Dot matrix multi-hopper intelligent stacking method and system

Abstract

This application relates to the field of intelligent control technology, and in particular to a matrix-type multi-silo intelligent stacking method and system. In the matrix-type multi-silo intelligent stacking method and system provided in this application, a three-dimensional model algorithm is used to create a three-dimensional model of multiple silos, resulting in a matrix-type three-dimensional model including multiple matrix blocks. Information parameters of the silos and materials are acquired, and a target silo is selected based on these parameters, wherein the target silo is a silo that matches the information parameters. It is determined whether the absolute value between the target matrix block and the edge matrix block is greater than a first preset threshold. If the absolute value is greater than the preset threshold, the material is stacked into the target silo. This not only enables automatic and uniform stacking of materials but also improves stacking efficiency, thereby reducing the labor intensity of workers. Furthermore, it enables intelligent selection of silos and intelligent operation of the stacking process.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology, and in particular to a dot matrix multi-bin intelligent stacking method and system. Background Technology

[0002] Coal is the core fuel for thermal power generation and one of my country's main energy sources for electricity supply, holding a pivotal position in the energy sector. In related technologies, the stacking of coal in silos is generally carried out manually by operators, who control robotic arms to perform the stacking operations.

[0003] However, in actual material stacking operations, uneven stacking often occurs, which makes subsequent material retrieval difficult and affects the efficiency of material retrieval operations. Summary of the Invention

[0004] To address some or all of the technical problems existing in the prior art, the present invention provides a dot matrix multi-bin intelligent stacking method and system.

[0005] The technical solution of this application embodiment is as follows:

[0006] Firstly, a matrix-style multi-bin intelligent stacking method includes:

[0007] A 3D modeling algorithm was used to create a 3D model of multiple silos, resulting in a lattice-type 3D model containing multiple lattice blocks.

[0008] Obtain information parameters of the silo and the material, and select a target silo based on the information parameters, wherein the target silo is a silo that matches the information parameters;

[0009] Determine whether there are any target dot matrix blocks in the dot matrix three-dimensional model that are not filled with material according to a preset order;

[0010] Determine the relationship between the absolute value between the target dot matrix block and the edge dot matrix block and a preset threshold;

[0011] If the absolute value is greater than or equal to the preset threshold, the material is piled into the target silo.

[0012] In some possible implementations, the method of using a 3D modeling algorithm to perform 3D modeling on multiple silos to obtain a lattice-type 3D model including multiple lattice blocks further includes:

[0013] The shape and three-dimensional dimensions of multiple silos are scanned to obtain point cloud data of the multiple silos;

[0014] Using the point cloud data and a 3D modeling algorithm, multiple silos are modeled in 3D to obtain a dot matrix 3D model.

[0015] In some possible implementations, the step of using the point cloud data to perform three-dimensional modeling of the silo using a three-dimensional modeling algorithm to obtain a raster-like three-dimensional model further includes:

[0016] Substituting the point cloud data into the three-dimensional model algorithm, a first three-dimensional model of the silo is obtained;

[0017] The first three-dimensional model is divided into virtual dot matrix segments according to a preset logic to obtain a dot matrix three-dimensional model with multiple dot matrix blocks.

[0018] In some possible implementations, the step of dividing the first three-dimensional model into virtual dot matrix segments according to preset logic to obtain a dot matrix three-dimensional model with multiple dot matrix blocks further includes:

[0019] Determine whether the distance between the first lattice block and the second lattice block located within the first three-dimensional model is less than a preset distance, wherein the first lattice block is located at the outer edge of the first three-dimensional model, and the second lattice block is located beside the first lattice block;

[0020] If the distance between the first dot matrix block and the second dot matrix block is less than the preset distance, the second dot matrix block is considered as part of the first dot matrix block.

[0021] In some possible implementations, after obtaining a raster-like 3D model comprising multiple raster blocks, the method further includes:

[0022] Measure the actual parameters of the material in the silo, wherein the actual parameters include the size and shape of the material;

[0023] The actual parameters are scaled to obtain a proportional second three-dimensional model;

[0024] The second three-dimensional model is substituted into the raster-type three-dimensional model and corresponds one-to-one with the raster blocks.

[0025] In some possible implementations, the step of acquiring information parameters of the material and the silo, and selecting the target silo based on the information parameters, further includes:

[0026] The type, weight, and storage capacity of the material are obtained, and multiple alternative storage silos are selected based on the type, weight, and storage capacity of the material.

[0027] Determine the distances between the multiple candidate silos and the stacker;

[0028] The candidate silo closest to the stacker is selected as the target silo.

[0029] In some possible implementations, determining whether there are target lattice blocks in the lattice-type 3D model that are not filled with material according to a preset order further includes:

[0030] Determine whether there are any target matrix blocks that are not filled with material in each layer of matrix blocks according to the top-to-bottom order;

[0031] When there is a target dot matrix block that is not filled with material in each layer of the dot matrix block, the step of determining the relationship between the absolute value of the target dot matrix block and the edge dot matrix block and the preset threshold is performed.

[0032] In some possible implementations, determining the relationship between the absolute value of the target dot matrix block and the edge dot matrix block and a preset threshold further includes:

[0033] Determine the relationship between the absolute value of the target direction between the target dot matrix block and the edge dot matrix block and a preset threshold.

[0034] Secondly, a matrix-type multi-bin intelligent stacking system includes:

[0035] The modeling module is used to perform three-dimensional modeling of multiple silos using three-dimensional modeling algorithms, resulting in a lattice-type three-dimensional model that includes multiple lattice blocks.

[0036] The selection module is used to obtain information parameters of the silo and the material, and select a target silo according to the information parameters, wherein the target silo is a silo that is compatible with the information parameters;

[0037] The first judgment module is used to judge whether there are target dot matrix blocks that are not filled with material in the dot matrix three-dimensional model according to a preset order.

[0038] The second judgment module is used to determine the relationship between the absolute value between the target dot matrix block and the edge dot matrix block and a preset threshold.

[0039] The stacking module is used to stack the material into the target silo when the absolute value is greater than or equal to the preset threshold.

[0040] The dot matrix multi-bin intelligent stacking method and system provided in this application have at least the following beneficial effects:

[0041] In the dot-matrix multi-silo intelligent stacking method and system provided in this application embodiment, a three-dimensional model algorithm is used to perform three-dimensional modeling of multiple silos, resulting in a dot-matrix three-dimensional model including multiple dot-matrix blocks; information parameters of the silos and materials are obtained, and a target silo is selected according to the information parameters, wherein the target silo is a silo that matches the information parameters; it is determined whether the absolute value between the target dot-matrix block and the edge dot-matrix block is greater than a first preset threshold; if the absolute value is greater than the preset threshold, the material is stacked into the target silo. This not only enables automatic and uniform stacking of materials but also improves the stacking efficiency, thereby reducing the labor intensity of workers. Furthermore, it enables intelligent selection of silos and intelligent operation of the stacking process. Attached Figure Description

[0042] The accompanying drawings, which are included to provide a further understanding of the embodiments of this application and constitute a part of the embodiments of this application, are illustrative embodiments of this application and their descriptions are used to explain the present invention, and do not constitute an improper limitation of the present invention.

[0043] In the attached diagram:

[0044] Figure 1 A flowchart of the dot matrix multi-bin intelligent stacking method provided in the embodiments of this application;

[0045] Figure 2 This is a schematic diagram of the silos in the dot matrix multi-silo intelligent stacking method provided in the embodiments of this application;

[0046] Figure 3 The system architecture diagram of the matrix-type multi-bin intelligent stacking method provided in the embodiments of this application is shown.

[0047] Figure 4 This is a structural diagram of the dot matrix multi-bin intelligent stacking system provided in an embodiment of this application. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0049] Coal is the core fuel for thermal power generation and one of my country's main energy sources for electricity supply, holding a pivotal position in the energy sector. In related technologies, the stacking of coal in silos is generally carried out manually by operators, who control robotic arms to perform the stacking operations.

[0050] However, in actual material stacking operations, the inventors discovered that the uniformity of material stacking is related to the operator's experience and understanding of the silo. Therefore, manual stacking can result in uneven material distribution. This leads to difficulties in subsequent material retrieval, thus affecting the efficiency of the retrieval operation.

[0051] <Method Implementation>

[0052] like Figure 1 As shown in the figure, the dot matrix multi-bin intelligent stacking method provided in this application includes steps S110 to S150, which are described below.

[0053] S110. Use a 3D modeling algorithm to perform 3D modeling on multiple silos to obtain a 3D matrix model including multiple lattice blocks.

[0054] A 3D modeling algorithm is a computer algorithm used to create 3D models. It can convert a silo containing materials into a 3D model and perform corresponding operations on a computer. A silo is a place for storing materials; in this embodiment, for example... Figure 2 As shown, multiple silos can be set up, and these silos are distributed side by side along a straight line within the factory building. A lattice block is the basic unit that makes up a lattice-based 3D model. Each silo's lattice-based 3D model includes multiple lattice blocks, and the superposition and combination of these blocks form the lattice-based 3D model in this embodiment. Therefore, after using a 3D modeling algorithm to perform 3D modeling on multiple silos in the factory building, a lattice-based 3D model can be obtained, where the lattice-based 3D model is formed by multiple superimposed and combined lattice blocks.

[0055] In some embodiments, step S110 may further include steps S111 to S113:

[0056] Step S111: Scan the shape and three-dimensional dimensions of multiple silos and obtain point cloud data of the multiple silos;

[0057] Step S112: Using point cloud data and a 3D modeling algorithm, perform 3D modeling on multiple silos to obtain a dot matrix 3D model.

[0058] Before creating a 3D model of the silo, a 3D laser scanning system is needed to scan its shape. In this embodiment, the stacker is located in a spacious factory building, above multiple silos arranged in a straight line. The reclaimer is located above the silos, typically positioned diagonally above them. Based on the silo layout, the repetitive movements of the stacker, and the reciprocating movement of the reclaimer, the 3D laser scanning system can be mounted on the reclaimer. Specifically, the system includes a radar, which can be suspended and fixed in the center of the reclaimer. When the reclaimer moves to the center of the silo, the 3D laser scanning system is also positioned in the center above the silo. Therefore, the 3D laser scanning system can provide full-view coverage of the silo, scanning its entire shape and 3D dimensions.

[0059] Specifically, the 3D scanning laser system can perform operations such as point cloud data acquisition, 3D imaging reconstruction, 3D point cloud processing, scan data coordinate transformation, and modeling on the silo. This method effectively overcomes the problems affecting actual scanning efficiency and accuracy caused by uneven stacking and the inability to measure local points. By scanning the silo's shape and 3D dimensions using the 3D scanning laser system, point cloud data regarding the silo's contour, volume, and shape can be obtained. Then, a 3D model algorithm is used to generate a raster-like 3D model of the silo based on the point cloud data.

[0060] In some embodiments, step S112 may further include steps S113 to S114:

[0061] Step S113: Substitute the point cloud data into the three-dimensional model algorithm to obtain a first three-dimensional model of the silo;

[0062] Step S114: The first three-dimensional model is divided into simulated dot matrix segments according to preset logic to obtain a dot matrix three-dimensional model with multiple dot matrix blocks.

[0063] Following the arrangement of multiple silos side-by-side in a straight line within the factory, a 3D scanning laser system was used to create 3D models of each silo, resulting in multiple first 3D models. These first 3D models of the silos were then sequentially labeled according to a specific orientation; for example, they were labeled LC1, LC2...LC from left to right. N Then, one of the first three-dimensional models of the silo can be divided into virtual lattice blocks. Specifically, an initial lattice block of a fixed volume is set at the center point of the bottom surface of the first three-dimensional model. The volume of the initial lattice block can be 1 cubic meter, 2 cubic meters, or 3 cubic meters, etc. The specific value of the initial lattice block volume can be determined according to actual needs.

[0064] In this embodiment, the first 3D model is divided into multiple lattice blocks of fixed size, centered on the initial lattice block, following a bottom-up and center-to-side order. Each lattice block can be labeled ZD(x, y, z). Here, x represents the number of lattice blocks from the center to the sides of the first 3D model, positive for rightward movement and negative for leftward movement, starting from 1. Similarly, y represents the number of lattice blocks from the center to the sides, positive for forward movement and negative for backward movement, starting from 1. z represents the layer number of the lattice blocks from bottom to top, with the bottommost lattice block layer numbered as 1. By dividing the first 3D model into virtual lattice blocks according to the above logic, a lattice-based 3D model with multiple lattice blocks can be obtained.

[0065] In some embodiments, step S114 may further include steps S115 to S116.

[0066] S115. Determine whether the distance between the first lattice block and the second lattice block located within the first three-dimensional model is less than a preset distance, wherein the first lattice block is located at the outer edge of the first three-dimensional model, and the second lattice block is located beside the first lattice block.

[0067] S116. If the distance between the first dot matrix block and the second dot matrix block is less than a preset distance, the second dot matrix block is regarded as part of the first dot matrix block.

[0068] Both the first and second lattice blocks are lattice blocks in a first 3D model. The first lattice block is located at the outer edge of the first 3D model, and the second lattice block is located beside the first lattice block. After dividing the first 3D model into virtual lattices according to preset logic, the fixed volume of the lattice blocks located at the outer edge of the first 3D model may be smaller than the fixed volume of the lattice blocks located on the inner edge. To facilitate the division of the lattice blocks located at the outer edge of the first 3D model, the second lattice block can be considered as part of the first lattice block if the distance between the first and second lattice blocks is less than a preset distance. The preset distance can be determined according to actual needs; for example, the preset distance can be 0.2m.

[0069] In some embodiments, after step S110, the method may further include S150 to S170, which will be described below.

[0070] S150. Measure the actual parameters of the material in the silo, wherein the actual parameters include the size and shape of the material;

[0071] S160. Scale the actual parameters to obtain a proportional second three-dimensional model;

[0072] S170. Substitute the second three-dimensional model into the matrix three-dimensional model and correspond one-to-one with the matrix blocks.

[0073] After establishing a lattice-based 3D model of multiple silos using a 3D modeling algorithm, a 3D scanning laser system is used to measure the actual parameters such as the size and shape of the materials inside the silos. The measurement results are then scaled proportionally to obtain a second 3D model of the materials. This second 3D model is then substituted into the lattice-based 3D model of the silos, and the second 3D model is divided into multiple lattice blocks to obtain a second 3D model of the materials inside the silos.

[0074] S120. Obtain information parameters of the silo and the material, and select a target silo according to the information parameters, wherein the target silo is a silo that matches the information parameters.

[0075] Before stacking materials, suitable silos need to be selected in advance. Different types and specifications of materials should be stacked separately and mixed. Furthermore, some materials require specific silos to meet their stacking requirements. Therefore, based on the information parameters of the silos and materials, the most suitable silo for that specific material needs to be selected for stacking. In practice, a multi-silo intelligent selection algorithm and a material intelligent stacking algorithm can be used to comprehensively judge the information parameters of the silos and materials to place the appropriate material into the appropriate silo.

[0076] In some embodiments, step S120 may further include S121 to S123:

[0077] S121. Obtain the type, weight, and storage capacity of the material in the silo, and select multiple alternative silos based on the type, weight, and storage capacity of the material in the silo.

[0078] S122. Determine the distance between multiple candidate silos and the stacker;

[0079] S123. Select the candidate silo closest to the stacker as the target silo.

[0080] In this embodiment, a multi-silo intelligent selection algorithm and a material intelligent stacking algorithm can be used to comprehensively determine the type and weight of the material to be stacked, as well as the type and storage capacity of the silos, and select several candidate silos from multiple silos. Since the stacker is located above multiple silos, in order to improve stacking efficiency, the distance between the stacker and the candidate silos can be determined based on the current parking position of the stacker, thereby selecting the closest candidate silo as the target silo.

[0081] S130. Determine whether there are target dot matrix blocks in the dot matrix three-dimensional model that are not filled with material according to a preset order.

[0082] After selecting the target silo for material storage, the lattice blocks in the 3D lattice model of the silo are evaluated according to a preset order to determine whether there is a target lattice block that is not filled with material. If no target lattice block is found, the evaluation of the other lattice blocks in the 3D lattice model continues according to the preset order until all evaluations are completed.

[0083] In some embodiments, step S130 may further include steps S131 to S132:

[0084] S131. Determine whether there are any target matrix blocks that are not filled with material in each layer of matrix blocks according to the top-to-bottom order;

[0085] S132. When there is a target dot matrix block that is not filled with material in each layer of the dot matrix block, perform the step of judging the relationship between the absolute value of the target dot matrix block and the edge dot matrix block and the preset threshold.

[0086] In practice, the presence of target lattice blocks that are not fully filled with material in each layer of the lattice model can be determined from bottom to top. For example, starting from the bottom of the lattice model, it can be determined whether there are target lattice blocks that are not fully filled with material in layer z. If there are no target lattice blocks in layer z, layer z is skipped, and then it is determined whether there are lattice blocks that are not fully filled with material in layer z+1. If there are target lattice blocks in layer z, then step S140 is executed.

[0087] S140. Determine the relationship between the absolute value between the target dot matrix block and the edge dot matrix block and a preset threshold.

[0088] S150. If the absolute value is greater than or equal to the preset threshold, the material is piled into the target silo.

[0089] When there are lattice blocks in the lattice-based 3D model that are not filled with material, the system compares the absolute value between the target lattice block and the edge lattice blocks with a preset threshold. The preset threshold can be a distance value set according to actual needs. When the absolute value between the target lattice block and the edge lattice block is greater than or equal to the preset threshold, it means that material can continue to be piled in the target hopper. When the absolute value between the target lattice block and the edge lattice block is less than the preset threshold, because the distance between the target lattice block and the edge lattice block is short, material can automatically slide down to replenish the target lattice block. Therefore, in this case, the stacker does not perform stacking processing.

[0090] In some embodiments, step S140 may further include S141:

[0091] S141. Determine the relationship between the absolute value of the target direction between the target dot matrix block and the edge dot matrix block and a preset threshold.

[0092] In this embodiment, if it is determined that there are unfilled target lattice blocks LC in layer z... N When calculating ZD(x, y, z), the absolute values ​​of the target lattice block and the edge lattice blocks in the x-direction are obtained, and these absolute values ​​are compared with a preset threshold. If the absolute value is less than the preset threshold, it indicates that the target lattice block and the edge lattice block are close enough that the material can slide down to the target lattice block for replenishment. If the absolute value is greater than or equal to the preset threshold, the stacker is controlled to move above the target hopper, and any unfilled target lattice blocks are stacked.

[0093] In this embodiment, the above methods are all implemented through an intelligent stacking system. The intelligent stacking system includes a stacker crane, a 3D laser scanning system, a positioning system, and a dot-matrix multi-bin stacking model algorithm system. For example... Figure 2 As shown, the intelligent stacking system is used to select appropriate stacking locations and execute stacking strategies. The stacker crane is used to perform stacking processing under the control of the intelligent stacking system. The 3D laser scanning system can scan the silo and then construct a 3D model using a dot-matrix multi-silo stacking model algorithm system, and further complete the construction of the dot-matrix 3D model with the assistance of the positioning system.

[0094] The positioning system uses a Gray busbar to calibrate the stacker's full-stroke position and precisely locate its movement. It employs the electromagnetic induction principle of a single-turn coil to generate an induced electromotive force in an alternating magnetic field. Further address encoding and decoding are performed to obtain the stacker's precise position value. The system takes the LC block of the topmost matrix point of each bin. N The intersection of the center position of ZD(x, y, z) and the vertical line of the stacker's running track is taken as the position of the stacker in LC. N ZD(x, y, z) represents the stopping point when the material block is stacked, denoted as LC. N The DLcx features a 200mm deceleration distance on both sides of each material stacking stop point for precise stopping control of the stacker.

[0095] <System Implementation Example>

[0096] like Figure 3 As shown in the embodiment of this application, the matrix-type multi-bin intelligent stacking system includes: a modeling module 310, a selection module 320, a first judgment module 330, a second judgment module 340, and a stacking module 350, wherein...

[0097] Modeling module 310 is used to perform three-dimensional modeling of multiple silos using a three-dimensional modeling algorithm to obtain a lattice-type three-dimensional model including multiple lattice blocks.

[0098] Selection module 320 is used to obtain information parameters of the silo and the material, and select a target silo according to the information parameters, wherein the target silo is a silo that is compatible with the information parameters;

[0099] The first judgment module 330 is used to judge whether there are target dot matrix blocks that are not filled with material in the dot matrix three-dimensional model according to a preset order.

[0100] The second judgment module 340 is used to judge the relationship between the absolute value between the target dot matrix block and the edge dot matrix block and a preset threshold.

[0101] The stacking module 350 is used to stack the material into the target silo when the absolute value is greater than or equal to the preset threshold.

[0102] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Additionally, the terms "front," "back," "left," "right," "upper," and "lower" in this document refer to the placement shown in the accompanying drawings.

[0103] 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 foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A matrix-type multi-bin intelligent stacking method, characterized in that, include: A 3D modeling algorithm was used to create a 3D model of multiple silos, resulting in a lattice-type 3D model containing multiple lattice blocks. Obtain information parameters of the silo and the material, and select a target silo based on the information parameters, wherein the target silo is a silo that matches the information parameters; Determine whether there are any target dot matrix blocks in the dot matrix three-dimensional model that are not filled with material according to a preset order; Determine the relationship between the absolute value between the target dot matrix block and the edge dot matrix block and a preset threshold; If the absolute value is greater than or equal to the preset threshold, the material is piled into the target silo. The step of using a 3D modeling algorithm to perform 3D modeling on multiple silos to obtain a dot matrix 3D model including multiple dot matrix blocks includes: scanning the shape and 3D dimensions of multiple silos to obtain point cloud data of the multiple silos; using the point cloud data and a 3D modeling algorithm to perform 3D modeling on the multiple silos to obtain a dot matrix 3D model. The step of using the point cloud data and a 3D modeling algorithm to create a 3D model of the silo and obtain a dot matrix 3D model includes: substituting the point cloud data into the 3D modeling algorithm to obtain a first 3D model of the silo; and dividing the first 3D model into virtual dot matrix segments according to a preset logic to obtain a dot matrix 3D model with multiple dot matrix blocks. The step of dividing the first three-dimensional model into virtual dot matrix segments according to a preset logic to obtain a dot matrix three-dimensional model with multiple dot matrix blocks includes: determining whether the distance between a first dot matrix block and a second dot matrix block located within the first three-dimensional model is less than a preset distance, wherein the first dot matrix block is located at the outer edge of the first three-dimensional model and the second dot matrix block is located beside the first dot matrix block; if the distance between the first dot matrix block and the second dot matrix block is less than the preset distance, the second dot matrix block is regarded as part of the first dot matrix block.

2. The dot-matrix multi-bin intelligent stacking method according to claim 1, characterized in that, After obtaining a raster-style 3D model comprising multiple raster blocks, the method further includes: Measure the actual parameters of the material in the silo, wherein the actual parameters include the size and shape of the material; The actual parameters are scaled to obtain a proportional second three-dimensional model; The second three-dimensional model is substituted into the raster-type three-dimensional model and corresponds one-to-one with the raster blocks.

3. The dot-matrix multi-bin intelligent stacking method according to claim 1, characterized in that, The step of acquiring information parameters of materials and silos, and selecting a target silo based on the information parameters, includes: The type, weight, and storage capacity of the material are obtained, and multiple alternative storage silos are selected based on the type, weight, and storage capacity of the material. Determine the distances between the multiple candidate silos and the stacker; The candidate silo closest to the stacker is selected as the target silo.

4. The dot-matrix multi-bin intelligent stacking method according to claim 1, characterized in that, The step of determining whether there are target dot matrix blocks in the dot matrix 3D model that are not filled with material according to a preset order includes: Determine whether there are any target matrix blocks that are not filled with material in each layer of matrix blocks according to the top-to-bottom order; When there is a target dot matrix block that is not filled with material in each layer of the dot matrix block, the step of determining the relationship between the absolute value of the target dot matrix block and the edge dot matrix block and the preset threshold is performed.

5. The intelligent stacking method for multi-bin lattice structures according to claim 1, characterized in that, The determination of the relationship between the absolute value of the target dot matrix block and the edge dot matrix block and a preset threshold includes: Determine the relationship between the absolute value of the target direction between the target dot matrix block and the edge dot matrix block and a preset threshold.

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