Intelligent line-following processing method and system

Abstract

The invention provides an intelligent line-following processing method and system. The intelligent line-following processing method comprises the following steps that S1, information of materials in a designated area is collected; s2, judging whether the materials can be processed or not according to the material information collected in the step S1; s3, if it is judged that the material can be processed, a to-be-processed label is generated, and if it is judged that the material cannot be processed, the to-be-processed label is not generated; s4, sorting the materials with the to-be-processed labels, and generating a to-be-processed task list; and S5, an execution unit carries out processing operation on the materials according to the sequence of the to-be-processed task list generated in the step S4. Through integration of advanced technologies such as machine vision, a sensor technology, automatic control and data analysis, the technical scheme of the invention is high in automation degree, the production efficiency can be remarkably improved, the consistency of product quality is ensured, and errors caused by manual operation are reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic food processing, and in particular to an intelligent on-line processing method and system. Background Art

[0002] With the global population growing and living standards improving, the demand for food is increasing, placing immense pressure on the food processing industry. Traditional food processing plants rely on extensive manpower and complex production lines, resulting in low efficiency and vulnerability to human factors, leading to inconsistent product quality and high production costs. Furthermore, frequent food safety issues pose a threat to public health. Therefore, there is an urgent need for flexible, efficient, intelligent, and automated food processing equipment to replace traditional manual operations.

[0003] The rapid development of artificial intelligence and robotics in recent years has brought new opportunities to the food processing industry. As an emerging food processing equipment, the intelligent flexible single station can automate the entire process, from raw material handling, processing, packaging, to quality inspection. Integrating advanced technologies such as machine vision, sensor technology, automated control, and data analysis, it can significantly improve production efficiency, ensure product quality, reduce production costs, and effectively avoid food safety risks caused by manual operation.

[0004] Currently, food processing plants widely use automated production lines to improve production efficiency. These lines typically include multiple processing equipment, each responsible for a specific task, such as loading and unloading, flipping and shaping, cutting, mixing, and cooking. These workstations are interconnected through traditional transmission mechanisms, sensors, and control systems, achieving a certain degree of automation.

[0005] The automated production line in the existing technology can only process materials at a specified location at a certain frequency, with poor intelligence and low work efficiency. Summary of the Invention

[0006] In view of the defects in the prior art, the purpose of the present invention is to provide an intelligent on-line processing method and system.

[0007] According to the present invention, an intelligent on-line processing method is provided, comprising the following steps:

[0008] Step S1: Collect information about materials in a designated area;

[0009] Step S2: judging whether the material can be processed according to the material information collected in step S1;

[0010] Step S3: If it is determined that the material can be processed, a label to be processed is generated; if it is determined that the material cannot be processed, no label to be processed is generated;

[0011] Step S4: sort the materials with the to-be-processed labels and generate a list of tasks to be processed;

[0012] Step S5: The execution unit processes the materials in the order of the list of tasks to be processed generated in step S4.

[0013] Preferably, for step S1, image information within a specified area is collected, and information of all materials in the image is extracted;

[0014] The material information includes position information, and the material information also includes at least one or more of material contour shape information, material surface pattern information, material size information, material color information, material volume information, material type information, gap size information between adjacent materials, and material processing probability information;

[0015] The material to be processed moves along the conveyor line, and the position of the material to be processed changes from being close to the processing area, to entering the processing area, and then moving away from the processing area. The position information of the material to be processed is updated in real time.

[0016] Preferably, for step S2, whether the material is within the processable range of the execution unit is determined based on the current position of the execution unit, the moving speed of the execution unit, the working range of the execution unit, and the moving speed of the material.

[0017] Preferably, for step S3, if it is determined that the material is within the processable range of the execution unit, it is further determined whether the material has been processed. If the material has been processed, no pending processing tag is generated; if the material has not been processed, a pending processing tag is generated;

[0018] If the material is judged to be outside the processable range of the execution unit, no label to be processed is generated.

[0019] Preferably, the information of the label to be processed includes one or more of the surface area of ​​the material, the contour shape of the material, and the gap between the material and another closest material.

[0020] Preferably, with respect to step S4, before sorting the materials with the labels to be processed, it is determined whether the information of the labels to be processed of the materials is within a preset range;

[0021] If the information of the material's to-be-processed label is within the preset range, the material's information and the information of the to-be-processed label are added to the to-be-processed task list for sorting;

[0022] If the information of the material's to-be-processed label is not within the preset range, the material's information and the information of the to-be-processed label will not be included in the to-be-processed task list.

[0023] Preferably, the order of the list of tasks to be processed is as follows: the materials included in the list of tasks to be processed that are closest to the execution module are processed first;

[0024] When multiple materials included in the list of tasks to be processed have the same distance from the execution module, the material closest to the target product parameters will be processed first.

[0025] Preferably, step S5 includes the following sub-steps:

[0026] Step S5.1: The working part of the execution unit moves to above the collision point;

[0027] Step S5.2: Determine whether the execution unit can be pressed down to complete the operation;

[0028] Step S5.3: If it is determined that the process can be completed, proceed to sub-step S5.4; if it is determined that the process cannot be completed, the execution unit is lifted, the process ends, and the abnormality is recorded;

[0029] Step S5.4: The material moves to the collision point while the execution unit is pressed down to the collision point;

[0030] Step S5.5: The execution unit tracks the position of the material and maintains the same speed as the material to be processed during processing, and is relatively stationary. After processing is completed, the execution unit is lifted and processing ends.

[0031] According to the present invention, an intelligent on-line processing method is provided for shaping fish fillets or meat fillets, comprising the following steps:

[0032] Step S1: Collect image information within a specified area and extract information about all materials in the image;

[0033] Step S2: judging whether the material is within the processable range of the execution unit according to the material information collected in step S1;

[0034] Step S3: If it is determined that the material is not within the processable range of the execution unit, no pending processing tag is generated; if it is determined that the material is within the processable range of the execution unit, then it is determined whether the material has been processed. If the material has been processed, no pending processing tag is generated; if the material has not been processed, a pending processing tag is generated;

[0035] The information of the label to be processed includes the surface area of ​​the material, the outline shape of the material, and the gap between the material and the closest material;

[0036] Step S4: Determine whether the information of the material's label to be processed is within a preset range; if the information of the material's label to be processed is within the preset range, then the information of the material and the information of the label to be processed are included in the list of tasks to be processed and sorted; if the information of the material's label to be processed is not within the preset range, then the information of the material and the information of the label to be processed are not included in the list of tasks to be processed;

[0037] The sorting method of the task list to be processed is: the material included in the task list with the closest distance to the execution module is processed first. If multiple materials included in the task list are at the same distance from the execution module, the material with the closest shape parameters to the target product is processed first.

[0038] Step S5: The execution unit processes the materials in the order of the list of tasks to be processed generated in step S4. The processing includes the following sub-steps:

[0039] Step S5.1: The working part of the execution unit moves to above the collision point;

[0040] Step S5.2: Determine whether the execution unit can be pressed down to complete the operation;

[0041] Step S5.3: If it is determined that the process can be completed, proceed to sub-step S5.4; if it is determined that the process cannot be completed, the execution unit is lifted, the process ends, and the abnormality is recorded;

[0042] Step S5.4: The material moves to the collision point while the execution unit is pressed down to the collision point;

[0043] Step S5.5: The execution unit tracks the position of the material and maintains the same speed as the material to be processed during processing, and is relatively stationary. After processing is completed, the execution unit is lifted and processing ends.

[0044] According to an intelligent on-line processing system provided by the present invention, the processing system includes:

[0045] Module M1 is used to collect information about materials in a specified area;

[0046] Module M2, used to determine whether the material can be processed based on the material information collected in step S1;

[0047] Module M3, for generating a to-be-processed label: if it is determined that the material can be processed, a to-be-processed label is generated; if it is determined that the material cannot be processed, no to-be-processed label is generated;

[0048] Module M4 is used to sort materials with labels to be processed and generate a list of tasks to be processed;

[0049] Module M5 is used to control the execution unit to process the materials in the order of the list of tasks to be processed generated in step S4.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. By integrating advanced technologies such as machine vision, sensor technology, automated control and data analysis, the present invention has a high degree of automation and can significantly improve production efficiency, ensure the consistency of product quality, and reduce errors caused by human operation.

[0052] 2. The present invention obtains incoming material data through AI recognition, and provides a variety of incoming material labels through label strategy analysis, matches incoming materials and labels, and updates the incoming material list; through the processing strategy analysis, the incoming material list is prioritized and updated to the processing task list. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Other features, objects and advantages of the present invention will become more apparent upon reading the detailed description of non-limiting embodiments with reference to the following drawings:

[0054] Figure 1 This is a flow chart of the intelligent on-line processing method mainly embodied in the present invention;

[0055] Figure 2 This is a schematic diagram of the overall structure of an intelligent on-line single station mainly embodied in the present invention;

[0056] Figure 3 This is a schematic diagram of the connection relationship of the intelligent on-line single-station electrical appliances mainly embodied in the present invention;

[0057] Figure 4 This is a schematic diagram of a detachable connection structure of the end effector mainly embodied in the present invention;

[0058] Figure 5 This is a schematic structural diagram of an end effector for shaping that is mainly embodied in the present invention;

[0059] Figure 6 This is a schematic structural diagram of an end-effector for grasping that mainly embodies the present invention.

[0060] Figure numerals: 1. Single station body; 2. Execution unit; 3. Vision unit; 4. Control unit; 5. Robotic arm; 6. End effector. DETAILED DESCRIPTION

[0061] The present invention is described in detail below with reference to specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, without departing from the scope of the present invention, a number of variations and improvements may be made by those skilled in the art. These all fall within the scope of protection of the present invention.

[0062] Example 1

[0063] like Figure 1 As shown, an intelligent on-line processing method provided by the present invention includes the following steps:

[0064] Step S1: Collect information about materials in a designated area.

[0065] Step S2: Determine whether the material can be processed based on the material information collected in step S1.

[0066] Step S3: If it is determined that the material can be processed, a label to be processed is generated; if it is determined that the material cannot be processed, no label to be processed is generated.

[0067] Step S4: sort the materials with the to-be-processed labels and generate a list of tasks to be processed.

[0068] Step S5: The execution unit 2 processes the materials in the order of the list of tasks to be processed generated in step S4.

[0069] The technical solution of the present application can use the image acquisition technology or sensor technology in the prior art to collect information about all materials in a specified area, and can extract material information such as position, contour shape, surface pattern, surface texture, surface area information, length information, width information, height information, color information, volume information, type information, gap size information between adjacent materials, and probability information of being processed. The technical solution of the present application can obtain different material information according to different processing scenarios, and the above material information is only an example for ease of understanding. The material information of the technical solution of the present application is not limited to the material information listed above. It should be noted that the collection and extraction of information on all materials in a specified area can be achieved by using AI cameras in the prior art.

[0070] The extracted material information is then used to determine whether the execution unit 2 can process the material. It should be noted that the execution unit 2 of the present application uses a robotic arm with 4 to 7 degrees of freedom, and the end of the robotic arm can perform corresponding processing on the material.

[0071] The material to be processed moves along the conveyor line, and its position will change from close to the processing area, to entering the processing area, and then away from the processing area. Execution Unit 2 can obtain the conveyor line speed in real time. After the vision unit collects the material position, it will track the position update of the material to be processed in real time.

[0072] For materials determined to be within the processing range of execution unit 2 and capable of processing, a pending processing tag is generated. The tags are then sorted based on their tag information to generate a pending processing task list. Execution unit 2 then performs processing operations according to the order listed. By generating pending processing tags and a pending processing task list, intelligent control of execution unit 2 is achieved, enabling execution unit 2 to process materials according to pre-set processing logic. This improves the automation level of the equipment, processing efficiency, and quality control stability.

[0073] Specifically, in step S1, image information within a designated area is collected, and information about all materials in the image is extracted. Material information includes location information, and at least one or more of the following: material shape information, material surface pattern information, material size information, material color information, material volume information, material type information, information about the gap between adjacent materials, and the probability of material processing. For example, material type information can include identification of fish species, such as grass carp and crucian carp. Furthermore, material type information can also include identification of fish organs, such as fish eyes and gills.

[0074] The material to be processed moves along the conveyor line, and the position of the material to be processed changes from being close to the processing area, to entering the processing area, and then moving away from the processing area. The position information of the material to be processed is updated in real time.

[0075] For step S2 , it is determined whether the material is within the processable range of the execution unit 2 based on the current position of the execution unit 2 , the moving speed of the execution unit 2 , the working range of the execution unit 2 , and the moving speed of the material.

[0076] For step S3, if it is determined that the material is within the processable range of the execution unit 2, then continue to determine whether the material has been processed. If the material has been processed, no label to be processed is generated. If the material has not been processed, a label to be processed is generated. If it is determined that the material is not within the processable range of the execution unit 2, no label to be processed is generated.

[0077] The information of the label to be processed includes one or more of the surface area of ​​the material, the contour shape of the material, and the gap between the material and another closest material.

[0078] For step S4, before sorting the materials with labels to be processed, determine whether the information of the labels to be processed of the materials is within a preset range; if the information of the labels to be processed of the materials is within the preset range, the information of the material and the information of the labels to be processed are included in the list of tasks to be processed for sorting; if the information of the labels to be processed of the materials is not within the preset range, the information of the material and the information of the labels to be processed are not included in the list of tasks to be processed.

[0079] The sorting method of the task list to be processed is: give priority to the materials included in the task list that are closest to the execution module; when multiple materials included in the task list to be processed have the same distance from the execution module, give priority to the materials closest to the target product parameters.

[0080] Step S5 includes the following sub-steps:

[0081] Step S5.1: The working part of the execution unit 2 moves to above the collision point.

[0082] Step S5.2: Determine whether the execution unit 2 can be pressed down to complete the operation.

[0083] Step S5.3: If it is determined that the process can be completed, continue to execute sub-step S5.4; if it is determined that the process cannot be completed, the execution unit 2 is lifted, the processing is completed, and the abnormality is recorded.

[0084] Step S5.4: The material moves to the collision point and the execution unit 2 is pressed down to the collision point.

[0085] Step S5.5: The execution unit 2 tracks the position of the material and keeps the same speed as the material to be processed during processing, and is relatively still. After the processing is completed, the execution unit 2 is lifted and the processing ends.

[0086] In a preferred embodiment, the intelligent on-line processing method of the present invention also has self-learning and optimization capabilities, which can continuously improve processing results based on production data, thereby enhancing production efficiency and product quality. For example, the abnormal data recorded in step S5.3 can be used to optimize the control of the execution module to avoid the recurrence of the same abnormal situation.

[0087] The present invention also provides an intelligent on-line processing system, the processing system comprising:

[0088] Module M1 is used to collect information about materials in a specified area.

[0089] Module M2 is used to determine whether the material can be processed based on the material information collected in step S1.

[0090] Module M3 is used to generate a label to be processed: if it is determined that the material can be processed, a label to be processed is generated; if it is determined that the material cannot be processed, no label to be processed is generated.

[0091] Module M4 is used to sort materials with labels to be processed and generate a list of tasks to be processed.

[0092] Module M5 is used to control the execution unit 2 to process the materials in the order of the list of tasks to be processed generated in step S4.

[0093] Example 2

[0094] Based on the first embodiment, according to the present invention, an intelligent on-line processing system is provided, which controls the hardware equipment to perform corresponding operations through the software system to achieve specific processing. Figure 2 、 Figure 3 、 Figure 4 、 Figure 5 as well as Figure 6 As shown, the hardware part of the technical solution of this application is an intelligent on-line processing single station, including a single station body 1, an execution unit 2, a visual unit 3 and a control unit 4. The execution unit 2 and the information acquisition unit are both arranged above the single station body 1, and the control unit 4 is integrated inside or outside the single station body 1.

[0095] The execution unit 2 includes a robotic arm 5 and an end-effector 6 detachably mounted on the robotic arm 5, for performing specific processing operations. The information acquisition unit includes a visual unit 3, which collects image information of the target area and transmits it to a control unit 4 for collecting information about the material in the specified area. The visual unit 3 of the technical solution of the present application can adopt an AI camera. The control unit 4 controls the execution unit 2 to process the target material in the image based on the image information collected by the visual unit 3. The control unit 4 is capable of executing computer-readable program code to control the execution unit 2.

[0096] Specifically, the end effector 6 includes a robot-end connector and a fixture-end connector. The robot-end connector is electrically connected to the robotic arm 5, and the fixture-end connector is electrically connected to the fixture. The robot-end connector and the fixture-end connector are detachably connected. The signal interface of the fixture-end connector and the signal interface of the robot-end connector are connected by metal contacts. The detachable connection method between the robot-end connector and the fixture-end connector can be any one of plug-in, connection using fasteners, and pneumatic clamps in the prior art. The end effector 6 of the present application not only realizes the detachable connection of the fixture, but also realizes the electrically controlled connection between the robotic arm 5 and the fixture, and can realize the control of the robotic arm 5 and the end effector 6 of the execution unit 2 by the control unit 4.

[0097] In a feasible implementation method: the robot-end connector is directly fixed to the end of the robot, and the fixture-end connector is pneumatically attracted to support manual / automatic control; the robot-end signal interface and the fixture-end signal interface are connected through metal contacts, and a waterproof sealing ring is fixed around the interface to support sensor signals, 485 communication, and 24V power supply access. The fixture-end pneumatic interface can be directly connected to the fixture cylinder and supports 6 channels.

[0098] It should be noted that the end effector 6 of the technical solution of this application includes at least a shaping fixture or a material gripping fixture to achieve different operations on the material. The control unit 4 can select the corresponding end effector 6 according to different working scenarios and match the corresponding control strategy.

[0099] More specifically, the control unit 4 includes an edge controller and an electrical box, and the vision unit 3 includes an AI camera. The edge controller and the AI ​​camera communicate via a data cable or network cable. The edge controller and the execution unit 2 communicate via a data cable or network cable. The edge controller and the electrical box communicate via a data cable or network cable.

[0100] The U-station main body 1 is also equipped with one or more of the following: a touch panel, a temperature and humidity detection module, a line speed detection module, operating buttons, an audible and visual alarm, and a cooling fan. Each of these components is connected to the electrical box via wires or a bus.

[0101] In actual use, the single-station body 1 is positioned to the side of the material conveyor. The AI ​​camera of the vision unit 3 extends above the material conveyor. The area below the AI ​​camera serves as the designated area for data collection. The end of the execution unit 2 extends above the material conveyor, thereby expanding the working range of the execution unit 2. In a preferred embodiment, the center of the execution unit 2's working range is located midway across the width of the material conveyor. The vision unit 3 is located on the incoming material side of the execution unit 2, collecting material information before processing.

[0102] The bottom of the station body 1 is equipped with rollers and a retractable positioning support device. This retractable positioning support device can be a retractable foot as is known in the art. When the station body 1 moves to a desired position, the retractable foot is lowered to secure the station body 1. When the station body 1 needs to be moved, the retractable foot is retracted, and the rollers are used to propel the station body 1 to the next target position, facilitating flexible layout of the processing site and adapting to different production lines.

[0103] This application proposes an intelligent, on-line food processing station. This station is centered around a hand-based execution unit 2, an eye-based visual unit 3, and a brain-based control unit 4. The end-of-line tooling can be changed based on different processing scenarios, enabling both on-line dynamic processing / grasping and static processing / grasping. The execution unit 2 can be configured with different execution modules and processing end-points to meet the needs of different scenarios. The execution modules can support collaborative robotic arms 5 with 4 to 7 degrees of freedom and force-controlled robots. The visual unit 3, located above the station body 1, houses an industrial monocular / binocular camera mounted on a visual support bracket. Multiple camera modules can be configured to meet different scenarios. The control unit 4, automatically or manually configures processing strategies based on the end-point and scenario. The station body 1 comprises a sealed, waterproof, and dustproof base frame that protects the control unit 4. The base features height-adjustable support feet and universal wheels, while the side panels provide electrical, gas, and network interfaces for quick access. The touch panel allows for configuring processing strategies, setting station parameters, performing station self-tests, and acquiring production data. Operation button box: contains emergency stop switch, start button and power switch.

[0104] The intelligent on-line single station of the technical solution of this application can quickly replace the processing end tools and flexibly adjust the processing parameters and processes according to different food processing scenarios and process requirements, thereby greatly improving the flexibility of production and adapting to the multi-variety and small-batch production model in the era of fast consumption.

[0105] The technical solution of this application improves production efficiency and quality: by integrating advanced technologies such as machine vision, sensor technology, automated control and data analysis, the present invention can significantly improve production efficiency, ensure the consistency of product quality, and reduce errors caused by human operation.

[0106] The technical solution of this application reduces production costs: the compact design of the intelligent single station reduces the demand for factory space and reduces the company's fixed asset investment. At the same time, its self-learning and optimization capabilities can continuously improve processing results, further reducing production costs.

[0107] The technical solution of this application enhances safety: the intelligent single station is equipped with safety measures such as emergency stop switches, which can effectively prevent accidents and ensure the safety of operators. At the same time, automated operation reduces human intervention and reduces food safety risks.

[0108] Easy to maintain and expand: The modular design of the intelligent single station makes maintenance more convenient and also facilitates future technology upgrades and function expansion.

[0109] To use the device, after powering it on, it must be initialized. You can then configure a work scenario, such as fillet / steak shaping, fish threading, or meat strip cutting. After selecting a scenario, select the corresponding end effector fixture. After selecting the fixture, install it. Once the fixture is installed, the device can begin operation.

[0110] Taking the shaping of fish fillets or meat fillets as an example, the shaping of fish fillets or meat fillets includes the following steps:

[0111] Step S1: The visual unit 3 collects image information in a specified area and extracts information about all materials in the image.

[0112] In step S2 , the control unit 4 determines whether the material is within the processable range of the execution unit 2 based on the material information collected in step S1 .

[0113] In step S3, if the control unit 4 determines that the material is not within the processable range of the execution unit 2, the label to be processed is not generated; if the control unit 4 determines that the material is within the processable range of the execution unit 2, it continues to determine whether the material has been processed. If the material has been processed, the label to be processed is not generated; if the material has not been processed, a label to be processed is generated.

[0114] The information of the label to be processed includes the surface area of ​​the material, the outline shape of the material, and the gap between the material and another closest material.

[0115] In step S4, the control unit 4 determines whether the information on the material's pending processing label is within a preset range. If the information on the material's pending processing label is within the preset range, the material information and the information on the pending processing label are added to the pending processing task list for sorting. If the information on the material's pending processing label is not within the preset range, the material information and the information on the pending processing label are not added to the pending processing task list. The material information added to the pending processing task list must include the material's location information.

[0116] The technical solution of this application provides a specific implementation method: a preset material is processable when its surface area is at least 10 square centimeters, when its outline shape includes a rectangle of at least 8 square centimeters, and when the gap between the preset material and the next closest material is greater than or equal to 5 centimeters. In this case, the material must simultaneously meet the preset surface area, outline shape, and gap between the material and the next closest material to be considered as having the information on the material's to-be-processed label within the preset range. It should be noted that the information data of the preset to-be-processed label can be adjusted according to actual conditions.

[0117] The list of pending tasks is sorted by prioritizing the materials closest to the execution module. If multiple materials are at the same distance from the execution module, the material closest to the target product's shape parameters is prioritized. The material closest to the target product's shape parameters can be understood as the material with the highest degree of similarity to the target product in one embodiment, or as the material with the closest contour shape and surface area to the target product in another embodiment.

[0118] Step S5: The execution unit 2 processes the material in the order of the list of tasks to be processed generated in step S4. The processing operation includes the following sub-steps:

[0119] Step S5.1: The working part of the execution unit 2 moves to above the collision point.

[0120] Step S5.2: The control unit 4 determines whether the execution unit 2 can be pressed down to complete the operation.

[0121] Step S5.3: If the control unit 4 determines that the process can be completed, the sub-step S5.4 is continued to be executed; if the control unit 4 determines that the process cannot be completed, the execution unit 2 is lifted, the process ends, and the abnormality is recorded.

[0122] Step S5.4: the control unit 4 controls the material to move to the collision point while the execution unit 2 is pressed down to the collision point.

[0123] Step S5.5: The control unit 4 controls the execution unit 2 to track the position of the material. During processing, the execution unit 2 maintains the same speed as the material to be processed and is relatively stationary. After the processing is completed, the execution unit 2 is lifted and the processing ends.

[0124] It should be noted that the on-line single station of the technical solution of this application obtains incoming material data through AI recognition, and the edge controller analyzes the label strategy to give a variety of incoming material labels, matches the incoming materials and labels, and updates the incoming material list; through the analysis of the pending processing strategy, the incoming material list is prioritized and updated to the pending processing task list. The pending processing strategy can dynamically adjust the label weight according to the analysis results of the incoming material data and production data, thereby affecting the priority.

[0125] Those skilled in the art will appreciate that, in addition to implementing the system and its various devices, modules, and units provided by the present invention in purely computer-readable program code, it is entirely possible to implement the same functions of the system and its various devices, modules, and units provided by the present invention in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; the devices, modules, and units for implementing various functions can also be considered as both software modules implementing the method and structures within the hardware component.

[0126] In the description of this application, it should be understood that the terms "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on this application.

[0127] The above describes specific embodiments of the present invention. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. The embodiments of this application and the features in the embodiments may be combined with each other in any manner unless there is a conflict.

Claims

1. An intelligent on-line processing method, characterized in that: The steps include: Step S1, collecting information of materials in a specified area; Step S2: judging whether the material can be processed according to the material information collected in step S1; Step S3: if it is determined that the material can be processed, a label to be processed is generated; if it is determined that the material cannot be processed, no label to be processed is generated; Step S4: sort the materials with the to-be-processed labels and generate a list of tasks to be processed; Step S5: The execution unit processes the materials in the order of the list of tasks to be processed generated in step S4.

2. The intelligent on-line processing method according to claim 1, characterized in that: With respect to step S1, image information within a specified area is collected, and information of all materials in the image is extracted; The material information includes position information, and the material information also includes at least one or more of material contour shape information, material surface pattern information, material size information, material color information, material volume information, material type information, gap size information between adjacent materials, and material processing probability information; The material to be processed moves along the conveyor line, and the position of the material to be processed changes from being close to the processing area, to entering the processing area, and then moving away from the processing area, and the position information of the material to be processed is updated in real time.

3. The intelligent on-line processing method according to claim 1, characterized in that: With respect to step S2, it is determined whether the material is within the processable range of the execution unit according to the current position of the execution unit, the moving speed of the execution unit, the working range of the execution unit, and the moving speed of the material.

4. The intelligent on-line processing method according to claim 3, characterized in that: For step S3, if it is determined that the material is within the processable range of the execution unit, it is further determined whether the material has been processed. If the material has been processed, no label to be processed is generated. If the material has not been processed, a label to be processed is generated. If it is determined that the material is not within the processable range of the execution unit, no label to be processed is generated.

5. The intelligent on-line processing method according to claim 4, characterized in that: The information of the label to be processed includes one or more of the surface area of ​​the material, the contour shape of the material, and the gap between the material and another closest material.

6. The intelligent on-line processing method according to claim 1, characterized in that: With respect to step S4, before sorting the materials with the labels to be processed, it is determined whether the information of the labels to be processed of the materials is within a preset range; If the information of the material's label to be processed is within the preset range, the information of the material and the label to be processed are included in the list of tasks to be processed for sorting; If the information of the material's label to be processed is not within the preset range, the material's information and the information of the label to be processed will not be included in the list of tasks to be processed.

7. The intelligent on-line processing method according to claim 6, characterized in that: The order of the list of tasks to be processed is as follows: the materials included in the list of tasks to be processed that are closest to the execution module are processed first; When multiple materials included in the list of tasks to be processed have the same distance from the execution module, the material closest to the target product parameters will be processed first.

8. The intelligent on-line processing method according to claim 1, characterized in that: Step S5 includes the following sub-steps: Step S5.1, the working part of the execution unit moves to above the collision point; Step S5.2, determine whether the execution unit can be pressed down to complete the operation; Step S5.3: If it is determined that the processing can be completed, then continue to execute sub-step S5.4; if it is determined that the processing cannot be completed, the execution unit is lifted, the processing is completed, and the abnormality is recorded; Step S5.4, the material moves to the collision point while the execution unit is pressed down to the collision point; Step S5.5, the execution unit tracks the position of the material, and during processing, maintains the same speed as the material to be processed and is relatively still. After the processing is completed, the execution unit is lifted and the processing ends.

9. An intelligent on-line processing method, characterized in that: Used for shaping fish steaks or meat steaks, comprising the following steps: Step S1, collecting image information in a specified area and extracting information of all materials in the image; Step S2: judging whether the material is within the processable range of the execution unit according to the material information collected in step S1; Step S3: If it is determined that the material is not within the processable range of the execution unit, no label to be processed is generated; if it is determined that the material is within the processable range of the execution unit, it is further determined whether the material has been processed. If the material has been processed, no label to be processed is generated; if the material has not been processed, a label to be processed is generated; The information of the label to be processed includes the surface area of ​​the material, the contour shape of the material, and the gap between the material and another closest material; Step S4, judging whether the information of the label to be processed of the material is within a preset range; if the information of the label to be processed of the material is within the preset range, the information of the material and the information of the label to be processed are included in the list of tasks to be processed for sorting; if the information of the label to be processed of the material is not within the preset range, the information of the material and the information of the label to be processed are not included in the list of tasks to be processed; The sorting method of the task list to be processed is: the materials included in the task list to be processed that are closest to the execution module are processed first. When multiple materials included in the task list to be processed are at the same distance from the execution module, the materials closest to the shape parameters of the target product are processed first. Step S5: The execution unit processes the material in the order of the list of tasks to be processed generated in step S4. The processing operation includes the following sub-steps: Step S5.1, the working part of the execution unit moves to above the collision point; Step S5.2, determine whether the execution unit can be pressed down to complete the operation; Step S5.3: If it is determined that the processing can be completed, then continue to execute sub-step S5.4; if it is determined that the processing cannot be completed, the execution unit is lifted, the processing is completed, and the abnormality is recorded; Step S5.4, the material moves to the collision point while the execution unit is pressed down to the collision point; Step S5.5, the execution unit tracks the position of the material, and during processing, maintains the same speed as the material to be processed and is relatively still. After the processing is completed, the execution unit is lifted and the processing ends.

10. An intelligent on-line processing system, characterized in that: For executing the intelligent on-line processing method according to any one of claims 1 to 8, the processing system comprises: Module M1, used to collect information about materials in a specified area; Module M2, used to determine whether the material can be processed according to the material information collected in step S1; Module M3, used to generate a label to be processed: if it is determined that the material can be processed, a label to be processed is generated; if it is determined that the material cannot be processed, no label to be processed is generated; Module M4 is used to sort the materials with labels to be processed and generate a list of tasks to be processed; Module M5 is used to control the execution unit to process the materials in the order of the list of tasks to be processed generated in step S4.

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