Intelligent Control Method and System for Edible Fungi under Dynamic Conveyance

Abstract

The present invention discloses an intelligent control method and system for edible fungi under dynamic transportation. The present invention relates to the technical field of dynamic transportation of edible fungi. The transportation route of the storage box is determined according to the overall quality grade of the edible fungi in the storage box, the transportation direction of the transportation line, and the shape of the transportation line, triggering the dynamic transportation of each edible fungus. The overall quality grade of the edible fungi in the storage box is introduced, ensuring the accuracy of the transportation route of the storage box. Therefore, if there is a damaged area of the edible fungus in the external image of the edible fungus, the damaged grade of the edible fungus is determined according to the identification of the damaged area of the edible fungus; the type of the edible fungus is collected, and the intelligent control mode of the transportation line is determined according to the type of the edible fungus, the damaged grade of the edible fungus, and the transportation speed of the transportation line, so as to smoothly transport each edible fungus in the storage box, fully considering the damaged grade of the edible fungus, and realizing the intelligent control of the edible fungus under dynamic transportation.

Description

Technical Field

[0001] The present invention relates to the technical field of dynamic transportation of edible fungi, and particularly to an intelligent control method and system for edible fungi under dynamic transportation. Background Art

[0002] With the development of technology, edible fungi are used as people's food ingredients and are cultivated in the edible fungi cultivation space. When the edible fungi reach the corresponding maturity, they are picked by a manipulator and transferred to the corresponding storage box. In the prior art, the storage box moves under the transportation of the conveyor line, and the edible fungi move along with the movement of the storage box. However, the conveyor line runs at a preset speed, and the edible fungi will collide with the storage box during the movement. Since the speed of the conveyor line does not change, the damaged area of the edible fungi is further enlarged, and the intelligent control of the edible fungi under dynamic transportation cannot be achieved. Summary of the Invention

[0003] The purpose of the present invention is to overcome the deficiencies of the prior art, and the present invention provides an intelligent control method and system for edible fungi under dynamic transportation.

[0004] An embodiment of the present invention provides an intelligent control method for edible fungi under dynamic transportation, including:

[0005] In the edible fungi cultivation space, trigger the picking of the edible fungi according to the spatial position and maturity of the edible fungi, so as to load each edible fungus into the corresponding storage box;

[0006] If the storage box is in a full-load state, then determine the overall quality grade of the edible fungi in the storage box according to the maturity and morphology of each edible fungus;

[0007] Transfer the storage box to the corresponding conveyor line, and determine the transportation route of the storage box according to the overall quality grade of the edible fungi in the storage box, the transportation direction of the conveyor line, and the morphology of the conveyor line, and trigger the dynamic transportation of each edible fungus;

[0008] During the transportation of the storage box, if there is a damaged area of the edible fungus in the external image of the edible fungus, then determine the damaged grade of the edible fungus according to the recognition of the damaged area of the edible fungus;

[0009] Collect the types of the edible fungi, and determine the intelligent control mode of the conveyor line according to the types of the edible fungi, the damaged grade of the edible fungus, and the transportation speed of the conveyor line, so as to smoothly transport each edible fungus in the storage box.

[0010] An embodiment of the present invention provides an intelligent control system for edible fungi under dynamic transportation. The intelligent control system for edible fungi under dynamic transportation is applied to the above-mentioned intelligent control method for edible fungi under dynamic transportation. The intelligent control system for edible fungi under dynamic transportation includes:

[0011] A picking module, which is used to trigger the picking of edible fungi in the edible fungi cultivation space according to the spatial position and maturity of the edible fungi, so as to load each edible fungus into the corresponding storage box;

[0012] An overall quality grade module, which is used to determine the overall quality grade of the edible fungi in the storage box according to the maturity and morphology of each edible fungus if the storage box is in a full-load state;

[0013] A dynamic transportation module, which is used to transfer the storage box to the corresponding conveyor line, determine the transportation route of the storage box according to the overall quality grade of the edible fungi in the storage box, the transportation direction of the conveyor line and the morphology of the conveyor line, and trigger the dynamic transportation of each edible fungus;

[0014] A damage grade module, which is used to determine the damage grade of the edible fungus according to the identification of the damaged area of the edible fungus if there is a damaged area of the edible fungus in the external image of the edible fungus during the transportation of the storage box;

[0015] An intelligent control mode module, which is used to collect the types of edible fungi, determine the intelligent control mode of the conveyor line according to the types of edible fungi, the damage grade of the edible fungi and the transportation speed of the conveyor line, so as to smoothly transport each edible fungus in the storage box.

[0016] Compared with the prior art, the beneficial effects of the present invention are:

[0017] In the embodiment of the present invention, through the method in the embodiment of the present invention, in the edible fungi cultivation space, the picking of edible fungi is triggered according to the spatial position and maturity of the edible fungi, so as to load each edible fungus into the corresponding storage box; if the storage box is in a full-load state, the overall quality grade of the edible fungi in the storage box is determined according to the maturity and morphology of each edible fungus; the storage box is transferred to the corresponding conveyor line, the transportation route of the storage box is determined according to the overall quality grade of the edible fungi in the storage box, the transportation direction of the conveyor line and the morphology of the conveyor line, and the dynamic transportation of each edible fungus is triggered. The overall quality grade of the edible fungi in the storage box is introduced, and the overall consideration of the overall quality grade of the edible fungi in the storage box, the transportation direction of the conveyor line and the morphology of the conveyor line is compatible, ensuring the accuracy of the transportation route of the storage box.

[0018] Therefore, during the transportation of the storage box, if there is a damaged area of the edible mushroom in the external image of the edible mushroom, the damage level of the edible mushroom is determined based on the recognition of the damaged area of the edible mushroom; the type of the edible mushroom is collected, and the intelligent control mode of the conveyor line is determined according to the type of the edible mushroom, the damage level of the edible mushroom, and the conveying speed of the conveyor line, so as to smoothly transport each edible mushroom in the storage box. The damage level of the edible mushroom is fully considered, and the overall consideration of the type of the edible mushroom, the damage level of the edible mushroom, and the conveying speed of the conveyor line is compatible, realizing the intelligent control of the edible mushroom under dynamic transportation. Description of the Drawings

[0019] Figure 1 is a schematic flowchart of the intelligent control method for edible mushrooms under dynamic transportation in an embodiment of the present invention;

[0020] Figure 2 is a schematic flowchart of step S11 in the intelligent control method for edible mushrooms under dynamic transportation in an embodiment of the present invention;

[0021] Figure 3 is a schematic flowchart of step S12 in the intelligent control method for edible mushrooms under dynamic transportation in an embodiment of the present invention;

[0022] Figure 4 is a schematic flowchart of step S13 in the intelligent control method for edible mushrooms under dynamic transportation in an embodiment of the present invention;

[0023] Figure 5 is a schematic flowchart of step S14 in the intelligent control method for edible mushrooms under dynamic transportation in an embodiment of the present invention;

[0024] Figure 6 is a schematic flowchart of step S15 in the intelligent control method for edible mushrooms under dynamic transportation in an embodiment of the present invention;

[0025] Figure 7 is a schematic diagram of the structural composition of the intelligent control system for edible mushrooms under dynamic transportation in an embodiment of the present invention. Detailed Embodiments

[0026] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0027] Please refer to Figures 1 to 7 , an intelligent control method for edible mushrooms under dynamic transportation, which is applied to the intelligent control scenario of edible mushrooms under dynamic transportation; the intelligent control method for edible mushrooms under dynamic transportation includes:

[0028] Step S11: In the edible mushroom cultivation space, trigger the picking of edible mushrooms according to the spatial position and maturity of the edible mushrooms, so as to load each edible mushroom into the corresponding storage box;

[0029] Step S12: If the storage box is in a full load state, then determine the overall quality grade of the edible mushrooms in the storage box according to the maturity of each edible mushroom and the morphology of the edible mushrooms;

[0030] Step S13: Transfer the storage box to the corresponding conveyor line, determine the conveying route of the storage box according to the overall quality grade of the edible mushrooms in the storage box, the conveying direction of the conveyor line and the morphology of the conveyor line, and trigger the dynamic conveying of each edible mushroom;

[0031] Step S14: During the conveying process of the storage box, if there is a damaged area of the edible mushroom in the external image of the edible mushroom, then determine the damage grade of the edible mushroom according to the recognition of the damaged area of the edible mushroom;

[0032] Step S15: Collect the types of edible mushrooms, determine the intelligent control mode of the conveyor line according to the types of edible mushrooms, the damage grade of the edible mushrooms and the conveying speed of the conveyor line, so as to smoothly convey each edible mushroom in the storage box;

[0033] Reference Figure 2 , in step S11, in the edible mushroom cultivation space, trigger the picking of edible mushrooms according to the spatial position and maturity of the edible mushrooms, so as to load each edible mushroom into the corresponding storage box;

[0034] In the specific implementation process of the present invention, the specific steps are as follows:

[0035] S111: The edible mushrooms are cultivated in the edible mushroom cultivation space. Based on the position detection of the edible mushrooms and the edible mushroom cultivation space, determine the spatial position of the edible mushrooms, and trigger the camera to take an online photo of the edible mushrooms according to the spatial position of the edible mushrooms;

[0036] S112: Based on the online photo of the edible mushrooms, determine the current image of the edible mushrooms. According to the recognition of the current image of the edible mushrooms, determine the morphological characteristics and color characteristics of the edible mushrooms, and determine the maturity of the edible mushrooms according to the morphological characteristics, color characteristics and types of the edible mushrooms;

[0037] S113: Determine the picking path of the edible mushrooms according to the spatial position, maturity of the edible mushrooms and the current position of the storage box. The robotic arm transfers the edible mushrooms to the storage box along the picking path. At this time, the storage box has multiple storage positions, and multiple edible mushrooms enter the corresponding storage positions in turn.

[0038] In an embodiment of the present application, the edible fungi are cultured in an edible fungi culture space. The spatial position of the edible fungi is determined based on the position detection of the edible fungi and the edible fungi culture space. According to the spatial position of the edible fungi, a camera is triggered to perform an on-line shooting of the edible fungi, and the on-line shooting of the edible fungi by the camera is introduced.

[0039] At this time, the edible fungi are cultured in a specific culture space (such as a greenhouse, a culture room or an automated culture rack). These culture spaces usually have suitable temperature, humidity, light and ventilation conditions to promote the growth of the edible fungi. At this time, multiple culture layers or racks are arranged in the culture space, and an edible fungi culture medium (such as sawdust, wheat bran, rice straw, etc.) is placed on each culture layer or rack. The edible fungi grow on these culture media to form fruiting bodies (i.e., the so-called edible fungi).

[0040] The positioning technology is used to determine the specific position of the edible fungi in the culture space, which is achieved by installing positioning sensors (such as RFID tags, ultrasonic sensors, infrared sensors or cameras, etc.) in the culture space. At the same time, an RFID reader-writer is installed on each culture rack, and an RFID tag is pasted on the edible fungi culture medium or culture container. When the edible fungi grow to a certain size, the RFID reader-writer reads the tag information and sends the position data to the central control system through a wireless signal. The central control system determines the specific position of each edible fungi according to the received data and the layout information of the culture rack.

[0041] Once the spatial position of the edible fungi is determined, the central control system triggers the camera to perform an on-line shooting of the edible fungi according to a preset rule or algorithm. At this time, a rule is set that when the edible fungi grow to a specific stage (such as when the cap expands to a certain extent), the camera is triggered to take a picture. The camera should be installed at a position where the edible fungi can be clearly photographed and have sufficient resolution and focal length to ensure the quality of the photographed image. When the central control system receives the position data and determines that the shooting condition is met, it sends a signal to the camera to trigger it to take a picture.

[0042] Specifically, assume there is an automated edible fungi culture system, which includes multiple culture racks, and multiple edible fungi culture media are placed on each culture rack. An RFID reader-writer and a camera are installed on each culture rack. When the edible fungi grow to a stage where the cap expands about 50%, it is necessary to take a picture of it to evaluate the maturity.

[0043] Edible fungi grow on the culture medium on the cultivation rack, forming fruiting bodies; when the edible fungi grow to a certain size, the RFID reader reads the RFID tag information attached to the culture medium and sends the location data to the central control system; the central control system determines whether the edible fungi meet the shooting conditions according to the received location data and preset rules (such as the cap unfolding by 50%); if the conditions are met, a signal is sent to the corresponding camera to trigger it to take a picture; the camera takes a clear image of the edible fungi, and this image is sent to the central control system for subsequent processing and analysis, such as maturity assessment, quality grading, etc.

[0044] Furthermore, based on the on-line shooting of the edible fungi, the current image of the edible fungi is determined. According to the recognition of the current image of the edible fungi, the morphological characteristics and color characteristics of the edible fungi are determined, and the maturity of the edible fungi is determined according to the morphological characteristics, color characteristics and the type of the edible fungi. It takes into account the morphological characteristics, color characteristics and the type of the edible fungi as a whole, ensuring the accuracy of the maturity of the edible fungi.

[0045] At this time, in step S111, when the edible fungi meet the preset shooting conditions, the camera will take an on-line picture of them, and these taken pictures are the current images of the edible fungi; at this time, the taken pictures should contain enough details for subsequent feature extraction and recognition, which requires the camera to have sufficient resolution and a suitable shooting angle; in addition, the light conditions of the shooting environment should also be kept stable to avoid overexposure or underexposure of the images.

[0046] The taken image of the edible fungi is analyzed using image recognition algorithms to extract its morphological characteristics and color characteristics; at the same time, the morphological characteristics include the size, shape, edge contour, etc. of the edible fungi; the color characteristics include hue, saturation, brightness, etc., and these characteristics are extracted through image processing techniques (such as edge detection, color space conversion, etc.); for example, deep learning models such as convolutional neural networks (CNNs) are used to extract features from the image, and these models can automatically learn and extract the key features in the image.

[0047] Combined with the type information of the edible fungi, machine learning or deep learning models are used to analyze the extracted morphological characteristics and color characteristics to judge the maturity of the edible fungi; specifically, different types of edible fungi will show different morphological and color changes during the maturation process; therefore, a special maturity judgment model needs to be trained for each type of edible fungi, and these models are trained based on a large amount of historical data (such as images at different maturity stages) to learn the characteristic change rules during the maturation process of the edible fungi; during judgment, the model will compare the extracted features with the trained model and output a probability or score representing the maturity; according to this score, a threshold is set to judge whether the edible fungi meet the picking standard.

[0048] Optionally, assume there is an edible mushroom called "oyster mushroom" and its maturity needs to be judged for picking. When the oyster mushroom grows to a certain size, a camera will take an online photo of it to obtain a clear image of the oyster mushroom. Use an image recognition algorithm (such as CNN) to analyze the taken oyster mushroom image and extract its morphological and color features. For example, extract features such as the diameter of the oyster mushroom, the edge contour, and the color of the cap. Combine the species information of the oyster mushroom (i.e., "oyster mushroom") and use the trained maturity judgment model to analyze the extracted features. The model will output a score representing the maturity of the oyster mushroom (such as a value between 0 and 1).

[0049] According to the score and a preset threshold (such as 0.8), judge whether the oyster mushroom meets the picking standard. If the score is greater than or equal to 0.8, it is considered that the oyster mushroom is mature and the picking action is triggered. If the score is less than 0.8, continue to wait for it to grow. Through this example, we can see the specific implementation method and operation process of step S112 in practical applications. This maturity judgment method based on image recognition greatly improves the accuracy and efficiency of edible mushroom picking and reduces the subjectivity and error of human judgment.

[0050] Therefore, determine the picking path of the edible mushroom according to the spatial position, maturity, and the current position of the storage box of the edible mushroom. The robotic arm transfers the edible mushroom to the storage box along the picking path. At this time, the storage box has multiple storage positions, and multiple edible mushrooms enter the corresponding storage positions in sequence, taking into account the overall situation of the spatial position, maturity, and the current position of the storage box of the edible mushroom, ensuring the accuracy of the picking path of the edible mushroom.

[0051] At this time, after determining the maturity and spatial position of the edible mushroom, it is necessary to plan an optimal picking path from the edible mushroom to the storage box in combination with the current position of the storage box. At this time, the planning of the picking path should consider multiple factors, including the straight-line distance between the edible mushroom and the storage box, the avoidance of obstacles (such as other cultivation racks, equipment, etc.), the movement range and flexibility of the robotic arm, etc. Usually, use path planning algorithms (such as A* algorithm, Dijkstra algorithm, RRT algorithm, etc.) to generate the optimal or sub-optimal path. In addition, the picking priority also needs to be considered, such as picking the edible mushrooms with higher maturity first, or adjusting the picking order according to the full-load situation of the storage box.

[0052] Once the picking path planning is completed, the robotic arm will move along this path to the position of the edible mushroom, and then perform the picking action to transfer the edible mushroom to the storage box. At the same time, the movement of the robotic arm is usually driven by a motor and realizes three-dimensional movement in space through the rotation and telescoping of the joints. During the picking process, the robotic arm needs to precisely control the position and posture of its end effector (such as gripper, suction cup, etc.) to ensure that it can stably grasp the edible mushroom and transfer it to the storage box. In addition, the robotic arm also needs to have sufficient flexibility and adaptability to handle edible mushrooms of different shapes, sizes and positions.

[0053] The storage box is usually designed with multiple storage positions to be able to accommodate multiple edible mushrooms. During the picking process, each edible mushroom will be placed in a specific position in the storage box. At the same time, the design of the storage positions should take into account factors such as the shape, size and fragility of the edible mushrooms to ensure that they can remain stable and undamaged during storage. In addition, the storage box also needs to have the ability to identify or record the information of the edible mushrooms in each storage position, such as variety, maturity, picking time, etc. These information are recorded and managed through barcodes, RFID tags, two-dimensional codes, etc.

[0054] Specifically, assume there is an automated edible mushroom picking system, which includes a robotic arm, multiple cultivation racks and a storage box with multiple storage positions. Now it is necessary to pick an edible mushroom called "oyster mushroom" and place it in the designated position in the storage box.

[0055] The system determines the spatial position and maturity of the oyster mushroom through a camera and sensors. Then, combined with the current position of the storage box, a path planning algorithm is used to generate an optimal picking path from the oyster mushroom to the storage box. This path avoids other cultivation racks and equipment, ensuring that the robotic arm can move smoothly. The robotic arm moves along the planned picking path to the position of the oyster mushroom. Then, its end effector (such as a gripper) precisely grasps the oyster mushroom and stably transfers it to a designated position in the storage box. During this process, the movement of the robotic arm is precisely controlled to ensure that the oyster mushroom is not damaged.

[0056] Each storage position in the storage box has a unique identifier (such as a barcode or RFID tag). When the oyster mushroom is placed in the storage position, the system will record the information of the oyster mushroom in this position (such as variety, maturity, picking time, etc.). In this way, it is convenient to track and manage each oyster mushroom in the storage box. Through this example, we can see the specific implementation method and operation process of step S113 in practical applications. This automated picking path planning and robotic arm operation technology greatly improves the efficiency and accuracy of edible mushroom picking, reducing manual intervention and errors.

[0057] In one embodiment of the present application, the picking path matching table includes the position of the edible fungus, the maturity, the position of the storage box, and the picking path; the picking path matching table is shown in Table 1:

[0058] Table 1 Picking Path Matching Table

[0059] Position of edible fungi Maturity Position of storage box Picking path Shelf No. 1 in Area A Ripe Box 1 - Position 1 Path 1 Shelf No. 2 in Area A Ripe Box 1 - Position 2 Path 2 Shelf No. 1 in Area B Half - ripe Box 2 - Position 1 Path 3

[0060] When the system detects that a certain edible fungus is mature and needs to be picked, it will look up the picking path matching table and determine the picking path based on the position of the edible fungus, the maturity, and the current free position of the storage box; for example, if it is detected that the edible fungus on Shelf No. 1 in Area A is mature and Position 1 of Storage Box 1 is free, then the system will select Path 1 as the picking path; once the picking path is determined, the robotic arm will move along this path to the position of the edible fungus, perform the picking action, and transfer the edible fungus to the designated position of the storage box.

[0061] At this time, it is detected that the edible fungus on Shelf No. 1 in Area A is mature; look up the picking path matching table, determine that the picking path is Path 1, and the storage position is Box 1 - Position 1; the robotic arm moves along Path 1 to Shelf No. 1 in Area A and picks the edible fungus; transfer the edible fungus to Position 1 of Storage Box 1.

[0062] Reference Figure 3 , in step S12, if the storage box is in a full-load state, then determine the overall quality grade of the edible fungi in the storage box according to the maturity of each edible fungus and the morphology of the edible fungi;

[0063] In the specific implementation process of the present invention, the specific steps are as follows:

[0064] S121: Monitor multiple storage positions of the storage box in real time. When multiple storage positions of the storage box are all storing edible fungi, the storage box is in a full-load state. Construct a distribution map of the edible fungi based on the morphology of the storage box and the multiple storage positions of the storage box, and mark the maturity of each edible fungus in the distribution map of the edible fungi;

[0065] S122: In the distribution map of the edible fungi, determine the morphology of each edible fungus based on the detection of multiple storage positions of the storage box, and determine the first quality coefficient according to the type of the edible fungus and the maturity of each edible fungus;

[0066] S123: Determine the second quality coefficient according to the type of the edible fungus and the morphology of each edible fungus; determine the overall quality grade of the edible fungi in the storage box based on the first quality coefficient, the second quality coefficient, and the quality grade mapping relationship.

[0067] In an embodiment of the present application, multiple storage locations of a storage box are monitored in real time. When all of the multiple storage locations of the storage box are storing edible fungi, the storage box is in a full-load state. A distribution map of the edible fungi is constructed based on the shape of the storage box and the multiple storage locations, and the maturity of each edible fungus is marked in the distribution map of the edible fungi. The distribution map of the edible fungi is introduced, and the maturity of each edible fungus is controlled.

[0068] At this time, sensors, cameras or other monitoring devices are used to obtain the status information of each storage location in the storage box in real time; at this time, the sensor detects whether there is an edible fungus at the storage location, and the camera captures the image inside the storage box for subsequent analysis; the system needs to collect these data regularly or continuously to ensure the timeliness of the information.

[0069] When all the preset storage locations of the storage box are occupied by edible fungi, the system determines that the storage box is full; at this time, the system will count the number of occupied storage locations according to the collected status information of the storage locations; if the number is equal to the total number of storage locations of the storage box, it is determined to be full load.

[0070] Combine the shape of the storage box (such as size, shape) and the information of the storage locations (such as position coordinates, occupancy status) to construct an intuitive distribution map of the edible fungi; at this time, the system will create a corresponding model in the virtual space according to the geometric shape of the storage box; then, according to the status information (occupied or idle) of each storage location, mark the position of the edible fungus on the model; the distribution map is two-dimensional (such as a grid map) or three-dimensional (such as a solid model), depending on the complexity of the storage box and the capabilities of the system.

[0071] On the constructed distribution map of the edible fungi, the system will add a maturity mark for each edible fungus according to the detected maturity information of the edible fungus; at this time, the system uses color coding (such as red for fully mature, green for immature), digital tags or other visualization means to represent the maturity, and these marks help users quickly understand the maturity status of the edible fungi in the storage box.

[0072] Specifically, assume there is an automated edible fungus storage system that includes a storage box with 10 storage locations; the system monitors the status of these storage locations in real time through cameras and sensors; the camera captures the image inside the storage box, and the sensor detects whether there is an edible fungus at each storage location; the system counts the number of occupied storage locations and finds that all 10 locations are occupied by edible fungi, so it determines that the storage box is full load.

[0073] The system creates a corresponding rectangular model in the virtual space according to the rectangular shape of the storage box; then, marks each occupied storage position on the model to form an intuitive two-dimensional distribution map; the system detects the maturity of each edible mushroom through image recognition technology and marks it with color coding on the distribution map; for example, a red mark indicates a fully mature edible mushroom, and a green mark indicates an immature edible mushroom; finally, the user sees a two-dimensional distribution map on the system that contains the positions of 10 edible mushrooms, and each position has a corresponding maturity mark. This distribution map helps the user quickly understand the distribution and maturity status of the edible mushrooms in the storage box, so as to make corresponding management decisions.

[0074] Furthermore, in the distribution map of the edible mushrooms, the morphology of each edible mushroom is determined based on the detection of multiple storage positions of the storage box. The first quality coefficient is determined according to the type of the edible mushroom and the maturity of each edible mushroom, taking into account the type of the edible mushroom and the maturity of each edible mushroom as a whole, and ensuring the accuracy of the first quality coefficient.

[0075] At this time, on the already constructed distribution map of the edible mushrooms, the morphological characteristics of each edible mushroom are further analyzed. At this time, the system uses image recognition technology or machine learning algorithms to analyze the images of each edible mushroom in the storage box. These technologies can identify the shape, size, color and other characteristics of the edible mushrooms and convert them into morphological data that can be used for subsequent analysis. The accuracy depends on the quality of the images and the precision of the recognition technology.

[0076] Combining the type information and maturity information of the edible mushrooms with the preset quality assessment criteria, the first quality coefficient of each edible mushroom is calculated. At the same time, different types of edible mushrooms have different quality standards. For example, some types value size more, while others value color or shape more. Similarly, maturity is also an important factor affecting quality. The system will assign a quality coefficient to each edible mushroom according to the preset quality assessment criteria (which are based on expert experience, industry standards or market feedback). This coefficient is usually a value between 0 and 1, and the higher the value, the better the quality.

[0077] Specifically, assume there is an automated edible mushroom quality assessment system. This system has obtained the images and maturity information of the edible mushrooms in the storage box through cameras and sensors and constructed a distribution map of the edible mushrooms. The system uses image recognition technology to analyze the images of each edible mushroom. For example, for enoki mushrooms, the system will identify its slender shape, uniform color and tight arrangement. For oyster mushrooms, the system will identify its umbrella-shaped shape, dark-colored cap and light-colored stalk.

[0078] Enoki Mushroom: Assume that the quality assessment criteria for enoki mushrooms mainly focus on their length and color uniformity. The system assigns a quality coefficient to each enoki mushroom according to these criteria. For example, an enoki mushroom with a length of 10 cm and uniform color will be assigned a relatively high quality coefficient, such as 0.9.

[0079] Oyster Mushroom: Assume that the quality assessment criteria for oyster mushrooms mainly focus on the size and thickness of the cap, as well as the length and thickness of the stalk. The system assigns a quality coefficient to each oyster mushroom according to these criteria. For example, an oyster mushroom with a cap diameter of 5 cm, moderate thickness, a stalk length of 3 cm, and uniform thickness will be assigned a relatively high quality coefficient, such as 0.85.

[0080] For each type of edible mushroom, the system also adjusts the quality coefficient according to its maturity. For example, for enoki mushrooms, fully mature enoki mushrooms will receive a higher quality coefficient than immature or overripe ones. The same is true for oyster mushrooms. Appropriate maturity usually receives a higher evaluation. Finally, the system generates a detailed report for each edible mushroom, including the type, morphology, maturity, and the first quality coefficient. These reports help users understand the quality status of the edible mushrooms in the storage box, so as to make corresponding management decisions. For example, users will give priority to selling high-quality edible mushrooms according to the quality coefficient, or use low-quality edible mushrooms for other purposes.

[0081] Therefore, determine the second quality coefficient according to the type of edible mushroom and the morphology of each edible mushroom; determine the overall quality grade of the edible mushrooms in the storage box based on the mapping relationship between the first quality coefficient, the second quality coefficient, and the quality grade, which incorporates the overall consideration of the first quality coefficient, the second quality coefficient, and the quality grade mapping relationship, ensuring the accuracy of the overall quality grade of the edible mushrooms in the storage box.

[0082] At this time, further analyze the type and morphological data of the edible mushrooms to determine the second quality coefficient for each edible mushroom. This coefficient is different from the first quality coefficient and focuses more on the impact of the morphological characteristics of the edible mushroom on its overall quality. At this time, the system analyzes the morphological data of each edible mushroom according to the preset morphological assessment criteria (these criteria are based on the biological characteristics, market preferences, or industry standards of the edible mushrooms); for example, for certain types of edible mushrooms, firm texture, smooth surface, or specific color are characteristics of high quality; the system assigns a second quality coefficient to each edible mushroom according to these criteria. This second quality coefficient is also a value between 0 and 1, indicating the level of its morphological quality.

[0083] Combine the first quality coefficient and the second quality coefficient, and assign an overall quality grade to each edible mushroom according to the preset quality grade mapping relationship. Meanwhile, the quality grade mapping relationship is a predefined table that maps the combination of the two quality coefficients to a specific quality grade, which is a simple classification (such as excellent, good, average, poor) and also a more detailed scoring system. The system will calculate an overall quality grade for each edible mushroom according to this mapping relationship, so as to provide a comprehensive quality assessment.

[0084] Specifically, assume there is an automated edible mushroom quality assessment system that has obtained the first quality coefficient and morphological data of the edible mushrooms in the storage box through previous steps.

[0085] Determination of the second quality coefficient: Flammulina velutipes: Assume that the morphological evaluation criteria for Flammulina velutipes include firm texture, smooth surface, and uniform color. The system assigns a second quality coefficient to each Flammulina velutipes according to these criteria. For example, a Flammulina velutipes with firm texture, smooth surface, and uniform color will be assigned a relatively high second quality coefficient, such as 0.8.

[0086] Pleurotus ostreatus: For Pleurotus ostreatus, assume that the morphological evaluation criteria include the integrity of the cap, the straightness of the stipe, and the overall symmetry. The system assigns a second quality coefficient to each Pleurotus ostreatus according to these criteria. For example, a Pleurotus ostreatus with an intact cap, straight stipe, and overall symmetry will be assigned a relatively high second quality coefficient, such as 0.85.

[0087] Assume there is the following quality grade mapping relationship: If both the first quality coefficient and the second quality coefficient are greater than 0.8, the overall quality grade is "excellent"; if at least one of the two coefficients is greater than 0.8 but less than or equal to 0.9, and the other coefficient is greater than 0.6, the overall quality grade is "good"; if both coefficients are greater than 0.6 but less than or equal to 0.8, the overall quality grade is "average"; if at least one of the two coefficients is less than or equal to 0.6, the overall quality grade is "poor". Apply this mapping relationship to assign an overall quality grade to each edible mushroom. For example: A Flammulina velutipes with a first quality coefficient of 0.9 and a second quality coefficient of 0.8 has an overall quality grade of "excellent"; a Pleurotus ostreatus with a first quality coefficient of 0.85 and a second quality coefficient of 0.75 has an overall quality grade of "good". Finally, the system will generate a detailed report for each edible mushroom that includes the variety, the first quality coefficient, the second quality coefficient, and the overall quality grade. These reports help users comprehensively understand the quality status of the edible mushrooms in the storage box, so as to make more informed management and sales decisions.

[0088] Reference Figure 4, in step S13, the storage box is transferred to the corresponding conveyor line. The conveying route of the storage box is determined according to the overall quality grade of the edible fungi in the storage box, the conveying direction of the conveyor line, and the form of the conveyor line, triggering the dynamic conveying of each edible fungus;

[0089] In the specific implementation process of the present invention, the specific steps are as follows:

[0090] S131: If the overall quality grade of the edible fungi in the storage box is greater than the preset overall quality grade threshold, trigger the transfer of the storage box by the robotic arm. The storage box is transferred to the corresponding conveyor line under the drive of the robotic arm. At this time, the storage box and the multiple edible fungi in the storage box move under the drive of the conveyor line;

[0091] S132: Collect the form of the conveyor line, and determine multiple edible fungus sorting nodes according to the form of the conveyor line. The multiple edible fungus sorting nodes cover the types of edible fungi and the corresponding overall quality grades; determine the first route according to the overall quality grade of the edible fungi in the storage box and the multiple edible fungus sorting nodes;

[0092] S133: Determine the second route according to the conveying direction of the conveyor line and the multiple edible fungus sorting nodes, and determine the conveying route of the storage box based on the synthesis of the first route and the second route. The storage box moves along this conveying route under the drive of the conveyor line to achieve the dynamic conveying of each edible fungus.

[0093] In the embodiment of the present application, if the overall quality grade of the edible fungi in the storage box is greater than the preset overall quality grade threshold, trigger the transfer of the storage box by the robotic arm. The storage box is transferred to the corresponding conveyor line under the drive of the robotic arm. At this time, the storage box and the multiple edible fungi in the storage box move under the drive of the conveyor line.

[0094] At this time, the overall quality grade of the edible fungi in the storage box is evaluated, which is usually based on multiple factors, such as the type, form, maturity, color, size, etc. of the edible fungi. These factors are obtained through methods such as image recognition and sensor data collection; the evaluation results will be compared with the preset overall quality grade threshold; the threshold is preset according to factors such as market demand, storage requirements of edible fungi, and sales strategies.

[0095] If the overall quality grade of the edible fungi in the storage box exceeds the preset threshold, the system will send a signal to the robotic arm controller to trigger the robotic arm to act; the actions of the robotic arm include steps such as stretching, grasping, lifting, rotating, and placing. These actions are precisely controlled to ensure that the storage box and the edible fungi inside are not damaged during the transfer process.

[0096] After receiving the trigger signal, the robotic arm will accurately grasp the storage box; the grasping points are usually specially designed to ensure a firm grip on the storage box while avoiding putting pressure on the storage box or the edible fungi inside; the robotic arm then lifts the storage box and moves it to the preset conveyor line position; during the movement, the motion of the robotic arm is smooth and continuous to avoid jolting the storage box due to sudden acceleration or deceleration.

[0097] When the robotic arm reaches the conveyor line position, it will smoothly place the storage box on the conveyor line; the placement point is usually a fixed position on the conveyor line, which has been optimized to ensure that the storage box can be smoothly docked with the conveyor line; the conveyor line is usually a continuously moving conveyor belt that can transport the storage box and the edible fungi inside from one position to another, such as a sorting area, a packaging area, or a storage area.

[0098] Once the storage box is placed on the conveyor line, the conveyor line will start to move, driving the storage box and the edible fungi inside along the preset path; the speed of the conveyor line is usually adjustable to adapt to different processing requirements; for example, a slower speed is required in the sorting area so that workers can accurately identify and sort the edible fungi; while a faster speed is needed in the packaging area to improve production efficiency.

[0099] Specifically, assume there is an automated edible fungi processing system, which includes a storage area, a quality assessment area, a robotic arm, a conveyor line, and a sorting area; in the storage area, there are multiple storage boxes containing different types and qualities of edible fungi; when a storage box is sent to the quality assessment area, the system conducts an overall quality grade assessment of the edible fungi inside through methods such as image recognition and sensor data collection.

[0100] Suppose the overall quality grade assessment of the shiitake mushrooms in a certain storage box is "excellent", exceeding the preset threshold (such as 0.8); the system then triggers the robotic arm to act, and the robotic arm grabs the storage box from the storage area and smoothly moves it to the conveyor line; the conveyor line starts to move at a moderate speed, driving the storage box and the shiitake mushrooms inside along the preset path towards the sorting area; in the sorting area, workers or automated equipment will further classify and process the shiitake mushrooms according to their types and quality grades, such as packaging, labeling, or sending them to different sales channels.

[0101] Furthermore, collect the form of the conveyor line, and determine multiple edible fungi sorting nodes according to the form of the conveyor line; the multiple edible fungi sorting nodes cover the types of edible fungi and the corresponding overall quality grades; determine the first route according to the overall quality grade of the edible fungi in the storage box and the multiple edible fungi sorting nodes, taking into account the overall quality grade of the edible fungi in the storage box and the multiple edible fungi sorting nodes, and ensuring the accuracy of the first route.

[0102] At this time, the system collects detailed information about the conveyor line through sensors or pre-set data, including its length, width, degree of bending, positions of branch points, etc. This information is crucial for subsequent determination of sorting nodes and routes; the form of the conveyor line varies due to factors such as factory layout, equipment configuration, processing procedures, etc.; therefore, the system needs to be able to flexibly adapt to different forms of conveyor lines.

[0103] Based on the collected information about the form of the conveyor line, the system determines the positions of multiple sorting nodes according to pre-set rules or algorithms. These nodes are the points where the edible fungi are sorted or redirected on the conveyor line; the determination of sorting nodes needs to consider multiple factors, such as the types of edible fungi, overall quality grades, market demand, subsequent processing procedures, etc.; for example, different types of edible fungi need to be sorted to different areas for packaging or storage; for the same type of edible fungi but with different quality grades, they also need to be sorted to different sales channels; each sorting node will have one or more associated processing devices or personnel responsible for performing sorting, redirection or other operations.

[0104] The system ensures that each sorting node can handle one or more types of edible fungi and is associated with a specific overall quality grade, which means that when the edible fungi in the storage box reach a certain sorting node, the system can accurately identify their types and quality grades and make corresponding processing decisions.

[0105] Determine the first route based on the overall quality grade and sorting node: After determining the sorting nodes, the system calculates an optimal path from the current position to the target sorting node according to the overall quality grade of the edible fungi in the storage box and the processing capabilities of each sorting node, that is, the first route; the determination of the first route involves multiple factors, such as the current state of the conveyor line, the busy degree of the sorting nodes, the storage time requirements of the edible fungi, etc.; the system needs to comprehensively consider these factors to ensure that the edible fungi can be sorted to the target position efficiently and accurately.

[0106] Specifically, assume there is an automated edible fungi sorting system, which includes a conveyor line, multiple sorting nodes and corresponding processing devices; the conveyor line is a straight line with a length of 100 meters and a width of 0.6 meters, but there is a 90-degree turning point in the middle, and there is a branch point before and after the turning point for connecting other processing areas; the system determines three sorting nodes according to the form of the conveyor line: Node A is located before the turning point and is responsible for sorting high-quality Pleurotus ostreatus; Node B is located after the turning point and is responsible for sorting medium-quality Pleurotus ostreatus; Node C is located at the end of the conveyor line and is responsible for sorting low-quality or other types of edible fungi.

[0107] The system ensures that each sorting node can handle the types and quality grades of edible fungi within its responsible scope; for example, node A only processes high-quality Pleurotus ostreatus, while node C processes all low-quality or other edible fungi that do not meet specific requirements; when a storage box containing high-quality Pleurotus ostreatus is placed on the conveyor line, the system calculates an optimal path from the starting point to node A as the first route based on the quality grade of the Pleurotus ostreatus and the position of the sorting node. This route involves operations such as straight-line movement, acceleration, deceleration, and turning to ensure that the storage box can reach node A smoothly and accurately for sorting.

[0108] Therefore, the second route is determined based on the conveying direction of the conveyor line and multiple edible fungi sorting nodes. The conveying route of the storage box is determined based on the synthesis of the first route and the second route. The storage box moves along this conveying route driven by the conveyor line to achieve the dynamic conveying of each edible fungus, taking into account the conveying direction of the conveyor line and multiple edible fungi sorting nodes as a whole, ensuring the accuracy of the second route. At the same time, taking into account the overall quality grade of the edible fungi in the storage box, the conveying direction of the conveyor line, and the shape of the conveyor line as a whole, it further ensures the accuracy of the conveying route of the storage box.

[0109] At this time, the system needs to accurately identify the current conveying direction of the conveyor line, which is usually achieved by directly reading the state of the conveyor line through sensors or the control system; the direction of the conveyor line changes due to production processes, equipment configurations, or operation requirements; therefore, the system needs to be able to monitor and adapt to these changes in real time.

[0110] When determining the second route, the system needs to comprehensively consider all relevant sorting nodes, which are located at different positions on the conveyor line and have different processing capabilities and priorities; the system needs to evaluate the applicability of each sorting node to the edible fungi in the current storage box and their relative positional relationships.

[0111] Determine the second route: Based on the direction of the conveyor line and the position of the sorting node, the system calculates an optimal path from the current position (or the end point of the first route) to the target sorting node, that is, the second route; the determination of the second route involves multiple factors, such as the speed of the conveyor line, the processing capacity of the sorting node, the priority of the storage box, etc.; the system needs to comprehensively consider these factors to ensure that the edible fungi can be efficiently and accurately conveyed to the target position.

[0112] Once both the first route and the second route are determined, the system combines them into a complete conveying route that starts from the starting point where the storage box is placed on the conveyor line and extends to the target sorting node; during the combination process, the system needs to ensure a smooth transition at the connection point of the two routes to avoid any conflicts or delays; meanwhile, driven by the conveyor line, the storage box moves along the combined conveying route; the system monitors the position and speed of the storage box in real time to ensure that it can accurately reach the target sorting node; during the movement process, the system needs to adjust the speed or direction of the conveyor line according to the actual situation to adapt to any emergencies or changes.

[0113] Specifically, assume there is an automated edible mushroom sorting system that includes a conveyor line, three sorting nodes (A, B, C) and corresponding processing equipment; the conveyor line is currently moving straight from west to east; node A is located slightly west of the middle section of the conveyor line and is responsible for sorting high-quality level shiitake mushrooms; node B is located slightly east of the middle section of the conveyor line and is responsible for sorting medium-quality level oyster mushrooms; node C is located at the end of the conveyor line and is responsible for sorting low-quality level or other types of edible mushrooms.

[0114] When a storage box containing medium-quality level oyster mushrooms is placed on the conveyor line, the system first determines the target sorting node as B according to the quality level of the oyster mushrooms; then, based on the direction of the conveyor line and the position of node B, the system calculates an optimal path from the current position (assumed to be near the starting point of the conveyor line) to node B as the second route, and this route involves operations such as straight movement and acceleration.

[0115] Since the storage box starts moving from the starting point of the conveyor line, the first route is actually a straight line segment from the starting point to the current position (in this example, near the assumed starting point); the system combines the first route and the second route into a complete conveying route, that is, from the starting point through the straight line segment to reach node B; driven by the conveyor line, the storage box moves from west to east along the combined conveying route, and after acceleration, it smoothly reaches node B for sorting; during the movement process, the system monitors the position and speed of the storage box in real time to ensure that it can accurately reach the target position.

[0116] In an embodiment of the present application, a conveying route matching table is collected, and the conveying route matching table is shown in Table 2:

[0117] Table 2 Conveying Route Matching Table

[0118] Starting point Target sorting node Conveyor route Point A Node B Move in a straight line from Point A to Node B Point A Node C Move in a straight line from Point A to the turning point, then turn north and move to Node C Point B Node A Move in the reverse direction from Point B to the turning point, then turn south and move to Node A

[0119] Assume the storage box starts moving from point A and the target sorting node is node C. After the system queries the conveying route matching table, the obtained conveying route is "move straight from point A to the turning point, and then turn north to move to node C".

[0120] Reference Figure 5 In step S14, during the conveyance of the storage box, if there is a damaged area on the external image of the edible mushroom, the damage level of the edible mushroom is determined based on the identification of the damaged area of the edible mushroom;

[0121] In the specific implementation process of the present invention, the specific steps are as follows:

[0122] S141: Monitor the conveyance of the storage box in real time, mark the conveyance process of the storage box, collect the external image of the edible mushroom, and determine a plurality of damaged features based on the detection of the external image of the edible mushroom;

[0123] S142: Determine the damaged area of the edible mushroom according to the positions of the plurality of damaged features, the shapes of the plurality of damaged features, and the shape of the edible mushroom, and the damaged area of the edible mushroom is presented on the surface of the edible mushroom;

[0124] S143: Collect the damaged area of the edible mushroom, determine a first damage level coefficient according to the area position of the damaged area of the edible mushroom and the shape of the edible mushroom, determine a second damage level coefficient according to the area of the damaged area of the edible mushroom and the type of the edible mushroom, and determine the damage level of the edible mushroom based on the first damage level coefficient, the second damage level coefficient, and the damage level mapping relationship.

[0125] In the embodiment of the present application, the conveyance of the storage box is monitored in real time, the conveyance process of the storage box is marked, the external image of the edible mushroom is collected, and a plurality of damaged features are determined based on the detection of the external image of the edible mushroom, and a plurality of damaged features are introduced.

[0126] At this time, the system uses a camera, a sensor or other monitoring devices to monitor the movement of the storage box on the conveyor line in real time; the purpose of the monitoring is to ensure the smooth conveyance process and timely detect any abnormalities or potential problems; the monitoring data (such as video stream, image sequence) is transmitted to the processing unit of the system in real time for analysis.

[0127] The system assigns a unique identifier (such as an ID number, a barcode, etc.) to each storage box and tracks it during the conveyance process; through time stamps, position marks or other tracking technologies, the entire conveyance process of the storage box from the starting point to the ending point is recorded, and these marked information helps subsequent data analysis and problem tracing.

[0128] During the conveyance process, the system collects the external image of the edible mushroom regularly or on demand; the image acquisition device (such as a camera) is usually installed above or on the side of the conveyor line to ensure that a comprehensive view of the edible mushroom can be captured; the collected images are stored as digital files for subsequent image processing and damaged feature detection.

[0129] Analyze the collected edible mushroom images; by comparing the edible mushroom images with preset damaged feature templates or databases, the system can identify damaged features such as scratches, spots, depressions, rot, etc.; detailed information such as the quantity, location, and size of the damaged features is recorded for use in subsequent steps.

[0130] Specifically, assume there is an automated edible mushroom sorting and packaging system, which includes a conveyor line, multiple cameras, and corresponding image processing software; the system monitors the conveying process of the storage boxes in real time through the cameras installed above the conveyor line; the video stream captured by the cameras is transmitted to the processing unit of the system in real time; each storage box is assigned a unique ID number when it is placed on the conveyor line and is recorded through RFID technology or a barcode scanner; the system tracks the movement path of the storage boxes on the conveyor line through timestamps and position sensors.

[0131] When a storage box passes by the camera, the system triggers an image acquisition operation to capture the external images of the edible mushrooms. These images are stored as files in JPEG or PNG format, along with the ID number of the storage box and the acquisition timestamp; the system uses image recognition algorithms to analyze the collected edible mushroom images; for example, the system detects two obvious scratches and a small depressed area on the surface of the shiitake mushrooms in an image. These damaged features are recorded and associated with the ID number of the storage box and the acquisition timestamp; through this example, the specific operations and processes of step S141 in practical applications can be seen; steps such as real-time monitoring, marking the conveying process, acquiring external images, and damaged feature detection together constitute the basis for detecting damaged edible mushrooms and provide an important basis for subsequent sorting, packaging, and quality control.

[0132] Furthermore, determine the damaged area of the edible mushroom based on the positions of multiple damaged features, the morphologies of multiple damaged features, and the morphology of the edible mushroom. The damaged area of the edible mushroom is presented on the surface of the edible mushroom, taking into account the overall considerations of the positions of multiple damaged features, the morphologies of multiple damaged features, and the morphology of the edible mushroom, ensuring the accuracy of the damaged area of the edible mushroom.

[0133] At this time, pay attention to the specific positions of each damaged feature on the surface of the edible mushroom; the position information includes the coordinates, directions of the damaged features relative to the whole edible mushroom, or the relative distances from key parts (such as the mushroom cap, mushroom stalk); through precise position analysis, the system can understand the distribution of damaged features on the edible mushroom.

[0134] Next, the system conducts a detailed assessment of the morphology of each damaged feature; the morphological analysis involves the shape (such as circular, linear, irregular) of the damaged feature, the size (such as diameter, area), the color (such as color change, spot color), etc. These morphological information helps the system further understand the nature and severity of the damaged features.

[0135] When determining the damaged area, the system also needs to consider the morphology of the edible mushroom itself; the morphology of the edible mushroom includes its overall shape (such as umbrella shape, spherical shape), size (such as diameter, height), color (such as natural color), texture (such as surface roughness), etc.; by comparing the similarities and differences between the damaged features and the morphology of the edible mushroom, the system can more accurately define the damaged area. Based on the above analysis, the system finally determines the damaged area of the edible mushroom; the damaged area is clearly defined as a specific area on the surface of the edible mushroom composed of damaged features; the system visually presents the damaged area on the image by means of image annotation, contour drawing or color filling, etc.

[0136] Specifically, the system is performing damage detection on a batch of Pleurotus ostreatus; the system first captures an image of a Pleurotus ostreatus and identifies two damaged features: one is located at the edge of the mushroom cap, near the connection with the stipe; the other is located in the middle of the mushroom cap, near the top; further analysis reveals that the damaged feature at the edge of the mushroom cap is a slender scratch, with a color slightly darker than the surrounding tissue; while the damaged feature in the middle of the mushroom cap is a circular spot, with a significantly lighter color.

[0137] The system also notes that the overall shape of this batch of Pleurotus ostreatus is umbrella-shaped, the mushroom cap is large and round, the color is uniform, and the surface has a certain luster; the system determines two damaged areas: one is the scratch area at the edge of the mushroom cap, and the other is the spot area in the middle of the mushroom cap, and both of these areas are clearly marked on the image for subsequent analysis and processing.

[0138] Therefore, collect the damaged area of the edible mushroom, determine the first damage level coefficient according to the regional position of the damaged area of the edible mushroom and the morphology of the edible mushroom, determine the second damage level coefficient according to the regional area of the damaged area of the edible mushroom and the type of the edible mushroom, and determine the damage level of the edible mushroom based on the first damage level coefficient, the second damage level coefficient and the damage level mapping relationship, which is compatible with the overall consideration of the first damage level coefficient, the second damage level coefficient and the damage level mapping relationship, and ensures the accuracy of the damage level of the edible mushroom.

[0139] At this time, the system accurately extracts these damaged areas from the collected images of edible mushrooms according to the previously determined damaged areas (step S142); the extraction process involves techniques such as image cropping, region segmentation or feature extraction to ensure the accuracy and integrity of the damaged areas.

[0140] Determine the first damage level coefficient: The system evaluates the impact of the damage on the overall quality of the edible mushroom according to the regional position of the damaged area (such as being located in the main edible part, edge part or non-critical area of the edible mushroom) and the morphology of the edible mushroom (such as overall size, shape, color, etc.); based on this evaluation, the system assigns a first damage level coefficient to each damaged area, and this first damage level coefficient reflects the importance of the damaged position to the quality of the edible mushroom.

[0141] Determine the second damage level coefficient: The system then further evaluates the severity of the damage based on the area of the damaged region (i.e., the proportion of the damaged part to the entire surface of the edible mushroom) and the type of the edible mushroom (different types of edible mushrooms have different tolerances to damage); by comparing the damaged area with a preset threshold or standard, the system assigns a second damage level coefficient to each damaged region, and this second damage level coefficient reflects the relative size of the damaged area and the direct impact on the quality of the edible mushroom.

[0142] The system combines the first damage level coefficient and the second damage level coefficient, as well as a preset damage level mapping relationship (one or more algorithms or rule sets), to determine the final damage level of the edible mushroom; the damage level is a classification label (such as "slightly damaged", "moderately damaged", "severely damaged", etc.), or a numerical score, which is used to quantify the degree of damage of the edible mushroom.

[0143] Specifically, the system is detecting and grading a batch of Pleurotus ostreatus; the system first crops the damaged part from the image of the Pleurotus ostreatus according to the previously determined damaged region; for example, there is an obvious sunken area at the top of a Pleurotus ostreatus in an image, and the system successfully extracts this area.

[0144] Determine the first damage level coefficient: The system evaluates the position of this sunken area and finds that it is located in the main edible part of the Pleurotus ostreatus (i.e., the top of the mushroom cap); since the mushroom cap of the Pleurotus ostreatus is its most important edible area, the system assigns a relatively high first damage level coefficient to this damaged region (such as 0.8, indicating that the damaged position has a significant impact on the quality).

[0145] Determine the second damage level coefficient: The system then measures the area of this sunken area and finds that it accounts for about 5% of the entire surface area of the Pleurotus ostreatus; considering that the Pleurotus ostreatus has a relatively low tolerance to sunken damage, the system assigns a medium second damage level coefficient to this damaged region (such as 0.6, indicating that the damaged area is relatively large but the impact on the quality is moderate). Finally, the system combines the first damage level coefficient (0.8) and the second damage level coefficient (0.6), as well as the preset damage level mapping relationship, and calculates that the final damage level of this Pleurotus ostreatus is "moderately damaged", and this level is used for subsequent sorting, packaging or quality control decisions.

[0146] Reference Figure 6 , in step S15, collect the type of the edible mushroom, and determine the intelligent control mode of the conveyor line according to the type of the edible mushroom, the damage level of the edible mushroom and the conveying speed of the conveyor line, so as to smoothly convey each edible mushroom in the storage box;

[0147] In the specific implementation process of the present invention, the specific steps are as follows:

[0148] S151: Collect the type of edible mushroom and the conveying speed of the conveyor line. Determine the first mode coefficient according to the type of edible mushroom and the damage level of the edible mushroom, and determine the second mode coefficient according to the damage level of the edible mushroom and the conveying speed of the conveyor line.

[0149] S152: Determine the intelligent control mode of the conveyor line based on the first mode coefficient, the second mode coefficient, and the control mode mapping relationship; control the conveying speed of the conveyor line along the intelligent control mode of the conveyor line, and control the activity range of the edible mushroom in the storage box to limit the collision between the edible mushroom and the storage box; the intelligent control mode includes a variable-speed conveying control mode, a constant-speed conveying control mode, and a deceleration conveying control mode.

[0150] In the embodiment of the present application, collecting the type of edible mushroom and the conveying speed of the conveyor line, determining the first mode coefficient according to the type of edible mushroom and the damage level of the edible mushroom, and determining the second mode coefficient according to the damage level of the edible mushroom and the conveying speed of the conveyor line, takes into account the overall consideration of the damage level of the edible mushroom and the conveying speed of the conveyor line, and ensures the accuracy of the second mode coefficient.

[0151] At this time, the system accurately obtains the information of the type of edible mushroom currently being conveyed on the conveyor line by reading barcodes, RFID tags, image recognition, or other automated means; the information of the type of edible mushroom includes its scientific name, common name, classification level, etc., and these information are crucial for determining the subsequent processing mode and coefficients.

[0152] The system obtains the current conveying speed of the conveyor line in real time through sensors or controllers; the conveying speed is usually expressed in meters per minute (m / min) or other speed units, which reflects the moving rate of the edible mushroom on the conveyor line.

[0153] Determine the first mode coefficient according to the type and damage level of the edible mushroom: The system consults the preset "edible mushroom type - damage level - first mode coefficient" mapping table or algorithm according to the collected type of edible mushroom and the damage level determined in the previous steps (such as S143); the mapping table or algorithm takes into account the physical characteristics (such as texture, shape, size) of different types of edible mushrooms, the influence of the damage level on the processing sensitivity, and the special requirements of specific types during the processing process;

[0154] Based on this information, the system assigns a first mode coefficient to each edible mushroom, and the first mode coefficient reflects the requirements of the edible mushroom type and damage state for the conveying and processing modes.

[0155] Determine the second mode coefficient based on the damage level and conveying speed: The system then looks up the preset "damage level - conveying speed - second mode coefficient" mapping table or algorithm according to the damage level and the current conveying speed; the mapping table or algorithm takes into account the impact of the conveying speed on the processing efficiency of edible fungi, the requirements of the damage level for processing safety, and the physical damage risk of edible fungi under different conveying speeds; based on this information, the system assigns a second mode coefficient to each edible fungus, and this second mode coefficient reflects the further adjustment requirements of the conveying speed and damage state for the conveying and processing modes.

[0156] Specifically, the system uses a conveyor line to transport different types of edible fungi from the storage area to the processing area; the system identifies that the currently conveyed one is "oyster mushroom" by reading the RFID tag; the system detects through a sensor that the current speed of the conveyor line is 50 meters per minute (50 m / min).

[0157] Determine the first mode coefficient: Oyster mushroom, as an edible fungus with a relatively soft texture and flat shape, is more sensitive to pressure and friction during the processing; assuming that the damage level of the oyster mushroom determined in the previous step is "slightly damaged", the system looks up the "edible fungus type - damage level - first mode coefficient" mapping table and finds that the coefficient matching "oyster mushroom - slightly damaged" is 0.75, and this coefficient reflects that the oyster mushroom requires relatively gentle processing conditions during the processing to reduce the risk of further damage.

[0158] Determine the second mode coefficient: Considering that the conveying speed is 50 meters per minute, which is a relatively fast speed, increasing the risk of damage to the oyster mushroom due to collision or friction during conveying; at the same time, since the oyster mushroom is already in the "slightly damaged" state, the system needs to handle it more carefully to avoid aggravating the damage; the system looks up the "damage level - conveying speed - second mode coefficient" mapping table and finds that the coefficient matching "slightly damaged - 50 m / min" is 0.6, and this coefficient reflects that at the given conveying speed, the system needs to adopt a slower or gentler processing method to reduce the damage to the oyster mushroom; through the above steps, the system determines two mode coefficients for the currently conveyed oyster mushroom: the first mode coefficient is 0.75, and the second mode coefficient is 0.6, and these coefficients will be used in the subsequent steps to determine the most suitable conveying and processing mode for the current situation.

[0159] Furthermore, an intelligent control mode of the conveyor line is determined based on the first mode coefficient, the second mode coefficient, and the control mode mapping relationship; the conveying speed of the conveyor line is controlled along the intelligent control mode of the conveyor line, and the activity range of the edible fungi in the storage box is controlled to limit the collision between the edible fungi and the storage box; the intelligent control mode includes a variable-speed conveying control mode, a constant-speed conveying control mode, and a decelerating conveying control mode, which takes into account the overall consideration of the first mode coefficient, the second mode coefficient, and the control mode mapping relationship, ensuring the accuracy of the intelligent control mode of the conveyor line. At the same time, it takes into account the overall consideration of the type of edible fungi, the damage level of the edible fungi, and the conveying speed of the conveyor line, and further realizes the intelligent control of the edible fungi under dynamic conveying.

[0160] At this time, the system combines the first mode coefficient and the second mode coefficient determined in the previous step (S151) and consults the preset "mode coefficient - intelligent control mode" mapping table or algorithm; the mapping table or algorithm takes into account the influence of different mode coefficient combinations on the conveyor line control mode, as well as the optimization of the edible fungi processing efficiency and safety by a specific control mode; based on this information, the system selects the most suitable intelligent control mode for the current situation; the intelligent control mode includes a variable-speed conveying control mode, a constant-speed conveying control mode, a decelerating conveying control mode, etc.

[0161] Once the intelligent control mode is determined, the system immediately adjusts the conveying speed of the conveyor line to meet the requirements of the selected mode; in the variable-speed conveying control mode, the system dynamically adjusts the conveying speed according to the preset speed curve or real-time feedback; in the constant-speed conveying control mode, the system keeps the conveying speed constant; in the decelerating conveying control mode, the system gradually reduces the conveying speed to ensure the safe deceleration of the edible fungi before the processing area.

[0162] To further reduce the collision between the edible fungi and the storage box, the system takes a series of measures to control the activity range of the edible fungi in the storage box. These measures include adjusting the size and shape of the storage box to adapt to different types of edible fungi, using buffer materials (such as foam, rubber pads) to reduce the impact force during collision, or setting limit devices in the storage box to limit the movement of the edible fungi; through these measures, the system ensures that the edible fungi remain relatively stable during transportation and reduces the damage caused by collision.

[0163] Specifically, the system uses an intelligent conveyor line to transport different types of edible fungi from the warehousing area to the processing area; according to the first mode coefficient (0.75) and the second mode coefficient (0.6) determined in the previous step (S151), the system consults the "mode coefficient - intelligent control mode" mapping table; assuming that the mapping table stipulates that when the first mode coefficient is between 0.7 and 0.8 and the second mode coefficient is less than 0.7, the "decelerating conveying control mode" is selected; therefore, the system selects the "decelerating conveying control mode" for the current situation.

[0164] Once the "decelerated conveyor control mode" is selected, the system immediately starts to adjust the conveyor speed of the conveyor line; assuming the current conveyor speed of the conveyor line is 50 meters per minute (50 m / min), the system gradually reduces it to 30 meters per minute (30 m / min) to ensure the safe deceleration of the edible mushrooms before the processing area; at the same time, to further reduce the collision between the Pleurotus ostreatus and the storage box, the system takes the following measures: adjusts the size and shape of the storage box to adapt to the size and shape of the Pleurotus ostreatus to ensure that the Pleurotus ostreatus can be stably placed in the storage box; lays a layer of foam pad at the bottom of the storage box to reduce the impact force when the Pleurotus ostreatus collides with the bottom of the storage box; sets a limiting device on the side of the storage box to limit the left and right movement range of the Pleurotus ostreatus during transportation; through these measures, the system successfully determines the intelligent control mode most suitable for the current situation, adjusts the conveyor speed of the conveyor line, and controls the movement range of the Pleurotus ostreatus in the storage box, thus ensuring the safety and stability of the Pleurotus ostreatus during transportation.

[0165] In an embodiment of the present application, an intelligent control mode matching table is collected, and the intelligent control mode matching table is shown in Table 3:

[0166] Table 3 Intelligent control mode matching table

[0167] Range of the first mode coefficient Range of the second mode coefficient Intelligent control mode 0.8-1.0 0.8-1.0 Variable - speed conveyor control mode 0.6-0.79 0.6-0.79 Constant - speed conveyor control mode 0.0-0.59 0.0-0.59 Decelerating conveyor control mode

[0168] Now, assume that the obtained first mode coefficient is 0.7 and the second mode coefficient is 0.65; according to the intelligent control mode matching table, these coefficients fall within the range of 0.6 - 0.79, so the system selects the "constant-speed conveyor control mode".

[0169] Please refer to Figure 7 , Figure 7 which is a schematic structural composition diagram of the intelligent control system for edible mushrooms under dynamic transportation in the embodiment of the present invention; the intelligent control system for edible mushrooms under dynamic transportation includes:

[0170] A picking module 21, which is used to trigger the picking of edible mushrooms according to the spatial position and maturity of the edible mushrooms in the edible mushroom cultivation space, so as to load each edible mushroom into the corresponding storage box;

[0171] An overall quality grade module 22, which is used to determine the overall quality grade of the edible mushrooms in the storage box according to the maturity and morphology of each edible mushroom if the storage box is in a full-load state;

[0172] A dynamic transportation module 23, which is used to transfer the storage box to the corresponding conveyor line, determine the transportation route of the storage box according to the overall quality grade of the edible mushrooms in the storage box, the transportation direction of the conveyor line, and the morphology of the conveyor line, and trigger the dynamic transportation of each edible mushroom;

[0173] The damaged level module 24 is configured to determine the damaged level of the edible mushroom according to the recognition of the damaged area of the edible mushroom if there is a damaged area of the edible mushroom in the external image of the edible mushroom during the conveying process of the storage box;

[0174] The intelligent control mode module 25 is configured to collect the types of edible mushrooms, and determine the intelligent control mode of the conveying line according to the types of the edible mushrooms, the damaged level of the edible mushrooms, and the conveying speed of the conveying line, so as to stably convey each edible mushroom in the storage box.

[0175] Any combination of the technical features of the above embodiments is made. For the sake of brevity of description, not all combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope described in this specification.

Claims

1. An intelligent control method for edible fungi under dynamic transportation, characterized in that, Including: In the edible mushroom cultivation space, pick the edible mushrooms according to the spatial position and maturity of the edible mushrooms, so as to load each edible mushroom into the corresponding storage box; If the storage box is in a full-load state, then determine the overall quality grade of the edible mushrooms in the storage box according to the maturity and morphology of each edible mushroom, including: constructing a distribution map of the edible mushrooms based on the morphology of the storage box and multiple storage positions of the storage box, and marking the maturity of each edible mushroom in the distribution map of the edible mushrooms; in the distribution map of the edible mushrooms, determine the morphology of each edible mushroom based on the detection of multiple storage positions of the storage box, and determine the first quality coefficient according to the type of the edible mushroom and the maturity of each edible mushroom; determine the second quality coefficient according to the type of the edible mushroom and the morphology of each edible mushroom; determine the overall quality grade of the edible mushrooms in the storage box based on the first quality coefficient, the second quality coefficient and the quality grade mapping relationship; the second quality coefficient is different from the first quality coefficient; Transfer the storage box to the corresponding conveyor line, and determine the conveying route of the storage box according to the overall quality grade of the edible mushrooms in the storage box, the conveying direction of the conveyor line and the morphology of the conveyor line, and trigger the dynamic conveying of each edible mushroom, including: if the overall quality grade of the edible mushrooms in the storage box is greater than the preset overall quality grade threshold, trigger the transfer of the storage box by the robotic arm, and the storage box is transferred to the corresponding conveyor line under the drive of the robotic arm. At this time, the storage box and multiple edible mushrooms in the storage box move under the drive of the conveyor line; collect the morphology of the conveyor line, and determine multiple edible mushroom sorting nodes according to the morphology of the conveyor line, and multiple edible mushroom sorting nodes cover the types of edible mushrooms and the corresponding overall quality grades; determine the first route according to the overall quality grade of the edible mushrooms in the storage box and multiple edible mushroom sorting nodes; determine the second route according to the conveying direction of the conveyor line and multiple edible mushroom sorting nodes, and determine the conveying route of the storage box based on the synthesis of the first route and the second route, and the storage box moves along this conveying route under the drive of the conveyor line to realize the dynamic conveying of each edible mushroom; the first route involves the current state of the conveyor line, the busy degree of the sorting nodes, and the storage time requirements of the edible mushrooms; the second route involves the speed of the conveyor line, the processing capacity of the sorting nodes, and the priority of the storage box; During the conveying process of the storage box, if there is a damaged area of the edible mushroom in the external image of the edible mushroom, then determine the damaged grade of the edible mushroom according to the identification of the damaged area of the edible mushroom; Collect the type of the edible mushroom, and determine the intelligent control mode of the conveyor line according to the type of the edible mushroom, the damaged grade of the edible mushroom and the conveying speed of the conveyor line, so as to smoothly convey each edible mushroom in the storage box.

2. The intelligent control method of edible fungi under dynamic conveying according to claim 1, characterized in that The step of picking the edible mushrooms according to the spatial position and maturity of the edible mushrooms in the edible mushroom cultivation space so as to load each edible mushroom into the corresponding storage box includes: The edible mushrooms are cultivated in the edible mushroom cultivation space. The spatial position of the edible mushrooms is determined based on the position detection of the edible mushrooms and the edible mushroom cultivation space, and the on-line shooting of the edible mushrooms by the camera is triggered according to the spatial position of the edible mushrooms; Determine the current image of the edible mushroom based on the on-line shooting of the edible mushroom, determine the morphological characteristics and color characteristics of the edible mushroom according to the recognition of the current image of the edible mushroom, and determine the maturity of the edible mushroom according to the morphological characteristics, color characteristics and type of the edible mushroom; Determine the picking path of the edible mushroom according to the spatial position of the edible mushroom, the maturity and the current position of the storage box. The robotic arm transfers the edible mushroom to the storage box along the picking path. At this time, the storage box has multiple storage positions, and multiple edible mushrooms enter the corresponding storage positions in sequence.

3. The intelligent control method of the edible fungus under dynamic conveying according to claim 1, characterized in that During the conveying process of the storage box, if there is a damaged area of the edible mushroom in the external image of the edible mushroom, determine the damage level of the edible mushroom according to the recognition of the damaged area of the edible mushroom, including: Monitor the conveying of the storage box in real time, mark the conveying process of the storage box, collect the external image of the edible mushroom, and determine multiple damaged characteristics according to the detection of the external image of the edible mushroom; Determine the damaged area of the edible mushroom according to the positions of multiple damaged characteristics, the morphology of multiple damaged characteristics and the morphology of the edible mushroom. The damaged area of the edible mushroom appears on the surface of the edible mushroom.

4. The intelligent control method of the edible mushroom under dynamic transportation according to claim 3, characterized in that, During the conveying process of the storage box, if there is a damaged area of the edible mushroom in the external image of the edible mushroom, determine the damage level of the edible mushroom according to the recognition of the damaged area of the edible mushroom, and further include: Collect the damaged area of the edible mushroom, determine the first damage level coefficient according to the regional position of the damaged area of the edible mushroom and the morphology of the edible mushroom, determine the second damage level coefficient according to the regional area of the damaged area of the edible mushroom and the type of the edible mushroom, and determine the damage level of the edible mushroom based on the first damage level coefficient, the second damage level coefficient and the damage level mapping relationship.

5. The intelligent control method of the edible fungus under dynamic transportation according to claim 1, wherein, Collect the type of the edible mushroom, determine the intelligent control mode of the conveying line according to the type of the edible mushroom, the damage level of the edible mushroom and the conveying speed of the conveying line, so as to convey each edible mushroom in the storage box smoothly, including: Collect the type of the edible mushroom and the conveying speed of the conveying line, determine the first mode coefficient according to the type of the edible mushroom and the damage level of the edible mushroom, and determine the second mode coefficient according to the damage level of the edible mushroom and the conveying speed of the conveying line.

6. The intelligent control method of the edible fungus under dynamic transportation according to claim 5, characterized in that, Collect the type of the edible mushroom, determine the intelligent control mode of the conveying line according to the type of the edible mushroom, the damage level of the edible mushroom and the conveying speed of the conveying line, so as to convey each edible mushroom in the storage box smoothly, and further include: Determine the intelligent control mode of the conveying line based on the first mode coefficient, the second mode coefficient and the control mode mapping relationship; control the conveying speed of the conveying line along the intelligent control mode of the conveying line, and control the activity range of the edible mushroom in the storage box to limit the collision between the edible mushroom and the storage box; the intelligent control mode includes a variable-speed conveying control mode, a constant-speed conveying control mode and a deceleration conveying control mode.

7. An intelligent control system for edible fungi under dynamic conveying, characterized in that, The intelligent control system of the edible mushroom under dynamic conveying is applied to the intelligent control method of the edible mushroom under dynamic conveying as described in any one of claims 1-6. The intelligent control system of the edible mushroom under dynamic conveying includes: The picking module is used to trigger the picking of edible fungi in the edible fungi cultivation space according to the spatial position and maturity of the edible fungi, so as to load each edible fungus into the corresponding storage box; The overall quality grade module is used to determine the overall quality grade of the edible fungi in the storage box according to the maturity of each edible fungus and the morphology of the edible fungi if the storage box is in a full-load state, including: constructing a distribution map of edible fungi based on the morphology of the storage box and multiple storage positions of the storage box, and marking the maturity of each edible fungus in the distribution map of the edible fungi; in the distribution map of the edible fungi, determining the morphology of each edible fungus based on the detection of multiple storage positions of the storage box, and determining the first quality coefficient according to the type of the edible fungus and the maturity of each edible fungus; determining the second quality coefficient according to the type of the edible fungus and the morphology of each edible fungus; determining the overall quality grade of the edible fungi in the storage box based on the first quality coefficient, the second quality coefficient and the quality grade mapping relationship; the second quality coefficient is different from the first quality coefficient; The dynamic conveying module is used to transfer the storage box to the corresponding conveying line, and determine the conveying route of the storage box according to the overall quality grade of the edible fungi in the storage box, the conveying direction of the conveying line and the morphology of the conveying line, and trigger the dynamic conveying of each edible fungus, including: if the overall quality grade of the edible fungi in the storage box is greater than the preset overall quality grade threshold, triggering the transfer of the storage box by the robotic arm, and the storage box is transferred to the corresponding conveying line under the drive of the robotic arm. At this time, the storage box and multiple edible fungi in the storage box move under the drive of the conveying line; collecting the morphology of the conveying line, and determining multiple edible fungus sorting nodes according to the morphology of the conveying line, and the multiple edible fungus sorting nodes cover the types of edible fungi and the corresponding overall quality grades; determining the first route according to the overall quality grade of the edible fungi in the storage box and the multiple edible fungus sorting nodes; determining the second route according to the conveying direction of the conveying line and the multiple edible fungus sorting nodes, and determining the conveying route of the storage box based on the synthesis of the first route and the second route, and the storage box moves along the conveying route under the drive of the conveying line to realize the dynamic conveying of each edible fungus; the first route involves the current state of the conveying line, the busy degree of the sorting nodes, and the storage time requirement of the edible fungi; the second route involves the speed of the conveying line, the processing capacity of the sorting nodes, and the priority of the storage box; The damage grade module is used to determine the damage grade of the edible fungus according to the identification of the damaged area of the edible fungus if there is a damaged area of the edible fungus in the external image of the edible fungus during the conveying process of the storage box; The intelligent control mode module is used to collect the type of the edible fungus, and determine the intelligent control mode of the conveying line according to the type of the edible fungus, the damage grade of the edible fungus and the conveying speed of the conveying line, so as to stably convey each edible fungus in the storage box.

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