Intelligent control method and system for edible mushrooms under dynamic conveying

Abstract

The invention discloses an intelligent control method and system for edible mushrooms under dynamic conveying, and relates to the technical field of edible mushroom dynamic conveying, and the method comprises the steps: determining a conveying path of a storage frame according to the overall quality grade of the edible mushrooms in the storage frame, the conveying direction of a conveying line and the form of the conveying line; dynamic conveying of all the edible mushrooms is triggered, the overall quality grade of the edible mushrooms in the storage frame is introduced, and the accuracy of the conveying route of the storage frame is guaranteed. Therefore, if the external image of the edible fungi has the damaged area of the edible fungi, the damage grade of the edible fungi is determined according to the identification of the damaged area of the edible fungi; the types of the edible mushrooms are collected, the intelligent control mode of the conveying line is determined according to the types of the edible mushrooms, the damage grade of the edible mushrooms and the conveying speed of the conveying line, the edible mushrooms in the storage frame are stably conveyed, the damage grade of the edible mushrooms is fully considered, and intelligent control over the edible mushrooms under dynamic conveying is achieved.

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 an edible fungi cultivation space. When the edible fungi reach the corresponding maturity level, they are picked by a manipulator and transferred to a corresponding storage box. In the prior art, the storage box moves under the transportation of a conveyor line, and the edible fungi move along with the movement of the storage box. However, the conveyor line operates at a preset speed, and the edible fungi will collide with the storage box during the movement. Moreover, the speed of the conveyor line does not change, resulting in further expansion of the damaged area of the edible fungi, 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: In an edible fungi cultivation space, trigger the picking of edible fungi according to the spatial position and maturity level of the edible fungi, so as to load each edible fungus into a corresponding storage box; 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 level and morphology of each edible fungus; 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; 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; Collect the types of edible fungi, and determine the intelligent control mode of the conveyor line according to the types of 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.

[0005] 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: A picking module, which is used to trigger the picking of edible fungi in the cultivation space of 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; 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; A dynamic conveying module, which is used to transfer the storage box to the corresponding conveying line, 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; 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 conveying process of the storage box; An intelligent control mode module, which is used to collect the types of edible fungi, determine the intelligent control mode of the conveying line according to the types of edible fungi, the damage grade of the edible fungus and the conveying speed of the conveying line, so as to smoothly convey each edible fungus in the storage box.

[0006] Compared with the prior art, the beneficial effects of the present invention are: In the embodiment of the present invention, by the method in the embodiment of the present invention, in the cultivation space of edible fungi, 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 conveying 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 conveying line and the morphology of the conveying line, and the dynamic conveying 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 conveying direction of the conveying line and the morphology of the conveying line is compatible, ensuring the accuracy of the conveying route of the storage box.

[0007] Therefore, during the conveying process of the storage box, if there is a damaged area of the edible fungus in the external image of the edible fungus, the damage grade of the edible fungus is determined according to the identification of the damaged area of the edible fungus; the types of edible fungi are collected, and the intelligent control mode of the conveying line is determined according to the types of edible fungi, the damage grade of the edible fungus and the conveying speed of the conveying line, so as to smoothly convey each edible fungus in the storage box. The damage grade of the edible fungus is fully considered, and the overall consideration of the types of edible fungi, the damage grade of the edible fungus and the conveying speed of the conveying line is compatible, realizing the intelligent control of the edible fungi under dynamic conveying. Description of the Drawings

[0008] Figure 1It is a schematic flow chart of the intelligent control method for edible fungi under dynamic transportation in the embodiments of the present invention; Figure 2 It is a schematic flow chart of step S11 in the intelligent control method for edible fungi under dynamic transportation in the embodiments of the present invention; Figure 3 It is a schematic flow chart of step S12 in the intelligent control method for edible fungi under dynamic transportation in the embodiments of the present invention; Figure 4 It is a schematic flow chart of step S13 in the intelligent control method for edible fungi under dynamic transportation in the embodiments of the present invention; Figure 5 It is a schematic flow chart of step S14 in the intelligent control method for edible fungi under dynamic transportation in the embodiments of the present invention; Figure 6 It is a schematic flow chart of step S15 in the intelligent control method for edible fungi under dynamic transportation in the embodiments of the present invention; Figure 7 It is a schematic diagram of the structural composition of the intelligent control system for edible fungi under dynamic transportation in the embodiments of the present invention. Detailed implementation manners

[0009] 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.

[0010] Please refer to Figures 1 to 7 , an intelligent control method for edible fungi under dynamic transportation, which is applied to the intelligent control scenario of edible fungi under dynamic transportation; the intelligent control method for edible fungi under dynamic transportation includes: Step S11: In the edible fungi cultivation space, trigger the picking of 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; 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 and morphology of each edible fungus; 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 fungi in the storage box, the conveying direction of the conveyor line and the morphology of the conveyor line, and trigger the dynamic transportation of each edible fungus; Step S14: During the conveying process 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 damage grade of the edible fungus according to the identification of the damaged area of the edible fungus; Step S15: Collect the types of edible fungi, and 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 conveying speed of the conveyor line, so as to stably convey each edible fungus in the storage box; Reference Figure 2 In step S11, in the edible mushroom cultivation space, the picking of the edible mushrooms is triggered according to the spatial position and maturity of the edible mushrooms, so as to load each edible mushroom into the corresponding storage box; In the specific implementation process of the present invention, the specific steps are as follows: 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, the spatial position of the edible mushrooms is determined, and the camera is triggered to take an online photo of the edible mushrooms according to the spatial position of the edible mushrooms; S112: Based on the online photo of the edible mushrooms, the current image of the edible mushrooms is determined. The morphological characteristics and color characteristics of the edible mushrooms are determined according to the recognition of the current image of the edible mushrooms, and the maturity of the edible mushrooms is determined according to the morphological characteristics, color characteristics and types of the edible mushrooms; S113: According to the spatial position, maturity of the edible mushrooms and the current position of the storage box, the picking path of the edible mushrooms is determined. 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 sequence.

[0011] In the embodiment of the present application, 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, the spatial position of the edible mushrooms is determined, and the camera is triggered to take an online photo of the edible mushrooms according to the spatial position of the edible mushrooms, and the online photo of the edible mushrooms by the camera is introduced.

[0012] At this time, the edible mushrooms are cultivated in a specific cultivation space (such as a greenhouse, a cultivation room or an automated cultivation rack), and these cultivation spaces usually have suitable temperature, humidity, light and ventilation conditions to promote the growth of the edible mushrooms; at this time, multiple cultivation layers or racks are arranged in the cultivation space, and each cultivation layer or rack is placed with an edible mushroom culture medium (such as sawdust, wheat bran, rice straw, etc.); the edible mushrooms grow on these culture media to form fruiting bodies (i.e., the so-called edible mushrooms).

[0013] The positioning technology is used to determine the specific position of the edible mushrooms in the cultivation space, which is realized by installing positioning sensors (such as RFID tags, ultrasonic sensors, infrared sensors or cameras, etc.) in the cultivation space; at the same time, RFID readers are installed on each cultivation rack, and RFID tags are pasted on the edible mushroom culture media or cultivation containers; when the edible mushrooms grow to a certain size, the RFID readers read the tag information and send the position data to the central control system through wireless signals; the central control system determines the specific position of each edible mushroom according to the received data and the layout information of the cultivation rack.

[0014] Once the spatial position of the edible mushroom is determined, the central control system triggers the camera to take an online photo of the edible mushroom according to preset rules or algorithms. At this time, a rule is set that when the edible mushroom grows to a specific stage (such as when the cap expands to a certain extent), the camera is triggered to take a photo. The camera should be installed at a position where the edible mushroom 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 conditions are met, it sends a signal to the camera to trigger it to take a photo.

[0015] Specifically, assume there is an automated edible mushroom cultivation system, which includes multiple cultivation racks, and multiple edible mushroom culture media are placed on each cultivation rack. An RFID reader and a camera are installed on each cultivation rack. When the edible mushroom grows to about 50% of its cap expanded, it needs to be photographed to evaluate its maturity.

[0016] The edible mushroom grows on the culture medium on the cultivation rack to form a fruiting body. When the edible mushroom grows to a certain size, the RFID reader reads the RFID tag information attached to the culture medium and sends the position data to the central control system. The central control system determines whether the edible mushroom meets the shooting conditions according to the received position data and preset rules (such as 50% cap expansion). If the conditions are met, a signal is sent to the corresponding camera to trigger it to take a photo. The camera takes a clear image of the edible mushroom, and this image is sent to the central control system for subsequent processing and analysis, such as maturity evaluation, quality grading, etc.

[0017] Furthermore, based on the online photo of the edible mushroom, the current image of the edible mushroom is determined. According to the recognition of the current image of the edible mushroom, the morphological characteristics and color characteristics of the edible mushroom are determined, and the maturity of the edible mushroom is determined according to the morphological characteristics, color characteristics and the type of the edible mushroom. It takes into account the overall consideration of the morphological characteristics, color characteristics and the type of the edible mushroom, ensuring the accuracy of the maturity of the edible mushroom.

[0018] At this time, in step S111, when the edible mushroom meets the preset shooting conditions, the camera takes an online photo of it, and these photographed images are the current images of the edible mushroom. At this time, the photographed images should contain sufficient 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 image.

[0019] Use image recognition algorithms to analyze the captured images of edible fungi and extract their morphological and color features. At the same time, the morphological features include the size, shape, edge contour, etc. of the edible fungi; the color features include hue, saturation, brightness, etc., and these features are extracted through image processing techniques (such as edge detection, color space conversion, etc.). For example, use deep learning models such as convolutional neural networks (CNNs) to extract features from the images, and these models can automatically learn and extract the key features in the images.

[0020] Combined with the species information of the edible fungi, use machine learning or deep learning models to analyze the extracted morphological and color features to judge the maturity of the edible fungi. Specifically, different species of edible fungi will show different morphological and color changes during the maturation process. Therefore, a dedicated maturity judgment model needs to be trained for each type of edible fungi. 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 the 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.

[0021] Optionally, assume there is an edible fungus called "oyster mushroom" and its maturity needs to be judged for picking. When the oyster mushroom grows to a certain size, the camera will take an online photo of it to obtain a clear image of the oyster mushroom. Use image recognition algorithms (such as CNNs) to analyze the captured oyster mushroom image and extract its morphological and color features. For example, extract features such as the diameter of the oyster mushroom, edge contour, and color of the cap, and combined with the species information of the oyster mushroom (i.e., "oyster mushroom"), 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).

[0022] According to the score and the 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 fungi picking and reduces the subjectivity and error of manual judgment.

[0023] Therefore, the picking path of the edible fungi is determined based on the spatial position, maturity of the edible fungi and the current position of the storage box. The robotic arm transfers the edible fungi to the storage box along the picking path. At this time, the storage box has multiple storage positions, and multiple edible fungi enter the corresponding storage positions in sequence, taking into account the overall situation of the spatial position, maturity of the edible fungi and the current position of the storage box, ensuring the accuracy of the picking path of the edible fungi.

[0024] At this time, after determining the maturity and spatial position of the edible fungi, it is necessary to combine the current position of the storage box to plan an optimal picking path from the edible fungi to the storage box; at this time, the planning of the picking path should consider multiple factors, including the straight-line distance between the edible fungi 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, path planning algorithms (such as A* algorithm, Dijkstra algorithm, RRT algorithm, etc.) are used to generate the optimal or sub-optimal path; in addition, the picking priority also needs to be considered, such as picking the edible fungi with a higher maturity first, or adjusting the picking order according to the full-load situation of the storage box.

[0025] Once the picking path is planned, the robotic arm will move along this path to the position of the edible fungi, and then perform the picking action to transfer the edible fungi to the storage box; at the same time, the movement of the robotic arm is usually driven by a motor, and three-dimensional movement in space is achieved 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 fungi and transfer them to the storage box; in addition, the robotic arm also needs to have sufficient flexibility and adaptability to handle edible fungi with different shapes, sizes and positions.

[0026] The storage box is usually designed with multiple storage positions to be able to accommodate multiple edible fungi; during the picking process, each edible fungus will be placed in a specific position in the storage box; at the same time, the design of the storage position should consider factors such as the shape, size and fragility of the edible fungi 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 fungi in each storage position, such as variety, maturity, picking time, etc., and these information are recorded and managed through barcodes, RFID tags, two-dimensional codes, etc.

[0027] Specifically, assume there is an automated edible fungi 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 fungus called "oyster mushroom" and place it in the designated position in the storage box.

[0028] The system determines the spatial position and maturity of Pleurotus ostreatus through cameras and sensors; then, combining with the current position of the storage box, it uses a path planning algorithm to generate an optimal picking path from the Pleurotus ostreatus 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 Pleurotus ostreatus; then, its end effector (such as a gripper) precisely grasps the Pleurotus ostreatus 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 Pleurotus ostreatus is not damaged.

[0029] Each storage position in the storage box has a unique identifier (such as a barcode or RFID tag); when the Pleurotus ostreatus is placed in the storage position, the system records the information of the Pleurotus ostreatus in this position (such as variety, maturity, picking time, etc.). In this way, it is convenient to track and manage each Pleurotus ostreatus in the storage box; through this example, the specific implementation method and operation process of step S113 in practical applications can be seen. 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.

[0030] In an embodiment of the present application, the picking path matching table includes the position of the edible mushroom, maturity, storage box position, and picking path; the picking path matching table is shown in Table 1: Table 1 Picking Path Matching Table 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 When the system detects that a certain edible mushroom is mature and needs to be picked, it will look up the picking path matching table and determine the picking path according to the position, maturity of the edible mushroom, and the current free position of the storage box; for example, if it is detected that the edible mushroom on Rack 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 mushroom, perform the picking action, and transfer the edible mushroom to the designated position of the storage box.

[0031] At this time, it is detected that the edible mushroom on Rack 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 Rack 1 in Area A, picks the edible mushroom; and transfers the edible mushroom to Position 1 of Storage Box 1.

[0032] 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 mushrooms in the storage box according to the maturity of each edible mushroom and the morphology of the edible mushrooms; In the specific implementation process of the present invention, the specific steps are as follows: S121: Monitor multiple storage locations of the storage box in real time. When all multiple storage locations of the storage box are storing edible fungi, the storage box is in a full-load state. Construct a distribution map of the edible fungi based on the shape of the storage box and the multiple storage locations of the storage box, and mark the maturity of each edible fungus in the distribution map of the edible fungi; S122: In the distribution map of the edible fungi, determine the shape of each edible fungus based on the detection of the multiple storage locations 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; S123: Determine the second quality coefficient according to the type of the edible fungus and the shape 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.

[0033] In the embodiments of the present application, monitor multiple storage locations of the storage box in real time. When all multiple storage locations of the storage box are storing edible fungi, the storage box is in a full-load state. Construct a distribution map of the edible fungi based on the shape of the storage box and the multiple storage locations of the storage box, and mark the maturity of each edible fungus 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.

[0034] At this time, use sensors, cameras or other monitoring devices 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 real-time nature of the information.

[0035] When all preset storage locations of the storage box are occupied by edible fungi, the system determines that the storage box is full-load; 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.

[0036] 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.

[0037] On the constructed edible mushroom distribution map, the system will add maturity marks for each edible mushroom according to the detected maturity information of the edible mushrooms; at this time, the system uses color coding (such as red for fully mature and green for immature), numerical tags or other visualization means to represent the maturity, and these marks help users quickly understand the maturity status of the edible mushrooms in the storage box.

[0038] Specifically, assume there is an automated edible mushroom 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 cameras capture images inside the storage box, and the sensors detect whether there are edible mushrooms in each storage location; the system counts the number of occupied storage locations and finds that all 10 locations are occupied by edible mushrooms, so it determines that the storage box is full.

[0039] 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 location 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 includes 10 edible mushroom locations, and each location has a corresponding maturity mark. This distribution map helps users quickly understand the distribution and maturity status of the edible mushrooms in the storage box, so as to make corresponding management decisions.

[0040] Furthermore, in the edible mushroom distribution map, the morphology of each edible mushroom is determined based on the detection of multiple storage locations of the storage box. According to the type of the edible mushroom and the maturity of each edible mushroom, the first quality coefficient is determined, which takes into account the overall consideration of the type of the edible mushroom and the maturity of each edible mushroom, and ensures the accuracy of the first quality coefficient.

[0041] At this time, on the already constructed edible mushroom distribution map, 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 available for subsequent analysis. The accuracy depends on the quality of the images and the precision of the recognition technology.

[0042] Combine the type information and maturity information of edible fungi with the preset quality assessment criteria to calculate the first quality coefficient for each edible fungus. At the same time, different types of edible fungi have different quality standards. For example, some types place more emphasis on size, while others place more emphasis on color or shape. Similarly, maturity is also an important factor affecting quality. The system will assign a quality coefficient to each edible fungus 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.

[0043] Specifically, assume there is an automated edible fungus quality assessment system that has obtained the images and maturity information of the edible fungi in the storage box through cameras and sensors and constructed a distribution map of the edible fungi. The system uses image recognition technology to analyze the images of each edible fungus. For example, for enoki mushrooms, the system will identify their slender shape, uniform color, and tight arrangement. For oyster mushrooms, the system will identify their umbrella-like shape, dark cap, and light-colored stalk.

[0044] Enoki mushrooms: 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.

[0045] Oyster mushrooms: 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.

[0046] For each type of edible fungus, the system will also adjust the quality coefficient according to its maturity. For example, for enoki mushrooms, fully mature enoki mushrooms will obtain a higher quality coefficient than immature or overripe enoki mushrooms. The same is true for oyster mushrooms. An appropriate maturity usually receives a higher evaluation. Finally, the system will generate a detailed report for each edible fungus containing the type, morphology, maturity, and the first quality coefficient. These reports help users understand the quality status of the edible fungi in the storage box, thereby making corresponding management decisions. For example, users will give priority to selling high-quality edible fungi according to the quality coefficient, or use low-quality edible fungi for other purposes.

[0047] Therefore, determine the second quality coefficient according to the type of edible fungi and the morphology of each edible fungus; based on the first quality coefficient, the second quality coefficient, and the quality grade mapping relationship, determine the overall quality grade of the edible fungi in the storage box, which takes into account the overall consideration of the first quality coefficient, the second quality coefficient, and the quality grade mapping relationship, and ensures the accuracy of the overall quality grade of the edible fungi in the storage box.

[0048] At this time, further analyze the type and morphological data of the edible fungi to determine the second quality coefficient of each edible fungus. This coefficient is different from the first quality coefficient and focuses more on the impact of the morphological characteristics of the edible fungi on its overall quality. At this time, the system will analyze the morphological data of each edible fungus according to the preset morphological evaluation criteria (these criteria are based on the biological characteristics, market preferences, or industry standards of the edible fungi); for example, for some types of edible fungi, firm flesh, smooth surface, or specific color are characteristics of high quality; the system will assign a second quality coefficient to each edible fungus according to these criteria. This second quality coefficient is also a value between 0 and 1, which is used to represent the level of its morphological quality.

[0049] Combine the first quality coefficient and the second quality coefficient, and assign an overall quality grade to each edible fungus according to the preset quality grade mapping relationship; at the same time, the quality grade mapping relationship is a predefined table that maps the combination of the two quality coefficients to a specific quality grade. This quality grade is a simple classification (such as excellent, good, average, poor), and it is also a more detailed scoring system; the system will calculate an overall quality grade for each edible fungus according to this mapping relationship, so as to provide a comprehensive quality assessment.

[0050] Specifically, assume there is an automated edible fungus quality assessment system that has obtained the first quality coefficient and morphological data of the edible fungi in the storage box through the previous steps; Determination of the second quality coefficient: Flammulina velutipes: Assume that the morphological evaluation criteria for Flammulina velutipes include firm flesh, 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 flesh, smooth surface, and uniform color will be assigned a relatively high second quality coefficient, such as 0.8.

[0051] Pleurotus ostreatus: For Pleurotus ostreatus, assume that the morphological evaluation criteria include the integrity of the mushroom 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 a complete mushroom cap, a straight stipe, and overall symmetry will be assigned a relatively high second quality coefficient, such as 0.85.

[0052] Suppose there is the following mapping relationship for quality grades: 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". Applying this mapping relationship, assign an overall quality grade to each edible mushroom; for example: A enoki mushroom 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.

[0053] Reference Figure 4 , in step S13, 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 shape of the conveyor line, triggering the dynamic conveying of each edible mushroom; In the specific implementation process of the present invention, the specific steps are as follows: S131: 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. 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 mushrooms in the storage box move under the drive of the conveyor line; S132: Collect the shape of the conveyor line, and determine multiple edible mushroom sorting nodes according to the shape of the conveyor line. The multiple edible mushroom sorting nodes cover the varieties 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 the multiple edible mushroom sorting nodes; S133: Determine the second route according to the conveying direction of the conveyor line and the 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. The storage box moves along this conveying route under the drive of the conveyor line to achieve the dynamic conveying of each edible mushroom.

[0054] In the embodiment of the present application, 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. 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 mushrooms in the storage box move under the drive of the conveyor line.

[0055] 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, morphology, maturity, color, size of the edible fungi, etc. These factors are obtained through methods such as image recognition and sensor data collection; the evaluation results are compared with a preset overall quality grade threshold; the threshold is preset according to factors such as market demand, storage requirements of edible fungi, and sales strategies.

[0056] 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 perform actions; the actions of the robotic arm include steps such as stretching, grasping, lifting, rotating, and placing, and these actions are precisely controlled to ensure that the storage box and the edible fungi inside are not damaged during the transfer process.

[0057] After receiving the trigger signal, the robotic arm will accurately grasp the storage box; the grasping points are usually specially designed to ensure that the storage box can be firmly grasped while avoiding applying pressure to 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 movement of the robotic arm is smooth and continuous to avoid impacts on the storage box caused by sudden acceleration or deceleration.

[0058] When the robotic arm reaches the conveyor line position, it will gently place the storage box on the conveyor line; the placement point is usually a fixed position on the conveyor line, and this position 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.

[0059] 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 to move 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 required in the packaging area to improve production efficiency.

[0060] Specifically, assume there is an automated edible fungi processing system, which includes a storage area, a quality evaluation 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 evaluation area, the system evaluates the overall quality grade of the edible fungi inside through methods such as image recognition and sensor data collection.

[0061] Suppose the overall quality grade of the shiitake mushrooms in a certain storage box is evaluated as "excellent", exceeding a preset threshold (such as 0.8); the system then triggers the robotic arm to move. The robotic arm grabs the storage box from the storage area and moves it smoothly onto the conveyor line; the conveyor line starts to move at a moderate speed, driving the storage box and the shiitake mushrooms inside along a 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.

[0062] Further, collect the shape of the conveyor line and determine multiple edible mushroom sorting nodes based on the shape of the conveyor line. The multiple edible mushroom sorting nodes cover the types of edible mushrooms and their corresponding overall quality grades; determine the first route based on the overall quality grade of the edible mushrooms in the storage box and the multiple edible mushroom sorting nodes, taking into account both the overall quality grade of the edible mushrooms in the storage box and the multiple edible mushroom sorting nodes, ensuring the accuracy of the first route.

[0063] 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, position of branch points, etc. This information is crucial for subsequent determination of sorting nodes and routes; the shape of the conveyor line varies due to factors such as factory layout, equipment configuration, and processing procedures; therefore, the system needs to be able to flexibly adapt to different conveyor line shapes.

[0064] Based on the collected conveyor line shape information, the system will determine the positions of multiple sorting nodes according to pre-set rules or algorithms. These nodes are the points where the edible mushrooms are sorted or redirected on the conveyor line; the determination of sorting nodes needs to consider multiple factors, such as the types of edible mushrooms, overall quality grades, market demand, subsequent processing procedures, etc.; for example, different types of edible mushrooms need to be sorted to different areas for packaging or storage; the same type of edible mushrooms but with different quality grades 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.

[0065] The system ensures that each sorting node can handle one or more types of edible mushrooms and is associated with a specific overall quality grade. This means that when the edible mushrooms in the storage box reach a certain sorting node, the system can accurately identify their types and quality grades and make corresponding processing decisions.

[0066] Determine the first route based on the overall quality grade and sorting nodes: After determining the sorting nodes, the system calculates an optimal path from the current location to the target sorting node, i.e., the first route, according to the overall quality grade of the edible fungi in the storage box and the processing capabilities of each sorting node. The determination of the first route involves multiple factors, such as the current state of the conveyor line, the busyness of the sorting nodes, and the storage time requirements of the edible fungi. The system needs to comprehensively consider these factors to ensure that the edible fungi can be sorted to the target location efficiently and accurately.

[0067] Specifically, assume there is an automated edible fungi sorting system that includes a conveyor line, multiple sorting nodes, and corresponding processing equipment. The conveyor line is a 100-meter-long and 0.6-meter-wide straight line, but there is a 90-degree turning point in the middle, and there is a branch point before and after the turning point to connect other processing areas. The system determines three sorting nodes according to the shape 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.

[0068] The system ensures that each sorting node can handle the types and quality grades of edible fungi within its responsible range. 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 according to 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.

[0069] Therefore, determine the second route according to the conveying direction of the conveyor line and multiple edible fungi 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 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, further ensures the accuracy of the conveying route of the storage box.

[0070] 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 control systems. The direction of the conveyor line changes due to production processes, equipment configurations, or operational requirements. Therefore, the system needs to be able to monitor and adapt to these changes in real time.

[0071] 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, as well as their relative positional relationships.

[0072] Determine the second route: Based on the direction of the conveyor line and the positions of the sorting nodes, the system calculates an optimal path from the current position (or the end point of the first route) to the target sorting node, which is the second route; the determination of the second route involves multiple factors, such as the speed of the conveyor line, the processing capabilities of the sorting nodes, the priorities of the storage boxes, etc.; the system needs to comprehensively consider these factors to ensure that the edible fungi can be efficiently and accurately transported to the target position.

[0073] Once the first route and the second route are both determined, the system combines them into a complete transportation route, which 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; at the same time, driven by the conveyor line, the storage box moves along the combined transportation 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 unexpected situations or changes.

[0074] Specifically, assume there is an automated edible fungi sorting system, which 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 in the middle west of the conveyor line and is responsible for sorting high-quality level shiitake mushrooms; node B is located in the middle east 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 fungi.

[0075] 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.

[0076] 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 (near the assumed starting point in this example); the system combines the first route and the second route into a complete conveyor 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 conveyor route, and after acceleration, it smoothly reaches node B for sorting; during the movement, the system monitors the position and speed of the storage box in real time to ensure that it can accurately reach the target position.

[0077] In an embodiment of the present application, a conveyor route matching table is collected, and the conveyor route matching table is shown in Table 2: Table 2 Conveyor Route Matching Table Starting point Target sorting node Conveyor route Point A Node B Move straight from Point A to Node B Point A Node C Move straight 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 Assume that the storage box starts moving from point A and the target sorting node is node C. After the system queries the conveyor route matching table, the obtained conveyor route is "move straight from point A to the turning point, and then turn north to move to node C".

[0078] Reference Figure 5 , in 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, the damaged grade of the edible mushroom is determined according to the recognition of the damaged area of the edible mushroom; In the specific implementation process of the present invention, the specific steps are as follows: S141: 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 features according to the detection of the external image of the edible mushroom; S142: Determine the damaged area of the edible mushroom according to 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; S143: Collect the damaged area of the edible mushroom, determine the first damaged grade coefficient according to the regional position of the damaged area of the edible mushroom and the morphology of the edible mushroom, determine the second damaged grade coefficient according to the regional area of the damaged area of the edible mushroom and the type of the edible mushroom, and determine the damaged grade of the edible mushroom based on the first damaged grade coefficient, the second damaged grade coefficient, and the damaged grade mapping relationship.

[0079] In the embodiment of the present application, the conveying of the storage box is monitored in real time, the conveying process of the storage box is marked, the external image of the edible mushroom is collected, and multiple damaged features are determined according to the detection of the external image of the edible mushroom, and multiple damaged features are introduced.

[0080] At this time, the system uses cameras, sensors 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 a smooth conveying process and promptly detect any abnormalities or potential problems; the monitoring data (such as video streams, image sequences) is transmitted in real time to the processing unit of the system for analysis.

[0081] The system assigns a unique identifier (such as an ID number, barcode, etc.) to each storage box and tracks it during the conveying process; through timestamps, location markers or other tracking technologies, the entire conveying process of the storage box from the starting point to the ending point is recorded, and this marker information helps with subsequent data analysis and problem tracing.

[0082] During the conveying process, the system periodically or as needed collects external images of the edible fungi; 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 fungi can be captured; the collected images are stored as digital files for subsequent image processing and damaged feature detection.

[0083] The collected images of the edible fungi are analyzed; by comparing the images of the edible fungi with a preset damaged feature template or database, 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.

[0084] Specifically, assume there is an automated edible fungi sorting and packaging system, which includes a conveyor line, multiple cameras and corresponding image processing software; the system uses the cameras installed above the conveyor line to monitor the conveying process of the storage box in real time; the video stream captured by the cameras is transmitted in real time to the processing unit of the system; 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 box on the conveyor line through timestamps and position sensors.

[0085] When the storage box passes by the camera, the system triggers an image acquisition operation to capture the external images of the edible fungi, and 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 images of the edible fungi; for example, the system detects two obvious scratches and a small depressed area on the surface of the shiitake mushrooms in an image, and 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, collecting external images, and damaged feature detection together form the basis for detecting damaged edible fungi and provide important evidence for subsequent sorting, packaging, and quality control.

[0086] Furthermore, the damaged area of the edible mushroom is determined 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.

[0087] 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 cap, the stalk). Through precise position analysis, the system can understand the distribution of damaged features on the edible mushroom.

[0088] 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.

[0089] 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.

[0090] Specifically, the system is conducting 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 cap, near the connection with the stalk; the other is located in the middle of the cap, near the top; further analysis reveals that the damaged feature located at the edge of the cap is a slender scratch, with a color slightly darker than the surrounding tissue; while the damaged feature located in the middle of the cap is a circular spot, with a significantly lighter color.

[0091] The system also notices that the overall shape of this batch of Pleurotus ostreatus is umbrella-shaped, the 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 cap, and the other is the spot area in the middle of the cap. These two areas are clearly marked on the image for subsequent analysis and processing.

[0092] Therefore, collect the damaged areas of the edible fungi, determine the first damaged grade coefficient according to the regional location of the damaged areas of the edible fungi and the morphology of the edible fungi, determine the second damaged grade coefficient according to the regional area of the damaged areas of the edible fungi and the types of the edible fungi, and determine the damaged grade of the edible fungi based on the first damaged grade coefficient, the second damaged grade coefficient and the damaged grade mapping relationship, which takes into account the overall consideration of the first damaged grade coefficient, the second damaged grade coefficient and the damaged grade mapping relationship, and ensures the accuracy of the damaged grade of the edible fungi.

[0093] At this time, the system accurately extracts these damaged areas from the collected images of the edible fungi according to the previously determined damaged areas (step S142); the extraction process involves technologies such as image cropping, region segmentation or feature extraction to ensure the accuracy and integrity of the damaged areas.

[0094] Determine the first damaged grade coefficient: The system evaluates the impact of the damage on the overall quality of the edible fungi according to the regional location of the damaged areas (such as in the main edible part, edge part or non-critical area of the edible fungi) and the morphology of the edible fungi (such as overall size, shape, color, etc.); based on this evaluation, the system assigns a first damaged grade coefficient to each damaged area, and this first damaged grade coefficient reflects the importance of the damaged location to the quality of the edible fungi.

[0095] Determine the second damaged grade coefficient: The system then further evaluates the severity of the damage according to the regional area of the damaged areas (that is, the proportion of the damaged part in the entire surface of the edible fungi) and the types of the edible fungi (different types of edible fungi have different tolerances to damage); by comparing the damaged area with a preset threshold or standard, the system assigns a second damaged grade coefficient to each damaged area, and this second damaged grade coefficient reflects the relative size of the damaged area and the direct impact on the quality of the edible fungi.

[0096] The system combines the first damaged grade coefficient and the second damaged grade coefficient, as well as a preset damaged grade mapping relationship (one or more algorithms or rule sets), to determine the final damaged grade of the edible fungi; the damaged grade is a classification label (such as "slightly damaged", "moderately damaged", "severely damaged", etc.), or a numerical score, which is used to quantify the damaged degree of the edible fungi.

[0097] Specifically, the system is detecting and grading the damage of 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 areas; for example, there is an obvious sunken area on the top of a Pleurotus ostreatus in an image, and the system successfully extracts this area.

[0098] Determine the first damage level coefficient: The system evaluated the location of this sunken area and found 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 assigned a relatively high first damage level coefficient to this damaged area (such as 0.8, indicating that the damaged location has a significant impact on the quality).

[0099] Determine the second damage level coefficient: The system then measured the area of this sunken area and found that it accounts for about 5% of the total surface area of the Pleurotus ostreatus; considering that the Pleurotus ostreatus has a relatively low tolerance to sunken damage, the system assigned a medium second damage level coefficient to this damaged area (such as 0.6, indicating that the damaged area is relatively large but has a moderate impact on the quality). Finally, the system combined 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 calculated 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.

[0100] Reference Figure 6 , in step S15, collect the types of edible fungi, and determine the intelligent control mode of the conveyor line according to the types of edible fungi, the damage levels of the edible fungi, and the conveying speed of the conveyor line, so as to smoothly convey each edible fungus in the storage box; In the specific implementation process of the present invention, the specific steps are as follows: S151: Collect the types of edible fungi and the conveying speed of the conveyor line, determine the first mode coefficient according to the types of edible fungi and the damage levels of the edible fungi, and determine the second mode coefficient according to the damage levels of the edible fungi and the conveying speed of the conveyor line; 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 fungi in the storage box 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.

[0101] In the embodiments of the present application, collecting the types of edible fungi and the conveying speed of the conveyor line, determining the first mode coefficient according to the types of edible fungi and the damage levels of the edible fungi, and determining the second mode coefficient according to the damage levels of the edible fungi and the conveying speed of the conveyor line takes into account the overall consideration of the damage levels of the edible fungi and the conveying speed of the conveyor line, and ensures the accuracy of the second mode coefficient.

[0102] At this time, the system accurately obtains the information of the edible mushroom species currently being conveyed on the conveyor line by reading barcodes, RFID tags, image recognition, or other automated means; the edible mushroom species information includes its scientific name, common name, taxonomic rank, etc., and this information is crucial for determining the subsequent processing mode and coefficients.

[0103] 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, and it reflects the moving rate of the edible mushrooms on the conveyor line.

[0104] Determine the first mode coefficient according to the species and damage level of the edible mushrooms: The system consults the preset "Edible Mushroom Species - Damage Level - First Mode Coefficient" mapping table or algorithm based on the collected edible mushroom species 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 edible mushroom species, the influence of the damage level on the processing sensitivity, and the special requirements of specific species during the processing; Based on this information, the system assigns a first mode coefficient to each edible mushroom, and this first mode coefficient reflects the requirements of the edible mushroom species and damage status for the conveying and processing modes.

[0105] Determine the second mode coefficient according to the damage level and conveying speed: The system then consults the preset "Damage Level - Conveying Speed - Second Mode Coefficient" mapping table or algorithm based on the damage level and the current conveying speed; the mapping table or algorithm takes into account the influence of the conveying speed on the processing efficiency of the edible mushrooms, the requirements of the damage level for processing safety, and the physical damage risk suffered by the edible mushrooms at different conveying speeds; based on this information, the system assigns a second mode coefficient to each edible mushroom, and this second mode coefficient reflects the further adjustment requirements of the conveying speed and damage status for the conveying and processing modes.

[0106] Specifically, the system uses the conveyor line to transport different types of edible mushrooms from the warehousing area to the processing area; the system identifies that the currently conveyed mushroom is "oyster mushroom" by reading the RFID tag; the system detects through sensors that the current speed of the conveyor line is 50 meters per minute (50 m / min).

[0107] Determine the first mode coefficient: As an edible mushroom with a relatively soft texture and a flat shape, oyster mushroom is 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 consults the "Edible Mushroom Species - 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 oyster mushroom requires relatively mild processing conditions during the processing to reduce the risk of further damage.

[0108] Determine the second mode coefficient: Considering that the conveying speed is 50 meters per minute, which is a relatively fast speed, it increases the risk of damage to Pleurotus ostreatus during conveying due to collision or friction. At the same time, since Pleurotus ostreatus is already in a "slightly damaged" state, the system needs to handle it more carefully to avoid further damage. The system consults the "damage level - conveying speed - second mode coefficient" mapping table and finds that the coefficient matching "slightly damaged - 50 m / min" is 0.6. This coefficient reflects that at the given conveying speed, the system needs to adopt a slower or gentler handling method to reduce the damage to Pleurotus ostreatus. Through the above steps, the system determines two mode coefficients for the currently conveyed Pleurotus ostreatus: the first mode coefficient is 0.75, and the second mode coefficient is 0.6. These coefficients will be used in the subsequent steps to determine the most suitable conveying and handling mode for the current situation.

[0109] Furthermore, based on the first mode coefficient, the second mode coefficient, and the control mode mapping relationship, determine the intelligent control mode of the conveyor line. 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 fungi in the storage box to limit the collision of the edible fungi with the storage box. The intelligent control mode includes variable-speed conveying control mode, constant-speed conveying control mode, and deceleration 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 to ensure 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.

[0110] 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 control mode of the conveyor line and the optimization of the processing efficiency and safety of the edible fungi 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 variable-speed conveying control mode, constant-speed conveying control mode, deceleration conveying control mode, etc.

[0111] 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 deceleration conveying control mode, the system gradually reduces the conveying speed to ensure the safe deceleration of the edible fungi before the processing area.

[0112] To further reduce the collision between edible fungi and the storage box, the system takes a series of measures to control the movement range of edible fungi within 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 collisions, or setting limit devices within the storage box to restrict the movement of edible fungi; through these measures, the system ensures that the edible fungi remain relatively stable during transportation and reduces damage caused by collisions.

[0113] 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 - 0.8 and the second mode coefficient is less than 0.7, the "decelerated conveyor control mode" is selected; therefore, the system selects the "decelerated conveyor control mode" for the current situation.

[0114] Once the "decelerated conveyor control mode" is selected, the system immediately starts to adjust the conveyor speed of the conveyor line; assuming that 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 fungi before the processing area; at the same time, to further reduce the collision between Pleurotus ostreatus and the storage box, the system takes the following measures: adjusting the size and shape of the storage box to adapt to the size and shape of Pleurotus ostreatus to ensure that Pleurotus ostreatus can be stably placed within the storage box; laying a layer of foam pad at the bottom of the storage box to reduce the impact force when Pleurotus ostreatus collides with the bottom of the storage box; setting limit devices on the sides of the storage box to restrict the left - right movement range of Pleurotus ostreatus during transportation; through these measures, the system successfully determines the most suitable intelligent control mode for the current situation, adjusts the conveyor speed of the conveyor line, and controls the movement range of Pleurotus ostreatus within the storage box, thus ensuring the safety and stability of Pleurotus ostreatus during transportation.

[0115] 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: Table 3 Intelligent Control Mode Matching Table 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 Now, assuming 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".

[0116] Please refer to Figure 7 , Figure 7It is a schematic structural diagram of the intelligent control system for edible fungi under dynamic transportation in the embodiments of the present invention; the intelligent control system for edible fungi under dynamic transportation includes: A picking module 21, which is used to trigger the picking of edible fungi according to the spatial position and maturity of the edible fungi in the edible fungi cultivation space, so as to load each edible fungus into the corresponding storage box; An overall quality grade module 22, 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; 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 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; A damage grade module 24, 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; An intelligent control mode module 25, 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.

[0117] Arbitrary combinations of the technical features of the above embodiments are made. For the sake of concise 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 as the scope recorded in this specification.

Claims

1. An intelligent control method for edible fungi under dynamic conveying, characterized in that, Including: In the edible mushroom cultivation space, picking of edible mushrooms is triggered according to the spatial position and maturity of the edible mushrooms, so as to load each edible mushroom into a corresponding storage box; When the storage box is in a full-load state, the overall quality grade of the edible mushrooms in the storage box is determined according to the maturity of each edible mushroom and the morphology of the edible mushrooms; The storage box is transferred to the corresponding conveyor line, and the conveying route of the storage box is determined 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 the dynamic conveying of each edible mushroom is triggered; 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, the damaged grade of the edible mushroom is determined according to the identification 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 damaged grade of the edible mushroom and the conveying speed of the conveyor line, so as to stably convey each edible mushroom in the storage box.

2. The intelligent control method for edible fungi under dynamic conveying according to claim 1, characterized in that, The step of, in the edible mushroom cultivation space, picking of edible mushrooms is triggered according to the spatial position and maturity of the edible mushrooms, so as to load each edible mushroom into a 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 a camera is triggered according to the spatial position of the edible mushrooms; The current image of the edible mushroom is determined based on the on-line shooting of the edible mushroom, the morphological characteristics and color characteristics of the edible mushroom are determined according to the recognition of the current image of the edible mushroom, and the maturity of the edible mushroom is determined according to the morphological characteristics, color characteristics and type of the edible mushroom; The picking path of the edible mushroom is determined according to the spatial position, maturity of the edible mushroom and the current position of the storage box, and 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 sequentially enter the corresponding storage positions.

3. The intelligent control method for edible fungi under dynamic conveying according to claim 1, characterized in that, The step of, when the storage box is in a full-load state, the overall quality grade of the edible mushrooms in the storage box is determined according to the maturity of each edible mushroom and the morphology of the edible mushrooms, includes: The multiple storage positions of the storage box are monitored in real time. When edible mushrooms are stored in all the multiple storage positions of the storage box, the storage box is in a full-load state. A distribution map of the edible mushrooms is constructed based on the morphology of the storage box and the multiple storage positions of the storage box, and the maturity of each edible mushroom is marked in the distribution map of the edible mushrooms; In the distribution map of the edible mushrooms, the morphology of each edible mushroom is determined based on the detection of the multiple storage positions of the storage box, and a first quality coefficient is determined according to the type of the edible mushroom and the maturity of each edible mushroom; A second quality coefficient is determined according to the type of the edible mushroom and the morphology of each edible mushroom; the overall quality grade of the edible mushrooms in the storage box is determined based on the first quality coefficient, the second quality coefficient and the quality grade mapping relationship.

4. The intelligent control method for edible fungi under dynamic conveying according to claim 1, characterized in that, The step of transferring the storage box to the corresponding conveyor line, determining 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 triggering the dynamic conveying of each edible mushroom, includes: If the overall quality grade of the edible fungi in the storage box is greater than the preset overall quality grade threshold, the robotic arm is triggered to transfer the storage box. The storage box is carried by the robotic arm and transferred to the corresponding conveyor line. At this time, the storage box and multiple edible fungi in the storage box move under the drive of the conveyor line; Collect the shape of the conveyor line, and determine multiple edible fungi sorting nodes according to the shape 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.

5. The intelligent control method for edible fungi under dynamic conveying according to claim 4, characterized in that, 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 fungi in the storage box, the conveying direction of the conveyor line, and the shape of the conveyor line, and trigger the dynamic conveying of each edible fungus. It also includes: Determine the second route according to the conveying direction of the conveyor line and the multiple edible fungi sorting nodes, 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 the conveying route under the drive of the conveyor line to achieve the dynamic conveying of each edible fungus.

6. The intelligent control method for edible fungi 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 fungus in the external image of the edible fungus, determine the damaged grade of the edible fungus according to the identification of the damaged area of the edible fungus, 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 fungus, and determine multiple damaged features according to the detection of the external image of the edible fungus; Determine the damaged area of the edible fungus according to the positions of the multiple damaged features, the shapes of the multiple damaged features, and the shape of the edible fungus. The damaged area of the edible fungus appears on the surface of the edible fungus.

7. The intelligent control method for edible fungi under dynamic conveying according to claim 6, characterized in that, During the conveying process of the storage box, if there is a damaged area of the edible fungus in the external image of the edible fungus, determine the damaged grade of the edible fungus according to the identification of the damaged area of the edible fungus. It also includes: Collect the damaged area of the edible fungus, determine the first damaged grade coefficient according to the regional position of the damaged area of the edible fungus and the shape of the edible fungus, determine the second damaged grade coefficient according to the regional area of the damaged area of the edible fungus and the type of the edible fungus, and determine the damaged grade of the edible fungus based on the first damaged grade coefficient, the second damaged grade coefficient, and the damaged grade mapping relationship.

8. The intelligent control method for edible fungi under dynamic conveying according to claim 1, characterized in that, Collect the type of the edible fungus, and determine the intelligent control mode of the conveyor line according to the type of the edible fungus, the damaged grade of the edible fungus, and the conveying speed of the conveyor line to smoothly convey each edible fungus in the storage box, including: Collect the type of the edible fungus and the conveying speed of the conveyor line, determine the first mode coefficient according to the type of the edible fungus and the damaged grade of the edible fungus, and determine the second mode coefficient according to the damaged grade of the edible fungus and the conveying speed of the conveyor line.

9. The intelligent control method for edible fungi under dynamic conveying according to claim 8, characterized in that, Collect the type of the edible fungus, and determine the intelligent control mode of the conveyor line according to the type of the edible fungus, the damaged grade of the edible fungus, and the conveying speed of the conveyor line to smoothly convey each edible fungus in the storage box. It also includes: 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 fungi in the storage box 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.

10. An intelligent control system for edible fungi under dynamic conveying, characterized in that, The intelligent control system for edible fungi under dynamic conveying is applied to the intelligent control method for edible fungi under dynamic conveying as described in any one of claims 1-9. The intelligent control system for edible fungi under dynamic conveying includes: 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; 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; A dynamic conveying module, which is used to 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 fungi 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 fungus; A damage grade module, which is used to determine the damage grade of the edible fungi according to the identification of the damaged area of the edible fungi if there is a damaged area of the edible fungi in the external image of the edible fungi during the conveying process of the storage box; 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 conveying speed of the conveyor line, so as to convey each edible fungus in the storage box smoothly.

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