the existing support platform, and the existing platform cannot automatically adjust the assembly mode to adapt to different bearing requirements, it may lead to problems such as structural instability or insufficient bearing capacity during the transplantation process. This application determines the set of assembly modes that meet the target bearing weight by obtaining the target bearing weight according to the overall weight of the big tree, combining the preset cross-shaped beam parameter data and all support pipe parameter data in the alternative library, and finally generates the assembly mode that meets the overall weight of the big tree according to the preset sorting rules, so as to be able to adjust the assembly mode in time and avoid the occurrence of structural instability or accidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a schematic structural diagram of an assembly-type big tree transplantation device based on machine vision according to an embodiment of this application;

[0061] Figure 2 is a schematic flow chart of a big tree transplantation method based on machine vision executed by a processor according to an embodiment of this application;

[0062] Figure 3 is a schematic flow chart of step S210 in the big tree transplantation method based on machine vision executed by a processor according to an embodiment of this application;

[0063] Figure 4 is a schematic flow chart of step S212 in the big tree transplantation method based on machine vision executed by a processor according to an embodiment of this application;

[0064] Figure 5 is a schematic flow chart of step S230 in the big tree transplantation method based on machine vision executed by a processor according to an embodiment of this application;

[0065] Figure 6 is a schematic flow chart of step S234 in the big tree transplantation method based on machine vision executed by a processor according to an embodiment of this application;

[0066] Figure 7 is an internal structural diagram of an electronic device according to an embodiment of this application.

[0067] Description of the reference numerals: 1. Grid beam; 11. Prefabricated connection point; 12. Main beam; 121. Connection hole; 13. Secondary beam; 14. Stiffening rib; 2. Support pipe; 3. Suspension beam platform; 4. Rope; 5. Sleeve. Specific embodiments

[0068] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term " / and" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0069] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0070] The embodiments of the present application will be further described in detail below with reference to the accompanying drawings of the specification.

[0071] In a first aspect, the present application provides a prefabricated large tree transplanting device based on machine vision. Referring to Figure 1 , it includes an assemblable grid beam 1, a plurality of support pipes 2, and a suspension beam platform 3. Among them, the grid beam 1 can be assembled to form a support frame, and a plurality of support pipes 2 are arranged on the support frame to form a support platform for carrying the transplanted large tree and its soil ball; the lower end of the suspension beam platform 3 is connected to the support frame through a rope 4, and the upper end is used to connect a hoisting device.

[0072] In this embodiment, the grid beam 1 includes two main beams 12 and two secondary beams 13. Both the main beam 12 and the secondary beam 13 are H-shaped steel I-beams. In order to improve the strength of the main beam 12, stiffening ribs 14 are provided on both sides of the H-shaped steel. A plurality of prefabricated connection points 11 are provided on one side of the two main beams 12 facing each other, and connection holes 121 matching the prefabricated connection points 11 are provided at both ends of the secondary beam 13. The main beam 12 and the secondary beam 13 are connected by bolts to form a support frame, and the size of the support frame can be changed by adjusting the assembly position. The support pipes 2 are arranged on the support frame. Sleeves 5 matching the support pipes 2 are connected to both ends of the support pipes 2. A plurality of sleeves 5 are fixedly connected to join a plurality of support pipes 2 into a support surface and are fixed to the support frame by a chain or other means to form a support platform.

[0073] Further, referring to Figure 2 , a processor, a camera, and an alarm are provided at the lower end of the suspension beam platform. The processor executes the following big tree transplantation method based on machine vision:

[0074] S210. Obtain the current support platform image captured by the camera, and calculate the safe load-bearing weight of the current support platform according to the current support platform image, the preset cross-shaped beam parameter data, and the current support pipe parameter data.

[0075] In this embodiment, a camera is provided at the lower end of the suspension beam platform for taking images of the current support platform. The camera can be a high-definition camera that can capture all details of the support platform. The processor receives the image captured by the camera and calculates the safe load-bearing weight of the current support platform according to the image data, the preset cross-shaped beam parameter data, and the current support pipe parameter data.

[0076] Specifically, the image data includes the layout and dimension information of the support platform. The processor identifies the key components of the support platform through image processing algorithms, such as the position of the cross-shaped beam and the number and dimensions of the support pipes. The preset cross-shaped beam parameter data includes the material properties, cross-sectional dimensions, and length information of the beam. The current support pipe parameter data includes the material properties, diameter, and wall thickness information of the support pipe. The processor uses mechanical formulas and structural analysis models to calculate the safe load-bearing weight of the current support platform according to the image data and parameter data. For example, through the finite element analysis method, the processor can calculate the stress distribution of the support platform under different load conditions, thereby obtaining the safe load-bearing weight.

[0077] S220. Obtain the big tree image captured by the camera, estimate the volume of the big tree and the volume of the soil ball according to the big tree image, and estimate the overall weight of the big tree according to the volume of the big tree, the volume of the soil ball, and the corresponding preset density.

[0078] In this embodiment, the camera on the suspension beam platform is also used to take images of the big tree and its soil ball for estimating the volume of the big tree and its soil ball; the processor receives the big tree image and estimates the volume of the big tree and the volume of the soil ball according to the image data.

[0079] Specifically, the big tree image is analyzed through image processing algorithms to identify the trunk, branches and leaves of the big tree, as well as the shape and size of the soil ball. Through the size ratio in the image and known reference sizes such as ground markings, the volume of the big tree and the soil ball can be estimated; then, according to the preset density data such as the average density of the tree and the soil, the total volume of the big tree and its soil ball is estimated, and the overall weight of the big tree is calculated.

[0080] It can be understood that when obtaining the weight of a large tree through a camera placed on the hanging beam platform, if the large tree has been transplanted onto the grid beam and it is found that the size of the device is inappropriate at this time, it is difficult to readjust the device. Therefore, the camera needs to capture images of the large tree and its soil ball before the large tree is transplanted onto the grid beam. When taking the pictures, the support platform can be captured at the same time. At this time, the known size of the main beam can be used as a reference size to estimate the volume of the large tree and the soil ball.

[0081] S230. Compare the safe load-bearing weight with the overall weight of the large tree. If the overall weight of the large tree is greater than the safe load-bearing weight, generate an assembly mode that meets the overall weight of the large tree according to the comparison result, and send an alarm signal through the alarm. The assembly mode includes the assembly position of the grid beam, the size and quantity of the support pipes.

[0082] In this embodiment, the processor compares the calculated safe load-bearing weight with the overall weight of the large tree. If the overall weight of the large tree is greater than the safe load-bearing weight, the processor generates a new assembly mode and sends an alarm signal through the alarm.

[0083] Specifically, the comparison result will determine whether it is necessary to adjust the assembly mode of the support platform. If it is found that the current platform is not sufficient to bear the overall weight of the large tree, the processor will generate a new assembly mode. The new assembly mode includes adjusting the assembly position of the grid beam, increasing or decreasing the quantity of the support pipes, and adjusting the size of the support pipes. The alarm reminds the construction personnel through sound or light signals, and at the same time, when conditions permit, such as when there is a display screen, specific adjustment suggestions are displayed.

[0084] S240. Transmit the assembly mode to the construction personnel according to a preset mode, so that the construction personnel can reassemble the support frame and the support platform according to the assembly mode.

[0085] In this embodiment, the processor transmits the generated assembly mode to the construction personnel according to a preset format, and the construction personnel reassemble the support frame and the support platform according to the assembly mode.

[0086] Specifically, the assembly mode is transmitted to the construction personnel through a display screen, printing, or other communication means. This information includes the new assembly position of the grid beam, the new size and quantity of the support pipes. The construction personnel adjust the position of the grid beam according to the received information and replace or add support pipes of corresponding sizes to ensure that the support platform can bear the overall weight of the large tree. Further, for the convenience of the construction personnel to understand, the assembly mode can include detailed graphic and text descriptions to ensure that the construction personnel can correctly perform the adjustment operation.

[0087] In one embodiment, refer to Figure 3, in step S210, obtain the current support platform image captured by the camera, and calculate the safe load-bearing weight of the current support platform according to the current support platform image, the preset well-shaped beam parameter data, and the current support pipe parameter data, which specifically includes the following steps:

[0088] S211. Obtain the current support platform image captured by the camera, input the current support platform image into a preset structure analysis model, and generate the structure data of the current support platform based on the current support platform image.

[0089] Among them, the structure analysis model is generated in the following way. First, collect the support platform images and corresponding structure data under different configurations. The structure data includes structure layout information and structure size information; record the structure performance data under different configurations according to a series of pre-designed and implemented structure tests; then, based on the support platform images and corresponding structure performance data under different configurations, train a preset machine learning model to generate the structure analysis model.

[0090] S212. Calculate the safe load-bearing weight of the current support platform through mechanical formulas based on the structure data, the preset well-shaped beam parameter data, and the current support pipe parameter data.

[0091] In this embodiment, the processor calculates the safe load-bearing weight of the current support platform through mechanical formulas based on the generated structure data, the preset well-shaped beam parameter data, and the current support pipe parameter data; among them, the preset well-shaped beam parameter data includes the material properties, cross-sectional dimensions, and length information of the beam, and the current support pipe parameter data includes the material properties, diameter, and wall thickness information of the support pipe.

[0092] In one embodiment, referring to Figure 4 , in step S212, calculate the safe load-bearing weight of the current support platform through mechanical formulas based on the structure data, the preset well-shaped beam parameter data, and the current support pipe parameter data, which specifically includes the following steps:

[0093] S2121. Process the structure data, the preset well-shaped beam parameter data, and the current support pipe parameter data based on the preset structure strength mapping rules to obtain the first relationship information between the structure strength and the assembly position of the well-shaped beam, and the second relationship information between the structure strength and the support pipe size.

[0094] Among them, since the assembly position of the well-shaped beam directly affects the structure strength distribution of the entire support platform, different assembly positions will affect the force state of the entire platform, and thus affect the safe load-bearing capacity of the platform; on the other hand, the size of the support pipe directly affects the load-bearing capacity of the support platform, and support pipes of different sizes can bear different loads. Therefore, it is necessary to adjust the size of the support pipe according to actual needs to ensure the safe load-bearing capacity of the platform.

[0095] Specifically, by processing the structural data, the parameters of the grid beam, and the parameters of the support pipe based on the structural strength mapping rules, the first relationship information between the structural strength and the assembly position of the grid beam, and the second relationship information between the structural strength and the size of the support pipe are obtained.

[0096] S2122. Calculate the safe load-bearing weight of the current support platform based on the first relationship information and the second relationship information.

[0097] Specifically, by obtaining the first relationship information, the contribution degree of the grid beam to the platform structural strength under different assembly positions can be determined. By obtaining the second relationship information, the contribution degree of the support pipe to the platform structural strength under different sizes can be determined, so as to find the optimal size of the support pipe. Finally, by calculating the safe load-bearing weight of the current support platform, the safety and effectiveness of the transplantation process can be ensured.

[0098] In one embodiment, referring to Figure 5 , in step S230, compare the safe load-bearing weight with the overall weight of the big tree. If the overall weight of the big tree is greater than the safe load-bearing weight, generate an assembly mode that meets the overall weight of the big tree according to the comparison result, which specifically includes the following steps:

[0099] S231. Compare the safe load-bearing weight with the overall weight of the big tree.

[0100] In this embodiment, the processor compares the overall weight of the big tree obtained with the previously calculated safe load-bearing weight of the current support platform to confirm whether the current support platform can safely carry the big tree to be transplanted and its soil ball.

[0101] S232. If the overall weight of the big tree is greater than the safe load-bearing weight, obtain the target load-bearing weight according to the overall weight of the big tree.

[0102] In this embodiment, if the processor finds that the overall weight of the big tree is greater than the safe load-bearing weight of the current support platform, a new target load-bearing weight needs to be obtained according to the overall weight of the big tree. The target load-bearing weight refers to the minimum safe load-bearing capacity that can carry the overall weight of the big tree and leave a safe weight threshold.

[0103] Specifically, the target load-bearing weight is usually slightly higher than the overall weight of the big tree to ensure a certain safety margin. For example, the target load-bearing weight is set to 1.1 times or higher of the overall weight of the big tree; the setting of the target load-bearing weight also considers the long-term stability of the support platform and the construction conditions. For example, in actual operation, the possible future additional loads may be considered, so as to appropriately increase the target load-bearing weight.

[0104] S233. Determine the set of assembly modes that meet the target load-bearing weight based on the target load-bearing weight, the preset cross-shaped beam parameter data, and all the support pipe parameter data in the alternative library.

[0105] In this embodiment, the processor generates a set of assembly modes that meet the target load-bearing weight according to the target load-bearing weight, the preset cross-shaped beam parameter data, and all the support pipe parameter data in the alternative library. The set of assembly modes includes various possible adjustment schemes to ensure that the support platform can meet the new load-bearing requirements.

[0106] Specifically, the processor generates multiple possible assembly modes according to the requirements of the target load-bearing weight, in combination with the parameter data of the cross-shaped beam and the support pipe. For example, adjust the position of the cross-shaped beam, increase or decrease the number of support pipes, or change the size of the support pipes to ensure that the support platform can bear the target load-bearing weight. The processor will search for all eligible support pipe parameter data in the alternative library and try different combinations to generate multiple possible assembly modes.

[0107] S234. In the set of assembly modes, finally generate the assembly mode that meets the overall weight of the big tree according to the preset sorting rules.

[0108] In this embodiment, after generating multiple possible assembly modes, the processor needs to select an optimal assembly mode according to the preset sorting rules.

[0109] Specifically, the sorting rules can be set according to multiple factors, such as cost, material utilization rate, structural stability, etc. For example, the processor can select the assembly mode with the lowest cost while ensuring that its structural stability meets the requirements. This can not only save costs but also ensure safety. In addition, the processor can also consider the convenience of construction and select the assembly mode that is easiest to construct. For example, if an assembly mode only requires a small amount of adjustment to meet the requirements of the target load-bearing weight, then this mode may be preferentially selected. The finally generated assembly mode will be notified to the construction personnel through an alarm and conveyed to the construction personnel through a display screen or printing, etc., so that they can re-assemble the support frame and support platform according to the new assembly mode.

[0110] In one embodiment, referring to Figure 6 , in step S234, in the set of assembly modes, finally generate the assembly mode that meets the overall weight of the big tree according to the preset sorting rules, which specifically includes the following steps:

[0111] S2341. Evaluate each assembly mode in the set of assembly modes, where the evaluation factors include material utilization rate, required time, and structural stability.

[0112] In this embodiment, the evaluation factors mainly include material utilization rate, required time, and structural stability.

[0113] Specifically, the material utilization rate refers to the ratio of the amount of materials required for the assembly mode to the existing materials, which helps to reduce waste and lower costs. The required time refers to the construction period required to complete the assembly mode, which directly affects the project schedule. Structural stability measures whether the assembly mode can safely support the overall weight of the big tree, which is the most basic and important evaluation criterion. For example, for the evaluation of the material utilization rate, it can be achieved by calculating the ratio of the amount of materials required for each assembly mode to the total amount of materials; for the evaluation of the required time, it can be carried out by estimating the number of working hours required under each assembly mode; and for the evaluation of structural stability, it can be completed by simulating the load-bearing conditions of various assembly modes through simulation software.

[0114] S2342. Obtain the current spare materials, urgency level, and safety level, and determine the evaluation weights corresponding to each evaluation factor according to the current spare materials, urgency level, and safety level.

[0115] In this embodiment, the system will collect the current spare material information, the urgency level of the project, and the safety level, so as to determine which factors should be given higher weights in the evaluation process.

[0116] Specifically, the information of the current spare materials is used to determine the importance of the material utilization rate. If the existing materials are limited, the weight of the material utilization rate is increased, and vice versa. The urgency level reflects the time pressure of the project. If the project is time-consuming, the weight of the evaluation factor of the required time will increase. The safety level determines the importance of structural stability. For projects with a high safety level, structural stability will become the most important evaluation factor. For example, if the current spare materials are less, the system may give a higher weight to the material utilization rate; if the project is in an emergency state, the weight of the required time will also increase accordingly; and for projects with a high safety level, the weight of structural stability is naturally the highest.

[0117] S2343. Perform weighted processing on each evaluation factor according to the evaluation weights corresponding to each evaluation factor to obtain the comprehensive score of each assembly mode.

[0118] Specifically, after determining the weights of each evaluation factor, perform weighted processing on these evaluation factors to calculate the comprehensive score of each assembly mode.

[0119] S2344. Determine the assembly mode with the highest comprehensive score as the final assembly mode.

[0120] Specifically, the system compares the comprehensive scores of all calculated assembly modes, finds the one with the highest score, marks it as the final selection, and the system will generate corresponding instructions to guide the construction team to operate according to this assembly mode. For example, if after calculation, the comprehensive score of a certain assembly mode reaches 8.5 points, while others are below 8 points, then this assembly mode will be selected as the final assembly mode. The construction team will receive the specific details of this assembly mode, including the bill of materials required, assembly steps, and any special precautions, to ensure that the assembly task can be completed smoothly.

[0121] It should be understood that the magnitudes of the sequence numbers of the steps in the above embodiments do not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0122] In a second aspect, the present application provides a big tree transplantation method based on machine vision, which is applied to the above-mentioned prefabricated big tree transplantation device based on machine vision. The method is used to execute the steps performed by the processor of the above-mentioned prefabricated big tree transplantation device based on machine vision.

[0123] In one embodiment, the present application provides an electronic device, which is arranged on the hanging beam platform of the big tree transplantation device. The internal structure diagram of this electronic device can be as Figure 7 shown. The electronic device includes a processor, a memory, and a network interface connected through a system bus. Among them, the processor of the electronic device is used to provide computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the electronic device is used to store data. The network interface of the electronic device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, it implements a big tree transplantation method based on machine vision.

[0124] Those skilled in the art can understand that Figure 7 the structure shown in

[0125] is only a block diagram of some structures related to the solution of the present application, and does not constitute a limitation to the electronic device to which the solution of the present application is applied. The specific electronic device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0126] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The above computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above various methods. Among them, any reference to a memory, storage, database, or other medium used in the various embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0127] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereby. Therefore, all equivalent changes made according to the structure, shape, and principle of the present application should be covered within the protection scope of the present application.

Claims

1. A machine vision-based assembled tree transplanting device, characterized in that: include: An assemblable crisscross beam (1), wherein the crisscross beam (1) can be assembled to form a supporting frame; A plurality of support tubes (2) are arranged on the support frame to form a support platform for carrying the transplanted tree and the soil ball; A hanging beam platform (3), the lower end of which is connected to the supporting frame via a rope (4), and the upper end of which is used to connect to the hanging equipment; Wherein, the cross-shaped beam (1) is provided with a plurality of assembly connection points (11) for changing the size of the support frame by adjusting the assembly position; The hanging beam platform (3) is provided with a processor, a camera and an alarm, and the processor performs the following steps: Acquire a current support platform image captured by a camera, input the current support platform image into a preset structural analysis model, and generate structural data of the current support platform based on the current support platform image; wherein the structural analysis model is generated by: collecting support platform images and corresponding structural data under different configurations, wherein the structural data includes structural layout information and structural dimension information; designing and implementing a series of structural tests, and recording structural performance data under different configurations; based on the support platform images and corresponding structural performance data under different configurations, training a preset machine learning model to generate a structural analysis model; Processing the structural data, preset cross-shaped beam parameter data and current support tube parameter data based on a preset structural strength mapping rule, obtaining first relationship information between the structural strength and the assembly position of the cross-shaped beam, and second relationship information between the structural strength and the size of the support tube, wherein the first relationship information determines the contribution degree of the cross-shaped beam to the structural strength of the platform at different assembly positions, and the second relationship information determines the contribution degree of the support tube to the structural strength of the platform at different sizes; Calculating the safe bearing weight of the current support platform based on the first relationship information and the second relationship information; Obtain an image of a tree taken by a camera, estimate the volume of the tree and the volume of a soil ball according to the image of the tree, and estimate the overall weight of the tree according to the volume of the tree, the volume of the soil ball and the corresponding preset density; Comparing the safe bearing weight with the overall weight of the tree, if the overall weight of the tree is greater than the safe bearing weight, generating an assembly mode that satisfies the overall weight of the tree according to the comparison result, and sending an alarm signal through the alarm, wherein the assembly mode includes the assembly position of the cross-shaped beam, the size and quantity of the support pipe; The assembly mode is communicated to the construction personnel according to a preset mode, so that the construction personnel can reassemble the support frame and the support platform according to the assembly mode.

2. The machine vision-based assembled tree transplanting device according to claim 1 is characterized in that: The safe bearing weight and the overall weight of the tree are compared. If the overall weight of the tree is greater than the safe bearing weight, an assembly mode that satisfies the overall weight of the tree is generated according to the comparison result, which specifically includes the following steps: Comparing the safe bearing weight with the entire weight of the tree; If the overall weight of the tree is greater than the safe bearing weight, obtaining a target bearing weight according to the overall weight of the tree; Determine an assembly mode set that meets the target load-bearing weight according to the target load-bearing weight, preset cross-shaped beam parameter data and all support pipe parameter data in the candidate library; In the assembly pattern set, according to a preset sorting rule, an assembly pattern that satisfies the overall weight of the tree is finally generated.

3. The machine vision-based assembled tree transplanting device according to claim 2 is characterized in that: In the assembly pattern set, according to the preset sorting rules, an assembly pattern that satisfies the overall weight of the tree is finally generated, which specifically includes the following steps: Evaluate each assembly mode in the assembly mode set, wherein evaluation factors include material utilization, required time, and structural stability; Obtaining current backup materials, urgency and safety level, and determining the evaluation weights corresponding to various evaluation factors according to the current backup materials, urgency and safety level; Each evaluation factor is weighted according to its corresponding evaluation weight to obtain a comprehensive score for each assembly mode; The assembly mode with the highest comprehensive score is determined as the final assembly mode.

4. A large tree transplanting method based on machine vision, applied to the assembled large tree transplanting device based on machine vision as claimed in claim 1, characterized in that: The steps include: Acquire a current support platform image captured by a camera, input the current support platform image into a preset structural analysis model, and generate structural data of the current support platform based on the current support platform image; wherein the structural analysis model is generated by: collecting support platform images and corresponding structural data under different configurations, wherein the structural data includes structural layout information and structural dimension information; designing and implementing a series of structural tests, and recording structural performance data under different configurations; based on the support platform images and corresponding structural performance data under different configurations, training a preset machine learning model to generate a structural analysis model; Processing the structural data, preset cross-shaped beam parameter data and current support tube parameter data based on a preset structural strength mapping rule, obtaining first relationship information between the structural strength and the assembly position of the cross-shaped beam, and second relationship information between the structural strength and the size of the support tube, wherein the first relationship information determines the contribution degree of the cross-shaped beam to the structural strength of the platform at different assembly positions, and the second relationship information determines the contribution degree of the support tube to the structural strength of the platform at different sizes; Calculating the safe bearing weight of the current support platform based on the first relationship information and the second relationship information; Obtain an image of a tree taken by a camera, estimate the volume of the tree and the volume of a soil ball according to the image of the tree, and estimate the overall weight of the tree according to the volume of the tree, the volume of the soil ball and the corresponding preset density; Comparing the safe bearing weight with the overall weight of the tree, if the overall weight of the tree is greater than the safe bearing weight, generating an assembly mode that satisfies the overall weight of the tree according to the comparison result, and sending an alarm signal through the alarm, wherein the assembly mode includes the assembly position of the cross-shaped beam, the size and quantity of the support pipe; The assembly mode is communicated to the construction personnel according to a preset mode, so that the construction personnel can reassemble the support frame and the support platform according to the assembly mode.

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