A method and device for configuring green plants in a limited space

Abstract

The present application discloses a method and device for configuring green plants in a confined space, which relate to the field of electronic digital data processing. The method determines the order of placing taller plants and shorter plants by identifying the distance between the confined space and external obstacles (fixed facilities such as walls and furniture). Since the matrix grid closer to the obstacle has a lower cardinality, the taller the plant is when it is located on the grid with a lower cardinality, the lower the configuration coefficient can be. The taller the plant is, the closer it is to the obstacle, and the less likely it is to block the lighting of other plants (the obstacle blocks light by default). At the same time, the configuration method of this embodiment is based on the minimum bounding box of the green plant, so that multiple green plants will not infringe on their respective minimum bounding boxes when configured, thereby achieving the purpose of reasonable dense planting.

Description

Technical Field

[0001] The present application relates to the technical field of electronic digital data processing, and in particular to a method and device for configuring green plants in a confined space. Background Art

[0002] In modern large-scale buildings, green plants have begun to be planted in restricted spaces (such as glass covers, partitions, curtain walls, etc.) to improve the indoor environment, provide fresh air to the indoor environment, and increase green and ecological sensory elements. Indoor cultivation of different plants can be achieved by adjusting the cultivation temperature, humidity, wind speed, etc. Planting green plants in restricted spaces is currently the mainstream greening and oxygenation method for indoor public environments and curtain walls.

[0003] The planting of green plants in confined spaces is usually done through artificial layout and configuration. Since the plants are of different sizes and heights, artificial planting often leads to mutual shading or unreasonable dense planting of green plants due to subjective errors and other factors. This is especially true when one or more sides of the confined space are against the wall. The disadvantages of mutual shading and external shading are more obvious, affecting the growth environment of the plants. Summary of the invention

[0004] The main purpose of the present application is to provide a method and device for configuring green plants in a confined space, so as to solve the problem in the prior art that there are subjective errors in the planting of green plants in a confined space, which is not conducive to plant growth.

[0005] To achieve the above objectives, this application provides the following technical solutions:

[0006] A method for configuring green plants in a confined space, wherein the confined space is enclosed by four transparent baffles covered by a cover plate and a substrate in sequence from top to bottom, and green plants of at least two different heights are planted on all substrates. The method for configuring green plants comprises:

[0007] Step S1, obtaining the minimum bounding box of each green plant respectively;

[0008] Step S2, defining a side of each minimum bounding box adjacent to the root of the green plant as a bottom surface, and each bottom surface is constrained to the upper surface of the substrate;

[0009] Step S3, evenly dividing the upper surface of the substrate into a plurality of grids;

[0010] Step S4, obtaining the distance between the current grid and the external obstacles along the normal direction of the four transparent baffles;

[0011] Step S5, obtaining the average value of the four interval distances of the current grid and defining it as the cardinality of the current grid;

[0012] Step S6, defining the inverse of the height value of the current minimum bounding box as the weight coefficient of the current minimum bounding box;

[0013] Step S7, summing up all bases of the grid covered by the current minimum bounding box and multiplying by the weight coefficient of the current minimum bounding box to obtain the configuration coefficient of the current minimum bounding box;

[0014] Step S8, iteratively updating the positions of all minimum bounding boxes through a global optimization algorithm so that the sum of the configuration coefficients of all minimum bounding boxes is minimized;

[0015] Step S9, obtaining the positions of all minimum bounding boxes that match the minimum sum of configuration coefficients as the configuration positions of all green plants.

[0016] As a further improvement of the present application, step S1, respectively obtaining the minimum bounding box of each green plant, includes:

[0017] Step S11, constructing a three-dimensional rectangular coordinate system with the upper surface of the substrate as the xy plane;

[0018] Step S12, obtaining a first projection, a second projection, and a third projection of the current green plant based on the xy plane, the xz plane, and the yz plane respectively;

[0019] Step S13, defining each coordinate axis of the three-dimensional coordinate system as a rotation axis;

[0020] Step S14, rotating the current green plant according to each rotation axis respectively, and obtaining the minimum value of the total area of ​​the first projection, the second projection, and the third projection by using the Floyd algorithm;

[0021] Step S15, obtaining a rotated form of the current green plant corresponding to the minimum value;

[0022] Step S16, obtaining the OBB bounding box in the rotated form, and defining the bounding box as the minimum bounding box.

[0023] As a further improvement of the present application, step S12, obtaining the first projection, the second projection, and the third projection of the current green plant based on the xy plane, the xz plane, and the yz plane, respectively, includes:

[0024] Step S121, obtaining a first visual image of the current green plant based on the z-axis, a second visual image based on the y-axis, and a third visual image based on the z-axis;

[0025] Step S122, acquiring a first boundary coordinate set of the first visual image, a second boundary coordinate set of the second visual image, and a third boundary coordinate set of the third visual image based on a boundary extraction algorithm;

[0026] Step S123, obtaining the enclosed area of ​​the first boundary coordinate set and defining it as the first projection;

[0027] Step S124, obtaining the enclosed area of ​​the second boundary coordinate set and defining it as the second projection;

[0028] Step S125: obtaining the enclosed area of ​​the third boundary coordinate set and defining it as the third projection.

[0029] As a further improvement of the present application, step S122, obtaining a first boundary coordinate set of the first visual image, a second boundary coordinate set of the second visual image, and a third boundary coordinate set of the third visual image based on a boundary extraction algorithm, includes:

[0030] Step S1221, binarizing all visual images respectively, and obtaining a black-and-white visual image based on one visual image;

[0031] Step S1222, defining a grid with the same resolution according to the resolutions of all visual images to divide all black and white visual images;

[0032] Step S1223, obtaining the RGB color value of each grid of the current black and white visual image;

[0033] Step S1224, assigning a value of 1 to the grid whose RGB color value is 0, 0, 0, and assigning a value of 0 to the grid whose RGB color value is 255, 255, 255;

[0034] Step S1225, defining each grid and eight adjacent grids as a 3×3 window;

[0035] Step S1226, traversing all grids through the 3×3 window with one grid as one step;

[0036] Step S1227, based on all traversal results, delete the grids assigned all values ​​1 and the grids assigned all values ​​0 in the 3×3 window;

[0037] Step S1228, obtain the retained grid and define it as a boundary and obtain the coordinate set of the boundary.

[0038] As a further improvement of the present application, in step S14, the current green plant is rotated according to each rotation axis, and the minimum value of the total area of ​​the first projection, the second projection, and the third projection is obtained by using the Floyd algorithm;

[0039] Step S141, defining a rotation step length for each rotation axis;

[0040] Step S142, traversing all rotation positions of each rotation axis respectively, and obtaining a first real-time projection, a second real-time projection, and a third real-time projection of the current green plant in each traversal;

[0041] Step S143, traversing the real-time total area of ​​each first real-time projection, each second real-time projection, and each third real-time projection according to the triple loop of the Floyd algorithm;

[0042] Step S144, obtaining the real-time total area with the smallest value among all the real-time total areas and marking it as the minimum value.

[0043] As a further improvement of the present application, step S8, iteratively updating the positions of all minimum bounding boxes through a global optimization algorithm so as to minimize the sum of the configuration coefficients of all minimum bounding boxes, includes:

[0044] Step S81, defining a plurality of random solutions for the minimum value of the sum of the configuration coefficients according to formula (1), and defining the optimization result of all random solutions as the minimum sum of the configuration coefficients of all minimum bounding boxes;

[0045] (1);

[0046] in, is the set of all random solutions, For each random solution, is the label of the random solution, is the number of all random solutions; is the set of velocities of all random solutions, are the speeds of each random solution respectively;

[0047] Step S82, initialize the position of each random solution, and update the current position and current speed respectively based on the same random solution according to formula (2):

[0048] (2);

[0049] in, For the The random solution is The speed of the step, For the The random solution is The speed inertia of the step, is the inertia coefficient, For the The self-perception representation of a random solution, For the social cognitive representation of a random solution; and are learning factors, for A random number, For the The individual optimal solution obtained by random solutions is For the The global optimal solution obtained by random solutions is For the Step 1 A random solution;

[0050] Step S83, iterate each random solution according to formula (2) to update each and each ;

[0051] Step S84, determine each Comparing the difference values ​​of the previous iteration to see whether they are less than or equal to the first preset adaptation threshold, if all the first difference values ​​are less than or equal to the first preset adaptation threshold, executing step S85;

[0052] Step S85, determine each Comparing the second difference values ​​of the previous iteration to see whether they are less than or equal to the second preset adaptation threshold, if all the second difference values ​​are less than or equal to the second preset adaptation threshold, executing step S86;

[0053] Step S86: determine whether an optimal solution with the smallest sum of configuration coefficients of all minimum bounding boxes has been obtained.

[0054] As a further improvement of the present application, in step S83, each random solution is iterated according to formula (2) to update each and each ,include:

[0055] Step S831, based on each iteration, the inertia coefficient is linearly reduced once according to formula (3):

[0056] (3);

[0057] in, For the The random solution is The inertia coefficient after step optimization, is the initial inertia coefficient, is the current iteration number, is the maximum number of iteration steps.

[0058] To achieve the above objectives, this application also provides the following technical solutions:

[0059] A green plant arrangement device in a limited space, the green plant arrangement device is applied to the green plant arrangement method as described above, and the green plant arrangement device comprises:

[0060] A minimum bounding box acquisition module is used to obtain the minimum bounding box of each green plant;

[0061] A minimum bounding box constraint module is used to define a side of each minimum bounding box adjacent to the root of the green plant as a bottom surface, and each bottom surface is constrained to the upper surface of the substrate;

[0062] A substrate upper surface division module, used for evenly dividing the upper surface of the substrate into a plurality of grids;

[0063] The external obstacle distance acquisition module is used to obtain the distance between the current grid and the external obstacles along the normal direction of the four transparent baffles;

[0064] A grid cardinality definition module is used to obtain the average value of the four interval distances of the current grid and define it as the cardinality of the current grid;

[0065] A minimum bounding box weight coefficient definition module is used to define the inverse of the height value of the current minimum bounding box as the weight coefficient of the current minimum bounding box;

[0066] A minimum bounding box configuration coefficient acquisition module is used to add all bases of the grid covered by the current minimum bounding box and multiply by the weight coefficient of the current minimum bounding box to obtain the configuration coefficient of the current minimum bounding box;

[0067] The configuration coefficient sum iteration module is used to iteratively update the positions of all minimum bounding boxes through a global optimization algorithm so as to minimize the sum of the configuration coefficients of all minimum bounding boxes;

[0068] The configuration position acquisition module is used to obtain the positions of all minimum bounding boxes that match the minimum sum of configuration coefficients, which are the configuration positions of all green plants.

[0069] To achieve the above objectives, this application also provides the following technical solutions:

[0070] An electronic device includes a processor and a memory coupled to the processor, wherein the memory stores program instructions executable by the processor; when the processor executes the program instructions stored in the memory, the green plant configuration method as described above is implemented.

[0071] To achieve the above objectives, this application also provides the following technical solutions:

[0072] A storage medium stores program instructions, and when the program instructions are executed by a processor, the above-mentioned green plant configuration method can be implemented.

[0073] The present application obtains the minimum bounding box of each green plant respectively; defines the root side of each minimum bounding box adjacent to the green plant as the bottom surface, and each bottom surface is constrained to the upper surface of the substrate; divides the upper surface of the substrate evenly into a number of grids; obtains the interval distance between the current grid and the external obstacles along the normal direction of the four transparent baffles; obtains the average value of the four interval distances of the current grid and defines it as the cardinality of the current grid; defines the inverse of the height value of the current minimum bounding box as the weight coefficient of the current minimum bounding box; adds up all the cardinalities of the grids covered by the current minimum bounding box and multiplies it by the weight coefficient of the current minimum bounding box to obtain the configuration coefficient of the current minimum bounding box; iteratively updates the positions of all minimum bounding boxes through a global optimization algorithm to minimize the sum of the configuration coefficients of all minimum bounding boxes; and obtains the positions of all minimum bounding boxes that match the minimum sum of the configuration coefficients, which are the configuration positions of all green plants. The present application determines the placement order of taller plants and shorter plants by identifying the distance between the restricted space and external obstacles (fixed facilities such as walls and furniture). Since the matrix grid closer to the obstacle has a lower cardinality, the taller plants can have a lower configuration coefficient when they are located on the grid with a lower cardinality, so that the taller plants are closer to the obstacle and are less likely to block the lighting of other plants (obstacles block light by default). At the same time, the configuration method of the present application is based on the minimum bounding box of the green plants, so that multiple green plants will not infringe on their respective minimum bounding boxes when configured, thereby achieving the purpose of reasonable dense planting. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] Figure 1 This is a schematic diagram of the steps of an embodiment of a method for configuring green plants in a limited space of the present application;

[0075] Figure 2 This is a functional module diagram of an embodiment of a green plant configuration device in a limited space of the present application;

[0076] Figure 3 This is a schematic diagram of the structure of an embodiment of the electronic device of the present application;

[0077] Figure 4 This is a schematic diagram of the structure of an embodiment of the storage medium of the present application. DETAILED DESCRIPTION

[0078] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0079] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0080] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0081] like Figure 1 As shown, this embodiment provides an embodiment of a method for configuring green plants in a confined space. In this embodiment, the confined space is surrounded by four transparent baffles that are covered by a cover plate and a matrix in sequence from top to bottom, and all matrices are planted with green plants of at least two different heights.

[0082] Preferably, this embodiment can be applied to an ecological cabin, which includes a hollow transparent column covered by an air circulation component and an oxygenation component in sequence from top to bottom, a substrate laid on the oxygenation component, and healthy plants located in the hollow transparent column and planted on the substrate.

[0083] Preferably, the eco-capsule is a prior art, and the eco-capsule is fixed between a first structural layer and a second structural layer at different heights, such as a floor and a ceiling, and the eco-capsule comprises:

[0084] The oxygen-enriching component includes a planting trough fixedly mounted on the floor, healthy plants planted on the planting trough, and a transparent enclosure fixedly mounted on the edge of the planting trough, wherein the transparent enclosure encloses the above-mentioned hollow transparent column.

[0085] The air circulation component includes a shell covering the top end of a hollow transparent column and a first air circulation component fixedly installed in the shell. The shell is fixedly connected to the transparent enclosure to form an air duct in the transparent enclosure. The first air inlet end of the first air circulation component passes through the shell and is connected to the air duct. The first air outlet end of the first air circulation component passes through the shell and is connected to the indoor space. The first air circulation component guides external air to pass through the air inlet, the air duct, the first air inlet end, and the first air outlet end in sequence to guide external air and oxygen produced by plants to the indoor space.

[0086] Preferably, the air circulation component also includes a second air circulation component fixedly installed in the shell, the second air inlet end of the second air circulation component passes through the shell and is connected to the indoor space, the second air outlet end of the second air circulation component passes through the shell and is connected to the outdoors, and the second air circulation component guides the air in the indoor space through the second air inlet end and the second air outlet end in sequence to guide the air in the indoor space to the outdoors.

[0087] Preferably, the oxygen enrichment component also includes a drip irrigation pipe fixedly installed in the planting trough, one end of the drip irrigation pipe is connected to an external water source, and the other end of the drip irrigation pipe is closed. At least one water outlet is opened on the side wall of the drip irrigation pipe, and a solenoid valve for conducting or blocking the external water source is fixedly installed in the drip irrigation pipe. The solenoid valve is located between the water outlet and the external water source, and the solenoid valve is opened or closed according to external instructions to conduct the external water source to the water outlet or block the external water source.

[0088] It is worth noting that the structure of the above-mentioned eco-cabin is already existing technology. For details, please refer to the patent "An air circulation device based on curtain wall", application number "202322289519.3". This embodiment does not improve the structure of the eco-cabin, but is a conventional application of the eco-cabin structure.

[0089] Specifically, the green plant configuration method includes the following steps:

[0090] Step S1, obtaining the minimum bounding box of each green plant.

[0091] Preferably, the minimum bounding box can be set as an AABB box or an OBB box. In this embodiment, the OBB box is preferred to reduce invalid occupied space.

[0092] Step S2, defining the side of each minimum bounding box adjacent to the root of the green plant as a bottom surface, and each bottom surface is constrained to the upper surface of the substrate.

[0093] Step S3, evenly dividing the upper surface of the substrate into a plurality of grids.

[0094] Preferably, the size of the grid should be smaller than the size of the bottom surface of the smallest bounding box, preferably one tenth the average size of the bottom surface of the smallest bounding box.

[0095] Step S4, obtaining the distance between the current grid and the external obstacles along the normal direction of the four transparent baffles.

[0096] Preferably, if the external obstacle is located in the diagonal direction of the bottom surface and does not pass through the normal direction, it can be ignored.

[0097] Step S5, obtaining an average value of the four interval distances of the current grid and defining it as the cardinality of the current grid.

[0098] Step S6: define the inverse of the height value of the current minimum bounding box as the weight coefficient of the current minimum bounding box.

[0099] Step S7, summing up all bases of the grid covered by the current minimum bounding box and multiplying by the weight coefficient of the current minimum bounding box to obtain the configuration coefficient of the current minimum bounding box.

[0100] Step S8, iteratively updating the positions of all minimum bounding boxes through a global optimization algorithm so as to minimize the sum of the configuration coefficients of all minimum bounding boxes.

[0101] Step S9, obtaining the positions of all minimum bounding boxes that match the minimum sum of configuration coefficients as the configuration positions of all green plants.

[0102] Furthermore, in step S1, the minimum bounding box of each green plant is obtained respectively, including:

[0103] Step S11, constructing a three-dimensional rectangular coordinate system with the upper surface of the substrate as the xy plane.

[0104] Step S12, obtaining a first projection, a second projection, and a third projection of the current green plant based on the xy plane, the xz plane, and the yz plane, respectively.

[0105] Step S13, defining each coordinate axis of the three-dimensional coordinate system as a rotation axis.

[0106] Step S14, rotating the current green plant according to each rotation axis, and obtaining the minimum value of the total area of ​​the first projection, the second projection, and the third projection by using the Floyd algorithm.

[0107] Step S15, obtaining a rotated form based on the current green plant corresponding to the minimum value.

[0108] Step S16, obtaining the OBB bounding box in the rotated form, and defining the bounding box as the minimum bounding box.

[0109] Preferably, Floyd's algorithm is an algorithm for finding the shortest path in a weighted graph with positive or negative edge weights (but no negative cycles). A single execution of the algorithm will find the lengths of the shortest paths (weighted) between all pairs of vertices. Although it does not return the details of the path itself, the path can be reconstructed by a simple modification of the algorithm. Versions of the algorithm can also be used to find the transitive closure of a relation R, or (related to the Schulze voting system) the shortest paths between all pairs of vertices in a weighted graph. Floyd's algorithm starts with an arbitrary single-edge path. The distance between all two points is the weight of the edge, which is infinite if there is no edge connecting the two points. For each pair of vertices u and v, see if there is a vertex w such that the path from u to w and then to v is shorter than the known path. If so, update v to w. The path graph is then represented by the adjacency matrix G. If there is a path from Vi to Vj, then G[i][j]=d, where d is the length of the path; otherwise G[i][j]=infinity. Define a matrix D to record the information of the inserted points. D[i][j] represents the points that need to be passed from Vi to Vj. Initialize D[i][j]=j. Insert each vertex into the graph and compare the distance after insertion with the original distance. G[i][j]=min(G[i][j],G[i][k]+G[k][j]). If the value of G[i][j] becomes smaller, then D[i][j]=k. G contains the information of the shortest path between two points, while D contains the information of the shortest path.

[0110] Preferably, the two angle values ​​between a rotation step in this embodiment are two adjacent points, and the projection areas corresponding to the two angle values ​​are the weights.

[0111] It should be noted that the meaning of the symbols in the above additional content is not interoperable with other parts of the embodiment.

[0112] Furthermore, step S12, obtaining a first projection, a second projection, and a third projection of the current green plant based on the xy plane, the xz plane, and the yz plane, respectively, includes:

[0113] Step S121, obtaining a first visual image of the current green plant based on the z-axis, a second visual image based on the y-axis, and a third visual image based on the z-axis.

[0114] Step S122 , obtaining a first boundary coordinate set of the first visual image, a second boundary coordinate set of the second visual image, and a third boundary coordinate set of the third visual image based on a boundary extraction algorithm.

[0115] Step S123, obtaining the enclosed area of ​​the first boundary coordinate set and defining it as the first projection.

[0116] Step S124, obtaining the enclosed area of ​​the second boundary coordinate set and defining it as the second projection.

[0117] Step S125, obtaining the enclosed area of ​​the third boundary coordinate set and defining it as the third projection.

[0118] Further, step S122, based on a boundary extraction algorithm, obtaining a first boundary coordinate set of the first visual image, a second boundary coordinate set of the second visual image, and a third boundary coordinate set of the third visual image, includes:

[0119] Step S1221, binarize all visual images respectively, and obtain a black and white visual image based on one visual image.

[0120] Step S1222: defining grids of the same resolution according to the resolutions of all visual images to divide all black and white visual images.

[0121] Step S1223, obtaining the RGB color value of each grid of the current black and white visual image.

[0122] Step S1224, assigning a value of 1 to the grid whose RGB color value is 0, 0, 0, and assigning a value of 0 to the grid whose RGB color value is 255, 255, 255.

[0123] Step S1225: define each grid and eight adjacent grids as a 3×3 window.

[0124] Step S1226, traverse all grids through the 3×3 window with one grid as one step.

[0125] Step S1227, based on all traversal results, delete the grids with all values ​​assigned to them as 1 and the grids with all values ​​assigned to them as 0 in the 3×3 window.

[0126] Step S1228, obtain the retained grid and define it as a boundary and obtain the coordinate set of the boundary.

[0127] Preferably, the grid defined according to the resolution will not split the pixels again to facilitate calculation, so the coordinate values ​​of the pixel coordinates at the same position are the same as the coordinate values ​​of the boundary coordinates.

[0128] Furthermore, in step S14, the current green plant is rotated according to each rotation axis, and the minimum value of the total area of ​​the first projection, the second projection, and the third projection is obtained by using the Floyd algorithm.

[0129] Step S141, defining a rotation step for each rotation axis.

[0130] Step S142, traversing all rotation positions of each rotation axis respectively, and obtaining a first real-time projection, a second real-time projection, and a third real-time projection of the current green plant in each traversal.

[0131] Step S143 , traversing the real-time total area of ​​each first real-time projection, each second real-time projection, and each third real-time projection according to the triple loop of the Floyd algorithm.

[0132] Step S144, obtaining the real-time total area with the smallest value among all the real-time total areas and marking it as the minimum value.

[0133] Furthermore, in step S8, the positions of all minimum bounding boxes are iteratively updated by a global optimization algorithm so that the sum of the configuration coefficients of all minimum bounding boxes is minimized, including:

[0134] Step S81, according to formula (1), a number of random solutions are defined for the minimum value of the sum of configuration coefficients, and the optimization result of all random solutions is defined as the minimum sum of configuration coefficients of all minimum bounding boxes.

[0135] (1).

[0136] in, is the set of all random solutions, For each random solution, is the label of the random solution, is the number of all random solutions; is the set of velocities of all random solutions, are the speeds of each random solution respectively.

[0137] Step S82, initialize the position of each random solution, and update the current position and current speed respectively based on the same random solution according to formula (2):

[0138] (2).

[0139] in, For the The random solution is The speed of the step, For the The random solution is The speed inertia of the step, is the inertia coefficient, For the The self-perception representation of a random solution, For the social cognitive representation of a random solution; and are learning factors, for A random number, For the The individual optimal solution obtained by random solutions is For the The global optimal solution obtained by random solutions is For the Step 1 A random solution.

[0140] Preferably, The value range is , preferably ; The value range is , preferably .

[0141] Step S83, iterate each random solution according to formula (2) to update each and each .

[0142] Step S84, determine each Whether the difference value of the previous iteration is less than or equal to the first preset adaptation threshold value is compared. If all the first difference values ​​are less than or equal to the first preset adaptation threshold value, step S85 is executed.

[0143] Step S85, determine each Compared with whether the second difference value of the previous iteration is less than or equal to the second preset adaptation threshold, if all the second difference values ​​are less than or equal to the second preset adaptation threshold, step S86 is executed.

[0144] Step S86: determine whether an optimal solution with the smallest sum of configuration coefficients of all minimum bounding boxes has been obtained.

[0145] Further, in step S83, each random solution is iterated according to formula (2) to update each and each ,include:

[0146] Step S831, based on each iteration, the inertia coefficient is linearly reduced once according to formula (3):

[0147] (3).

[0148] in, For the The random solution is The inertia coefficient after step optimization, is the initial inertia coefficient, is the current iteration number, is the maximum number of iteration steps.

[0149] Preferably, the initial inertia coefficient is generally set to 0.5, and the maximum number of iteration steps is generally set according to actual needs, and can be set to 100 times in this embodiment.

[0150] This embodiment obtains the minimum bounding box of each green plant respectively; defines the root side of each minimum bounding box adjacent to the green plant as the bottom surface, and each bottom surface is constrained to the upper surface of the substrate; divides the upper surface of the substrate evenly into a number of grids; obtains the interval distance between the current grid and the external obstacles along the normal direction of the four transparent baffles; obtains the average value of the four interval distances of the current grid and defines it as the cardinality of the current grid; defines the inverse of the height value of the current minimum bounding box as the weight coefficient of the current minimum bounding box; adds up all the cardinalities of the grids covered by the current minimum bounding box and multiplies it by the weight coefficient of the current minimum bounding box to obtain the configuration coefficient of the current minimum bounding box; iteratively updates the positions of all minimum bounding boxes through a global optimization algorithm to minimize the sum of the configuration coefficients of all minimum bounding boxes; and obtains the positions of all minimum bounding boxes that match the minimum sum of configuration coefficients, which are the configuration positions of all green plants. This embodiment determines the placement order of taller plants and shorter plants by identifying the distance between the restricted space and external obstacles (fixed facilities such as walls and furniture). Since the matrix grid closer to the obstacle has a lower cardinality, the taller plants can have a lower configuration coefficient when they are located on the grid with a lower cardinality, so that the taller plants are closer to the obstacle and are less likely to block the lighting of other plants (obstacles block light by default). At the same time, the configuration method of this embodiment is based on the minimum bounding box of the green plants, so that multiple green plants will not infringe on their respective minimum bounding boxes when configured, thereby achieving the purpose of reasonable dense planting.

[0151] like Figure 2 As shown, this embodiment provides an embodiment of a green plant configuration device in a confined space. In this embodiment, the green plant configuration device is applied to the green plant configuration method as in the above-mentioned embodiment.

[0152] Specifically, the green plant configuration device includes a minimum bounding box acquisition module 1, a minimum bounding box constraint module 2, a substrate upper surface division module 3, an external obstacle spacing distance acquisition module 4, a grid base definition module 5, a minimum bounding box weight coefficient definition module 6, a minimum bounding box configuration coefficient acquisition module 7, a configuration coefficient sum iteration module 8, and a configuration position acquisition module 9, which are electrically connected in sequence.

[0153] Among them, the minimum bounding box acquisition module 1 is used to obtain the minimum bounding box of each green plant respectively; the minimum bounding box constraint module 2 is used to define the root side of each minimum bounding box adjacent to the green plant as the bottom surface, and each bottom surface is constrained to the upper surface of the matrix; the matrix upper surface division module 3 is used to evenly divide the upper surface of the matrix into a number of grids; the external obstacle interval distance acquisition module 4 is used to obtain the interval distance between the current grid and the external obstacles along the normal direction of the four transparent baffles; the grid cardinality definition module 5 is used to obtain the average value of the four interval distances of the current grid and define it as the cardinality of the current grid; the minimum bounding box weight coefficient definition module Definition module 6 is used to define the inverse of the height value of the current minimum bounding box as the weight coefficient of the current minimum bounding box; minimum bounding box configuration coefficient acquisition module 7 is used to add all cardinalities of the grid covered by the current minimum bounding box and multiply by the weight coefficient of the current minimum bounding box to obtain the configuration coefficient of the current minimum bounding box; configuration coefficient sum iteration module 8 is used to iteratively update the positions of all minimum bounding boxes through a global optimization algorithm to minimize the sum of configuration coefficients of all minimum bounding boxes; configuration position acquisition module 9 is used to obtain the positions of all minimum bounding boxes that match the minimum sum of configuration coefficients, which are the configuration positions of all green plants.

[0154] Furthermore, the minimum bounding box acquisition module 1 specifically includes a first minimum bounding box acquisition submodule, a second minimum bounding box acquisition submodule, a third minimum bounding box acquisition submodule, a fourth minimum bounding box acquisition submodule, a fifth minimum bounding box acquisition submodule, and a sixth minimum bounding box acquisition submodule, which are electrically connected in sequence; the sixth minimum bounding box acquisition submodule is electrically connected to the minimum bounding box constraint module 2.

[0155] Among them, the first minimum bounding box acquisition submodule is used to construct a three-dimensional rectangular coordinate system with the upper surface of the matrix as the xy plane; the second minimum bounding box acquisition submodule is used to obtain the first projection, second projection, and third projection of the current green plant based on the xy plane, xz plane, and yz plane respectively; the third minimum bounding box acquisition submodule is used to define each coordinate axis of the three-dimensional coordinate system as a rotation axis; the fourth minimum bounding box acquisition submodule is used to rotate the current green plant according to each rotation axis, and obtain the minimum value of the total area of ​​the first projection, the second projection, and the third projection through the Floyd algorithm; the fifth minimum bounding box acquisition submodule is used to obtain the rotated form based on the current green plant corresponding to the minimum value; the sixth minimum bounding box acquisition submodule is used to obtain the OBB bounding box of the rotated form, and define the bounding box as the minimum bounding box.

[0156] Furthermore, the second minimum enclosing box acquisition submodule specifically includes a first minimum enclosing box acquisition unit, a second minimum enclosing box acquisition unit, a third minimum enclosing box acquisition unit, a fourth minimum enclosing box acquisition unit, and a fifth minimum enclosing box acquisition unit that are electrically connected in sequence; the first minimum enclosing box acquisition unit is electrically connected to the first minimum enclosing box acquisition submodule, and the fifth minimum enclosing box acquisition unit is electrically connected to the fourth minimum enclosing box acquisition submodule.

[0157] Among them, the first minimum bounding box acquisition unit is used to acquire a first visual image based on the z-axis, a second visual image based on the y-axis, and a third visual image based on the z-axis of the current green plant; the second minimum bounding box acquisition unit is used to acquire a first boundary coordinate set of the first visual image, a second boundary coordinate set of the second visual image, and a third boundary coordinate set of the third visual image based on a boundary extraction algorithm; the third minimum bounding box acquisition unit is used to acquire the enclosed area of ​​the first boundary coordinate set and define it as the first projection; the fourth minimum bounding box acquisition unit is used to acquire the enclosed area of ​​the second boundary coordinate set and define it as the second projection; the fifth minimum bounding box acquisition unit is used to acquire the enclosed area of ​​the third boundary coordinate set and define it as the third projection.

[0158] Further, the second minimum enclosing box acquisition unit specifically includes a first minimum enclosing box acquisition sub-unit, a second minimum enclosing box acquisition sub-unit, a third minimum enclosing box acquisition sub-unit, a fourth minimum enclosing box acquisition sub-unit, a fifth minimum enclosing box acquisition sub-unit, a sixth minimum enclosing box acquisition sub-unit, a seventh minimum enclosing box acquisition sub-unit, and an eighth minimum enclosing box acquisition sub-unit, which are electrically connected in sequence: the first minimum enclosing box acquisition sub-unit is electrically connected to the first minimum enclosing box acquisition unit, and the eighth minimum enclosing box acquisition sub-unit is electrically connected to the third minimum enclosing box acquisition unit.

[0159] Among them, the first minimum bounding box acquisition subunit is used to perform binarization processing on all visual images respectively, and obtain a black and white visual image based on one visual image; the second minimum bounding box acquisition subunit is used to define grids of the same resolution according to the resolution of all visual images to divide all black and white visual images; the third minimum bounding box acquisition subunit is used to obtain the RGB color value of each grid of the current black and white visual image; the fourth minimum bounding box acquisition subunit is used to assign 1 to the grid with RGB color value of 0, 0, 0, and assign 0 to the grid with RGB color value of 255, 255, 255; the fifth minimum bounding box acquisition subunit is used to define each grid and the eight adjacent grids as a 3×3 window; the sixth minimum bounding box acquisition subunit is used to traverse all grids through the 3×3 window with one grid as a step on all grids; the seventh minimum bounding box acquisition subunit is used to delete the grids with all values ​​1 and the grids with all values ​​0 in the 3×3 window based on all traversal results; the eighth minimum bounding box acquisition subunit is used to obtain the retained grids and define them as boundaries and obtain the coordinate set of the boundaries.

[0160] Furthermore, the fourth minimum enclosing box acquisition submodule includes a sixth minimum enclosing box acquisition unit, a seventh minimum enclosing box acquisition unit, an eighth minimum enclosing box acquisition unit, and a ninth minimum enclosing box acquisition unit that are electrically connected in sequence; the sixth minimum enclosing box acquisition unit is electrically connected to the third minimum enclosing box acquisition submodule, and the ninth minimum enclosing box acquisition unit is electrically connected to the fifth minimum enclosing box acquisition submodule.

[0161] Among them, the sixth minimum bounding box acquisition unit is used to define a rotation step for each rotation axis respectively; the seventh minimum bounding box acquisition unit is used to traverse all rotation positions of each rotation axis respectively, and obtain the first real-time projection, the second real-time projection, and the third real-time projection of the current green plant in each traversal; the eighth minimum bounding box acquisition unit is used to traverse the real-time total area of ​​each first real-time projection, each second real-time projection, and each third real-time projection according to the triple loop of Floyd's algorithm; the ninth minimum bounding box acquisition unit is used to obtain the real-time total area with the smallest value among all real-time total areas and mark it as the minimum value.

[0162] Furthermore, the configuration coefficient sum iteration module 8 specifically includes a first configuration coefficient sum iteration submodule, a second configuration coefficient sum iteration submodule, a third configuration coefficient sum iteration submodule, a fourth configuration coefficient sum iteration submodule, a fifth configuration coefficient sum iteration submodule, and a sixth configuration coefficient sum iteration submodule, which are electrically connected in sequence; the first configuration coefficient sum iteration submodule is electrically connected to the minimum bounding box configuration coefficient acquisition module 7, and the sixth configuration coefficient sum iteration submodule is electrically connected to the configuration position acquisition module 9.

[0163] The first configuration coefficient sum iterator module is used to define a number of random solutions for the minimum value of the configuration coefficient sum according to formula (1), and define the optimization result of all random solutions as the minimum sum of configuration coefficients of all minimum bounding boxes.

[0164] (1).

[0165] in, is the set of all random solutions, For each random solution, is the label of the random solution, is the number of all random solutions; is the set of velocities of all random solutions, are the speeds of each random solution respectively.

[0166] The second configuration coefficient sum iterator module is used to initialize the position of each random solution, and based on the same random solution, update the current position and current velocity according to formula (2):

[0167] (2).

[0168] in, For the The random solution is The speed of the step, For the The random solution is The speed inertia of the step, is the inertia coefficient, For the The self-perception representation of a random solution, For the social cognitive representation of a random solution; and are learning factors, for A random number, For the The individual optimal solution obtained by random solutions is For the The global optimal solution obtained by random solutions is For the Step 1 A random solution.

[0169] The third configuration coefficient sum iterator module is used to iterate each random solution according to formula (2) to update each and each .

[0170] The fourth configuration coefficient sum iterator module is used to determine each Whether the difference compared with the previous iteration is less than or equal to the first preset adaptation threshold.

[0171] The fifth configuration coefficient sum iterative submodule is used to determine each if all first differences are less than or equal to the first preset adaptation threshold. Whether the second difference compared to the previous iteration is less than or equal to the second preset adaptation threshold.

[0172] The sixth configuration coefficient sum iteration submodule is used to determine that an optimal solution with a minimum sum of configuration coefficients of all minimum bounding boxes has been obtained if all second difference values ​​are less than or equal to a second preset adaptation threshold.

[0173] Furthermore, the third configuration coefficient sum iterator module is specifically used to linearly decrease the inertia coefficient once according to formula (3) based on each iteration step:

[0174] (3).

[0175] in, For the The random solution is The inertia coefficient after step optimization, is the initial inertia coefficient, is the current iteration number, is the maximum number of iteration steps.

[0176] It should be noted that this embodiment is an apparatus embodiment based on the above method embodiment. Additional contents such as the preference, extension, limitation, and example illustration of this embodiment can be found in the above method embodiment, and will not be elaborated in this embodiment.

[0177] This embodiment obtains the minimum bounding box of each green plant respectively; defines the root side of each minimum bounding box adjacent to the green plant as the bottom surface, and each bottom surface is constrained to the upper surface of the substrate; divides the upper surface of the substrate evenly into a number of grids; obtains the interval distance between the current grid and the external obstacles along the normal direction of the four transparent baffles; obtains the average value of the four interval distances of the current grid and defines it as the cardinality of the current grid; defines the inverse of the height value of the current minimum bounding box as the weight coefficient of the current minimum bounding box; adds up all the cardinalities of the grids covered by the current minimum bounding box and multiplies it by the weight coefficient of the current minimum bounding box to obtain the configuration coefficient of the current minimum bounding box; iteratively updates the positions of all minimum bounding boxes through a global optimization algorithm to minimize the sum of the configuration coefficients of all minimum bounding boxes; and obtains the positions of all minimum bounding boxes that match the minimum sum of configuration coefficients, which are the configuration positions of all green plants. This embodiment determines the placement order of taller plants and shorter plants by identifying the distance between the restricted space and external obstacles (fixed facilities such as walls and furniture). Since the matrix grid closer to the obstacle has a lower cardinality, the taller plants can have a lower configuration coefficient when they are located on the grid with a lower cardinality, so that the taller plants are closer to the obstacle and are less likely to block the lighting of other plants (obstacles block light by default). At the same time, the configuration method of this embodiment is based on the minimum bounding box of the green plants, so that multiple green plants will not infringe on their respective minimum bounding boxes when configured, thereby achieving the purpose of reasonable dense planting.

[0178] Figure 3 Schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 3 As shown, the electronic device 10 includes a processor 101 and a memory 102 coupled to the processor 101 .

[0179] The memory 102 stores program instructions for implementing a method for configuring green plants in a limited space according to any of the above embodiments.

[0180] The processor 101 is used to execute program instructions stored in the memory 102 to configure green plants in a limited space.

[0181] The processor 101 may also be referred to as a CPU (Central Processing Unit). The processor 101 may be an integrated circuit chip having a signal processing capability. The processor 101 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0182] Further, Figure 4 This is a schematic diagram of the structure of a storage medium according to an embodiment of the present application. Figure 4 The storage medium 11 of the embodiment of the present application stores program instructions 111 that can implement all the above methods, wherein the program instructions 111 can be stored in the above storage medium in the form of a software product, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) or a processor to execute all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes, or terminal devices such as computers, servers, mobile phones, tablets, etc.

[0183] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0184] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A method for arranging green plants in a confined space, wherein the confined space is enclosed by four transparent baffles covered by a cover plate and a matrix in sequence from top to bottom, and at least two green plants of different heights are planted on all matrices, characterized in that: The green plant configuration method comprises: Step S1, obtaining the minimum bounding box of each green plant respectively; Step S2, defining a side of each minimum bounding box adjacent to the root of the green plant as a bottom surface, and each bottom surface is constrained to the upper surface of the substrate; Step S3, evenly dividing the upper surface of the substrate into a plurality of grids; Step S4, obtaining the distance between the current grid and the external obstacles along the normal direction of the four transparent baffles; Step S5, obtaining the average value of the four interval distances of the current grid and defining it as the cardinality of the current grid; Step S6, defining the inverse of the height value of the current minimum bounding box as the weight coefficient of the current minimum bounding box; Step S7, summing up all bases of the grid covered by the current minimum bounding box and multiplying by the weight coefficient of the current minimum bounding box to obtain the configuration coefficient of the current minimum bounding box; Step S8, iteratively updating the positions of all minimum bounding boxes through a global optimization algorithm so that the sum of the configuration coefficients of all minimum bounding boxes is minimized; Step S9, obtaining the positions of all minimum bounding boxes that match the minimum sum of configuration coefficients as the configuration positions of all green plants; Step S1, respectively obtain the minimum bounding box of each green plant, including: Step S11, constructing a three-dimensional rectangular coordinate system with the upper surface of the substrate as the xy plane; Step S12, obtaining a first projection, a second projection, and a third projection of the current green plant based on the xy plane, the xz plane, and the yz plane respectively; Step S13, defining each coordinate axis of the three-dimensional coordinate system as a rotation axis; Step S14, rotating the current green plant according to each rotation axis respectively, and obtaining the minimum value of the total area of ​​the first projection, the second projection, and the third projection by using the Floyd algorithm; Step S15, obtaining a rotated form of the current green plant corresponding to the minimum value; Step S16, obtaining the OBB bounding box in the rotated form, and defining the bounding box as the minimum bounding box; Step S8, iteratively updating the positions of all minimum bounding boxes through a global optimization algorithm so as to minimize the sum of the configuration coefficients of all minimum bounding boxes, including: Step S81, defining a plurality of random solutions for the minimum value of the sum of the configuration coefficients according to formula (1), and defining the optimization result of all random solutions as the minimum sum of the configuration coefficients of all minimum bounding boxes; （1）； in, is the set of all random solutions, For each random solution, is the label of the random solution, is the number of all random solutions; is the set of velocities of all random solutions, are the speeds of each random solution respectively; Step S82, initialize the position of each random solution, and update the current position and current speed respectively based on the same random solution according to formula (2): （2）； in, For the The random solution is The speed of the step, For the The random solution is The speed inertia of the step, is the inertia coefficient, For the The self-perception representation of a random solution, For the social cognitive representation of a random solution; and are learning factors, for A random number, For the The individual optimal solution obtained by random solutions is For the The global optimal solution obtained by random solutions is For the Step 1 A random solution; Step S83, iterate each random solution according to formula (2) to update each and each ; Step S84, determine each Comparing the difference values ​​of the previous iteration to see whether they are less than or equal to the first preset adaptation threshold, if all the first difference values ​​are less than or equal to the first preset adaptation threshold, executing step S85; Step S85, determine each Comparing the second difference values ​​of the previous iteration to see whether they are less than or equal to the second preset adaptation threshold, if all the second difference values ​​are less than or equal to the second preset adaptation threshold, executing step S86; Step S86: determine whether an optimal solution with the smallest sum of configuration coefficients of all minimum bounding boxes has been obtained.

2. The green plant configuration method according to claim 1, characterized in that: Step S12, obtaining a first projection, a second projection, and a third projection of the current green plant based on the xy plane, the xz plane, and the yz plane, respectively, includes: Step S121, obtaining a first visual image of the current green plant based on the z-axis, a second visual image based on the y-axis, and a third visual image based on the z-axis; Step S122, acquiring a first boundary coordinate set of the first visual image, a second boundary coordinate set of the second visual image, and a third boundary coordinate set of the third visual image based on a boundary extraction algorithm; Step S123, obtaining the enclosed area of ​​the first boundary coordinate set and defining it as the first projection; Step S124, obtaining the enclosed area of ​​the second boundary coordinate set and defining it as the second projection; Step S125: obtaining the enclosed area of ​​the third boundary coordinate set and defining it as the third projection.

3. The green plant configuration method according to claim 2, characterized in that: Step S122, obtaining a first boundary coordinate set of the first visual image, a second boundary coordinate set of the second visual image, and a third boundary coordinate set of the third visual image based on a boundary extraction algorithm, includes: Step S1221, binarizing all visual images respectively, and obtaining a black-and-white visual image based on one visual image; Step S1222, defining a grid with the same resolution according to the resolutions of all visual images to divide all black and white visual images; Step S1223, obtaining the RGB color value of each grid of the current black and white visual image; Step S1224, assigning a value of 1 to the grid whose RGB color value is 0, 0, 0, and assigning a value of 0 to the grid whose RGB color value is 255, 255, 255; Step S1225, defining each grid and eight adjacent grids as a 3×3 window; Step S1226, traversing all grids through the 3×3 window with one grid as one step; Step S1227, based on all traversal results, delete the grids assigned all values ​​1 and the grids assigned all values ​​0 in the 3×3 window; Step S1228, obtain the retained grid and define it as a boundary and obtain the coordinate set of the boundary.

4. The green plant configuration method according to claim 1, characterized in that: Step S14, rotating the current green plant according to each rotation axis respectively, and obtaining the minimum value of the total area of ​​the first projection, the second projection, and the third projection by using the Floyd algorithm; Step S141, defining a rotation step length for each rotation axis; Step S142, traversing all rotation positions of each rotation axis respectively, and obtaining a first real-time projection, a second real-time projection, and a third real-time projection of the current green plant in each traversal; Step S143, traversing the real-time total area of ​​each first real-time projection, each second real-time projection, and each third real-time projection according to the triple loop of the Floyd algorithm; Step S144, obtaining the real-time total area with the smallest value among all the real-time total areas and marking it as the minimum value.

5. The green plant configuration method according to claim 1, characterized in that: Step S83, iterate each random solution according to formula (2) to update each and each ,include: Step S831, based on each iteration, the inertia coefficient is linearly reduced once according to formula (3): （3）； in, For the The random solution is The inertia coefficient after step optimization, is the initial inertia coefficient, is the current iteration number, is the maximum number of iteration steps.

6. A green plant arrangement device in a limited space, the green plant arrangement device being applied to the green plant arrangement method according to any one of claims 1 to 5, characterized in that: The green plant configuration device comprises: A minimum bounding box acquisition module is used to obtain the minimum bounding box of each green plant; A minimum bounding box constraint module is used to define a side of each minimum bounding box adjacent to the root of the green plant as a bottom surface, and each bottom surface is constrained to the upper surface of the substrate; A substrate upper surface division module, used for evenly dividing the upper surface of the substrate into a plurality of grids; The external obstacle distance acquisition module is used to obtain the distance between the current grid and the external obstacles along the normal direction of the four transparent baffles; A grid cardinality definition module is used to obtain the average value of the four interval distances of the current grid and define it as the cardinality of the current grid; A minimum bounding box weight coefficient definition module is used to define the inverse of the height value of the current minimum bounding box as the weight coefficient of the current minimum bounding box; A minimum bounding box configuration coefficient acquisition module is used to add all bases of the grid covered by the current minimum bounding box and multiply by the weight coefficient of the current minimum bounding box to obtain the configuration coefficient of the current minimum bounding box; The configuration coefficient sum iteration module is used to iteratively update the positions of all minimum bounding boxes through a global optimization algorithm so as to minimize the sum of the configuration coefficients of all minimum bounding boxes; The configuration position acquisition module is used to obtain the positions of all minimum bounding boxes that match the minimum sum of configuration coefficients, which are the configuration positions of all green plants.

7. An electronic device, characterized in that: It includes a processor and a memory coupled to the processor, wherein the memory stores program instructions that can be executed by the processor; when the processor executes the program instructions stored in the memory, the green plant configuration method as described in any one of claims 1 to 5 is implemented.

8. A storage medium, characterized in that: The storage medium stores program instructions, and when the program instructions are executed by the processor, the green plant configuration method as described in any one of claims 1 to 5 can be implemented.

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