Intelligent conveying and stacking method for small workbins of automobile parts based on visual identification

Abstract

The invention discloses an automobile part small material box intelligent conveying and stacking method based on visual identification, and the method comprises the steps: obtaining the model information of a target small material box in response to the request information for stacking the small material boxes; based on the model information, a stacking mode is determined, the stacking mode is used for placing the target small material box to the target position, and the stacking mode comprises a recommendation mode and a memory mode; and in response to the stacking mode, a control instruction set is generated, and the control instruction set is used for controlling the mechanical arm execution end to place the target small material box to a target position matched with the model information. According to the system, automatic classification, optimized stacking and efficient carrying of the material boxes are achieved through the advanced visual recognition technology and the intelligent algorithm, the precision and efficiency of logistics automatic operation are greatly improved, the operation cost is reduced, human errors are reduced, optimization and upgrading of logistics management are achieved, and the logistics management efficiency is improved. And therefore, the technical problem that the overall efficiency of existing workbin stacking operation is low is solved.

Description

Technical Field

[0001] The present invention relates to intelligent logistics equipment technology, and in particular to an intelligent conveying and stacking method for small boxes of automobile parts based on visual recognition. Background Art

[0002] In today's automotive manufacturing industry, efficient parts management and rapid turnover are key to improving production efficiency. Therefore, bins, as the primary means of carrying and transporting these parts, play a crucial role on the production line. However, due to the wide variety and specifications of the equipment contained within bins, accurately sorting, stacking, and handling these bins has become a major challenge for many companies.

[0003] Traditional processing methods rely primarily on manual identification and stacking, or simple, repetitive operations performed by pre-programmed robotic arms. Under manual operation, the device model information marked on the bins often cannot be read immediately and accurately, leading to frequent misclassification and improper stacking. Existing automated mechanical solutions mostly use fixed stacking routines, which are slow to respond to the diversity of device models and the requirements for production line flexibility, making it difficult to achieve efficient and accurate stacking operations.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The embodiment of the present invention provides a method for intelligently conveying and stacking small bins of automotive parts based on visual recognition, so as to at least solve the technical problem of low overall efficiency of existing bin stacking operations.

[0006] According to one aspect of an embodiment of the present invention, in order to achieve the above-mentioned purpose, according to one aspect of the present invention, there is provided a method for intelligent conveying and stacking of small boxes of automotive parts based on visual recognition, comprising: in response to request information for stacking small boxes, obtaining model information of the target small box; based on the model information, determining the stacking mode, the stacking mode is used to place the target small box to the target position, wherein the stacking mode includes: a recommended mode and a memory mode; in response to the stacking mode, generating a control instruction set, the control instruction set is used to control the execution end of the robotic arm to place the target small box to the target position that is compatible with the model information.

[0007] Furthermore, based on the model information, the stacking mode is determined, including: based on the historical material box database, the model information is matched with the historical material box database to obtain a matching result, and the matching result includes: successful matching and unsuccessful matching; in response to the matching result being a successful matching, the stacking mode is determined to be a memory mode; in response to the matching result being an unsuccessful matching, the stacking mode is determined to be a recommended mode.

[0008] Furthermore, in response to the stacking mode being the memory mode, a control instruction set is generated, including: determining stacking mode data corresponding to the target material box based on the model information; and generating a first control instruction corresponding to the stacking mode data based on the stacking mode data.

[0009] Furthermore, in response to the stacking mode being the recommended mode, a control instruction set is generated, including: obtaining a plurality of image data of the target material box, and determining the target material box size information based on the plurality of image data; obtaining the target material box quantity data, and determining the target stacking mode data based on the target material box size information; and generating a second control instruction corresponding to the target stacking mode data based on the target stacking mode data.

[0010] Furthermore, based on the target material box size information, the target stacking mode data is determined, including: obtaining the type data of the current supporting pallet, and determining the current supporting pallet size data based on the historical pallet database; determining the target stacking sequence data based on the current supporting pallet size data and the target material box size information; and determining the target stacking mode data based on the target stacking sequence data and the target material box quantity data.

[0011] Furthermore, based on the current supporting pallet size data and the target material box size information, the target stacking sequence data is determined, including: determining the preliminary stacking method based on the current supporting pallet size data and the target material box size information; determining the number of single-layer stacking based on the preliminary stacking method; determining the target stacking path data based on the single-layer stacking number; and determining the target stacking sequence data based on the target stacking path data.

[0012] Furthermore, after generating the second control instruction corresponding to the target stacking pattern data based on the target stacking pattern data, the method further includes: storing the target stacking pattern data and corresponding model information in a historical material box database.

[0013] According to one embodiment of the present invention, there is also provided an intelligent conveying and stacking device for small boxes of automotive parts based on visual recognition, comprising: an acquisition module for acquiring model information of a target box in response to box stacking request information; a determination module for determining a stacking mode based on the model information, the stacking mode being used to place the target box to a target position, wherein the stacking mode includes a recommended mode and a memory mode; a control module for generating a control instruction set in response to the stacking mode, the control instruction set being used to control a robotic arm execution end to place the target box to the target position that matches the model information.

[0014] According to one embodiment of the present invention, a computer-readable storage medium is also provided, which includes a stored executable program, wherein when the executable program is running, the device where the storage medium is located is controlled to execute the above-mentioned intelligent conveying and stacking method of small boxes of automotive parts based on visual recognition.

[0015] According to one embodiment of the present invention, a computer program product is also provided, including a computer program, which, when executed by a processor, implements the above-mentioned method for intelligent conveying and stacking of small boxes of automotive parts based on visual recognition.

[0016] In an embodiment of the present invention, the model information of the target material box is obtained in response to the material box stacking request information, and the stacking mode is determined based on the model information. The stacking mode is used to place the target material box to the target position, wherein the stacking mode includes: a recommendation mode and a memory mode; in response to the stacking mode, a control instruction set is generated, and the control instruction set is used to control the robot arm execution end to place the target material box to the target position that is compatible with the model information. Through advanced visual recognition technology and intelligent algorithms, automatic classification, optimized stacking and efficient transportation of material boxes are achieved, which not only greatly improves the accuracy and efficiency of logistics automation operations, but also reduces operating costs, reduces human errors, realizes the optimization and upgrade of logistics management, and thus solves the technical problem of low overall efficiency of existing material box stacking operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 This is a flow chart of a method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition according to one embodiment of the present invention;

[0019] Figure 2 This is a flow chart of a method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition according to one embodiment of the present invention;

[0020] Figure 3 The present invention is a block diagram of an intelligent conveying and stacking device for small boxes of automotive parts based on visual recognition according to one embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0022] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0023] In modern automotive production, lean management and smooth movement of parts, like the precise meshing of gears, are directly linked to improved overall production efficiency. Against this backdrop, the importance of bins—an indispensable vehicle for the movement of parts—is undeniable. They shuttle through every corner of the production line, carrying parts and tools of varying shapes and sizes, forming a busy and orderly artery in the manufacturing industry. However, it is precisely this seemingly routine flow that holds the key to improving logistics efficiency. This is especially true when it comes to selecting and managing the diverse loading devices within bins. Accurate classification, efficient stacking, and agile handling have become pressing challenges for the industry.

[0024] Under the traditional operating model, the identification and stacking of bins are mainly undertaken by experienced workers who rely on vision and memory to distinguish the model identification on the bins, and then manually move them to the designated location for stacking. This process is not only labor-intensive, but also easily interfered with by individual factors, resulting in misjudgment and stacking errors. Especially under high-intensity working environments and time pressure, the probability of identification errors increases significantly. At the same time, some companies have tried to introduce automated robotic arms to replace manual operations, completing bin handling and stacking through preset programming. However, such systems often stick to established stacking templates and lack the ability to respond to complex on-site situations. They cannot meet the growing demand for diversity and flexibility of automobile production lines in the new energy era. Especially when handling bins of different specifications and models, the extensive nature of mechanical operations is exposed, resulting in waste of resources and reduced work efficiency.

[0025] According to an embodiment of the present invention, a method embodiment of an intelligent conveying and stacking method for small boxes of automotive parts based on visual recognition is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] The method embodiment can be executed in an electronic device or similar computing device that includes a memory and a processor. Taking running on a vehicle terminal as an example, the vehicle terminal may include one or more processors (processors may include but are not limited to central processing units (CPUs), graphics processing units (GPUs), digital signal processing (DSP) chips, microcontroller units (MCUs), field programmable gate arrays (FPGAs), neural network processors (NPUs), tensor processing units (TPUs), artificial intelligence (AI) type processors, etc.) and a memory for storing data. Optionally, the vehicle terminal may also include transmission equipment, input and output equipment, and display equipment for communication functions. Those skilled in the art will understand that the above structural description is only illustrative and does not limit the structure of the vehicle terminal. For example, the vehicle terminal may also include more or fewer components than those described above, or have a configuration different from that described above.

[0027] The memory can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition in the embodiments of the present invention. The processor executes the computer program stored in the memory to perform various functional applications and data processing, thereby implementing the above-mentioned method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition. The memory can include high-speed random access memory and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory can further include memory remotely located relative to the processor, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0028] The transmission device is used to receive or send data via a network. Specific examples of the aforementioned network may include a wireless network provided by the mobile terminal's communications provider. In one embodiment, the transmission device includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In one embodiment, the transmission device may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0029] The display device can be, for example, a touch-screen liquid crystal display (LCD) and a touch display (also referred to as a "touch screen" or "touch display screen"). The liquid crystal display can enable the user to interact with the user interface of the mobile terminal. In some embodiments, the mobile terminal has a graphical user interface (GUI), and the user can interact with the GUI by finger contacts and / or gestures on the touch-sensitive surface. The human-computer interaction functions here optionally include the following interactions: creating web pages, drawing, word processing, making electronic documents, games, video conferencing, instant messaging, sending and receiving emails, call interfaces, playing digital videos, playing digital music and / or web browsing, etc. The executable instructions for performing the above-mentioned human-computer interaction functions are configured / stored in a computer program product or readable storage medium executable by one or more processors.

[0030] Figure 1 FIG. 1 is a flow chart of a method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition according to one embodiment of the present invention. Figure 1 As shown, the method includes the following steps:

[0031] Step S110, in response to the request information for stacking the small material boxes, obtaining the model information of the target small material box;

[0032] In step S110, in response to a request for intelligent stacking of small containers, obtaining the target container model information is the first and most crucial step in achieving automated logistics management. This process, by integrating advanced visual recognition and information communication technologies, ensures the accuracy and efficiency of subsequent stacking operations.

[0033] When a production line or warehouse system issues a command to stack small boxes, the intelligent logistics system immediately responds. The system's visual recognition modules (such as industrial-grade cameras and image sensors) are activated to focus on and capture images or video of the target small boxes. These vision devices are typically installed at strategic locations on robotic arms or conveyor belts to capture clear, complete images of the boxes at the optimal time.

[0034] After capturing the image of the small box, the image processing software in the visual recognition system goes to work. Using optical character recognition (OCR) technology or more complex image analysis algorithms, the software quickly interprets the model information marked on the small box. This information may be presented in the form of a barcode, a QR code, or simply as a combination of numbers and letters. The recognition process must ensure high accuracy, as any recognition errors can cause confusion in subsequent operations, such as incorrect stacking or sorting.

[0035] Once the model information is accurately acquired, it is transmitted in real time to the Central Control Unit (CCU). The CCU is the brain of the entire intelligent logistics system, responsible for receiving, processing, and analyzing data from various sensors and identification modules. Here, the model information is matched with the system's internal container database, which stores detailed information on all containers, including dimensions, weight, and permitted stacking methods. This ensures that subsequent stacking strategies are based on the most accurate data.

[0036] Step S120: determining a stacking mode based on the model information, wherein the stacking mode is used to place the target small material box at a target location, wherein the stacking mode includes a recommended mode and a memory mode;

[0037] In step S120, stacking pattern selection and control command generation are key steps in achieving efficient automated operations. This process deeply integrates data matching, historical record learning, and intelligent recommendation mechanisms to ensure that each small container is stacked appropriately, improving storage space utilization while ensuring the safety and stability of the logistics process.

[0038] When a request is received to palletize a small container, the system first matches the container model information against the historical container database. This database is a vast repository of detailed records of various containers accumulated from previous palletizing operations, including but not limited to key parameters such as container size, weight, and previously successful palletizing methods.

[0039] If the model information exactly matches the record in the database, the system determines it's a "successful match" and enters "Memory Mode." In Memory Mode, the system directly calls upon the stacking strategy, action sequence, and parameter settings used in a previous successful stacking of the same model bin—the first control instruction—to guide the robotic arm's execution. This effectively "remembers" past experience, allowing for rapid replication and application, significantly improving stacking efficiency and success rates.

[0040] Conversely, if the model information fails to find a match in the historical bin database (i.e., a "match failure"), the system automatically switches to "recommendation mode." In this mode, more detailed analysis and calculation are required to determine the stacking strategy and generate control instructions.

[0041] Step S140: generating a control instruction set in response to the stacking mode, wherein the control instruction set is used to control the robot arm execution end to place the target small material box to the target position adapted to the model information;

[0042] In step S140, multi-angle image data of the target material box is first obtained through visual equipment such as high-definition industrial cameras, and then the size, shape and surface features of the material box are accurately analyzed.

[0043] Next, based on the acquired bin quantity and size information, combined with the type and size of the current supporting pallet, an intelligent algorithm begins to work to determine the optimal stacking sequence and stacking method.

[0044] In memory mode, the first control command generated is based on existing stacking pattern data. The system will automatically retrieve and extract previous successful stacking examples, including the specific stacking sequence, pallet positioning information, robot arm movement trajectory, etc., to guide the current stacking operation.

[0045] In the recommended mode, the generation of control instructions involves more real-time calculations and reasoning. The specific steps are as follows:

[0046] Based on the current size information of pallets and containers, the intelligent algorithm first generates several possible stacking methods, taking into account factors such as space utilization and structural stability.

[0047] Based on the preliminary stacking method, calculate the number of boxes that can be stacked on each layer to ensure that the carrying capacity of the pallet is not exceeded and to maintain appropriate gaps between boxes for subsequent stacking operations.

[0048] Taking into account the number of single-layer stacks and the number of target containers, the system will plan an optimal stacking path and define how the robotic arm should grab and place the containers in sequence to achieve the best stacking layout and the highest operating efficiency.

[0049] Based on the finalized stacking path and sequence, the system generates a second control instruction, which includes specific robot arm movements, pallet movement instructions, stacking strategy details, etc., to guide the entire stacking process.

[0050] In addition, whether in memory mode or recommendation mode, each time a stacking is successfully completed, the system will store the mode data used for this stacking (including the model information of the material box, stacking strategy, pallet information, etc.) into the historical material box database to form a new "memory" so that it can be directly called when encountering the same or similar situations in the future, reducing calculation time and improving response speed and overall efficiency.

[0051] Through the detailed analysis, matching, and calculation process described above, the intelligent logistics system can dynamically determine the most appropriate stacking pattern based on the model information of the small material boxes, and generate the corresponding control instruction set to guide the robotic arm execution end to complete the stacking task efficiently and accurately, fully demonstrating the huge potential and advantages of intelligent logistics technology in improving the level of automation in automotive parts logistics.

[0052] Based on the above steps S10 to S40, in an embodiment of the present invention, the model information of the target material box is obtained in response to the material box stacking request information, and the stacking mode is determined based on the model information. The stacking mode is used to place the target material box to the target position, wherein the stacking mode includes: a recommendation mode and a memory mode; in response to the stacking mode, a control instruction set is generated, and the control instruction set is used to control the robotic arm execution end to place the target material box to the target position that is compatible with the model information. Through advanced visual recognition technology and intelligent algorithms, automatic classification, optimized stacking and efficient transportation of material boxes are achieved, which not only greatly improves the accuracy and efficiency of logistics automation operations, but also reduces operating costs, reduces human errors, realizes the optimization and upgrade of logistics management, and thus solves the technical problem of low overall efficiency of existing material box stacking operations.

[0053] The intelligent conveying and stacking method for small bins of automotive parts based on visual recognition in an embodiment of the present invention determines the stacking pattern based on model information, including: matching the model information with the historical bin database based on a historical bin database to obtain a matching result, wherein the matching results include: a successful match and an unsuccessful match; in response to a successful match, determining the stacking pattern to be a memory mode; in response to an unsuccessful match, determining the stacking pattern to be a recommended mode. The method of determining the stacking pattern based on model information not only significantly improves the efficiency and accuracy of the automated logistics system, but also has strong learning and adaptability, can effectively optimize storage space, enhance the flexibility and scalability of the system, reduce operating costs, and promote the formation of industry standards and standardized operating procedures.

[0054] In an embodiment of the present invention, the visual recognition-based intelligent conveying and stacking method for small auto parts bins generates a control instruction set in response to the stacking mode being in memory mode. The method includes: determining the stacking pattern data corresponding to the target bin based on the model information; and generating a first control instruction corresponding to the stacking pattern data based on the stacking pattern data. Generating control instructions in memory mode not only significantly accelerates the response speed of the intelligent logistics system but also ensures the accuracy and continuity of stacking operations.

[0055] Furthermore, in response to the stacking mode being the recommended mode, a control instruction set is generated, including: acquiring multiple images of a target container and determining target container dimensions based on the images; acquiring target container quantity data and determining target stacking pattern data based on the target container dimensions; and generating, based on the target stacking pattern data, a second control instruction corresponding to the target stacking pattern data. Generating the control instruction set in the recommended mode not only significantly improves the automated logistics system's adaptability and flexibility to new container models, but also optimizes stacking strategies through scientific data analysis, reducing manual intervention and lowering operating costs.

[0056] In one exemplary embodiment, determining target stacking pattern data based on target bin size information includes: obtaining the type data of the current supporting pallet and determining the current supporting pallet size data based on a historical pallet database; determining target stacking sequence data based on the current supporting pallet size data and the target bin size information; and determining target stacking pattern data based on the target stacking sequence data and the target bin quantity data. This process of determining target stacking pattern data based on target bin size information not only ensures efficient use of storage space and stability of the stacking structure, but also enables automated, dynamic optimization, and cost control of logistics operations.

[0057] In this embodiment, target stacking sequence data is determined based on the current supporting pallet size data and target bin size information, including: determining a preliminary stacking method based on the current supporting pallet size data and target bin size information; determining the number of single-layer stacks based on the preliminary stacking method; determining target stacking path data based on the number of single-layer stacks; and determining target stacking sequence data based on the target stacking path data. This process of determining target stacking sequence data based on the current supporting pallet size data and target bin size information not only enables refined management of logistics space and dynamic optimization of stacking strategies, but also significantly improves performance and saves costs for automated logistics systems by reducing manual intervention, improving stacking efficiency and accuracy, and promoting operational standardization.

[0058] In one exemplary embodiment, after generating a second control instruction corresponding to the target stacking pattern data based on the target stacking pattern data, the system further includes storing the target stacking pattern data and corresponding model information in a historical bin database. Storing the target stacking pattern data and corresponding model information in the historical bin database not only enhances the system's intelligent decision-making capabilities but also provides robust data support and decision-making basis for intelligent logistics management through multiple optimizations, such as experience accumulation, decision acceleration, and cost control. This is a key technical measure for improving logistics automation and supply chain management efficiency.

[0059] Figure 2 Another method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition according to one embodiment of the present invention is as follows: Figure 2 As shown, the method includes the following steps:

[0060] Step S201, in response to a request for stacking small boxes, obtaining model information of a target small box;

[0061] Step S202, based on the historical material box database, matching the model information with the historical material box database to obtain a matching result;

[0062] Step S203, in response to the matching result being a successful match, determining that the stacking mode is a memory mode;

[0063] Step S204: determining stacking mode data corresponding to the target container based on the model information;

[0064] Step S205: generating a first control instruction corresponding to the stacking mode data based on the stacking mode data;

[0065] Step S206, in response to the matching result being unsuccessful, determining the stacking mode as a recommended mode;

[0066] Step S207, obtaining a plurality of image data of the target container, and determining the size information of the target container based on the plurality of image data;

[0067] Step S208, obtaining target bin quantity data, and determining target stacking pattern data based on the target bin size information;

[0068] Step S209 : generating a second control instruction corresponding to the target stacking mode data based on the target stacking mode data.

[0069] Step S210: storing the target stacking mode data and the corresponding model information in the historical material box database.

[0070] Based on the above steps S201 to S210, in an embodiment of the present invention, the model information of the target material box is obtained in response to the material box stacking request information, and the stacking mode is determined based on the model information. The stacking mode is used to place the target material box to the target position, wherein the stacking mode includes: a recommendation mode and a memory mode; in response to the stacking mode, a control instruction set is generated, and the control instruction set is used to control the robotic arm execution end to place the target material box to the target position that matches the model information, thereby achieving automatic classification, optimized stacking and efficient transportation of material boxes through advanced visual recognition technology and intelligent algorithms, which not only greatly improves the accuracy and efficiency of logistics automation operations, but also reduces operating costs, reduces human errors, realizes the optimization and upgrade of logistics management, and thus solves the technical problem of low overall efficiency of existing material box stacking operations.

[0071] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in each embodiment of the present invention.

[0072] In an embodiment of the present invention, a device for intelligently conveying and stacking small boxes of automotive parts based on visual recognition is also provided. The device is used to implement the above-mentioned embodiments and preferred embodiments, and the details that have been described will not be repeated here. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0073] Figure 3 This is a block diagram of a device for intelligently conveying and stacking small boxes of automotive parts based on visual recognition according to one embodiment of the present invention. Figure 3 As shown, the device includes:

[0074] An acquisition module 301 is configured to acquire model information of a target container in response to a container stacking request;

[0075] A determination module 302 is configured to determine a stacking mode based on the model information, where the stacking mode is used to place the target container at the target location, wherein the stacking mode includes a recommended mode and a memory mode;

[0076] The control module 303 is used to generate a control instruction set in response to the stacking mode, and the control instruction set is used to control the robot arm execution end to place the target material box to the target position adapted to the model information.

[0077] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0078] According to one embodiment of the present invention, an electronic device is also provided, comprising: a memory storing an executable program; and a processor for running the program, wherein when the program is running, the above-mentioned method for intelligent conveying and stacking of small boxes of automotive parts based on visual recognition is executed.

[0079] Optionally, in this embodiment, the processor may be configured to execute the following steps through a computer program:

[0080] Step S1, in response to a request for stacking small boxes, obtaining model information of a target small box;

[0081] Step S2: determining a stacking mode based on the model information, wherein the stacking mode is used to place the target small material box at a target position, wherein the stacking mode includes a recommendation mode and a memory mode;

[0082] Step S3: generating a control instruction set in response to the stacking mode, wherein the control instruction set is used to control the robot arm execution end to place the target small material box to the target position adapted to the model information.

[0083] According to one embodiment of the present invention, a computer-readable storage medium is also provided, which includes a stored executable program, wherein when the executable program is running, the device where the storage medium is located is controlled to execute the above-mentioned intelligent conveying and stacking method of small boxes of automotive parts based on visual recognition.

[0084] Optionally, in this embodiment, the storage medium may be configured to store a computer program for performing the following steps:

[0085] Step S1, in response to a request for stacking small boxes, obtaining model information of a target small box;

[0086] Step S2: determining a stacking mode based on the model information, wherein the stacking mode is used to place the target small material box at a target position, wherein the stacking mode includes a recommendation mode and a memory mode;

[0087] Step S3: generating a control instruction set in response to the stacking mode, wherein the control instruction set is used to control the robot arm execution end to place the target small material box to the target position adapted to the model information.

[0088] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store computer programs.

[0089] According to one embodiment of the present invention, a computer program product is also provided, including a computer program, which, when executed by a processor, implements the above-mentioned method for intelligent conveying and stacking of small boxes of automotive parts based on visual recognition.

[0090] Optionally, in this embodiment, the computer program product may be configured as a computer program for executing the following steps:

[0091] Step S1, in response to a request for stacking small boxes, obtaining model information of a target small box;

[0092] Step S2: determining a stacking mode based on the model information, wherein the stacking mode is used to place the target small material box at a target position, wherein the stacking mode includes a recommendation mode and a memory mode;

[0093] Step S3: generating a control instruction set in response to the stacking mode, wherein the control instruction set is used to control the robot arm execution end to place the target small material box to the target position adapted to the model information.

[0094] Optionally, specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0095] In the above embodiments of the present invention, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0096] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0097] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0098] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0099] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program codes.

[0100] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for intelligent conveying and stacking of small boxes of automotive parts based on visual recognition, characterized in that: include: In response to request information for stacking small material boxes, obtaining model information of target small material boxes; Determine a stacking mode based on the model information, wherein the stacking mode is used to place the target small material box at a target position, wherein the stacking mode includes: a recommendation mode and a memory mode; In response to the stacking mode, a control instruction set is generated, and the control instruction set is used to control the robot arm execution end to place the target small material box to the target position adapted to the model information.

2. The method for intelligent conveying and stacking small boxes of automotive parts based on visual recognition according to claim 1 is characterized in that: Determining the stacking mode based on the model information includes: Based on a historical material box database, the model information is matched with the historical material box database to obtain a matching result, wherein the matching result includes: a successful match and an unsuccessful match; In response to the matching result being a successful match, determining that the stacking mode is a memory mode; In response to the matching result being unsuccessful, the stacking mode is determined to be a recommended mode.

3. The method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition according to claim 2 is characterized in that: In response to the stacking mode being the memory mode, generating the control instruction set includes: Determining stacking mode data corresponding to the target container based on the model information; Based on the stacking mode data, a first control instruction corresponding to the stacking mode data is generated.

4. The method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition according to claim 2 is characterized in that: In response to the stacking mode being the recommended mode, generating the control instruction set includes: Acquire a plurality of image data of a target container, and determine the size information of the target container based on the plurality of image data; Obtaining target bin quantity data, and determining target stacking pattern data based on the target bin size information; Based on the target stacking pattern data, a second control instruction corresponding to the target stacking pattern data is generated.

5. The method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition according to claim 4, It is characterized by: Determining target stacking pattern data based on the target bin size information includes: Acquire type data of a current supporting pallet, and determine size data of the current supporting pallet based on a historical pallet database; Determining target stacking sequence data based on the current supporting pallet size data and the target container size information; The target stacking pattern data is determined based on the target stacking sequence data and the target bin quantity data.

6. The method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition according to claim 5 is characterized in that: Determining the target stacking sequence data based on the current supporting pallet size data and the target bin size information includes: Determining a preliminary stacking method based on the current supporting pallet size data and the target container size information; Determining the number of single-layer stacking based on the preliminary stacking method; Determining target stacking path data based on the single-layer stacking quantity; Based on the target stacking path data, the target stacking sequence data is determined.

7. The method for intelligently conveying and stacking small boxes of automotive parts based on visual recognition according to claim 4 is characterized in that: After generating the second control instruction corresponding to the target stacking pattern data based on the target stacking pattern data, the method further includes: storing the target stacking pattern data and the corresponding model information in the historical material box database.

8. An intelligent conveying and stacking device for small boxes of automobile parts based on visual recognition, characterized in that: include: An acquisition module, configured to acquire model information of a target material box in response to a material box stacking request; a determination module, configured to determine a stacking mode based on the model information, wherein the stacking mode is used to place the target material box at a target position, wherein the stacking mode includes: a recommended mode and a memory mode; The control module is used to generate a control instruction set in response to the stacking mode, and the control instruction set is used to control the robot arm execution end to place the target material box to the target position adapted to the model information.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the storage medium is located is controlled to execute the intelligent conveying and stacking method of small boxes of automobile parts based on visual recognition as described in any one of claims 1 to 7.

10. A computer program product, characterized in that The invention comprises a computer program, which, when executed by a processor, implements the method for intelligent conveying and stacking of small boxes of automobile parts based on visual recognition according to any one of claims 1 to 7.

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