Intelligent decision-making method for steel grabbing machine in bulk material scene of steel scrap yard

Abstract

The invention relates to an intelligent decision-making method for a steel grabbing machine in a bulk material scene of a scrap steel stock yard, and belongs to the technical field of steel grabbing decision-making, and the method comprises the steps: S1, comprehensively considering the single-bucket grabbing rate and the single-time grabbing movement time, and selecting a grabbing point, specifically, introducing a grabbing window and a mass distribution value, analyzing the three-dimensional feature distribution of bulk materials, and calculating the single-bucket grabbing rate; calculating the moving time of the steel grabbing machine during grabbing; the single-bucket grabbing rate and the single-time grabbing moving time are comprehensively considered, double-target optimization is carried out, and the point with the highest weighted score is selected as a grabbing point; s2, placing points are selected by comprehensively considering manual operation experience and safety operation rule requirements, and the method comprises the following two selection modes: mode 1: for the manual experience, placing points are selected from the rear part of a truck carriage forwards during placing; and mode 2: aiming at the requirements of safety operation rules, considering the defects of hardware equipment in a scrap steel stock yard, judging whether a stock pile shielding sensing condition exists from front to back, and selecting a placement point.

Description

Technical Field

[0001] The present invention belongs to the technical field of steel grabbing decision-making, and relates to an intelligent decision-making method for a steel grabbing machine in the bulk material scenario of a scrap yard. Background Art

[0002] With the continuous acceleration of the industrialization process and urbanization construction, the demand for steel has also been continuously expanding. As an important downstream base for steel processing, the internal operation burden of the scrap yard has also been continuously increasing. At present, the degree of intelligentization in the steel industry is constantly deepening, and the scrap yard is gradually developing towards the direction of intelligentization, and the unmanned operation environment has become a new direction. The unmanned treatment of the scrap yard can improve operation efficiency, reduce labor costs, and reduce the safety risks of personnel operation. In the field of scrap steel treatment, unmanned steel grabbing machines have begun to be widely used. The core of the autonomous operation of an unmanned steel grabbing machine lies in automatically selecting the grabbing points and placement points of scrap steel.

[0003] The operation efficiency of the scrap yard depends heavily on the grabbing and placement operations of the steel grabbing machine. Bulk materials usually show irregular accumulations, with large volumes and complex shapes. When the steel grabbing machine is performing the grabbing operation, the key lies in finding the points within the grabable range that are relatively raised in the material pile; when performing the placement operation, the key lies in finding a safe position in the truck carriage for placement to ensure that the waste materials will not leak or collapse, causing safety hazards.

[0004] At present, when manual operation of a steel grabbing machine is performing continuous high-intensity bulk material loading and unloading operations, the following multiple problems may occur:

[0005] (1) Efficiency problem: Manual operation of the steel grabbing machine overly relies on experience, and the degree of freedom in the operation process is relatively large, making it difficult to ensure that the points can be efficiently selected for operation every time;

[0006] (2) Safety problem: The safety risks faced by operators are relatively large. When operating the steel grabbing machine to handle scrap steel, there may be safety risks such as the falling of scrap steel. The stability and safety of the operation are affected due to operator fatigue or inconsistent skills;

[0007] (3) Cost problem: Over-reliance on manual operation brings high labor costs. Long-term physical labor not only increases the burden on personnel but also affects the overall cost-effectiveness. Summary of the Invention

[0008] In view of this, the purpose of the present invention is to provide an intelligent decision-making method for a steel grabbing machine in the bulk material scenario of a scrap yard.

[0009] To achieve the above purpose, the present invention provides the following technical solutions:

[0010] An intelligent decision-making method for a steel grabbing machine in the bulk material scenario of a scrap yard, including:

[0011] S1: Selection of Grabbing Points: Considering the single-bucket grabbing rate and the moving time for a single grab comprehensively, select the grabbing points, which specifically include: introducing a grabbing window and a mass distribution value, analyzing the three-dimensional feature distribution of the bulk materials, and calculating the single-bucket grabbing rate; calculating the moving time of the grab crane during grabbing; considering the single-bucket grabbing rate and the moving time for a single grab comprehensively, conducting a two-objective optimization, and selecting the point with the highest weighted score as the grabbing point;

[0012] S2: Selection of Placing Points: Select the placing points considering the manual operation experience and the requirements of safety operation rules, including the following two selection modes:

[0013] Mode 1: For manual experience, consider placing from the rear of the truck carriage forward to select the placing points;

[0014] Mode 2: For the requirements of safety operation rules, consider the hardware equipment defects in the scrap yard, and judge whether there is a situation of material pile occlusion perception from front to back to select the placing points.

[0015] Furthermore, the steps for selecting the grabbing points are as follows:

[0016] A1: Obtain the three-dimensional data of the bulk materials;

[0017] A2: Screen out the grabable material pile grid data according to the grabable range;

[0018] A3: Set the size of the grabbing window according to the opening area of the grab of the grab crane, and calculate the mass distribution characteristic values of the bulk materials within the grabbing window;

[0019] A4: Slide the grabbing window regularly through the sliding step length, and calculate the mass distribution characteristic values of the bulk materials in the entire bulk material area;

[0020] A5: Calculate the moving time from the grabbing point to the placing point;

[0021] A6: Conduct a two-objective optimization according to the mass distribution characteristic values of the bulk materials in the entire bulk material area and the moving time from the grabbing point to the placing point, calculate the weighted sum, and output the grabbing point with the largest score value.

[0022] Furthermore, the specific operations of steps A2 - A4 are as follows:

[0023] Calculate the distance from the material pile grid to the grab crane. If it is outside the maximum and minimum operating ranges of the grab crane, then eliminate the grid data of the material pile for the material pile grid data screening;

[0024] Fill the screened material pile grid data into the height vector;

[0025] Generate a grasping window based on the size of the grab bucket of the steel grabber, fix the sliding step length, start from the lower left corner of the height matrix, slide from left to right and from bottom to top, and find the information of the grasping window with the largest value, which is the optimal grasping window information.

[0026] Furthermore, the steps for selecting the placement point are as follows:

[0027] B1: Obtain the real-time data of the stockpile, the position information of the truck, and the information of the steel grabber and initialize them;

[0028] B2: Calculate the points on the central axis of the truck bed according to a certain sliding step length;

[0029] B3: Update the information of the available placement points according to the maximum and minimum operating ranges of the steel grabber;

[0030] B4: Determine whether the truck is full. If it is full, stop the calculation. Otherwise, select the placement point generation mode according to the need, including Mode 1 and Mode 2;

[0031] In Mode 1, calculate the average height of the grid in the placement window from back to front, and determine whether it exceeds the threshold. If it exceeds, slide forward and continue the calculation. Otherwise, output this point as the placement point;

[0032] In Mode 2, check whether the stockpile in the placement window exceeds the height threshold of the truck bed from the front to the back of the carriage. If it exceeds, it is considered that the subsequent data will be blocked, and select the point before this point as the placement point.

[0033] Furthermore, during the selection process of the grasping point and the placement point, after obtaining the grid data, first judge whether the data source is correct, check whether there are abnormal values. If the data source is incorrect or there are abnormal values, end the process and give priority to handling the errors.

[0034] The beneficial effects of the present invention are as follows:

[0035] Independence. This technology does not require manual intervention, can generate appropriate grasping points and placement points according to the input data, and realizes the independent and continuous operation of the unmanned steel grabber.

[0036] High efficiency. This technology has verified the correctness and effectiveness of the grasping points and placement points through tests, can improve the efficiency of the autonomous operation of the unmanned steel grabber, and reduce the labor cost.

[0037] Safety. This technology can realize the unmanned operation of the scrap steel yard during use, and reduce the potential safety hazards existing in manual operations.

[0038] Other advantages, objects, and features of the present invention will, in part, be set forth in the following description, and in part, will be obvious to those skilled in the art upon examination of the following, or may be learned from the practice of the present invention. The objects and other advantages of the present invention may be realized and obtained by the following description of the specification. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to make the objects, technical solutions, and advantages of the present invention clearer, the present invention will be described in detail below with reference to the accompanying drawings, where:

[0040] Figure 1 is a flowchart for selecting a grasping point;

[0041] Figure 2 is a flowchart for calculating a grasping point;

[0042] Figure 3 is a flowchart for writing a file;

[0043] Figure 4 is a flowchart for checking grid data;

[0044] Figure 5 is a flowchart for updating stockpile data;

[0045] Figure 6 is a flowchart for filling height data;

[0046] Figure 7 is a flowchart for screening the best window;

[0047] Figure 8 is a flowchart for calculating data of all grasping windows;

[0048] Figure 9 is a flowchart for calculating the eigenvalue of a single grasping window;

[0049] Figure 10 is a flowchart for calculating the eigenvalue of a grasping sub-window;

[0050] Figure 11 is a flowchart for calculating the eigenvalue of a grasping window;

[0051] Figure 12 is a flowchart for selecting a placement point;

[0052] Figure 13 is a flowchart for calculating a placement point;

[0053] Figure 14 is a flowchart for checking grid data;

[0054] Figure 15 is a flowchart for calculating a point position;

[0055] Figure 16Flow chart for determining whether it exceeds the boundary;

[0056] Figure 17 Flow chart for determining whether the truck is full;

[0057] Figure 18 Flow chart for Mode 1;

[0058] Figure 19 Flow chart for point position judgment;

[0059] Figure 20 Flow chart for Mode 2;

[0060] Figure 21 Flow chart for file writing;

[0061] Figure 22 Visual schematic diagram for gripper point test;

[0062] Figure 23 Visual schematic diagram for placement point test. Detailed implementation manners

[0063] The following uses specific specific examples to illustrate the implementation manners of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific implementation manners. The details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0064] It should be noted that the diagrams provided in the following embodiments only illustrate the basic concept of the present invention in a schematic manner. Therefore, only the components related to the present invention are shown in the diagrams, rather than being drawn according to the number, shape, and size of the components in actual implementation. The type, quantity, and ratio of each component in actual implementation can be arbitrarily changed, and the component layout type may also be more complex.

[0065] In the following description, a large number of details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0066] Embodiment 1:

[0067] The present invention provides an intelligent decision-making method for a steel grabber in the bulk material scenario of a scrap yard. Aiming at the problem of selecting the grabbing point and placing point of an unmanned steel grabber in the bulk material scenario, an optimization decision algorithm for the grabbing point based on three-dimensional feature distribution and an optimization decision algorithm for the placing point based on artificial rules are respectively designed to optimize the operation process of the unmanned steel grabber.

[0068] When selecting the grabbing point, this algorithm comprehensively considers the single-bucket grabbing rate and the single-grab moving time to improve the operation efficiency. For the single-bucket grabbing rate index, the algorithm introduces concepts such as the grabbing window and the mass distribution value to analyze the three-dimensional feature distribution of the bulk materials. At the same time, considering the moving time of the steel grabber during grabbing, comprehensive consideration is made according to the static data and dynamic data of the steel grabber, and two-objective optimization is carried out for these two indicators.

[0069] When selecting the placing point, this algorithm comprehensively considers the manual operation experience and the safety operation rules. For the manual experience, consider selecting the point from the rear of the truck carriage forward when placing. For the requirements of the safety operation rules, considering the hardware equipment defects in the scrap yard, it is proposed to judge whether there is a situation of material pile occlusion perception from front to back to optimize the point position.

[0070] The present invention can quickly generate appropriate grabbing points and placing points when the unmanned steel grabber operates autonomously, improving the efficiency of the overall operation process. During the operation of the steel grabber, no human intervention is required. Based on the static data such as the input material pile data and the dynamic data such as the position and attitude of the steel grabber, the selected grabbing points and placing points can be output autonomously to achieve autonomous operation. When selecting the grabbing point, mainly consider the three-dimensional feature information of the material pile, and find the point with the highest convexity as the candidate grabbing point. When selecting the placing point, comprehensively consider the manual operation experience and the actual operation safety rules to ensure that the placing point is correct and there are no safety hazards.

[0071] Embodiment 2:

[0072] In this embodiment, the overall process of the grabbing point selection algorithm is as Figure 1 shown. When calculating the grabbing point, comprehensively consider the single-bucket grabbing rate and the single-grab moving time. First, obtain the three-dimensional data of the bulk materials, screen out the grabable material pile grid data according to the grabable range, set the size of the grabbing window according to the opening area of the grab bucket of the steel grabber, and calculate the mass distribution characteristic value of the bulk materials within the grabbing window. Slide the grabbing window regularly through the sliding step length, and cover and record the entire bulk material area. For the single-grab moving time, the algorithm comprehensively considers the static data and dynamic data of the steel grabber, and calculates the moving time from the grabbing point to the placing point. Two-objective optimization is carried out for these two indicators, and the grabbing point with the largest score value is output after obtaining the weighted sum.

[0073] The flow of the grabbing point selection algorithm is as Figure 2As shown. The grab point algorithm first obtains inputs including empty coordinates, error information, trucks, material pile grid data, and feeding points, and then saves all data to a file for retention. Then the correctness of the input grid data value is calculated. If the input is an abnormal value, an error message is output and the calculation is terminated. Otherwise, the material pile data will be updated according to the maximum and minimum operating range of the steel grabber. The updated material pile grid data is filled into the height vector for storage. The grab window is generated with the size of the steel grabber bucket, the sliding step is fixed, and it starts from the lower left corner of the height matrix, slides from left to right and from bottom to top to obtain the optimal grab window information. The updated grid data, grab point information, truck and other data are stored in the file, and the calculation is terminated.

[0074] In a specific embodiment, the file writing process is as follows: Figure 3 As shown, when the scene data is obtained, the data needs to be written into a file for storage to facilitate subsequent archiving and recording.

[0075] In a specific embodiment, the grid data checking process is as follows: Figure 4 As shown, after obtaining the grid data, it is necessary to determine whether the data source is correct and process it by checking whether there are abnormal values.

[0076] In a specific embodiment, the process of updating the stockpile data is as follows: Figure 5 As shown, the grid data is processed, and the grid information within the operating range is filtered and saved according to the operable range of the steel grabber, and the remaining information is discarded.

[0077] In one specific embodiment, the filling height data flow is as follows Figure 6 As shown, the stockpile data is filled into a highly two-dimensional matrix for subsequent use.

[0078] In a specific embodiment, the optimal window screening process is as follows Figure 7 As shown, first calculate the characteristic value data of all grab windows, and then obtain the data of the best grab window, wherein all grab hook information is calculated, specifically, the data of all grab windows is calculated according to the sliding step rule, such as Figure 8 As shown, the calculation of the capture window information represents the calculation of the characteristic value data of each capture window. The specific process is as follows Figure 9 As shown. The process of calculating the characteristic value of each captured sub-window is as follows Figure 10 As shown, the process of calculating the characteristic value of the capture window is as follows Figure 11 shown.

[0079] Embodiment 3:

[0080] In this embodiment, the placement point selection process is as follows: Figure 12As shown in the figure, when selecting the placement points, first obtain the real-time data of the stockpile, the position information of the truck, the information of the grapple crane, etc. for initialization. Then calculate the points on the central axis of the truck bed according to a certain sliding step length. Next, update the available placement point information according to the maximum and minimum operating ranges of the grapple crane. Determine whether the truck is full. If it is full, stop the calculation. Otherwise, select the placement point generation mode as needed. In mode one, calculate the average height of the grid within the placement window from the back to the front and determine whether it exceeds the threshold. If it exceeds, slide forward and continue the calculation. Otherwise, output the point position. In mode two, check whether the stockpile within the placement window exceeds the height threshold of the truck bed from the front to the back of the carriage. If it exceeds, it is considered that the subsequent data will be blocked. Select the point before this point as the placement point and store the information in a file.

[0081] The placement point selection algorithm is as Figure 13 shown. First, calculate the correctness of the input grid data value. If the input is an abnormal value, output an error message and end the calculation. Otherwise, calculate the point position on the central axis of the carriage, determine whether the truck is full. If it is full, stop the calculation. Otherwise, perform the corresponding process according to the placement point generation mode, and finally output the placement point information.

[0082] In a specific embodiment, the grid data check process is as Figure 14 shown. After obtaining the grid data, it is necessary to determine whether the data source is correct and process it by checking for abnormal values.

[0083] In a specific embodiment, the point position calculation process is as Figure 15 shown. Calculate the points on the central axis of the truck bed for later use. Figure 15 In Figure 16 shown, the process of determining whether the position of the placement window exceeds the carriage boundary is as

[0084] In a specific embodiment, the process of determining whether the truck is full is as Figure 17 shown. Calculate and determine whether the stockpile data in the truck bed exceeds the height of the truck bed. Among them, determining whether the grid has intersections represents determining whether the grid is within the rectangle.

[0085] In a specific embodiment, as Figure 18 shown, mode one is to determine whether a placement point is found from the back to the front of the carriage. The process of determining whether the grids intersect is as Figure 19 shown.

[0086] In a specific embodiment, as Figure 20 shown, mode two is to determine whether the number of grids exceeding the height of the truck bed exceeds the threshold and optimize the placement points from the back to the front.

[0087] In a specific embodiment, the file writing process is as Figure 21As shown, writing data into a file for storage facilitates subsequent archival records.

[0088] In a specific embodiment, actual tests are conducted on the selection of the grasping points, and the algorithms are verified for stockpiles of different scales and shapes. For example, Figure 22 as shown, the red represents the position of the grasping window, and the colored three-dimensional broken line represents the stockpile. It can be seen that the grasping window is selected at the bulge of the stockpile, meeting the requirements of grasping efficiency and the limitation of the operation range.

[0089] When conducting actual tests on the placement points, the algorithms are verified for the data of carriages at different angles and different full-load levels. For example, Figure 23 as shown, the red rectangle represents the placement window, the blue rectangle represents the carriage, and the internal small grid represents the existing stockpile in the carriage. It can be seen that the safety placement requirements are met.

[0090] Embodiment 4:

[0091] An electronic device includes a memory and a processor;

[0092] The memory is used to store a computer program;

[0093] The processor is used to implement the method as described in Embodiment 1 when executing the computer program.

[0094] Embodiment 5:

[0095] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method as described in Embodiment 1 is implemented.

[0096] Embodiment 6:

[0097] A computer program product includes a computer program, and when the computer program is executed by a processor, the method as described in Embodiment 1 is implemented.

[0098] In the above embodiments, the reference to "this embodiment" in the specification means that the specific features, structures, or characteristics described in combination with the embodiments are included in at least some embodiments, but not necessarily all embodiments. The multiple occurrences of "this embodiment" do not necessarily all refer to the same embodiment.

[0099] In the above embodiments, although the present invention has been described in combination with specific embodiments of the present invention, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the previous description. For example, other storage structures (such as dynamic RAM (DRAM)) can be used in the discussed embodiments. The embodiments of the present invention are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims.

[0100] For the computer-readable storage medium in this embodiment, those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to a computer program. The aforementioned computer program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps including the above method embodiments; and the aforementioned storage medium includes: various media such as ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0101] The electronic terminal provided in this embodiment includes a processor, a memory, a transceiver, and a communication interface. The memory and the communication interface are connected to the processor and the transceiver and complete communication with each other. The memory is used to store a computer program, the communication interface is used for communication, and the processor and the transceiver are used to run the computer program so that the electronic terminal executes each step of the above method.

[0102] In this embodiment, the memory may include a random access memory (Random Access Memory, abbreviated as RAM), and may also include a non-volatile memory, such as at least one disk memory.

[0103] The above-mentioned processor can be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), etc.; it can also be a digital signal processor (Digital Signal Processing, abbreviated as DSP), an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), a field-programmable gate array (Field-Programmable Gate Array, abbreviated as FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0104] The present invention can be used in many general-purpose or special-purpose computing system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on.

[0105] The present invention may be described in the general context of computer-executable instructions, such as program modules, executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The present invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including storage devices.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the present technical solution, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An intelligent decision-making method for a steel grabber in a bulk material scene in a scrap steel yard, characterized by: include: S1: Grasping point selection: The grasping rate of a single bucket and the moving time of a single grasping are comprehensively considered to select the grasping points, including: introducing the grasping window and mass distribution value, analyzing the three-dimensional characteristic distribution of bulk materials, and calculating the grasping rate of a single bucket; calculating the moving time of the steel grabber during grasping; comprehensively considering the grasping rate of a single bucket and the moving time of a single grasping, performing dual-objective optimization, and selecting the point with the highest weighted score as the grasping point; S2: Placement point selection: The placement point is selected based on the manual operation experience and safety operation rule requirements, including the following two selection modes: Mode 1: Based on manual experience, the placement point is selected from the rear of the truck compartment to the front; Mode 2: In response to the requirements of safe operating rules, considering the defects of hardware equipment in the scrap steel yard, determine from front to back whether there is any obstruction in the perception of the material pile, and select the placement point.

2. The intelligent decision-making method for steel grabbers in bulk material scenarios in scrap steel yards according to claim 1 is characterized by: The steps of grabbing point selection are as follows: A1: Obtain bulk material 3D data; A2: Filter the grid data of the grabbable material pile according to the grabbable range; A3: Set the grab window size according to the grab bucket opening area of ​​the steel grabber, and calculate the mass distribution characteristic value of the bulk material in the grab window; A4: Slide the capture window regularly by sliding the step length to calculate the bulk material mass distribution characteristic value of the entire bulk material area; A5: Calculate the moving time from the grabbing point to the placing point; A6: Perform dual-objective optimization based on the bulk material mass distribution characteristic value of the entire bulk material area and the moving time from the grabbing point to the placement point, calculate the weighted sum, and output the grabbing point with the largest score.

3. The intelligent decision-making method for steel grabbers in bulk material scenarios in scrap steel yards according to claim 1 is characterized by: The specific operations of steps A2-A4 are as follows: Calculate the distance from the stockpile grid to the steel grabber. If it is outside the maximum and minimum operating range of the steel grabber, remove the grid data of the stockpile, thereby screening the stockpile grid data; Fill the filtered stockpile grid data into the height vector; Generate a grab window based on the grab bucket size of the steel grabber, fix the sliding step length, start from the lower left corner of the height matrix, slide from left to right and from bottom to top, and find the grab window information with the largest value, which is the optimal grab window information.

4. The intelligent decision-making method for steel grabbers in bulk material scenarios in scrap steel yards according to claim 1 is characterized by: The steps for selecting the placement point are as follows: B1: Obtain real-time data of the stockpile, truck location information, steel grabber information and initialize; B2: Calculate the point on the center axis of the bucket according to a certain sliding step length; B3: Update the information of the possible placement points according to the maximum and minimum operating ranges of the steel grabber; B4: Determine whether the truck is full. If so, stop the calculation. Otherwise, select the placement point generation mode according to the needs, including mode 1 and mode 2. Mode 1 is to calculate the average height of the grid in the placement window from back to front, and determine whether it exceeds the threshold. If it exceeds, slide forward to continue calculating, otherwise output the point as the placement point; Mode 2 is to check from the front to the back of the carriage whether the material pile in the placement window exceeds the height threshold of the carriage. If it exceeds, it is considered that it will block the data behind it, and the point before this point is selected as the placement point.

5. The intelligent decision-making method for steel grabbers in bulk material scenarios in scrap steel yards according to claim 1 is characterized by: In the process of grabbing and placing points selection, after obtaining the grid data, we first determine whether the data source is correct and check whether there are abnormal values. If the data source is incorrect or there are abnormal values, the process is terminated and errors are handled first.

6. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the intelligent decision-making method for a steel grabber in a bulk material scenario in a scrap steel yard as described in any one of claims 1 to 5 when executing the computer program.

7. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the intelligent decision-making method for a steel grabber in a bulk material scenario in a scrap steel yard as described in any one of claims 1 to 5 is implemented.

8. A computer program product, characterized in that: It comprises a computer program which, when executed by a processor, implements the intelligent decision-making method for a steel grabber in a bulk material scenario in a scrap steel yard as described in any one of claims 1 to 5.

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