Mushroom picking sequence planning method, system and equipment based on dynamic shielding analysis and medium

Abstract

The invention discloses a mushroom picking sequence planning method, system and device based on dynamic shielding analysis and a medium. The method comprises the steps that mushrooms are clustered into an unaffected mushroom group and an affected mushroom group; sorting the mushrooms in the unaffected mushroom group according to the radius from large to small; sorting the mushrooms in the affected mushroom group according to the radius from large to small; selecting unshielded mushrooms with the maximum radius as initial picking points, putting the mushrooms into a sorting queue, and if a plurality of candidates exist, selecting the mushroom with the highest influence factor; and re-calculating the average shielded rate and the influence factor of each remaining mushroom, updating the unaffected mushroom group and the affected mushroom group, selecting the mushroom with the minimum average shielded rate and the maximum radius, putting the mushroom into a sorting queue, if a plurality of candidates exist, selecting the mushroom with the highest influence factor, and repeating the process until all the mushrooms in the image enter the sorting queue. Precise picking can be achieved, and the product quality is improved.

Description

Technical Field

[0001] The present invention relates to the planning of mushroom picking order, and particularly to a method, system, device and medium for planning mushroom picking order based on dynamic occlusion analysis. Background Art

[0002] Currently, mushroom picking robots are gradually replacing manual labor and being applied to mushroom picking operations. Mushroom picking robots mainly achieve automatic picking through visual positioning and path optimization. Picking order planning is crucial for improving operation efficiency and reducing mushroom damage rate, especially in the scenario of dense mushroom clusters. A method for classifying Agaricus bisporus groups and an automatic picking strategy for Agaricus bisporus disclosed in CN116597307A propose the following solutions: spiral picking path: pick mushrooms sequentially along a spiral trajectory from the center of the mushroom bed to the outside to reduce the empty travel of the robotic arm; static clustering strategy: divide the mushroom cluster into multiple sub-clusters according to the mushroom spacing, and preferentially pick mushrooms within a single cluster; overlap rate determination: calculate the overlapping area of mushrooms through RGB images, and when the set threshold (overlap rate > 30%) is reached, it is determined as occlusion. The existing technical problems are as follows:

[0003] (1) Poor adaptability to dynamic occlusion: A fixed spiral picking sequence is generated in the initialization stage, unable to respond dynamically to changes; the update of the occlusion relationship lags behind. When a certain mushroom is picked, the newly exposed mushrooms in its occlusion area cannot be inserted into the queue to be picked in time; using the radius or position as a fixed sorting basis ("from the center to the outside" strategy), the real-time reachability change after occlusion release is not considered.

[0004] (2) Disconnection between the clustering strategy and the order planning: The clustering method (DBSCAN clustering algorithm based on Euclidean distance) is only used for spatial division and does not have a linkage logic with the picking order. If the mushrooms within the same cluster are mutually occluded and actually need to be picked alternately across clusters, the original plan enforces the execution according to the cluster order.

[0005] (3) Risk of path conflict: The picking sequence generated by static planning may contain spatial topological contradictions. If the outer mushrooms are picked first, it may cause the inner mushrooms to be occluded and unreachable. Summary of the Invention

[0006] Object of the Invention: The object of the present invention is to provide a method, system, device and medium for planning mushroom picking order based on dynamic occlusion analysis, which can dynamically update parameters and plan the mushroom picking order, so as to perform automatic mushroom picking more accurately and improve product quality.

[0007] Technical Solution: A method for planning mushroom picking order based on dynamic occlusion analysis according to the present invention includes:

[0008] (1) Obtain a mushroom image and identify n mushroom targets through a deep learning algorithm;

[0009] (2) Calculate the radius of mushroom M i and sort the mushrooms in descending order of radius; calculate the spacing d i between adjacent mushrooms M j and M ij , and obtain the spacing set D i of mushroom M i ; cluster the mushrooms according to the spacing set D i to obtain the non - influencing mushroom group S and the influencing mushroom group G;

[0010] (3) Calculate the gap angle α i between adjacent mushrooms M j and M ij , and obtain the gap angle set α(i) of mushroom M i ; calculate the average occlusion rate and the average being - occluded rate of the mushrooms in the influencing mushroom group G;

[0011] (4) According to the average occlusion rate O i and the gap angle α i of mushroom M i , calculate the influence factor F i of mushroom M i ;

[0012] (5) The picking order is planned as follows:

[0013] (501) Sort the mushrooms in the non - influencing mushroom group S in descending order of radius;

[0014] (502) Sort the mushrooms in the influencing mushroom group G in descending order of radius;

[0015] (503) Select the mushroom M1 with the largest radius and not being occluded as the initial picking point and put it into the sorting queue T. If there are multiple candidates, select the one with the highest influence factor;

[0016] (504) Recalculate the average being - occluded rate o i and the influence factor F i of each remaining mushroom, and update the non - influencing mushroom group S and the influencing mushroom group G;

[0017] (505) Select the mushroom M j with the smallest average being - occluded rate and the largest radius and put it into the sorting queue T. If there are multiple candidates, select the one with the highest influence factor;

[0018] (506) Repeat steps (504) - (505) until all the mushrooms in the mushroom image enter the sorting queue T.

[0019] Further, step (2) includes:

[0020] (201) Calculate the radius r of mushroom M i of i :

[0021]

[0022] where (Lx i , Ly i ) are the upper left coordinates of mushroom M i , and (Rx i , Ry i ) are the lower right coordinates of mushroom M i ; The mushroom queue Q = {M1, M2, …, M n}, r1 ≥ r2 ≥ … ≥ r n .

[0023] Furthermore, step (2) also includes:

[0024] (202) Calculate the center coordinates (x i , y i ) of mushroom M i :

[0025]

[0026] Calculate the center gap D i between adjacent mushrooms M j and M ij :

[0027]

[0028] Then, the distance between adjacent mushrooms M i and M j is d ij :

[0029] d ij = D ij - r i - r j

[0030] where r i and r j are the radii of mushrooms M i and M j respectively;

[0031] The distance set D i of mushroom M i＝ {d 1i , d 2i ,..., d (n-1)i}, and the adjacent mushroom set N(i) of mushroom M i = {M k | d ik<0, k≠i}。

[0032] Furthermore, step (2) also includes:

[0033] (203) Cluster the mushrooms:

[0034] If for mushroom M i the spacing values in the spacing set D i are all greater than or equal to 0, then put mushroom M i into the non - influencing mushroom group S;

[0035] If for mushroom M i the spacing values in the spacing set D i have values less than 0, then put it into the influencing mushroom group G.

[0036] Furthermore, step (3) includes:

[0037] (301) Calculate the gap angle α i between adjacent mushrooms M j and M ij :

[0038] Taking the line connecting the center points C i and C j of the two mushrooms as the reference line L ij , respectively make the outer common tangents of the two mushrooms, such that they are on both sides of the reference line, obtaining two common tangents L1 and L2. The included angle between the common tangent L1 and the reference line L ij is θ1, and the included angle between the common tangent L2 and the reference line L ij is θ2. The gap angle α ij = θ1 + θ2, When r i = r j :

[0039] The gap angle set α(i) of mushroom M i = {α ij , α ik , …, α io |j, k, …, o ∈ N(i)}.

[0040] Furthermore, step (3) also includes:

[0041] (302) Calculate the average occlusion rate and average being - occluded rate of the mushrooms:

[0042] If there is an overlapping relationship between mushroom M i and mushroom M j , and mushroom M j occludes mushroom M i , that is, mushroom M j is within mushroom M iWhen above, mushroom M j has an overlap rate of S ij is the overlapping area of the mushrooms; then when there are m mushrooms around being blocked by mushroom M j mushroom M j has an average blocking rate of Similarly, at this time, mushroom M i is blocked by mushroom M j mushroom M i has an overlap rate of When there are n mushrooms blocking mushroom M i mushroom M i has an average blocked rate of

[0043] Furthermore, traverse the affected mushroom group G and calculate the overlapping area of the mushrooms:

[0044] S ij =S 扇i +S 扇j -S Δoij -S ΔNij

[0045] where the area of sector i angle θ is: θ = θ1 + θ2, where the area of sector j is the same; the area of Δoij the area of ΔNij is the same.

[0046] Furthermore, step (4) includes:

[0047] Calculate the influence factor F i of mushroom M i :

[0048]

[0049] where O i is the average blocking rate of mushroom M i ; ∈ = 1°; is the minimum gap angle of mushroom M i .

[0050] Based on the same inventive concept, a mushroom picking sequence planning system based on dynamic occlusion analysis of the present invention includes:

[0051] A mushroom target recognition module for implementing step (1) in the above mushroom picking sequence planning method;

[0052] A geometric relationship calculation module for implementing steps (2) to (4) in the above mushroom picking sequence planning method;

[0053] The picking order planning module is used to implement step (5) in the above mushroom picking order planning method.

[0054] Based on the same inventive concept, a mushroom picking order planning device of the present invention includes a processor and a memory. Computer instructions are stored in the memory, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the steps of the above mushroom picking order planning method.

[0055] Based on the same inventive concept, a computer-readable storage medium of the present invention stores a computer program, which when executed by a processor, implements the steps of the above mushroom picking order planning method.

[0056] Advantages: Compared with the prior art, the present invention has the following remarkable advantages:

[0057] (1) Improve picking accuracy: Through the dynamic occlusion analysis technology, accurately identify the spatial relationship between mushrooms, significantly reduce the extrusion damage to adjacent mushrooms during the picking process, and ensure the integrity of the mushroom cap and the accuracy of picking positioning.

[0058] (2) Realize cross-cluster alternate picking: The present invention adopts a method of linking cluster division and picking order, making the picking operation more flexible and efficient, meeting the requirements of actual application scenarios. By calculating the influence factor of each mushroom and combining its radius size and occlusion situation, an optimal picking path can be effectively planned, avoiding the cross-cluster obstacle problem that may occur in traditional static cluster division strategies.

[0059] (3) Solve path conflicts: Effectively avoid the problem that the internal mushrooms cannot be reached due to picking the outer mushrooms first. The present invention updates the occlusion rate and influence factor of mushrooms in real time, adjusts the picking order in a timely manner, ensures that all mushrooms can be picked in the best state, and reduces the remaining number of mushrooms in the picked area. Description of the Drawings

[0060] Figure 1 is a flowchart of the mushroom picking order planning method based on dynamic occlusion analysis provided by an embodiment of the present invention;

[0061] Figure 2 is a flowchart of the geometric relationship calculation in an embodiment of the present invention;

[0062] Figure 3 is a schematic diagram of the gap angle between mushrooms in an embodiment of the present invention;

[0063] Figure 4 is a schematic diagram of the overlapping area between mushrooms in an embodiment of the present invention;

[0064] Figure 5 It is the flowchart of the picking sequence planning algorithm in the embodiment of the present invention;

[0065] Figure 6 It is the mushroom image sorted by radius size in the embodiment of the present invention. Specific embodiments

[0066] The present invention will be further described below with reference to the accompanying drawings.

[0067] Embodiment 1

[0068] As Figure 1 shown, Embodiment 1 provides a method for planning the picking sequence of mushrooms based on dynamic occlusion analysis, including the following steps:

[0069] (1) Obtain the mushroom image and identify n mushroom targets through a deep learning algorithm, which is the prior art;

[0070] (2) Combine Figure 2 to calculate the radius of mushroom M i , sort the mushrooms in descending order of radius; calculate the distance d between adjacent mushrooms M i and M j ij , obtain the distance set D of mushroom M i i ; cluster the mushrooms according to the distance set D i to obtain the non-influenced mushroom group S and the influenced mushroom group G;

[0071] (201) Calculate the radius r i of mushroom M i :

[0072]

[0073] where (Lx i , Ly i ) is the upper left coordinate of mushroom M i , (Rx i , Ry i ) is the lower right coordinate of mushroom M i , and the coordinates are automatically generated when the mushrooms are identified by the deep learning algorithm, which is the prior art;

[0074] The mushroom queue Q = {M1, M2,..., M n}, r1 ≥ r2 ≥... ≥ r n .

[0075] (202) Calculate the center coordinates (x i , y i ) of mushroom M i :

[0076]

[0077] Calculate the central gap D between adjacent mushrooms M i and M j : ij :

[0078]

[0079] Then, the spacing between adjacent mushrooms M i and M j is d ij :

[0080] d ij = D ij - r i - r j

[0081] where r i and r j are the radii of mushrooms M i and M j respectively;

[0082] The set of spacings D of mushroom M i is {d i＝ , d 1i ,..., d 2i ,..., d (n-1)i}, and the set of adjacent mushrooms N(i) of mushroom M i is {M k | d ik < 0, k ≠ i}.

[0083] (203) Cluster the mushrooms:

[0084] If all the spacing values in the set of spacings D of mushroom M i are greater than or equal to 0, then put mushroom M i into the non - influencing mushroom group S; i into the non - influencing mushroom group S;

[0085] If there are spacing values less than 0 in the set of spacings D of mushroom M i put it into the influencing mushroom group G. i put it into the influencing mushroom group G.

[0086] (3) Calculate the gap angle α i between adjacent mushrooms M j and M ij , obtain the set of gap angles α(i) of mushroom M i ; Calculate the average occlusion rate and average being - occluded rate of the mushrooms in the influencing mushroom group G;

[0087] (301) Calculate the adjacent mushroom M iand M j The gap angle α between ij :

[0088] As Figure 3 shown, with the line connecting the centers C i and C j of the two mushrooms as the reference line L ij , draw the outer common tangents of the two mushrooms respectively, making them on both sides of the reference line, and obtain two common tangents L1 and L2. The included angle between the common tangent L1 and the reference line L ij is θ1, and the included angle between the common tangent L2 and the reference line L ij is θ2. The gap angle α ij = θ1 + θ2, When r i = r j ,

[0089] The set of gap angles α(i) of mushroom M i = {α ij , α ik , …, α io |j, k, …, o ∈ N(i)}.

[0090] (302) Calculate the average occlusion rate and average occluded rate of the mushrooms:

[0091] As Figure 4 shown, traverse the affected mushroom group G and calculate the overlapping area of the mushrooms:

[0092] S ij = S 扇i + S 扇j - S Δoij - S ΔNij

[0093] Among them, the area of sector i The angle θ is: θ = θ1 + θ2, where The area of sector j is the same; the area of Δoij The area of ΔNij is the same.

[0094] If there is an overlapping relationship between mushroom M i and mushroom M j , and mushroom M j occludes mushroom M i , that is, when mushroom M j is above mushroom M i , the overlapping rate of mushroom M j is Then when there are m mushrooms around being occluded by mushroom M j , the average occlusion rate of mushroom M j is Similarly, at this time, mushroom M i is blocked by mushroom M j and the overlapping rate of mushroom M i is When there are n mushrooms blocking mushroom M i , the average blocking rate of mushroom M i is

[0095] (4) According to the average blocking rate O i of mushroom M i and the gap angle α i , calculate the influence factor F i of mushroom M i ;

[0096]

[0097] where O i is the average blocking rate of mushroom M i ; ∈ = 1°; is the minimum gap angle of mushroom M i .

[0098] (5) Combining Figure 5 , the picking order is planned as follows:

[0099] (501) Sort the mushrooms in the mushroom group S without influence in descending order of radius;

[0100] (502) Sort the mushrooms in the mushroom group G with influence in descending order of radius;

[0101] (503) Select the mushroom M1 with the largest radius and not blocked as the initial picking point and put it into the sorting queue T. If there are multiple candidates, select the one with the highest influence factor;

[0102] (504) Recalculate the average blocking rate o i and the influence factor F i of each remaining mushroom, and update the mushroom group S without influence and the mushroom group G with influence;

[0103] (505) Select the mushroom M j with the smallest average blocking rate and the largest radius and put it into the sorting queue T. If there are multiple candidates, select the one with the highest influence factor;

[0104] (506) Repeat steps (504) to (505) until all the mushrooms in the mushroom image enter the sorting queue T.

[0105] Example 2

[0106] Embodiment 2 provides a mushroom picking order planning system based on dynamic occlusion analysis, specifically including:

[0107] A mushroom target recognition module, which is used to implement step (1) in the mushroom picking order planning method;

[0108] A geometric relationship calculation module, which is used to implement steps (2) to (4) in the mushroom picking order planning method;

[0109] A picking order planning module, which is used to implement step (5) in the mushroom picking order planning method.

[0110] Embodiment 3

[0111] Embodiment 3 provides a mushroom picking order planning device based on dynamic occlusion analysis, including a processor and a memory. Computer instructions are stored in the memory, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the steps of the method described in the above embodiment and can achieve the same technical effects as the above method.

[0112] The memory may include a computer system readable medium in the form of volatile memory, such as random access memory (RAM) and / or cache memory. The device may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, the memory may be used to read and write non-removable, non-volatile magnetic media (commonly referred to as "hard disk drives"). Programs / utilities with a set of (at least one) program modules may be stored in the memory, such as an operating system, one or more application programs, other program modules, and program data. Implementations of network environments may be included in each or some combination of these examples. Program modules generally execute the functions and / or methods in the embodiments described in the present invention.

[0113] The processor executes various functional applications and data processing by running the programs stored in the memory, such as implementing the method provided in the embodiment of the present invention.

[0114] Embodiment 4

[0115] Embodiment 4 provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, it implements the steps of the method described in the above embodiment and can achieve the same technical effects as the above method.

[0116] The computer storage medium of an embodiment of the present invention may adopt any combination of one or more computer-readable media. The computer-readable media may be computer-readable signal media or computer-readable storage media. The computer-readable storage media may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage media include: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer-readable storage media may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0117] The computer-readable signal media may include a data signal propagated in a baseband or as part of a carrier wave, which carries the computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal media may also be any computer-readable media other than the computer-readable storage media, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0118] The program code contained on the computer-readable media may be transmitted by any appropriate medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the above.

[0119] The computer program code for performing the operations of the present invention may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0120] Of course, for a storage medium containing computer-executable instructions provided by an embodiment of the present invention, the computer-executable instructions are not limited to the above method operations, and can also execute relevant operations in the methods provided by any embodiment of the present invention.

[0121] A specific example is given below.

[0122] As Figure 6 shown, it is a mushroom image after calculating the radius and sorting according to the radius size.

[0123] Initial state:

[0124] Non-influential mushroom group S (2): ['a15', 'a20']

[0125] Influential mushroom group G (20): ['a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9', 'a10', 'a11', 'a12', 'a13', 'a14', 'a16', 'a17', 'a18', 'a19', 'a21', 'a22']

[0126]

[0127]

[0128] Step 1: Pick a15

[0129] Selection strategy: Non-influential mushroom group S (the largest radius → the highest influence factor)

[0130] Non-influential mushroom group S (1): ['a20']

[0131] Influential mushroom group G (20): ['a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9', 'a10', 'a11', 'a12', 'a13', 'a14', 'a16', 'a17', 'a18', 'a19', 'a21', 'a22']

[0132] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a2 97 2.88％ 34.22° 0.0008 a16, a22 a3 92 2.23％ 0.67° 0.0133 a4, a7 a4 91 8.58％ 0.67° 0.0513 a3, a7, a8, a17 a5 86 18.12％ 9.83° 0.0167 a1, a10, a11, a18 a6 84 1.61％ 2.97° 0.0040 a7 a7 80 9.08％ 2.97° 0.0299 a3, a4, a6, a8 a8 75 20.51％ 4.21° 0.0393 a4, a7, a12 a9 70 2.87％ 22.75° 0.0012 a18 a10 68 3.27％ 14.37° 0.0021 a5, a21 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a13 63 3.39％ 16.16° 0.0020 a19 a14 62 3.19％ 17.75° 0.0017 a22 a16 55 6.80％ 10.08° 0.0061 a2, a22 a17 52 27.09％ 5.35° 0.0426 a4, a18 a18 49 41.95％ 5.35° 0.0660 a5, a9, a17 a19 49 5.61％ 16.16° 0.0033 a13 a20 48 0.00％ 180.00° 0.0000 a21 47 15.61％ 21.98° 0.0068 a10, a11 a22 47 18.23％ 10.08° 0.0165 a2, a14, a16

[0133] Step 2: Pick a20

[0134] Selection strategy: Non-influential mushroom group S (the largest radius → the highest influence factor)

[0135] Non-influential mushroom group S (0): []

[0136] Influential mushroom group G (20): ['a1', 'a2', 'a3', 'a4', 'a5', 'a6', 'a7', 'a8', 'a9', 'a10', 'a11', 'a12', 'a13', 'a14', 'a16', 'a17', 'a18', 'a19', 'a21', 'a22']

[0137]

[0138]

[0139] Step 3: Pick a6

[0140] Selection strategy: Influential mushroom group G (lowest occlusion rate → largest radius → highest influence factor)

[0141] Non - influential mushroom group S (0): []

[0142] Influential mushroom group G (19): ['a1', 'a2', 'a3', 'a4', 'a5', 'a7', 'a8', 'a9', 'a10', 'a11', 'a12', 'a13', 'a14', 'a16', 'a17', 'a18', 'a19', 'a21', 'a22']

[0143] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a2 97 2.88％ 34.22° 0.0008 a16, a22 a3 92 2.23％ 0.67° 0.0133 a4, a7 a4 91 8.58％ 0.67° 0.0513 a3, a7, a8, a17 a5 86 18.12％ 9.83° 0.0167 a1, a10, a11, a18 a7 80 7.31％ 4.21° 0.0140 a3, a4, a8 a8 75 20.51％ 4.21° 0.0393 a4, a7, a12 a9 70 2.87％ 22.75° 0.0012 a18 a10 68 3.27％ 14.37° 0.0021 a5, a21 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a13 63 3.39％ 16.16° 0.0020 a19 a14 62 3.19％ 17.75° 0.0017 a22 a16 55 6.80％ 10.08° 0.0061 a2, a22 a17 52 27.09％ 5.35° 0.0426 a4, a18 a18 49 41.95％ 5.35° 0.0660 a5, a9, a17 a19 49 5.61％ 16.16° 0.0033 a13 a21 47 15.61％ 21.98° 0.0068 a10, a11 a22 47 18.23％ 10.08° 0.0165 a2, a14, a16

[0144] Step 4: Pick a3

[0145] Selection strategy: Influential mushroom group G (lowest occlusion rate → largest radius → highest influence factor)

[0146] Non - influential mushroom group S (0): []

[0147] Influential mushroom group G (18): ['a1', 'a2', 'a4', 'a5', 'a7', 'a8', 'a9', 'a10', 'a11', 'a12', 'a13', 'a14', 'a16', 'a17', 'a18', 'a19', 'a21', 'a22']

[0148] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a2 97 2.88％ 34.22° 0.0008 a16, a22 a4 91 6.32％ 7.94° 0.0071 a7, a8, a17 a5 86 18.12％ 9.83° 0.0167 a1, a10, a11, a18 a7 80 7.28％ 4.21° 0.0140 a4, a8 a8 75 20.51％ 4.21° 0.0393 a4, a7, a12 a9 70 2.87％ 22.75° 0.0012 a18 a10 68 3.27％ 14.37° 0.0021 a5, a21 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a13 63 3.39％ 16.16° 0.0020 a19 a14 62 3.19％ 17.75° 0.0017 a22 a16 55 6.80％ 10.08° 0.0061 a2, a22 a17 52 27.09％ 5.35° 0.0426 a4, a18 a18 49 41.95％ 5.35° 0.0660 a5, a9, a17 a19 49 5.61％ 16.16° 0.0033 a13 a21 47 15.61％ 21.98° 0.0068 a10, a11 a22 47 18.23％ 10.08° 0.0165 a2, a14, a16

[0149] Step 5: Pick a9

[0150] Selection strategy: Influential mushroom group G (lowest occlusion rate → largest radius → highest influence factor)

[0151] Non - influential mushroom group S (0): []

[0152] There are affected mushroom groups G (17): ['a1', 'a2', 'a4', 'a5', 'a7', 'a8', 'a10', 'a11', 'a12', 'a13', 'a14', 'a16', 'a17', 'a18', 'a19', 'a21', 'a22']

[0153] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a2 97 2.88％ 34.22° 0.0008 a16, a22 a4 91 6.32％ 7.94° 0.0071 a7, a8, a17 a5 86 18.12％ 9.83° 0.0167 a1, a10, a11, a18 a7 80 7.28％ 4.21° 0.0140 a4, a8 a8 75 20.51％ 4.21° 0.0393 a4, a7, a12 a10 68 3.27％ 14.37° 0.0021 a5, a21 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a13 63 3.39％ 16.16° 0.0020 a19 a14 62 3.19％ 17.75° 0.0017 a22 a16 55 6.80％ 10.08° 0.0061 a2, a22 a17 52 27.09％ 5.35° 0.0426 a4, a18 a18 49 36.09％ 5.35° 0.0568 a5, a17 a19 49 5.61％ 16.16° 0.0033 a13 a21 47 15.61％ 21.98° 0.0068 a10, a11 a22 47 18.23％ 10.08° 0.0165 a2, a14, a16

[0154] Step 6: Pick a2

[0155] Selection strategy: Affected mushroom group G (lowest occlusion rate → largest radius → highest influence factor)

[0156] There are no affected mushroom groups S (0): []

[0157] There are affected mushroom groups G (16): ['a1', 'a4', 'a5', 'a7', 'a8', 'a10', 'a11', 'a12', 'a13', 'a14', 'a16', 'a17', 'a18', 'a19', 'a21', 'a22']

[0158] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a4 91 6.32％ 7.94° 0.0071 a7, a8, a17 a5 86 18.12％ 9.83° 0.0167 a1, a10, a11, a18 a7 80 7.28％ 4.21° 0.0140 a4, a8 a8 75 20.51％ 4.21° 0.0393 a4, a7, a12 a10 68 3.27％ 14.37° 0.0021 a5, a21 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a13 63 3.39％ 16.16° 0.0020 a19 a14 62 3.19％ 17.75° 0.0017 a22 a16 55 3.54％ 10.08° 0.0032 a22 a17 52 27.09％ 5.35° 0.0426 a4, a18 a18 49 36.09％ 5.35° 0.0568 a5, a17 a19 49 5.61％ 16.16° 0.0033 a13 a21 47 15.61％ 21.98° 0.0068 a10, a11 a22 47 10.41％ 10.08° 0.0094 a14, a16

[0159] Step 7: Pick a14

[0160] Selection strategy: Affected mushroom group G (lowest occlusion rate → largest radius → highest influence factor)

[0161] There are no affected mushroom groups S (0): []

[0162] There are affected mushroom groups G (15): ['a1', 'a4', 'a5', 'a7', 'a8', 'a10', 'a11', 'a12', 'a13', 'a16', 'a17', 'a18', 'a19', 'a21', 'a22']

[0163] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a4 91 6.32％ 7.94° 0.0071 a7, a8, a17 a5 86 18.12％ 9.83° 0.0167 a1, a10, a11, a18 a7 80 7.28％ 4.21° 0.0140 a4, a8 a8 75 20.51％ 4.21° 0.0393 a4, a7, a12 a10 68 3.27％ 14.37° 0.0021 a5, a21 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a13 63 3.39％ 16.16° 0.0020 a19 a16 55 3.54％ 10.08° 0.0032 a22 a17 52 27.09％ 5.35° 0.0426 a4, a18 a18 49 36.09％ 5.35° 0.0568 a5, a17 a19 49 5.61％ 16.16° 0.0033 a13 a21 47 15.61％ 21.98° 0.0068 a10, a11 a22 47 4.85％ 10.08° 0.0044 a16

[0164] Step 8: Pick a10

[0165] Selection strategy: Affected mushroom group G (lowest occlusion rate → largest radius → highest influence factor)

[0166] There are no affected mushroom groups S (0): []

[0167] There are affected mushroom groups G (14): ['a1', 'a4', 'a5', 'a7', 'a8', 'a11', 'a12', 'a13', 'a16', 'a17', 'a18', 'a19', 'a21', 'a22']

[0168] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a4 91 6.32％ 7.94° 0.0071 a7, a8, a17 a5 86 16.53％ 9.83° 0.0153 a1, a11, a18 a7 80 7.28％ 4.21° 0.0140 a4, a8 a8 75 20.51％ 4.21° 0.0393 a4, a7, a12 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a13 63 3.39％ 16.16° 0.0020 a19 a16 55 3.54％ 10.08° 0.0032 a22 a17 52 27.09％ 5.35° 0.0426 a4, a18 a18 49 36.09％ 5.35° 0.0568 a5, a17 a19 49 5.61％ 16.16° 0.0033 a13 a21 47 14.09％ 26.05° 0.0052 a11 a22 47 4.85％ 10.08° 0.0044 a16

[0169] Step 9: Pick a13

[0170] Selection strategy: Influential mushroom group G (lowest occlusion rate → largest radius → highest influence factor)

[0171] Non-influential mushroom group S (1): ['a19']

[0172] Influential mushroom group G (12): ['a1', 'a4', 'a5', 'a7', 'a8', 'a11', 'a12', 'a16', 'a17', 'a18', 'a21', 'a22']

[0173] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a4 91 6.32％ 7.94° 0.0071 a7, a8, a17 a5 86 16.53％ 9.83° 0.0153 a1, a11, a18 a7 80 7.28％ 4.21° 0.0140 a4, a8 a8 75 20.51％ 4.21° 0.0393 a4, a7, a12 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a16 55 3.54％ 10.08° 0.0032 a22 a17 52 27.09％ 5.35° 0.0426 a4, a18 a18 49 36.09％ 5.35° 0.0568 a5, a17 a19 49 0.00％ 180.00° 0.0000 a21 47 14.09％ 26.05° 0.0052 a11 a22 47 4.85％ 10.08° 0.0044 a16

[0174] Step 10: Pick a19

[0175] Selection strategy: Non-influential mushroom group S (largest radius → highest influence factor)

[0176] Non-influential mushroom group S (0): []

[0177] Influential mushroom group G (12): ['a1', 'a4', 'a5', 'a7', 'a8', 'a11', 'a12', 'a16', 'a17', 'a18', 'a21', 'a22']

[0178]

[0179]

[0180] Step 11: Pick a16

[0181] Selection strategy: Influential mushroom group G (lowest occlusion rate → largest radius → highest influence factor), Non-influential mushroom group S (1): ['a22']

[0182] Influential mushroom group G (10): ['a1', 'a4', 'a5', 'a7', 'a8', 'a11', 'a12', 'a17', 'a18', 'a21']

[0183] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a4 91 6.32％ 7.94° 0.0071 a7, a8, a17 a5 86 16.53％ 9.83° 0.0153 a1, a11, a18 a7 80 7.28％ 4.21° 0.0140 a4, a8 a8 75 20.51％ 4.21° 0.0393 a4, a7, a12 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a17 52 27.09％ 5.35° 0.0426 a4, a18 a18 49 36.09％ 5.35° 0.0568 a5, a17 a21 47 14.09％ 26.05° 0.0052 a11 a22 47 0.00％ 180° 0.0000

[0184] Step 12: Pick a22

[0185] Selection strategy: Non-influential mushroom group S (largest radius → highest influence factor)

[0186] Influential mushroom group S (0): []

[0187] Influential mushroom groups G (10): ['a1', 'a4', 'a5', 'a7', 'a8', 'a11', 'a12', 'a17', 'a18', 'a21']

[0188] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a4 91 6.32％ 7.94° 0.0071 a7, a8, a17 a5 86 16.53％ 9.83° 0.0153 a1, a11, a18 a7 80 7.28％ 4.21° 0.0140 a4, a8 a8 75 20.51％ 4.21° 0.0393 a4, a7, a12 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a17 52 27.09％ 5.35° 0.0426 a4, a18 a18 49 36.09％ 5.35° 0.0568 a5, a17 a21 47 14.09％ 26.05° 0.0052 a11

[0189] Step 13: Pick a4

[0190] Selection strategy: Influential mushroom groups G (lowest occlusion rate → largest radius → highest influence factor), Non - influential mushroom groups S (0): []

[0191] Influential mushroom groups G (9): ['a1', 'a5', 'a7', 'a8', 'a11', 'a12', 'a17', 'a18', 'a21']

[0192]

[0193]

[0194] Step 14: Pick a7

[0195] Selection strategy: Influential mushroom groups G (lowest occlusion rate → largest radius → highest influence factor), Non - influential mushroom groups S (0): []

[0196] Influential mushroom groups G (8): ['a1', 'a5', 'a8', 'a11', 'a12', 'a17', 'a18', 'a21']

[0197] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a1 99 8.28％ 9.83° 0.0076 a5, a11 a5 86 16.53％ 9.83° 0.0153 a1, a11, a18 a8 75 10.47％ 10.52° 0.0091 a12 a11 68 12.83％ 14.97° 0.0080 a1, a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a17 52 23.42％ 5.35° 0.0369 a18 a18 49 36.09％ 5.35° 0.0568 a5, a17 a21 47 14.09％ 26.05° 0.0052 a11

[0198] Step 15: Pick a1

[0199] Selection strategy: Influential mushroom groups G (lowest occlusion rate → largest radius → highest influence factor), Non - influential mushroom groups S (0): []

[0200] Influential mushroom groups G (7): ['a5', 'a8', 'a11', 'a12', 'a17', 'a18', 'a21']

[0201] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a5 86 6.26％ 14.97° 0.0039 a11, a18 a8 75 10.47％ 10.52° 0.0091 a12 a11 68 11.70％ 14.97° 0.0073 a5, a21 a12 65 13.94％ 10.52° 0.0121 a8 a17 52 23.42％ 5.35° 0.0369 a18 a18 49 36.09％ 5.35° 0.0568 a5, a17 a21 47 14.09％ 26.05° 0.0052 a11

[0202] Step 16: Pick a5

[0203] Selection strategy: Influential mushroom groups G (lowest occlusion rate → largest radius → highest influence factor), Non - influential mushroom groups S (0): []

[0204] Influential mushroom groups G (6): ['a8', 'a11', 'a12', 'a17', 'a18', 'a21']

[0205] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a8 75 10.47％ 10.52° 0.0091 a12 a11 68 6.73％ 26.05° 0.0025 a21 a12 65 13.94％ 10.52° 0.0121 a8 a17 52 23.42％ 5.35° 0.0369 a18 a18 49 26.38％ 5.35° 0.0415 a17 a21 47 14.09％ 26.05° 0.0052 a11

[0206] Step 17: Pick a11

[0207] Selection strategy: Influential mushroom groups G (lowest occlusion rate → largest radius → highest influence factor), Non - influential mushroom group S (1): ['a21']

[0208] Influential mushroom groups G (4): ['a8', 'a12', 'a17', 'a18']

[0209] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a8 75 10.47％ 10.52° 0.0091 a12 a12 65 13.94％ 10.52° 0.0121 a8 a17 52 23.42％ 5.35° 0.0369 a18 a18 49 26.38％ 5.35° 0.0415 a17 a21 47 0.00％ 180.00° 0.0000

[0210] Step 18: Pick a21

[0211] Selection strategy: Non - influential mushroom group S (largest radius → highest influence factor)

[0212] Influential mushroom group S (0): []

[0213] Influential mushroom groups G (4): ['a8', 'a12', 'a17', 'a18']

[0214] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a8 75 10.47％ 10.52° 0.0091 a12 a12 65 13.94％ 10.52° 0.0121 a8 a17 52 23.42％ 5.35° 0.0369 a18 a18 49 26.38％ 5.35° 0.0415 a17

[0215] Step 19: Pick a8

[0216] Selection strategy: Influential mushroom groups G (lowest occlusion rate → largest radius → highest influence factor), Non - influential mushroom group S (1): ['a12']

[0217] Influential mushroom groups G (2): ['a17', 'a18']

[0218] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a12 65 0.00％ 180° 0.0000 a17 52 23.42％ 5.35° 0.0369 a18 a18 49 26.38％ 5.35° 0.0415 a17

[0219] Step 20: Pick a12

[0220] Selection strategy: Non - influential mushroom group S (largest radius → highest influence factor)

[0221] Non - influential mushroom group S (0): []

[0222] Influential mushroom groups G (2): ['a17', 'a18']

[0223] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a17 52 23.42％ 5.35° 0.0369 a18 a18 49 26.38％ 5.35° 0.0415 a17

[0224] Step 21: Pick a17

[0225] Selection strategy: Influential mushroom group G (lowest occlusion rate → largest radius → highest influence factor), Non-influential mushroom group S (1): ['a18']

[0226] Influential mushroom group G (0): []

[0227] ID Radius Occlusion rate Minimum angle Influence factor Adjacent mushrooms a18 49 0％ 180° 0.0000

[0228] Step 22: Pick a18

[0229] Selection strategy: Non-influential mushroom group S (largest radius → highest influence factor)

[0230] The final picking order is: a15 → a20 → a6 → a3 → a9 → a2 → a14 → a10 → a13 → a19 → a16 → a22 → a4 → a7 → a1 → a5 → a11 → a21 → a8 → a12 → a17 → a18.

Claims

1. A method for planning the mushroom picking sequence based on dynamic occlusion analysis, characterized in that, Including: (1) Obtain mushroom images and identify n mushroom targets through deep learning algorithms; (2) Calculate the radius of mushroom M i and sort the mushrooms in descending order of radius; Calculate adjacent mushrooms M i and M j The spacing between ij , get mushroom M i The spacing set D i ; According to the spacing set D i Cluster the mushrooms to obtain the unaffected mushroom group S and the affected mushroom group G; (3) Calculate the adjacent mushrooms M i and M j The gap angle α between ij , obtain the gap angle set α(i) of the mushroom M i ; Calculate the average occlusion rate and average occluded rate of mushrooms in the affected mushroom group G; (4) According to the average occlusion rate O i of the mushroom M i and the gap angle α i , calculate the influence factor F i of the mushroom M i ; (5) The picking order is planned as follows: (501) Sort the mushrooms in the non - affected mushroom group S in descending order of radius; (502) Sort the mushrooms in the affected mushroom group G in descending order of radius; (503) Select the mushroom M1 with the largest radius and not occluded as the initial picking point and put it into the sorting queue T. If there are multiple candidates, select the one with the highest influence factor; (504) Recalculate the average occlusion rate o of each remaining mushroom i and the influence factor F i , and update the mushroom group S without influence and the mushroom group G with influence; (505) Select the mushroom M with the smallest average occlusion rate and the largest radius j Put it into the sorting queue T. If there are multiple candidates, select the one with the highest influence factor; (506) Repeat steps (504) - (505) until all mushrooms in the mushroom image enter the sorting queue T.

2. The method for planning the mushroom picking sequence based on dynamic occlusion analysis according to claim 1, wherein Step (2) includes: (201) Calculate the radius r of mushroom M i of i : where (Lx i , Ly i ) is the upper left coordinate of mushroom M i , and (Rx i , Ry i ) is the lower right coordinate of mushroom M i ; the mushroom queue Q = {M1, M2, …, M n}, and r1 ≥ r2 ≥ … ≥ r n .

3. The mushroom picking sequence planning method based on dynamic occlusion analysis according to claim 2, wherein Step (2) also includes: (202) Calculate the center coordinates (x i , y i ) of mushroom M i : Calculate adjacent mushrooms M i and M j The center gap D ij : Then, the spacing between adjacent mushrooms M i and M j is d ij : d ij = D ij - r i - r j Among them, r i and r j are the radii of mushroom M i and M j respectively; Mushroom M i spacing set D i＝ {d 1i , d 2i ,..., d (n-1)i}, Mushroom M i adjacent mushroom set N(i) = {M k |d ik <0, k ≠ i}.

4. The method for planning the mushroom picking sequence based on dynamic occlusion analysis according to claim 3, wherein, Step (2) also includes: (203) Cluster the mushrooms; If mushroom M i in the spacing set D i has spacing values all greater than or equal to 0, then put mushroom M i into the non-influential mushroom group S; If mushroom M i in the spacing set D i has a spacing value less than 0, it is placed in the affected mushroom group G.

5. The method for planning the mushroom picking sequence based on dynamic occlusion analysis according to claim 4, wherein Step (3) includes: (301) Calculate the gap angle α between adjacent mushrooms M i and M j : ij : With the line connecting the centers C i and C j as the reference line L ij , respectively make the outer common tangents of the two mushrooms, making them located on both sides of the reference line, obtaining two common tangents L1 and L2. The included angle between the common tangent L1 and the reference line L ij is θ1, and the included angle between the common tangent L2 and the reference line L ij is θ2. The gap angle α ij =θ1 + θ2, When r i =r j , Mushroom M i The set of void angles α(i) = {α ij , α ik , …, α io | j, k, …, o ∈ N(i)}.

6. The method for mushroom picking sequence planning based on dynamic occlusion analysis according to claim 5, wherein Step (3) also includes: (302) Calculate the average occlusion rate and average occluded rate of mushrooms: If mushroom M i and mushroom M j have an overlapping relationship, and mushroom M j obscures mushroom M i , that is, when mushroom M j is above mushroom M i , the overlapping rate of mushroom M j is S ij is the overlapping area of the mushrooms; then when there are m mushrooms around being obscured by mushroom M j , the average obscuring rate of mushroom M j is Similarly, at this time mushroom M i is obscured by mushroom M j , and the overlapping rate of mushroom M i is When there are n mushrooms obscuring mushroom M i , the average obscured rate of mushroom M i is 7. The method for planning the mushroom picking sequence based on dynamic occlusion analysis according to claim 6, wherein Step (4) includes: Calculate the influence factor F of mushroom M i i :​ Among them, O i is the average occlusion rate of mushroom M i ; ∈ = 1°; is the minimum gap angle of mushroom M i .

8. A mushroom picking sequence planning system based on dynamic occlusion analysis, characterized in that, Including: A mushroom target recognition module for implementing step (1) in the mushroom picking order planning method described in any one of claims 1 to 7; A geometric relationship calculation module for implementing steps (2) to (4) in the mushroom picking order planning method described in any one of claims 1 to 7; A picking order planning module for implementing step (5) in the mushroom picking order planning method described in any one of claims 1 to 7.

9. A mushroom picking sequence planning device based on dynamic occlusion analysis, characterized in that, Including a processor and a memory. Computer instructions are stored in the memory, and the processor is used to execute the computer instructions stored in the memory. When the computer instructions are executed by the processor, the device implements the steps of the mushroom picking order planning method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer - readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the mushroom picking order planning method described in any one of claims 1 to 7.

Images