A corrugated board finished product multi-specification automatic sorting and intelligent scheduling method and system

Abstract

The present application relates to the technical field of corrugated board production, and particularly relates to a method for automatically sorting and intelligently scheduling corrugated board products of multiple specifications, which comprises the following steps: obtaining a paperboard original image on a corrugated board production line, extracting characteristic specification parameters of the paperboard according to the paperboard original image, and generating a paperboard attribute data set; obtaining empty capacity data of each outlet of a sorting line and downstream process requirement information, and calculating a sorting target position priority weight according to the empty capacity data and the downstream process requirement information. Through multi-link collaborative optimization, the present application solves the problems existing in the prior art in a targeted manner, effectively avoids resource idling and emergency order delay, solves the problems of transportation delay and equipment performance mismatch caused by static paths, ensures the continuity of the buffer area circulation and the priority execution of emergency transfer tasks, and finally realizes efficient, accurate and stable corrugated board product sorting and scheduling in the whole process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of corrugated board production, in particular to a method and system for automatic sorting and intelligent scheduling of corrugated board products of multiple specifications. BACKGROUND

[0002] In the field of corrugated board production, as the packaging industry continues to demand product specification diversity, production efficiency and delivery timeliness, the sorting and scheduling of corrugated board products has become a key node connecting production and downstream processes. In the mainstream corrugated board production process, after the paperboard is processed and formed by the assembly line, it needs to be sorted by specification first, and then transported to the downstream processes such as stacking, packaging or loading by AGV (Automatic Guided Vehicle) or RGV (Rail Guided Vehicle) and other transport vehicles. The entire process needs to be executed in coordination with multi-dimensional information such as production demand, equipment status and workshop environment. Among them, the sorting link usually relies on mechanical arms and visual recognition technology to complete paperboard specification recognition and grabbing sorting, and the scheduling link needs to allocate tasks and plan travel paths according to the transport request and the state of the transport vehicle to realize efficient flow of paperboard from sorting to downstream processes.

[0003] However, the existing sorting and scheduling technology of corrugated board products still has many problems to be solved in practical application, and it is difficult to fully adapt to the production demand of multiple specifications and high timeliness. On the one hand, the priority determination of the sorting target position does not fully integrate the real-time spare capacity of the sorting line outlet and the dynamic demand of the downstream process (such as order delivery time, production rhythm), which may lead to high utilization rate of some outlet space and idle of some outlet resources, or delay of delivery due to not responding to urgent orders in priority; on the other hand, the path planning link is mostly based on the static environment map of the workshop to generate fixed paths, which are not adjusted in real time combined with the traffic heat situation of the workshop, and the AGV / RGV may be delayed when driving into a high congestion area, and the path generation does not fully consider the equipment motion parameters (such as maximum speed, turning radius), which may lead to mismatch between the path and the equipment performance, increasing the risk of equipment failure. SUMMARY

[0004] The main purpose of the present application is to provide a method and system for automatic sorting and intelligent scheduling of corrugated board products of multiple specifications, which aims to solve the technical problems raised in the background.

[0005] The present application provides a method and system for automatic sorting and intelligent scheduling of corrugated board products of multiple specifications, characterized by comprising:

[0006] Obtaining a paperboard original image on a corrugated board production assembly line, extracting characteristic specification parameters of the paperboard according to the paperboard original image, and generating a paperboard attribute data set;

[0007] obtaining empty capacity data of each outlet of the sorting line and downstream process demand information, calculating a sorting target position priority weight according to the empty capacity data and the downstream process demand information;

[0008] generating a grabbing instruction and a joint motion instruction of a sorting mechanical arm according to the paperboard attribute data set and the sorting target position priority weight;

[0009] obtaining buffer zone state data of an output end of the sorting line, obtaining a paperboard stacking height according to the buffer zone state data, and generating a transfer request containing paperboard specifications and quantity information when the paperboard stacking height reaches a preset threshold;

[0010] obtaining a transfer request and real-time state data of an AGV / RGV, and generating a task queue according to the transfer request and the real-time state data of the AGV / RGV;

[0011] obtaining a workshop environment map and a workshop traffic heat map, generating a planning path according to the task queue, the workshop environment map and the workshop traffic heat map, and scheduling the corrugated paperboard based on the planning path.

[0012] Preferably, the step of calculating the sorting target position priority weight according to the empty capacity data and the downstream process demand information comprises:

[0013] calculating a space utilization rate of each outlet according to the empty capacity data of each outlet of the sorting line;

[0014] extracting order delivery time and production beat parameters according to the downstream process demand information;

[0015] calculating a space adaptation coefficient according to the space utilization rate;

[0016] calculating a time urgency coefficient according to the order delivery time and the production beat parameters;

[0017] obtaining the space adaptation coefficient and the time urgency coefficient, and calculating an initial priority weight through a weighting algorithm;

[0018] obtaining historical sorting efficiency data, and dynamically correcting the initial priority weight according to the historical sorting efficiency data to obtain the sorting target position priority weight.

[0019] Preferably, the step of generating the grabbing instruction and the joint motion instruction of the sorting mechanical arm according to the paperboard attribute data set and the sorting target position priority weight comprises:

[0020] Obtaining size, weight parameters and paperboard fluting parameters in the paperboard attribute data set, determining the grabbing force and the number of grabbing points of the mechanical arm according to the size and weight parameters, and determining the placing angle and light placing buffer parameters of the mechanical arm through the paperboard fluting parameters;

[0021] Integrating the grabbing force, the number of grabbing points, the placing angle and the light placing buffer parameters to generate the grabbing instruction of the sorting mechanical arm;

[0022] According to the sorting target position priority weight, a plurality of candidate drop point positions with the highest weight are screened out;

[0023] According to the spatial coordinates of a plurality of candidate drop point positions, the motion trajectory parameters of the mechanical arm are calculated, and the joint motion instruction of the sorting mechanical arm is generated according to the motion trajectory parameters.

[0024] Preferably, the step of obtaining the paperboard stacking height according to the buffer area state data comprises:

[0025] Obtaining a preset threshold value of the paperboard stacking height, the preset threshold value comprising a first preset threshold value and a second preset threshold value;

[0026] Comparing the paperboard stacking height with the preset threshold value:

[0027] When the paperboard stacking height reaches the first preset threshold value, a pre-warning signal is generated and transmitted to the AGV / RGV scheduling center;

[0028] When the paperboard stacking height reaches the second preset threshold value, the number and corresponding specification parameters of the paperboards in the buffer area are counted;

[0029] Obtaining the statistical result of comparing the paperboard stacking height with the preset threshold value, and generating a transfer request containing the paperboard specification, quantity and priority according to the statistical result.

[0030] Preferably, the step of generating a task queue according to the transfer request and the real-time state data of the AGV / RGV comprises:

[0031] Determining the required transportation vehicle type and the required number of transportation vehicles according to the transfer request;

[0032] Obtaining the real-time state data of the AGV / RGV, including the current position, power, load state and fault information;

[0033] According to the transportation vehicle type and the real-time state data of the AGV / RGV, screening out available transportation equipment;

[0034] obtaining priorities of the transport requests, and sorting the transport requests according to the priorities from high to low;

[0035] allocating the sorted transport requests to specific AGVs / RGVs according to the required number of transport vehicles;

[0036] generating a task queue including task numbers, target positions, and execution time limits.

[0037] Preferably, the step of generating a planning path according to the task queue, the workshop environment map, and the workshop traffic heat map comprises:

[0038] extracting information of passageways, intersections, and obstacle coordinates in the workshop environment map;

[0039] identifying high-congestion areas and smooth-passing areas according to the workshop traffic heat map;

[0040] generating an initial path based on the target positions in the task queue;

[0041] obtaining congestion data of areas through which the initial path passes, and adjusting weights of the initial path;

[0042] obtaining device motion parameters of AGVs / RGVs;

[0043] generating a planning path including speed instructions according to the adjusted initial path and the device motion parameters.

[0044] Preferably, the step of generating an initial path based on the target positions in the task queue comprises:

[0045] obtaining AGV / RGV current position coordinates and target positions in the task queue, and determining a path start point and a path end point according to the AGV / RGV current position coordinates and the target positions;

[0046] constructing a grid model according to the workshop environment map, labeling passable nodes and obstacle positions, and determining a path search range;

[0047] obtaining a current node, and defining a cost function for evaluating path cost based on an actual distance from the path start point to the current node and a Manhattan distance estimation value from the current node to the path end point;

[0048] initializing an open list and a closed list, adding the path start point to the open list, and calculating a cost function value corresponding to the path start point according to the cost function;

[0049] iteratively selecting a node with the minimum cost function value in the open list, expanding adjacent passable nodes of the node and updating costs of the adjacent passable nodes, until the path end point is searched;

[0050] generate an initial path composed of consecutive coordinate points by tracing back the parent-child relationship between the nodes of the path end to the path start.

[0051] The application also provides a corrugated board finished product multi-specification automatic sorting and intelligent scheduling system, comprising:

[0052] A data acquisition module acquires a paperboard original image on a corrugated board production line, extracts characteristic specification parameters of the paperboard according to the paperboard original image, and generates a paperboard attribute data set;

[0053] A weight calculation module acquires empty capacity data of each outlet of a sorting line and downstream process requirement information, and calculates a sorting target position priority weight according to the empty capacity data and the downstream process requirement information;

[0054] A cooperative control module generates a grabbing instruction and a joint motion instruction of a sorting mechanical arm according to the paperboard attribute data set and the sorting target position priority weight;

[0055] A request generation module acquires buffer area state data of an output end of the sorting line, acquires a paperboard stacking height according to the buffer area state data, and generates a transfer request containing paperboard specification and quantity information when the paperboard stacking height reaches a preset threshold;

[0056] A task generation module acquires the transfer request and real-time state data of an AGV / RGV, and generates a task queue according to the transfer request and the real-time state data of the AGV / RGV;

[0057] A scheduling module acquires a workshop environment map and a workshop traffic heat map, generates a planning path according to the task queue, the workshop environment map and the workshop traffic heat map, and schedules the corrugated board based on the planning path.

[0058] The application also provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the corrugated board finished product multi-specification automatic sorting and intelligent scheduling method when executing the computer program.

[0059] The application also provides a computer readable storage medium storing a computer program, wherein the computer program implements the steps of the corrugated board finished product multi-specification automatic sorting and intelligent scheduling method when executed by a processor.

[0060] The beneficial effects of the present application are: the present application solves the problems existing in the prior art through multi-link cooperative optimization, in the priority judgment of the sorting target position, the space adaptation coefficient is calculated by combining the empty capacity data of each outlet of the sorting line, the time urgency coefficient is calculated by combining the order delivery time and production rhythm of the downstream process, and then the priority weight is obtained through the weighting algorithm and the dynamic correction of the historical sorting efficiency data, which effectively avoids resource idling and urgent order delay, improves the utilization rate of the outlet space of the sorting line and the response speed of the urgent order; in the path planning, the physical constraint information in the workshop environment map is extracted first, then the congestion area is identified by combining the traffic heat map of the workshop, the initial path is generated based on the task queue, the path weight is adjusted according to the congestion data, and the planning path containing speed instructions is generated by combining the motion parameters of AGV / RGV equipment, which solves the problems of transportation delay and equipment performance mismatch caused by static path, and guarantees the timeliness and safety of AGV / RGV running; in the buffer zone transfer request triggering, the first preset threshold and the second preset threshold are set, the pre-warning signal is generated when the first threshold is reached, and the transfer request containing priority is generated when the second threshold is reached, which avoids buffer zone overflow and urgent task delay, ensures the continuity of buffer zone circulation and the priority execution of urgent transfer task, and finally realizes the efficient, accurate and stable of the whole process of corrugated board finished product sorting and scheduling. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 It is a method flowchart of an embodiment of the present application.

[0062] Figure 2 It is a system structure schematic diagram of an embodiment of the present application.

[0063] The implementation, functional features and advantages of the present application will be further described with reference to the embodiments and the accompanying drawings. DETAILED DESCRIPTION

[0064] It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0065] As shown in the drawings, the present application provides a corrugated board finished product multi-specification automatic sorting and intelligent scheduling method, comprising: Figure 1

[0066] S1, acquiring a paperboard original image on a corrugated board production line, extracting characteristic specification parameters of the paperboard according to the paperboard original image, and generating a paperboard attribute data set;

[0067] S2, acquiring empty capacity data of each outlet of the sorting line and downstream process demand information, calculating the priority weight of the sorting target position according to the empty capacity data and the downstream process demand information;

[0068] ​S3, generating grabbing instructions and joint motion instructions of the sorting mechanical arm according to the paperboard attribute data set and the sorting target position priority weight;

[0069] S4, obtaining buffer area state data of the sorting line output end, obtaining a paperboard stacking height according to the buffer area state data, and generating a transfer request containing paperboard specifications and quantity information when the paperboard stacking height reaches a preset threshold;

[0070] S5, obtaining real-time state data of the transfer request and AGV / RGV, and generating a task queue according to the transfer request and the real-time state data of the AGV / RGV;

[0071] S6, obtaining a workshop environment map and a workshop traffic heat map, generating a planning path according to the task queue, the workshop environment map and the workshop traffic heat map, and scheduling the corrugated paperboard based on the planning path.

[0072] As described above in steps S1-S6, the present application can realize the full-process automatic collaborative scheduling of corrugated paperboard from the production line output to the AGV / RGV transfer, solve the problems of high manual dependence, disordered resource allocation, poor equipment action adaptability, low transfer efficiency and other problems in traditional sorting scheduling, and improve the overall precision and efficiency of multi-specification paperboard sorting and transfer.

[0073] Specifically:

[0074] First, by obtaining the original image of the corrugated paperboard on the production line, the characteristic specification parameters of the paperboard are extracted according to the original image of the paperboard, and a paperboard attribute data set is generated. The original image of the paperboard is obtained by a high-resolution 3D camera array on the production line, processed by a visual recognition system (noise reduction, edge detection, texture matching, etc.), and parameters such as size, thickness, flute type, load-bearing grade, etc. are extracted and a paperboard attribute data set is generated. Machine vision replaces manual work, greatly improves identification efficiency and accuracy, and avoids subsequent sorting decision deviation. Among them, the original image is collected by a 3D camera covering the full width of the production line, the sampling frequency is matched with the production line speed, and each paperboard is imaged completely; the load-bearing grade in the paperboard attribute data set is derived from the thickness, material density (preset industry standard value) and mechanical formula.

[0075] Second step, obtain the empty capacity data of each outlet of the sorting line and the downstream process requirement information, calculate the sorting target position priority weight according to the empty capacity data and the downstream process requirement information, and obtain the initial priority weight through the weighting algorithm, and finally dynamically correct it combined with the historical sorting efficiency data, to ensure the accurate matching of sorting position allocation and capacity and demand, and improve the efficiency of downstream process connection.

[0076] Third step, generate the grabbing instruction and joint motion instruction of the sorting mechanical arm according to the paperboard attribute data set and the sorting target position priority weight, to solve the problems of paperboard damage and landing deviation caused by fixed action of traditional mechanical arm. This step determines the grabbing force and grabbing point number according to the size and weight of the paperboard, determines the placement angle and light placement buffer parameters combined with the corrugation type, integrates to generate the grabbing instruction; at the same time, the candidate landing position is selected according to the priority weight, the motion trajectory parameters of the mechanical arm are calculated and the joint motion instruction is generated, so that the action of the mechanical arm is accurately matched with the paperboard specifications and the landing position, the paperboard damage rate is reduced, and the grabbing success rate is improved.

[0077] Fourth step, obtain the buffer area state data at the output end of the sorting line, and obtain the paperboard stacking height according to the buffer area state data. When the paperboard stacking height reaches the preset threshold, a transfer request containing the paperboard specifications and quantity information is generated, to avoid the accumulation risk caused by traditional single threshold warning. This step first obtains the stacking height standard containing the first and second preset thresholds (set according to the total height of the buffer area and the response time of AGV / RGV), then obtains the real-time stacking height through the laser ranging sensor, and after comparison with the threshold, it is processed in stages - when the first threshold is reached, a pre-warning signal is sent to schedule AGV / RGV in advance; when the second threshold is reached, the number and specifications of the paperboard are counted, and a transfer request containing priority is generated, to ensure that there is no accumulation in the buffer area and avoid shutdown of the sorting line.

[0078] Fifth step, obtain the real-time state data of the transfer request and AGV / RGV, and generate a task queue according to the transfer request and the real-time state data of the AGV / RGV, to solve the problem of uneven equipment in traditional scheduling. This step first determines the type and number of carriers according to the paperboard load rating in the transfer request (such as using RGV for B-level load and AGV for other loads), then obtains the real-time state of the equipment (position, power, load, fault, from UWB positioning and vehicle-mounted system), selects the available equipment, and assigns tasks according to the request priority to generate a queue containing task number, target position and execution time limit, to realize the balanced matching of equipment and tasks, and improve the equipment utilization rate and task completion rate.

[0079] In the sixth step, the workshop environment map and the workshop traffic heat map are obtained, a planning path is generated according to the task queue, the workshop environment map and the workshop traffic heat map, and the corrugated board is dispatched based on the planning path, so that the transportation delay problem caused by the traditional fixed path ignoring congestion is solved. In this step, the workshop environment map (pre-constructed digital twin map, containing channels, intersections and obstacles) and the traffic heat map (generated based on the real-time position of the equipment, marking the congestion / smoother area) are extracted first. After the initial path is generated by the algorithm, the path weight is adjusted combined with the congestion data, and the AGV / RGV motion parameters (maximum speed and turning radius) are integrated to generate a planning path containing speed instructions, so that efficient transportation and collision-free are ensured, and the transportation time is shortened.

[0080] In summary, the present application forms a complete process of data acquisition-decision calculation-instruction generation-state monitoring-task allocation-path planning, solves the pain points of traditional sorting scheduling, and improves the automation level and overall efficiency of multi-specification corrugated board sorting and transfer.

[0081] In an embodiment of the present application, the step of calculating the sorting target position priority weight according to the empty capacity data and the downstream process demand information comprises:

[0082] S21, calculating the space utilization rate of each outlet according to the empty capacity data of each outlet of the sorting line;

[0083] S22, extracting the order delivery time and production rhythm parameters according to the downstream process demand information;

[0084] S23, calculating the space adaptation coefficient according to the space utilization rate;

[0085] S24, calculating the time urgency coefficient according to the order delivery time and the production rhythm parameters;

[0086] S25, obtaining the space adaptation coefficient and the time urgency coefficient, and calculating the initial priority weight by a weighting algorithm;

[0087] S26, obtaining historical sorting efficiency data, and dynamically correcting the initial priority weight according to the historical sorting efficiency data to obtain the sorting target position priority weight.

[0088] As described in steps S21-S26 above, the present application combines the actual capacity condition of each outlet of the sorting line and the demand urgency of the downstream process to generate a scientific and dynamic priority weight, provides a precise basis for paperboard sorting position allocation, solves the technical problem of "only allocating outlets in fixed order, not linking capacity and demand, causing part of the outlets to accumulate and part of the outlets to be idle, and then affecting the efficiency of the downstream process connection" in the traditional sorting scheduling, and ensures the efficient matching of sorting resources and production demand.

[0089] In a traditional corrugated paper sorting scenario, the allocation of sorting target positions often relies on manual experience or fixed round rules, such as allocating paper boards in order of sorting line outlet numbers, without considering the spare capacity of each outlet - if a certain outlet has accumulated a lot of paper boards, continuously allocating new paper boards will exacerbate the accumulation and even cause the sorting line to stop; without considering the downstream process requirements - if a certain stacking process downstream needs to urgently process paper boards of a specific specification to meet order delivery, but cannot obtain them in priority due to disordered outlet allocation, order delivery will be delayed. To address these problems, the present application realizes precise optimization through the hierarchical design of "data collection-parameter calculation-weight generation-dynamic correction":

[0090] First, the space utilization rate is calculated according to the spare capacity data of each outlet of the sorting line, providing a basic index for subsequent judgment of outlet receiving capacity. The spare capacity data of each outlet of the sorting line here comes from the infrared distance sensor installed on the side wall of the buffer area of each outlet. The sensor detects the height of the stacked paper boards in the buffer area and the total height of the buffer area in real time, and calculates the spare capacity by the formula "spare capacity = (total height of buffer area - stacked height) x bottom area of buffer area", with data updated every 2 seconds and transmitted to the control system. For example, if the total height of the outlet buffer area is 2m, the bottom area is 1.5㎡, and the current stacked height is 0.8m, the spare capacity of the outlet is (2-0.8) x 1.5 = 1.8m³; the calculation logic of space utilization rate is "space utilization rate = 1 - (spare capacity / total capacity of buffer area)", and the total capacity of buffer area = total height x bottom area. Using the above example, the total capacity of the buffer area is 2 x 1.5 = 3m³, so the space utilization rate = 1 - (1.8 / 3) = 0.4, i.e. 40%. This step converts the abstract "spare capacity" into the concrete "space utilization rate", which is convenient for intuitive judgment of the receiving capacity of each outlet. The lower the space utilization rate, the stronger the outlet's ability to receive new paper boards, providing a direct basis for subsequent space adaptation coefficient calculation.

[0091] Next, the order delivery time and production rhythm parameters are extracted from the downstream process requirement information to clarify the urgency of the downstream paperboard requirements and processing capacity. The downstream process requirement information in this case comes from the factory's production management system (MES system), which stores the corresponding order information and production plans for each downstream process (such as stacking, packing, and loading). The order delivery time refers to the final delivery deadline for the paperboard processed by the process, for example, a certain batch of paperboard processed by the downstream stacking process requires packing and loading to be completed within 3 hours, so the order delivery time for this process is "remaining 3 hours". The production rhythm parameter refers to the number of paperboards that can be processed by the downstream process per unit time, for example, the stacking process can process 60 paperboards per hour, so the production rhythm parameter is "60 per hour". By extracting these two parameters, the "time urgency" (the closer the delivery time, the more urgent) and "processing capacity limit" (the production rhythm determines the rate at which the process can accept paperboards) of the downstream process for paperboards can be clarified, providing key inputs for subsequent time urgency coefficient calculation.

[0092] Then, according to the space utilization rate calculated in step S21, the space adaptation coefficient is calculated to quantify the degree of adaptation of the outlet to new paperboards. The core logic of the space adaptation coefficient is that the lower the space utilization rate, the stronger the ability of the outlet to accept new paperboards, and the higher the adaptation coefficient. Its calculation formula is preset as "space adaptation coefficient = 1 - space utilization rate", ensuring that the parameter value range is between 0 and 1, which is convenient for subsequent weighted calculation. For example, if the space utilization rate of an outlet in step S21 is 40% (i.e. 0.4), then the space adaptation coefficient of the outlet is 1 - 0.4 = 0.6; if the space utilization rate of another outlet is 70%, then the space adaptation coefficient is 1 - 0.7 = 0.3. Through this calculation, the space utilization rate is converted into a coefficient that directly reflects the "adaptability of the outlet", and the higher the adaptation coefficient of the outlet, the easier it is to be selected in the subsequent priority weight calculation, effectively avoiding the allocation of too much paperboard to high utilization rate (low adaptability) outlets, which leads to accumulation.

[0093] Then, the time urgency coefficient is calculated by combining the order delivery time extracted in step S22 and the production rhythm parameter, quantifying the urgency of the downstream process to the paperboard. The calculation of the time urgency coefficient needs to consider both the "delivery remaining time" and the "production rhythm matching degree": first, the basic urgency coefficient is calculated according to the order delivery time, the formula is "basic urgency coefficient = 1 / remaining delivery hours", for example, if the remaining delivery time is 3 hours, the basic urgency coefficient = 1 / 3 ≈ 0.33; if the remaining delivery time is 1 hour, the basic urgency coefficient is 1, and the urgency is significantly improved. Then, combined with the production rhythm parameter, if the production rhythm of the downstream process is higher than the average level (for example, the average production rhythm of the factory is 50 sheets / hour, and this process is 60 sheets / hour), it means that its processing capacity is stronger, and the urgency coefficient can be appropriately increased to match its processing efficiency. The correction formula is "time urgency coefficient = basic urgency coefficient × (production rhythm of this process / average production rhythm)", using the above example, the time urgency coefficient = 0.33 × (60 / 50) ≈ 0.4; if the production rhythm is lower than the average level, then reduce the urgency coefficient according to the same logic. This step corrects the double parameters, which not only ensures that the orders with urgent delivery time are prioritized, but also takes into account the actual processing capacity of the downstream process, avoiding assigning too high urgency to the paperboard to the process with insufficient processing capacity, leading to process overload.

[0094] Next, the initial priority weight is calculated by combining the space adaptation coefficient of step S23 and the time urgency coefficient of step S24 through a weighted algorithm. The weighted algorithm here presets fixed weight coefficients α and β, where α is the weight of the space adaptation coefficient, β is the weight of the time urgency coefficient, and α + β = 1. According to the actual needs of the corrugated paper sorting scene, "capacity guarantee is prior to demand urgency", α is preset to 0.6, and β is preset to 0.4. The calculation formula of the initial priority weight is "initial priority weight = α × space adaptation coefficient + β × time urgency coefficient", for example, if the space adaptation coefficient of an export space is 0.6, and the time urgency coefficient of the downstream process is 0.4, then the initial priority weight = 0.6 × 0.6 + 0.4 × 0.4 = 0.36 + 0.16 = 0.52; if another export space adaptation coefficient is 0.3, and the time urgency coefficient is 0.8, then the initial priority weight = 0.6 × 0.3 + 0.4 × 0.8 = 0.18 + 0.32 = 0.5. Through weighted calculation, the parameters of "export capacity" and "downstream demand" in two dimensions are integrated into a single priority weight, and the export space with higher weight should be prioritized for paperboard allocation, providing a clear quantitative basis for sorting location decision-making.

[0095] Finally, the initial priority weight is dynamically corrected by historical sorting efficiency data to improve the actual adaptability of the weight. The historical sorting efficiency data here comes from the sorting-related data of each outlet stored by the control system within the past 72 hours, including the "sorting completion rate" (the actual completed sorting quantity / plan sorting quantity), "stacking occurrence rate" (the number of times of paperboard stacking / total sorting times) and "downstream process satisfaction" (the timeliness rate of downstream processes obtaining paperboard according to demand) of each outlet under different initial priority weights. The logic of dynamic correction is: if the stacking occurrence rate of an outlet is higher than 5% (preset threshold) after the initial priority weight is assigned to the task, it means that the actual receiving capacity of the outlet is lower than the initial weight expectation, and the space adaptation coefficient needs to be adjusted downward by 0.1, and the initial priority weight is recalculated as the final weight; if the downstream process satisfaction is lower than 90% (preset threshold), it means that the urgency of the downstream demand corresponding to the outlet is not fully met, and the time urgency coefficient needs to be adjusted upward by 0.1, and the final weight is recalculated. For example, the initial priority weight of an outlet is 0.52, but its historical stacking occurrence rate is 7%, so the space adaptation coefficient is first adjusted from 0.6 to 0.5, and the weight is recalculated as 0.6*0.5+0.4*0.4=0.3+0.16=0.46, which is the corrected sorting target position priority weight. This step corrects the initial weight through historical data feedback to avoid the deviation caused by not considering the actual performance of the equipment, environmental interference and other factors, so that the final weight is more suitable for the actual production conditions.

[0096] In summary, steps S21-S26 form a complete sorting target position priority weight calculation logic: steps S21 and S23 start from the "sorting line capacity" dimension, and quantify the outlet receiving capacity through space utilization rate and space adaptation coefficient; steps S22 and S24 start from the "downstream demand" dimension, and quantify the demand urgency through order delivery time, production rhythm and time urgency coefficient; step S25 integrates the two-dimensional parameters to generate the initial weight through a weighted algorithm; and step S26 dynamically corrects to ensure the accuracy of the weight.

[0097] In an embodiment of the present application, the step of generating the grabbing instruction and joint motion instruction of the sorting mechanical arm according to the paperboard attribute data set and the sorting target position priority weight comprises:

[0098] S31, obtaining the size, weight parameter and paperboard lumen type parameter in the paperboard attribute data set, determining the grabbing force and grabbing point number of the mechanical arm according to the size and weight parameter, and determining the placing angle and light placing buffer parameter of the mechanical arm through the paperboard lumen type parameter;

[0099] S32, integrating the grabbing force, the grabbing point number, the placing angle and the light placing buffer parameter to generate the grabbing instruction of the sorting mechanical arm;

[0100] S33, screening a plurality of candidate drop point positions with the highest weight according to the sorting target position priority weight;

[0101] S34, calculating the motion trajectory parameters of the mechanical arm according to the spatial coordinates of the plurality of candidate drop point positions, and generating joint motion instructions of the sorting mechanical arm according to the motion trajectory parameters.

[0102] As described in steps S31-S34, the present application generates precise sorting mechanical arm grabbing instructions and joint motion instructions based on the specification characteristics of the paperboard itself and the priority of the sorting target position, ensuring that the mechanical arm action can adapt to the physical characteristics (size, weight, and corrugation type) of different specifications of corrugated paperboard and precisely match the highest priority sorting drop point position, solving the technical problems of "fixed action parameters, mismatch with paperboard specifications leading to paperboard damage, and single drop point position, not combined with priority leading to low sorting efficiency" in traditional mechanical arm sorting, and realizing non-destructive and efficient sorting of multiple specifications of paperboard.

[0103] Firstly, according to the key parameters in the paperboard attribute dataset, the core action parameters of the mechanical arm grabbing and placing are determined, which provides a basis for subsequent instruction generation. The paperboard attribute dataset here comes from the S1 step in the previous text, and the original image of the paperboard is obtained by the pipeline 3D camera. The size (such as length L, width W), weight (calculated by size x thickness x preset material density, and the density of ordinary corrugated paper is preset to 0.6 g / cm³), and paperboard fluting parameters (such as A fluting, B fluting, C fluting) are extracted by visual recognition and integrated into a structured dataset. In S31, the above parameters are extracted from the dataset, and the mechanical arm action parameters are determined by dimension: for the grabbing force and the number of grabbing points, the size and weight parameters need to be combined, for example, when the paperboard weight ≤1 kg, the size ≤500 mm x 500 mm, the grabbing force is set to 30-40 N to avoid falling off, and 2 grabbing points (symmetrically distributed at 1 / 3 of the paperboard diagonal) are used to reduce the damage to the paperboard; when the paperboard weight is between 1-3 kg and the size is between 500 mm x 500 mm-1200 mm x 800 mm, the grabbing force needs to be increased to 60-80 N to ensure stable grabbing, and the number of grabbing points needs to be increased to 4 (uniformly distributed near the midpoint of the paperboard four edges) to avoid deformation of the paperboard caused by excessive stress on a single point; when the paperboard weight >3 kg and the size >1200 mm x 800 mm, the grabbing force is set to 100-120 N, and the number of grabbing points is set to 6 to further improve the grabbing stability. For the placement angle and the light placement buffer parameters, the paperboard fluting parameters need to be combined: the A fluting paperboard has a larger fluting height (about 4.5-5 mm) and a wider fluting distance (about 10 mm), and has weaker resistance to vertical impact. If placed horizontally, the fluting top will be deformed under stress, so the placement angle is set to 3-5° (slightly inclined along the length direction of the fluting type), and the light placement buffer parameter (i.e. the end speed when the mechanical arm drops to the drop point) is set to 0.05 m / s to reduce the impact force in the contact moment; the B fluting paperboard has a smaller fluting height (about 2.5-3 mm) and a closer fluting distance (about 5 mm), and has stronger impact resistance. The placement angle is set to 0° (horizontal placement), and the light placement buffer parameter is set to 0.1 m / s to improve the placement efficiency on the premise of no damage; the C fluting paperboard has performance between A fluting and B fluting, the placement angle is set to 2-3°, and the light placement buffer parameter is set to 0.08 m / s. For example, a certain paperboard attribute data is: size 1200 mm x 800 mm, weight 2.5 kg, fluting B, according to the above rules, the grabbing force is determined to be 70 N, the number of grabbing points is 4, the placement angle is 0°, and the light placement buffer parameter is 0.1 m / s. These parameters directly determine the adaptability of the mechanical arm grabbing and placing, and avoid paperboard damage caused by parameter mismatch from the source.

[0104] Then, the complete sorting mechanical arm grabbing instruction is generated by integrating the grabbing related parameters determined in S31. In the traditional process, parameters such as grabbing force and grabbing point number are often set separately, and need to be adjusted one by one manually, which is easy to miss or conflict with parameters. In S32, the grabbing force, grabbing point number, placement angle and light placement buffer parameters determined in S31 are structured and integrated to form an instruction data packet containing specific execution values. For example, the grabbing instruction corresponding to the 1200mm x 800mm B-lumber paperboard will be marked as "grabbing force: 70N; grabbing point number: 4; grabbing point coordinates (based on the lower left corner of the paperboard as the origin): (100mm, 100mm), (1100mm, 100mm), (100mm, 700mm), (1100mm, 700mm); placement angle: 0°; light placement buffer speed: 0.1m / s", and the instruction also contains grabbing trigger conditions (such as triggering grabbing when the paperboard reaches the center of the mechanical arm working area) and placement confirmation conditions (such as triggering light placement buffer when the pressure sensor detects that the paperboard contacts the drop point platform). The instruction data packet is transmitted to the mechanical arm controller through the industrial bus, and the controller can directly analyze and drive the mechanical arm to execute without manual intervention, effectively avoiding execution errors caused by parameter dispersion, while ensuring the integrity and consistency of the grabbing action, reducing the paperboard damage rate in the grabbing link to below 0.5%.

[0105] Then according to the sorting target position priority weight, the highest weight of multiple candidate drop point positions is screened out, solving the sorting interruption problem caused by traditional single drop point. The sorting target position priority weight here comes from the previous S2 step, in which the priority weight of each outlet is calculated by integrating the spare capacity data of each outlet of the sorting line (collected by infrared sensors) and the downstream process demand information (obtained by the MES system). For example, among the 8 outlets of the sorting line, the priority weight of outlet 3 is 0.85, outlet 5 is 0.78, and outlet 7 is 0.72, and the priority weight of the remaining outlets is less than 0.7. In S33, candidate drop point positions need to be selected according to the weight, and the selection rule is preset as "select the top 3 outlets with the highest weight as candidate drop points", which not only ensures the high priority of the drop point position, but also retains redundant options to deal with unexpected situations. For example, in the above example, the candidate drop point positions are outlet 3, outlet 5 and outlet 7, and the spatial coordinates of each candidate drop point are preset in the system database (such as outlet 3 coordinates (12m, 6m, 0.8m), outlet 5 coordinates (15m, 8m, 0.8m), and outlet 7 coordinates (18m, 6m, 0.8m)). These coordinates are calibrated by laser ranging in the early stage, with an accuracy of ±5mm, ensuring the accuracy of subsequent motion trajectory calculation. If only one candidate drop point is selected, when the outlet cannot receive due to sensor failure or paper accumulation, the robot needs to wait for manual processing, causing the sorting to stop, while the setting of 3 candidate drop points can make the robot automatically switch to the next highest weight drop point when the current drop point is unavailable, reducing the sorting interruption rate to below 1%, significantly improving the continuity of sorting.

[0106] Finally, the spatial coordinates of the candidate drop point positions are calculated to obtain the motion trajectory parameters of the robot arm, and joint motion instructions are generated to ensure that the robot arm moves accurately to the target drop point. First, the spatial coordinates of the multiple candidate drop point positions screened out in S33 are retrieved from the system database, and the current position coordinates of the robot arm (real-time collected by the encoder of the robot arm, including the spatial positions corresponding to the joint angles) are obtained. With the current position of the robot arm as the starting point and the candidate drop point position as the end point, the motion trajectory parameters, including the rotation angles, motion speeds and accelerations of each joint of the robot arm (such as the base, the large arm, the small arm and the wrist), are calculated by the forward and inverse kinematics algorithms. During the calculation process, the fixed obstacles in the workshop (such as the sorting line support and the sensor mounting rod, whose coordinates are preset in the motion trajectory calculation model) need to be avoided. For example, if the current position coordinates of the robot arm are (8m, 6m, 0.8m) and the coordinates of the candidate drop point outlet 3 are (12m, 6m, 0.8m), the base needs to be rotated by 0° (since the starting point and the end point are on the same horizontal straight line), the large arm needs to be rotated by 30°, and the small arm needs to be rotated by 45°, so that the end of the robot arm moves from the starting point to the end point, and the motion speed is set to 0.3m / s (a higher speed can be used in the open area to improve efficiency), and the acceleration is set to 0.1m / s² to avoid vibration of the robot arm caused by sudden changes in speed. If the candidate drop point is outlet 5 (15m, 8m, 0.8m), the base needs to be rotated by 37° (calculated according to the right triangle angle relationship, the horizontal distance is 3m, the vertical distance is 2m, and the included angle is arctan(2 / 3)≈37°), and the rotation angles of the large arm and the small arm need to be adjusted to ensure that the trajectory does not interfere with other equipment. After the motion trajectory parameters are calculated, they are converted into specific control instructions for each joint of the robot arm (such as the base joint rotating by 37° at a speed of 0.3m / s, the large arm joint rotating by 35° at a speed of 0.25m / s, etc.), joint motion instructions are formed and transmitted to the robot arm controller, and the controller drives each joint to execute according to the instructions, achieving accurate arrival of the end at the drop point position. Through this step, the positioning error of the drop point position of the robot arm can be controlled within ±10mm, fully meeting the precision requirements of corrugated paperboard sorting, and the motion trajectory is optimized, with a motion time reduced by 10%-15% compared with the traditional fixed trajectory, further improving the sorting efficiency.

[0107] In summary, the present application reduces the damage rate of corrugated paperboard sorting, improves the sorting efficiency, and reduces the need for manual intervention, providing key technical support for the automated sorting of multiple specifications of paperboard.

[0108] In an embodiment of the present application, when the paperboard stacking height reaches a preset threshold, the step of generating a transfer request containing the paperboard specifications, quantity and priority based on the buffer area state data includes:

[0109] S41, a preset threshold of the paperboard stacking height is obtained, the preset threshold comprising a first preset threshold and a second preset threshold;

[0110] S42, the paperboard stacking height is compared with the preset threshold:

[0111] S43, when the paperboard stacking height reaches the first preset threshold, a pre-warning signal is generated and transmitted to the AGV / RGV scheduling center;

[0112] S44, when the paperboard stacking height reaches the second preset threshold, the number and corresponding specification parameters of the paperboard in the buffer area are counted;

[0113] S45, a statistical result of the comparison of the paperboard stacking height with the preset threshold is obtained, and a transfer request containing the paperboard specification, number and priority is generated according to the statistical result.

[0114] As described in steps S41-S45 above, the present application realizes seamless connection between the buffer area and the transfer link by real-time monitoring and threshold judgment of the paperboard stacking height in the buffer area at the output end of the sorting line, triggering pre-warning and formal transfer request in stages, accurately counting the paperboard specification, number and giving priority, ensuring that the buffer area is not stacked, and the transfer demand is efficiently delivered to the AGV / RGV scheduling center, solving the technical problems of "single threshold warning leading to response lag, incomplete transfer request information (lack of specification / number), and no priority sorting leading to AGV / RGV scheduling confusion" in traditional buffer area management.

[0115] First, the preset threshold value of the paperboard stack height (including the first and second preset threshold values) is obtained, which provides a judgment standard for subsequent height comparison. The preset threshold value here is not a fixed value, but is set comprehensively considering the sorting line output efficiency, AGV / RGV average response time and buffer zone maximum carrying capacity, and the data is stored in the system parameter configuration module and can be dynamically adjusted according to the production rhythm. The specific setting logic is: the first preset threshold value is 60%-70% of the maximum carrying height of the buffer zone, which is used to trigger a preliminary warning and reserve AGV / RGV scheduling preparation time; the second preset threshold value is 80%-90% of the maximum carrying height of the buffer zone, which is used to trigger a formal transfer request to avoid accumulation caused by near full load. For example, the maximum carrying height of the buffer zone at the output end of a sorting line is 2m (about 50 standard corrugated paperboards can be stacked), the average response time of AGV / RGV from receiving the request to arriving at the buffer zone is 3 minutes, and the sorting line outputs 3 paperboards per minute. Within 3 minutes, 9 new paperboards will be added, so the first preset threshold value is set to 1.2m (corresponding to 30 paperboards, 60% of the maximum height), at which point the AGV / RGV can arrive within 3 minutes after triggering a preliminary warning, avoiding the stack height from rapidly rising to full load; the second preset threshold value is set to 1.6m (corresponding to 40 paperboards, 80% of the maximum height), if the height reaches this value, AGV / RGV needs to be dispatched for transfer immediately to prevent the stack height from exceeding 2m within the next 3 minutes. By setting double threshold values, compared with a single threshold value (such as only setting 1.8m), the preparation action can be triggered in advance to avoid the risk of accumulation caused by AGV / RGV response delay.

[0116] Then, the real-time obtained paperboard stack height is compared with the preset threshold value set in S41 to provide a basis for the graded response. The paperboard stack height here comes from the laser ranging sensor installed at the top of the buffer zone at the output end of the sorting line. The sensor emits a laser signal to the bottom of the buffer zone every 1 second, and the stack height is calculated by "stack height = total height of buffer zone - laser ranging value". The data is transmitted to the control system in real time, with an accuracy of ±2mm, ensuring the accuracy of height monitoring. For example, if the total height of the buffer zone is 2m and the current detection distance of the laser ranging sensor is 0.8m, then the paperboard stack height = 2-0.8 = 1.2m. At this time, the control system will compare the preset threshold value (1.2m is the first preset threshold value and 1.6m is the second preset threshold value) in S41 to determine whether the current stack height has reached the first preset threshold value. If the laser ranging value is 0.4m, the stack height = 2-0.4 = 1.6m, then it is determined that the second preset threshold value is reached. This step converts the abstract "height data" into a clear "threshold trigger signal" through real-time comparison, providing a direct basis for the subsequent graded actions of S43 and S44, avoiding the lag and errors of manual inspection.

[0117] Then when the cardboard stack height reaches the first preset threshold, a pre-warning signal is generated and transmitted to the AGV / RGV scheduling center, realizing the advance start of transfer preparation. The pre-warning signal is not an official transfer instruction, but a pre-warning information containing "buffer zone number, current stack height, time to reach the second preset threshold". The "time to reach the second preset threshold" is calculated by the current sorting line output rate (obtained from the MES system, such as 3 pieces per minute) and the difference between the current height and the second preset threshold. For example, the current stack height is 1.2m (30 pieces), the second preset threshold is 1.6m (40 pieces), the difference is 10 pieces, and the output rate is 3 pieces per minute. Therefore, the estimated time = 10 ÷ 3 ≈ 3.3 minutes. This information can help the AGV / RGV scheduling center plan idle equipment in advance and avoid equipment shortage caused by temporary scheduling. The signal transmission uses industrial Ethernet, and the delay is controlled within 100ms to ensure that the scheduling center receives quickly. After receiving the signal, the system interface will pop up a yellow warning prompt and automatically mark the pre-scheduling requirement of the corresponding buffer zone, such as "buffer zone 3, current height 1.2m (first threshold), estimated 3.3 minutes later for transfer". The scheduling personnel can arrange the AGV / RGV that has completed the current task to move near the buffer zone in advance, shortening the equipment arrival time during the subsequent official transfer. Through this step, the average response time of AGV / RGV is shortened from 3 minutes to 1.5 minutes, effectively improving the timeliness of transfer.

[0118] Then, when the cardboard stack height reaches the second preset threshold, the number of cardboard sheets and their corresponding specifications in the buffer zone are counted to provide core data support for the formal transfer request. The quantity statistics here are cross-validated in two ways: First, based on the stack height, i.e., "Quantity = Current stack height ÷ Single cardboard sheet thickness (obtained from the cardboard attribute dataset of S1, such as 4mm / sheet)". For example, if the current stack height is 1.6m and the single sheet thickness is 4mm, then the quantity = 1600 ÷ 4 = 40 sheets. Second, through the photoelectric counter installed at the entrance of the buffer zone, the counter increments by 1 for each cardboard sheet detected entering the buffer zone. The error between the two methods must be ≤1 sheet to ensure accurate quantity. Specification parameter statistics are achieved by reading the RFID tags on the cardboard within the buffer area (tag information is bound to the cardboard attribute dataset of S1, including size, flute type, load-bearing capacity, etc.). The RFID reader installed on the side of the buffer area can read tag data in batches without scanning each card individually, and the statistical time is ≤2 seconds. If the RFID tag of a cardboard fails, the system will automatically trigger the 3D camera (from the same source as S1) on the top of the buffer area to take an image and supplement the specification parameters through visual recognition to avoid data loss. For example, the statistical result is "Quantity 40 sheets, Specifications: 1200mm×800mm, B flute, load-bearing capacity B (30 sheets); 1000mm×600mm, C flute, load-bearing capacity A (10 sheets)". This data will be used as key content for subsequent transfer requests to ensure that the AGV / RGV scheduling can match the corresponding vehicle type (e.g., RGV is required for load-bearing capacity A), avoiding transfer failures caused by mismatch between the vehicle and the cardboard specifications.

[0119] Finally, the comparison result between the cardboard stacking height and the preset threshold is obtained (i.e., whether the second preset threshold is reached, the quantity and specifications are statistically analyzed), and a transfer request containing cardboard specifications, quantity and priority is generated, completing the closed loop from "threshold triggering" to "instruction generation". The priority setting of the transfer request is based on two factors: one is the difference between the stacking height of the buffer area and the maximum load-bearing height. The smaller the difference (closer to full load), the higher the priority. For example, if the current height of a buffer area is 1.8m (0.2m difference from the maximum height of 2m), the priority is set to "high". The other factor is the urgency of the downstream process demand (obtained from the MES system, such as a batch of cardboard that needs to be loaded urgently). If the cardboard in the buffer area corresponds to the urgent downstream demand, the priority is automatically increased by one level. For example, among the 40 cardboard sheets statistically analyzed above, 10 Class A load-bearing cardboard sheets correspond to an urgent order in the downstream loading process (delivery time remaining 1 hour). Then the priority of this transfer request is set to "highest", the specifications and quantity follow the statistical results of S44, and "target location (workstation 5 in the downstream stacking area, obtained from the MES system)" is added. After a transfer request is generated, it is transmitted to the AGV / RGV dispatch center via an encrypted protocol. Upon receiving the request, the center automatically marks it as a red urgent task and prioritizes equipment allocation. For example, "Buffer Zone 3, Transfer Request: 40 sheets (30 sheets of B-flute 1200×800, 10 sheets of A-grade C-flute 1000×600), highest priority, target location: stacking area, workstation 5." The dispatch system immediately filters available and matching carriers (e.g., 1 RGV for A-grade cardboard, 1 AGV for B-grade cardboard) and generates a preliminary task allocation plan. Through this step, the information completeness of the transfer request is improved from the traditional 70% to 100%, and the task execution accuracy of AGV / RGV is improved to over 99.5%.

[0120] In summary, this invention can improve the scheduling efficiency of AGV / RGV, ensuring the continuous flow of corrugated cardboard from sorting to transfer, while reducing the cost of manual intervention and meeting the needs of automated production.

[0121] In one embodiment of the present invention, the step of generating a task queue based on the transfer request and the real-time status data of the AGV / RGV includes:

[0122] S51, determine the required type and quantity of transport vehicles based on the transfer request;

[0123] S52 acquires real-time status data of AGV / RGV, including current location, power level, load status, and fault information;

[0124] S53, Based on the type of transport vehicle and the real-time status data of the AGV / RGV, select available transport equipment;

[0125] S54, obtain the priority of each of the transfer requests and sort them from high to low priority;

[0126] S55, based on the required number of transport vehicles, assigns the sorted requests to specific AGVs / RGVs;

[0127] S56 generates a task queue containing task number, target location, and execution time limit.

[0128] As described in steps S51-S56 above, this invention completes the entire process of "vehicle matching - equipment screening - request sorting - task allocation - queue generation" based on the specific requirements of the transfer request and the real-time status of the AGV / RGV. This ensures that the transfer task and the transportation equipment are accurately matched and efficiently allocated, solving the technical problems in traditional AGV / RGV scheduling such as "transfer failure due to mismatch between vehicle type and cardboard specifications, waste of resources due to lack of real-time synchronization of equipment status, and delay of emergency tasks due to lack of priority sorting". This achieves optimal utilization of transportation resources and orderly execution of transfer tasks.

[0129] First, the required type and quantity of transport vehicles are determined based on the transfer request, providing clear criteria for subsequent equipment selection. This transfer request originates from step S45 above and includes key information such as cardboard specifications (size, flute type, load-bearing capacity), quantity, and priority. The load-bearing capacity is the core basis for determining the type of transport vehicle—the system presets matching rules between vehicle type and load-bearing capacity: When the load-bearing capacity is Class A (single sheet load ≥ 50kg) or the total weight of stacked cardboard is ≥ 300kg, a higher-capacity RGV (Automated Guided Vehicle) should be selected because its fixed track and high load-bearing stability prevent cardboard displacement caused by bumps during AGV (Automated Guided Vehicle) movement; when the load-bearing capacity is Class B (single sheet load 20-50kg) and the total weight is < 300kg, an AGV can be used, offering greater flexibility and suitability for multi-path transfers. The required number of transport vehicles is calculated based on the rated load capacity of a single vehicle and the total number of cardboard sheets in the transfer request. For example, if a transfer request contains 40 sheets of Grade B cardboard, and a single AGV has a rated load capacity of 20 sheets, then the required number of transport vehicles = 40 ÷ 20 = 2 vehicles. If a transfer request contains 30 sheets of Grade A cardboard, and a single RGV has a rated load capacity of 15 sheets, then the required number = 30 ÷ 15 = 2 vehicles. For example, if a transfer request is "Specifications: 1200mm × 800mm, Grade A load capacity (55kg per sheet), quantity 30 sheets, total weight 1650kg", according to the matching rules, the vehicle type is determined to be RGV, and a single RGV has a rated load capacity of 15 sheets, therefore the required number of transport vehicles = 30 ÷ 15 = 2 vehicles. This step, by clarifying the type and quantity of carriers, avoids type mismatch (such as overload failure caused by using AGV to transport heavy Grade A cardboard) or insufficient quantity (such as one carrier not being able to complete the transport of 30 cardboard sheets, requiring two round trips and delaying time) caused by the traditional scheduling method of "allocating carriers based on experience" and lays the foundation for accurate equipment selection in the future.

[0130] Next, real-time status data of the AGV / RGV is acquired to comprehensively understand the current availability of the equipment and provide data support for selecting usable equipment. Real-time status data is collected in two ways: first, equipment location data, acquired by UWB positioning modules installed on the AGV / RGV, with a positioning accuracy of ±10cm, providing real-time feedback of the equipment's current coordinates; second, equipment operating status data (battery level, load status, fault information), transmitted in real-time from the vehicle control system to the dispatch center via an industrial bus—battery level data is collected by the battery management system, and when it is below 20%, it is considered low battery and requires priority charging; load status is detected by the vehicle-mounted weight sensor, displaying "no load," "half load," and "full load," with only "no load" equipment able to participate in new tasks; fault information is generated by the equipment self-checking system, such as motor failures and sensor malfunctions, with faulty equipment automatically marked as "unavailable." For example, real-time status data obtained by the dispatch center shows: AGV1 (coordinates: 5m, 8m), 65% battery, no load, no fault; AGV2 (coordinates: 12m, 6m), 18% battery, no load, no fault; RGV1 (coordinates: 8m, 10m), 70% battery, fully loaded, no fault; RGV2 (coordinates: 15m, 9m), 60% battery, no load, no fault. This data is updated every second to ensure the dispatch center has the latest equipment status and avoids task interruption caused by using low-battery or faulty equipment.

[0131] Then, based on the transport vehicle type determined in S51 and the real-time status data obtained in S52, available transport equipment that meets the requirements is selected, and equipment that does not meet the conditions is removed. The selection rules are executed in two steps: First, the selection is based on the vehicle type, retaining only equipment of the type determined in S51. For example, if S51 determines the vehicle type to be RGV, then RGV1, RGV2, and other RGV type equipment are selected from all equipment. Second, the selection is based on the real-time status data, retaining equipment with "power ≥ 20% (ensuring the completion of a single transfer task, with a preset average power consumption of 15% per transfer), no load status, and no fault information". For example, continuing the above example, if S51 determines that the vehicle type is RGV and the quantity is 2 units, first filter out RGV1 and RGV2; then check the status data: RGV1 is fully loaded, which does not meet the "no-load" requirement, so it is eliminated; RGV2 has 60% battery, is no-load, and has no faults, which meets the requirements. At the same time, other RGVs need to be filtered—if there is also RGV3 in the system (coordinates: 10m, 7m), with 55% battery, no-load, and no faults, it also meets the requirements. Finally, two usable devices, RGV2 and RGV3, are selected. If S51 determines that the vehicle type is AGV and the quantity is 2 units, first filter out AGV1 and AGV2; AGV2's battery is 18% < 20%, so it is eliminated; AGV3 (coordinates: 9m, 5m) needs to be added to the filter, with 72% battery, no-load, and no faults. Finally, AGV1 and AGV3 are selected. This step, through double screening, ensures that available equipment fully meets the transfer requirements, avoiding the problems of "selecting low-power equipment leading to power outages" or "selecting load-bearing equipment that cannot receive new cardboard" in traditional scheduling. The accuracy rate of available equipment screening reaches 100%.

[0132] Next, the priority of each transfer request is obtained and sorted from high to low to ensure that urgent tasks are executed first. The priority of transfer requests comes from step S45, and the priority is divided into four levels: "highest", "high", "medium", and "low". The determination criteria are as follows: when the difference between the stack height of the buffer area and the maximum carrying height is <0.3m and the corresponding downstream urgent order (delivery time remaining <2 hours), the priority is "highest"; when the difference is 0.3-0.5m or the delivery time remaining is 2-4 hours, the priority is "high"; when the difference is 0.5-0.8m or the delivery time remaining is 4-8 hours, the priority is "medium"; when the difference is >0.8m and the delivery time remaining is >8 hours, the priority is "low". When the system receives multiple transfer requests simultaneously, they must be sorted by priority. For example, if there are three transfer requests: Request A (highest priority, buffer difference 0.2m, delivery time 1.5 hours), Request B (higher priority, buffer difference 0.4m, delivery time 3 hours), and Request C (medium priority, buffer difference 0.6m, delivery time 6 hours), the sorting result would be Request A > Request B > Request C. If there are requests with the same priority, they are then sorted by their creation time, with the earlier creation time taking precedence. This priority sorting avoids the delays in urgent tasks caused by the "out-of-order execution of all requests" in traditional scheduling (such as assigning low-priority requests to idle devices while high-priority requests wait, missing order delivery times), reducing the average waiting time for the highest priority request from the traditional 15 minutes to less than 5 minutes.

[0133] Subsequently, based on the required number of transport vehicles, the sorted transfer requests from S54 are allocated to specific AGVs / RGVs, achieving precise matching between tasks and equipment. The allocation logic follows the principle of "priority first + proximity": high-priority requests are processed first, and from the available devices filtered by S53, the device closest to the corresponding buffer of the request is selected first to shorten the time for the device to arrive at the buffer; if the number of available devices is greater than or equal to the required number, the corresponding number of devices are directly allocated; if the number of available devices is insufficient, existing devices are allocated to perform part of the task, while new device scheduling is triggered (such as notifying devices that are charging and about to complete preparation). For example, for request A (vehicle type RGV, quantity 2 units, buffer area coordinates 12m, 6m), the available RGVs selected by S53 are RGV2 (coordinates 15m, 9m) and RGV3 (coordinates 10m, 7m). The straight-line distance between the devices and the buffer area is calculated as follows: RGV2 distance = √[(15-12)² + (9-6)²] = √18 ≈ 4.24m, RGV3 distance = √[(10-12)² + (7-6)²] = √5 ≈ 2.24m. Therefore, RGV3 and RGV2, which are closer, are prioritized to ensure that both devices can quickly reach the buffer area. If request A requires 2 units, but S53 only selects 1 available RGV (RGV3), then RGV3 is first assigned to perform the transfer task of 15 cardboard sheets. Simultaneously, the system detects that RGV4 is charging (90% battery, expected to complete in 5 minutes), so the remaining 15 cardboard sheets are pre-assigned to RGV4 and executed immediately after charging. This step, through a dual consideration of "priority + distance," ensures that urgent tasks are executed first, while also shortening equipment movement time by allocating equipment to nearby locations. The average arrival time of equipment is reduced from the traditional 8 minutes to 3 minutes, improving transfer efficiency.

[0134] Finally, a task queue containing task number, target location, and execution time limit is generated, providing a complete basis for task execution for subsequent path planning. The task number adopts the format of "date + request number + equipment number", such as "20230220-A02-RGV3", which facilitates system traceability and management; the target location is derived from the downstream process location corresponding to the transfer request (obtained from the MES system, such as workstation 5 in the stacking area, coordinates 20m, 12m), clearly defining the destination of the equipment transfer; the execution time limit is calculated based on the priority of the transfer request and the estimated execution time of the equipment—the execution time limit for the highest priority request = current time + 30 minutes (the transfer must be completed within 30 minutes), high priority is current time + 60 minutes, medium priority is current time + 120 minutes, and low priority is current time + 240 minutes, ensuring that the task has clear time constraints. For example, a task assigned to RGV3, with task number "20230220-A02-RGV3", has a target location of workstation 5 in the stacking area (20m, 12m), the highest priority, and a current time of 10:00, therefore the execution time limit is 10:30. The task queue is stored in a structured list format, containing information such as task number, equipment number, starting point (buffer area coordinates), target location, execution time limit, cardboard specifications and quantity, etc. This information can be synchronized in real-time to the AGV / RGV's onboard control system and the dispatch center monitoring interface, allowing dispatchers to visually view the execution status of each task (pending execution, in progress, completed). The task queue generated in this step avoids execution deviations caused by "fragmented task information" in traditional dispatching (such as unclear target location or execution time for equipment, requiring manual confirmation), improving task execution accuracy to 99.5%, and providing clear targets and time constraints for subsequent path planning in step S6.

[0135] In summary, this invention improves the accuracy of vehicle matching, increases equipment utilization, and enhances the on-time completion rate of emergency tasks. It also reduces the workload of manual scheduling and better meets the intelligent needs of automated production.

[0136] In one embodiment of the present invention, the step of generating a planned route based on the task queue, the workshop environment map, and the workshop traffic heat map includes:

[0137] S61, extract the coordinate information of passageways, intersections and obstacles from the workshop environment map;

[0138] S62, based on the workshop traffic heat map, identify high-congestion areas and smooth-flowing areas;

[0139] S63, Generate an initial path based on the target position in the task queue;

[0140] S64, Obtain congestion data of the areas traversed by the initial path, and adjust the weight of the initial path;

[0141] S65, obtain the device motion parameters of AGV / RGV;

[0142] S66, Based on the adjusted initial path and the device motion parameters, generate a planned path containing speed commands.

[0143] As described in steps S61-S66 above, this invention combines the execution requirements of the task queue, the physical constraints of the workshop environment, and real-time traffic conditions. Through a progressive process of "environmental analysis - congestion identification - initial path generation - path optimization - parameter adaptation - instruction generation," it generates planned paths for AGVs / RGVs that take into account safety, timeliness, and equipment adaptability. This solves the technical problems in traditional path planning, such as "failure to consider dynamic congestion in the workshop leading to transportation delays, failure to consider the motion characteristics of equipment leading to unexecutable paths, and failure to extract key environmental information leading to collision risks." This enables efficient and safe operation of AGVs / RGVs during corrugated cardboard transfer.

[0144] First, the coordinates of passageways, intersections, and obstacles are extracted from the workshop environment map to build a basic physical environment framework for subsequent path planning, avoiding equipment collisions caused by ignoring fixed obstacles during path planning. The workshop environment map here is a high-precision map generated in the early stage using laser SLAM (simultaneous localization and mapping) technology, stored in the system database, containing the coordinate information of all static facilities in the workshop, and can be updated according to the workshop layout. When extracting information, the system uses an image segmentation algorithm to structure the map: passageways are divided into main passageways (width ≥ 3m, allowing two-way traffic) and branch passageways (width 1.5-2.5m, allowing one-way traffic). The starting point, ending point coordinates, and direction of travel of each passageway must be marked during extraction. The center point coordinates and the number of passageways connected to intersections (such as T-junctions and crossroads) must be marked at intersections. Deceleration or avoidance logic must be set at these intersections in subsequent path planning. Obstacles include fixed equipment (such as sorting line supports and shelves), walls, and columns. The boundary coordinates of obstacles must be marked during extraction (such as the coordinate range of a shelf being (10m-12m, 8m-10m)) to ensure that the path planning avoids this area. For example, from the workshop environment map, the following can be extracted: Main aisle 1 (starting point (0m, 5m), ending point (20m, 5m), two-way traffic), branch aisle 2 (starting point (10m, 5m), ending point (10m, 15m), one-way traffic), crossroads (coordinates (10m, 5m)), and obstacle 1 (shelf, coordinates (8m-10m, 12m-14m)). This step transforms the abstract map into structured coordinate information, providing clear "feasible areas" and "prohibited areas" for subsequent path searching. This avoids information omissions caused by relying on manual obstacle labeling in traditional planning and reduces the risk of equipment collisions.

[0145] Next, the system identifies high-congestion and smooth-flowing areas based on the workshop traffic heatmap, providing dynamic traffic condition data for route optimization and preventing AGVs / RGVs from entering congested areas and causing delays. The workshop traffic heatmap data comes from the real-time location data of the AGVs / RGVs (synchronously acquired from the real-time status data in step S52, updated every second). The system uses a density clustering algorithm to count the number of devices in each area (divided into 5m×5m grids): when the number of devices in a grid is ≥3, it is identified as a high-congestion area, marked in red on the heatmap; when the number of devices is 1-2, it is a normal traffic area, marked in yellow; when the number of devices is 0, it is a smooth-flowing area, marked in green. Simultaneously, the system supplements and corrects the real-time heatmap by incorporating historical traffic data (e.g., the concentration of devices near intersections (10m, 5m) from 10:00-11:00 daily, indicating potential congestion). For example, a heat map at a certain moment might show that there are 4 AGVs / RGVs within a grid (10m-15m, 5m-10m), which is identified as a high-congestion area; while there are no devices within a grid (15m-20m, 10m-15m), which is a smooth-flowing area. This step allows for real-time monitoring of traffic dynamics within the workshop, avoiding the uncontrollable transportation time caused by traditional route planning that "only plans based on the shortest distance without considering congestion," and providing a basis for subsequent adjustments to route weights.

[0146] Then, an initial path is generated based on the target position in the task queue to complete the basic path search from the "starting point to the ending point". The task queue comes from step S56 and contains the task starting point of the AGV / RGV (corresponding to the buffer coordinates, obtained from the transfer request in step S45) and the target position (corresponding to the downstream process position, obtained from the MES system). The initial path generation adopts an improved A* algorithm (see steps S631-S636 for details). The core is to balance the path length and search efficiency through a cost function to ensure that the generated initial path is a feasible path with "shorter distance and no obstacles". For example, the starting point of an AGV's task is buffer zone 3 (coordinates (12m, 6m)), and the target location is workstation 5 in the stacking area (coordinates (20m, 12m)). Combining the passageway and obstacle information extracted by S61, the generated initial path is: from (12m, 6m) along the main passageway 1 (20m, 5m) to (15m, 6m), turn into branch passageway 3 (15m, 6m-12m), and finally reach (20m, 12m). This path avoids obstacle 1 (8m-10m, 12m-14m) and has a shorter total distance. The generation of the initial path does not consider real-time congestion, but only ensures "physical feasibility," laying the foundation for subsequent optimization based on congestion conditions and avoiding excessive computation caused by directly generating complex paths.

[0147] Next, acquire congestion data for the areas traversed by the initial path (extracted from the heatmap in step S62) and adjust the weights of the initial path to achieve congestion avoidance path optimization. The adjustment logic is as follows: assign a congestion weight to each grid area traversed by the initial path—a weight of 5 for highly congested areas, 2 for generally passable areas, and 1 for smoothly passable areas; calculate the total weight of the initial path (the sum of the weights of each grid); if the total weight is ≥10 (i.e., the path contains many highly congested areas), trigger a path re-search, prioritizing alternative paths in smoothly passable areas; if the total weight is <10, retain the initial path, only marking "requires slowing down and avoiding" sections passing through highly congested areas. For example, the initial path of the AGV mentioned above passes through grid (10m-15m, 5m-10m) (high congestion area, weight 5) and grid (15m-20m, 10m-15m) (smooth area, weight 1), with a total weight of 5+1=6<10. The initial path is retained, but the section passing through the high congestion area (12m-15m, 6m-10m) is marked "decelerate to 0.2m / s". If the initial path passes through two high congestion areas, the total weight is 5+5=10. Then the path is searched again. For example, it is adjusted to travel from (12m, 6m) along branch channel 4 (12m, 6m-12m) to (12m, 12m), and then turn into main channel 2 (12m-20m, 12m) to reach the target position. The new path passes through smooth areas, and the total weight is reduced to 3. This step integrates dynamic congestion information into route planning, avoiding delays caused by the initial route passing through highly congested areas and improving the timeliness of route travel.

[0148] Subsequently, the motion parameters of the AGV / RGV are acquired to provide a basis for generating speed commands, ensuring that the planned path matches the equipment performance. These motion parameters are stored in the system's equipment database, categorized by vehicle type: AGV motion parameters include maximum travel speed (typically 1.2 m / s), maximum acceleration (0.3 m / s²), and minimum turning radius (1.5 m); RGVs, traveling along tracks, have parameters including track speed (fixed at 1.5 m / s) and start / stop acceleration (0.5 m / s²). These parameters are provided by the equipment manufacturer and verified through on-site testing before being entered into the system to ensure consistency with the actual equipment performance. For example, the motion parameters for AGV1 performing the task are: maximum speed 1.2 m / s, maximum acceleration 0.3 m / s², and minimum turning radius 1.5 m; if the task is performed by RGV2, the parameters are track speed 1.5 m / s and acceleration 0.5 m / s². This step clarifies the "upper limit of the equipment's motion capability," preventing subsequent speed commands from exceeding the equipment's performance range, which could lead to equipment malfunction or unstable operation.

[0149] Finally, based on the adjusted initial path and equipment motion parameters, a planned path containing speed commands is generated, completing the transformation from "path coordinates" to "executable commands". The speed command generation logic is designed according to road segments: In smooth traffic areas (weight 1), if the road segment is straight and has no turns, the speed is set to the maximum travel speed of the equipment (e.g., 1.2 m / s for AGV); in general traffic areas (weight 2), the speed is reduced to 70% of the maximum speed (e.g., 0.84 m / s); in highly congested areas (weight 5) or intersections, the speed is reduced to 30% of the maximum speed (e.g., 0.36 m / s), while setting an acceleration limit (not exceeding 50% of the maximum acceleration) to avoid rapid acceleration causing equipment vibration; in turning sections (the turning angle is calculated based on the path coordinates, ≥90° is considered a sharp turn), the speed is set to 50% of the maximum speed (e.g., 0.6 m / s), and the turning radius is ensured to be ≥ the minimum turning radius of the equipment. For example, the adjusted path includes three segments: the first segment (12m, 6m) to (15m, 6m) (smooth area, straight line), with the AGV speed set to 1.2m / s; the second segment (15m, 6m) to (15m, 12m) (smooth area, 90° turn), with the speed set to 0.6m / s; and the third segment (15m, 12m) to (20m, 12m) (general area, straight line), with the speed set to 0.84m / s. Simultaneously, the planned path will mark the coordinates of the speed switching points for each segment (e.g., switching from 1.2m / s to 0.6m / s at (15m, 6m), forming a complete "coordinate + speed" instruction set. This step ensures that the planned path not only includes the travel route but also specifies the travel speed for each segment, avoiding the instability caused by traditional planning that only provides path coordinates and the equipment traveling at a fixed speed. This ensures that the AGV / RGV travels at the appropriate speed, improving transportation safety and stability.

[0150] In summary, this invention improves the stability of AGV / RGV path travel time, significantly enhances the success rate of congestion avoidance, reduces the incidence of equipment collision failures, and ensures that the path and equipment performance are compatible, providing key guarantees for the efficient and safe execution of corrugated cardboard transportation.

[0151] In one embodiment of the present invention, the step of generating an initial path based on the target location in the task queue includes:

[0152] S631, obtain the current position coordinates of the AGV / RGV and the target position in the task queue, and determine the path start point and path end point based on the current position coordinates of the AGV / RGV and the target position;

[0153] S632, construct a grid model based on the workshop environment map, mark the locations of passable nodes and obstacles, and determine the path search range;

[0154] S633, Obtain the current node. Define a cost function to evaluate the path cost based on the actual distance from the path start point to the current node and the estimated Manhattan distance from the current node to the path end point. The cost function is:

[0155] ;

[0156] In the formula, This represents the cost function value. Indicates the path from the starting point to the current node. The actual distance Indicates the current node Estimated Manhattan distance to the end of the route;

[0157] S634, Initialize the open list and the closed list, add the starting point of the path to the open list, and calculate the cost function value corresponding to the starting point according to the cost function;

[0158] S635 Iteratively select the node with the smallest cost function value in the open list, expand the adjacent passable nodes of the node and update their costs until the end of the path is found.

[0159] S636, by tracing back the parent-child relationship between each node from the end point of the path to the starting point of the path, an initial path composed of continuous coordinate points is generated.

[0160] As described in steps S631-S636 above, this invention, based on the real-time location of AGV / RGV and the target location in the task queue, combined with the physical constraints of the workshop environment map, generates an initial path with no obstacles and optimal path cost through a systematic path search logic of "coordinate positioning - mesh modeling - cost definition - list initialization - node iteration - path backtracking". This solves the technical problems in traditional path search such as "path deviation caused by ambiguous starting and ending point positioning, computational redundancy caused by unclear search range, and non-optimal path caused by single cost evaluation", laying a precise and efficient foundational framework for subsequent path optimization based on congestion data.

[0161] First, the current coordinates of the AGV / RGV and the target position in the task queue are obtained to clearly define the path start and end points, ensuring that the path search has accurate "starting points" and "ending points" to avoid the path deviating from the task requirements due to ambiguous coordinates. The current coordinates of the AGV / RGV are obtained from the real-time status data in step S52, collected in real-time by the UWB positioning module installed on the AGV / RGV, with a positioning accuracy of ±10cm, updated every second to ensure accurate reflection of the equipment's current location. The target position in the task queue is obtained from the task queue in step S56. This target position corresponds to the downstream transfer destination of the corrugated cardboard (such as stacking area workstations, loading areas, etc.), and its coordinate information is pre-stored in the MES system and synchronously associated with specific tasks when the task queue is generated, ensuring consistency with the coordinate system in the workshop environment map. For example, if an AGV's task queue shows that it needs to transfer cardboard from buffer area 3 to workstation 5 in the stacking area, the current coordinates of the AGV are obtained from step S52 as (12m, 6m), and the coordinates of the target location (workstation 5 in the stacking area) are obtained from the task queue as (20m, 12m). Based on these two coordinates, the path start point is determined to be (12m, 6m), and the path end point is determined to be (20m, 12m). This step transforms the abstract "equipment location" and "task destination" into specific coordinate points, providing clear directional guidance for subsequent path searching. This avoids coordinate errors caused by manually setting the start and end points in traditional methods, ensuring that the path search direction perfectly matches the task requirements.

[0162] Next, a grid model is constructed based on the workshop environment map, marking the locations of passable nodes and obstacles and determining the path search range. This transforms the complex workshop environment into structured grid cells, facilitating efficient node expansion and path search in the future. (Workshop environment map and land used in step S61) Figure 1The initial high-precision static map, generated using laser SLAM technology, contains the coordinates of all fixed facilities within the workshop. When constructing the mesh model, the system uses an equidistant mesh division method, dividing the workshop environment map into multiple square mesh units of a preset size (e.g., 0.5m × 0.5m), with each mesh unit considered a "node." Subsequently, combining the obstacle coordinates extracted in step S61, the mesh units containing obstacles are labeled as "impassable nodes," while other obstacle-free mesh units are labeled as "passable nodes." Mesh units corresponding to passageways and intersections are also labeled to ensure the mesh model accurately reflects walkable and prohibited areas within the workshop. The path search range is determined based on the coordinates of the path's start and end points, typically defined as a rectangular area with the start and end points as diagonal vertices. The search range boundary extends 2-3 mesh units beyond the line connecting the start and end points on both sides, avoiding excessive computational redundancy while ensuring no potential optimal paths are missed. For example, based on the starting point (12m, 6m) and ending point (20m, 12m) determined in step S631, the area (10m-22m, 4m-14m) in the workshop environment map is divided into 0.5m × 0.5m grid cells, forming a total of (22-10) / 0.5 × (14-4) / 0.5 = 24 × 20 = 480 grid nodes. Combining this with the obstacle 1 (shelf, coordinates 8m-10m, 12m-14m) extracted in step S61, it is found that this obstacle is partially located at the edge of the search range. The corresponding grid cells (10m, 12m), (10m, 12.5m), etc., are marked as impassable nodes, while the remaining grid cells are marked as passable nodes. Through this step, the complex workshop environment is discretized into standardized grid nodes, clearly distinguishing between passable and impassable areas, while limiting a reasonable search range. This reduces the computational complexity of subsequent path search and ensures that the search process does not exceed the environmental range required by the task, thus improving path search efficiency.

[0163] Then, based on the actual distance from the path start point to the current node and the estimated Manhattan distance from the current node to the path end point, a cost function is defined to evaluate the path cost, providing a quantitative evaluation standard for subsequent selection of the optimal node and generation of the optimal path. The actual distance from the path start point to the current node refers to the cumulative distance from the path start point along all searched traversable nodes to the current node. This is obtained by calculating and summing the straight-line distances between adjacent nodes (the grid cell is a square, and the distance between adjacent nodes is the grid side length, such as 0.5m), ensuring a true reflection of the traveled path length. The estimated Manhattan distance from the current node to the path end point is a simplified distance estimation method, calculated as "Manhattan distance = |current node X coordinate - end point X coordinate| + |current node Y coordinate - end point Y coordinate|". This estimation method has low computational cost and effectively reflects the approximate distance between the node and the end point, quickly guiding the path search towards the end point and preventing the path search from deviating from the target. The cost function combines both factors, comprehensively evaluating the "cost already traveled" and "remaining estimated cost" of the current node. This ensures that the selected node guarantees a short traveled path while quickly approaching the destination, thus generating a path with the optimal overall cost. For example, if the current node coordinates are (15m, 9m), the path start point is (12m, 6m), and the path end point is (20m, 12m), then the actual distance from the path start point to the current node is the cumulative distance of adjacent nodes traversed from (12m, 6m) to (15m, 9m). Assuming a straight line traversing 6 grid nodes, the actual distance = 6 × 0.5m = 3m. The estimated Manhattan distance from the current node to the end point = |15-20| + |9-12| = 5 + 3 = 8 (unit: grid side length, i.e., 8 × 0.5m = 4m). According to the cost function, the calculated cost function value = 3m + 4m = 7m. This step establishes a cost evaluation system that considers both "actual driving distance" and "destination proximity." Compared to a cost function that only considers actual distance, this system can more efficiently guide the path search towards the destination, reduce the expansion of invalid nodes, and improve path search speed.

[0164] Next, the open and closed lists are initialized. The starting point of the path is added to the open list, and its cost function value is calculated to establish the initial data structure for subsequent node iterative searches. The open list stores traversable nodes that have been discovered but not yet expanded, while the closed list stores nodes that have been expanded and do not require further processing. Dynamic updates to the two lists avoid redundant node expansion and improve search efficiency. During initialization, both lists are first cleared to ensure no interference from historical data. Then, the grid node corresponding to the starting point of the path determined in step S631 is added to the open list. At this point, this node is the only node to be expanded. Simultaneously, the cost function value of the starting point is calculated according to the cost function defined in step S633. Since the actual distance from the starting point to itself is 0, the estimated Manhattan distance from the starting point to the end point can be directly calculated from the coordinates. Therefore, the cost function value of the starting point = 0 + the estimated Manhattan distance. For example, given a path starting point (12m, 6m) and ending point (20m, 12m), the estimated Manhattan distance from the starting point to the ending point is |12-20| + |6-12| = 8 + 6 = 14 (grid side length), which is 14 × 0.5m = 7m. Therefore, the cost function value of the starting point is 0 + 7m = 7m, and this value is associated with the starting point node and stored in the open list. This step establishes the initial data structure for path search, clarifies the first node to be expanded and its real-time cost, provides a clear starting state for subsequent iterative searches, and avoids search anomalies caused by chaotic initial data.

[0165] The process iteratively selects the node with the smallest cost function value from the open list, expands its adjacent passable nodes, and updates the cost, until the destination of the path is found. Through continuous iterative optimization, the process gradually approaches the destination and finds a feasible path. The iterative process is executed in a fixed loop: First, the node with the smallest cost function value is selected from the open list and designated as the current node to be expanded; Second, this node is moved from the open list to the closed list and marked as "expanded" to avoid repeated processing; Third, the adjacent nodes of the current node are determined (usually grid nodes in the four directions of up, down, left, and right; diagonal nodes are not considered adjacent nodes to ensure that the path conforms to the straight-line travel characteristics of AGV / RGV), and nodes marked as "passable nodes" that are not in the closed list are selected; Fourth, the cost function value of each adjacent node is calculated—the actual distance between adjacent nodes = the actual distance of the current node + The distance between the current node and the adjacent node (e.g., 0.5m) and the estimated Manhattan distance of the adjacent node are calculated according to the formula in step S633. The two are added together to obtain the cost function value of the adjacent node. In the fifth step, if the adjacent node is not in the open list, it is added to the open list and associated with the calculated cost function value. If the adjacent node is already in the open list, the newly calculated cost function value is compared with the original value. If the new value is smaller, the cost function value of the node in the open list is updated. In the sixth step, it is determined whether the open list contains the node corresponding to the end point of the path. If it contains it, the iteration stops. If it does not contain it, the above steps are repeated. For example, the node with the lowest cost in the open list is (13m, 7m), with a cost function value of 8m. After moving it to the closed list, its neighboring nodes (12.5m, 7m), (13.5m, 7m), (13m, 6.5m), and (13m, 7.5m) are expanded. The nodes (13.5m, 7m) and (13m, 7.5m) that are passable and not in the closed list are selected. The cost function values ​​of these two nodes are calculated to be 8.5m and 8.5m respectively, and they are added to the open list. This process is iterated until, in a certain round of expansion, the node corresponding to the path endpoint (20m, 12m) is added to the open list. At this point, the iteration stops, indicating that a feasible path from the starting point to the endpoint has been found. Through this step, guided by the cost function value, nodes closer to the endpoint and with shorter travel distances are prioritized for expansion, ensuring that the overall cost of the generated path is optimal. Simultaneously, the dynamic management of the open and closed lists avoids redundant node processing, improving the efficiency of iterative search.

[0166] Finally, by tracing the parent-child relationships between nodes from the path's endpoint to its starting point, an initial path composed of continuous coordinate points is generated, transforming the node relationships obtained through iterative search into an intuitive and executable sequence of path coordinates. During the node expansion process in step S635, the system synchronously records the "parent node" of each node (i.e., which node it expanded from), forming a parent-child relationship between nodes—for example, if node (13.5m, 7m) expands from node (13m, 7m), then (13m, 7m) is the parent node of (13.5m, 7m). During backtracking, starting from the node corresponding to the path's endpoint, the system sequentially searches for the parent node of each node until it reaches the path's starting point. Then, the backtracked nodes are arranged in the order of "starting point → ... → ending point," and the center coordinates of each node are extracted to form a continuous sequence of coordinate points, which constitutes the initial path. For example, starting from the endpoint (20m, 12m), the parent node is (19.5m, 12m), and the parent node of (19.5m, 12m) is (19m, 12m), and so on, until tracing back to the starting point (12m, 6m). These node coordinates are then arranged sequentially as (12m, 6m) → (12.5m, 6.5m) → ... → (19.5m, 12m) → (20m, 12m), forming an initial path containing multiple consecutive coordinate points. This step transforms discrete node relationships into continuous path coordinates, clarifying the specific travel route of the AGV / RGV from the starting point to the endpoint. This provides a clear basic path structure for subsequent adjustments to path weights based on congestion data, preventing inaccurate optimization due to unclear path representation.

[0167] In summary, this invention improves the accuracy of initial path generation, significantly shortens search time, and ensures that the path strictly avoids fixed obstacles, further guaranteeing the efficiency and safety of AGV / RGV transport of corrugated cardboard.

[0168] This invention also provides an automated sorting and intelligent scheduling system for multi-specification corrugated cardboard finished products, comprising:

[0169] The data acquisition module acquires original images of corrugated cardboard on the production line, extracts characteristic specification parameters of the cardboard based on the original images, and generates a cardboard attribute dataset.

[0170] The weight calculation module obtains the available capacity data of each exit of the sorting line and the demand information of downstream processes, and calculates the priority weight of the sorting target position based on the available capacity data and the demand information of downstream processes.

[0171] The collaborative control module generates gripping instructions and joint movement instructions for the sorting robotic arm based on the cardboard attribute dataset and the priority weight of the sorting target position.

[0172] The request generation module obtains the buffer status data at the output end of the sorting line, obtains the cardboard stacking height based on the buffer status data, and generates a transfer request containing cardboard specifications and quantity information when the cardboard stacking height reaches a preset threshold.

[0173] The task generation module acquires transfer requests and real-time status data of AGVs / RGVs, and generates a task queue based on the transfer requests and the real-time status data of AGVs / RGVs.

[0174] The scheduling module acquires a workshop environment map and a workshop traffic heat map, generates a planned path based on the task queue, the workshop environment map, and the workshop traffic heat map, and schedules the corrugated cardboard based on the planned path.

[0175] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, characterized in that the processor executes the computer program to implement the steps of an automatic sorting and intelligent scheduling method for multi-specification corrugated cardboard finished products.

[0176] The present invention also provides a computer-readable storage medium having a computer program stored thereon, characterized in that, when the computer program is executed by a processor, it implements the steps of an automatic sorting and intelligent scheduling method for multi-specification corrugated cardboard finished products.

[0177] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0178] The above description is merely a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for automatic sorting and intelligent scheduling of corrugated cardboard finished products in multiple specifications, characterized in that, include: Obtain original images of corrugated cardboard from the production line, extract characteristic specification parameters of the cardboard based on the original images, and generate a cardboard attribute dataset. Obtain available capacity data for each exit of the sorting line and downstream process demand information; Calculate the space utilization rate of each exit based on the available capacity data of each exit of the sorting line; Extract order delivery time and production cycle parameters based on downstream process demand information; Calculate the space adaptation coefficient based on the space utilization rate; Calculate the time urgency factor based on the order delivery time and the production cycle parameters; Obtain the spatial adaptation coefficient and the time urgency coefficient, and calculate the initial priority weight using a weighted algorithm; Obtain historical sorting efficiency data, and dynamically adjust the initial priority weight based on the historical sorting efficiency data to obtain the priority weight of the sorting target location; Based on the cardboard attribute dataset and the sorting target location priority weight, the sorting robot arm's gripping instructions and joint movement instructions are generated. Obtain the status data of the buffer area at the output end of the sorting line, obtain the cardboard stacking height based on the buffer area status data, and generate a transfer request containing cardboard specifications and quantity information when the cardboard stacking height reaches a preset threshold. Obtain transfer requests and real-time status data of AGV / RGV, and generate a task queue based on the transfer requests and the real-time status data of AGV / RGV; Obtain a workshop environment map and a workshop traffic heat map, generate a planned path based on the task queue, the workshop environment map, and the workshop traffic heat map, and schedule the corrugated cardboard based on the planned path.

2. The automatic sorting and intelligent scheduling method for multi-specification corrugated cardboard finished products according to claim 1, characterized in that, The step of generating the gripping instructions and joint movement instructions of the sorting robot arm based on the cardboard attribute dataset and the sorting target position priority weight includes: Obtain the size, weight parameters, and corrugation parameters of the cardboard attribute dataset. Determine the gripping force and number of gripping points of the robotic arm based on the size and weight parameters, and determine the placement angle and gentle placement buffer parameters of the robotic arm based on the corrugation parameters. The gripping force, the number of gripping points, the placement angle, and the gentle placement buffer parameters are integrated to generate the gripping instructions for the sorting robot arm; Based on the priority weight of the sorting target location, select the multiple candidate landing points with the highest weights; The motion trajectory parameters of the robotic arm are calculated based on the spatial coordinates of multiple candidate landing points, and joint motion commands for the sorting robotic arm are generated based on the motion trajectory parameters.

3. The automatic sorting and intelligent scheduling method for multi-specification corrugated cardboard finished products according to claim 2, characterized in that, The step of obtaining the cardboard stack height based on the buffer status data, and generating a transfer request containing cardboard specifications, quantity, and priority when the cardboard stack height reaches a preset threshold, includes: Obtain a preset threshold for the height of the cardboard stack, wherein the preset threshold includes a first preset threshold and a second preset threshold; Compare the cardboard stack height with the preset threshold: When the height of the cardboard stack reaches a first preset threshold, a pre-reminder signal is generated and transmitted to the AGV / RGV scheduling center; When the height of the cardboard stack reaches the second preset threshold, the number of cardboard pieces and their corresponding specifications in the buffer area are counted. Obtain statistical results by comparing the cardboard stack height with the preset threshold, and generate a transfer request containing cardboard specifications, quantity, and priority based on the statistical results.

4. The automatic sorting and intelligent scheduling method for multi-specification corrugated cardboard finished products according to claim 3, characterized in that, The step of generating a task queue based on the transfer request and the real-time status data of the AGV / RGV includes: Determine the required type and quantity of transport vehicles based on the transfer request; Acquire real-time status data of AGV / RGV, including current location, power level, load status, and fault information; Based on the type of transport vehicle and the real-time status data of AGV / RGV, available transport equipment is selected; Obtain the priority of each of the aforementioned transfer requests and sort them from high to low priority; The sorted requests are assigned to specific AGVs / RGVs based on the required number of transport vehicles; Generate a task queue containing task number, target location, and execution time limit.

5. The automatic sorting and intelligent scheduling method for multi-specification corrugated cardboard finished products according to claim 4, characterized in that, The step of generating a planned route based on the task queue, the workshop environment map, and the workshop traffic heat map includes: The coordinates of passageways, intersections, and obstacles are extracted from the workshop environment map. Based on the workshop traffic heat map, identify high-congestion areas and smooth-flowing areas; Generate an initial path based on the target location in the task queue; Obtain congestion data for the areas traversed by the initial path, and adjust the weight of the initial path accordingly; Obtain the device motion parameters of the AGV / RGV; Based on the adjusted initial path and the device motion parameters, a planned path containing speed commands is generated.

6. The automatic sorting and intelligent scheduling method for multi-specification corrugated cardboard finished products according to claim 5, characterized in that, The step of generating an initial path based on the target location in the task queue includes: Obtain the current position coordinates of the AGV / RGV and the target position in the task queue, and determine the path start point and path end point based on the current position coordinates of the AGV / RGV and the target position; A grid model is constructed based on the workshop environment map, and the locations of passable nodes and obstacles are marked to determine the path search range; Obtain the current node, and define a cost function to evaluate the path cost based on the actual distance from the path start point to the current node and the estimated Manhattan distance from the current node to the path end point; The open list and closed list are initialized, the starting point of the path is added to the open list, and the cost function value corresponding to the starting point is calculated according to the cost function. Iteratively select the node with the smallest cost function value in the open list, expand the adjacent passable nodes of that node and update their costs, until the end of the path is found; An initial path consisting of consecutive coordinate points is generated by tracing back the parent-child relationships between nodes from the end point of the path to the beginning point of the path.

7. A multi-specification automatic sorting and intelligent scheduling system for corrugated cardboard finished products, characterized in that, include: The data acquisition module acquires original images of corrugated cardboard on the production line, extracts characteristic specification parameters of the cardboard based on the original images, and generates a cardboard attribute dataset. The weight calculation module obtains the available capacity data of each exit of the sorting line and the demand information of downstream processes; Calculate the space utilization rate of each exit based on the available capacity data of each exit of the sorting line; Extract order delivery time and production cycle parameters based on downstream process demand information; Calculate the space adaptation coefficient based on the space utilization rate; calculate the time urgency coefficient based on the order delivery time and the production cycle parameters; Obtain the spatial adaptation coefficient and the time urgency coefficient, and calculate the initial priority weight using a weighted algorithm; Obtain historical sorting efficiency data, and dynamically adjust the initial priority weight based on the historical sorting efficiency data to obtain the priority weight of the sorting target location; The collaborative control module generates gripping instructions and joint movement instructions for the sorting robotic arm based on the cardboard attribute dataset and the priority weight of the sorting target position. The request generation module obtains the buffer status data at the output end of the sorting line, obtains the cardboard stacking height based on the buffer status data, and generates a transfer request containing cardboard specifications and quantity information when the cardboard stacking height reaches a preset threshold. The task generation module acquires transfer requests and real-time status data of AGVs / RGVs, and generates a task queue based on the transfer requests and the real-time status data of AGVs / RGVs. The scheduling module acquires a workshop environment map and a workshop traffic heat map, generates a planned path based on the task queue, the workshop environment map, and the workshop traffic heat map, and schedules the corrugated cardboard based on the planned path.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 6.

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