AGV (Automatic Guided Vehicle) scheduling method, system and equipment for photovoltaic cell workshop and storage medium

Abstract

The invention relates to the technical field of intelligent storage route planning, in particular to an AGV scheduling method, system and device for a photovoltaic cell workshop and a storage medium, and the method comprises the steps: calculating the driving distance of an AGV, the average speed of the AGV and the docking time of the AGV according to a drawn AGV driving map, and calculating the potential completion time of the AGV; and performing priority ranking and rolling optimization on the AGVs according to the length of the potential completion time. The number of interface flower baskets, the production time of a single flower basket and the explosion time of a computer table are obtained through monitoring the state of the computer table; performing priority ranking and rolling optimization on the machines according to the length of the machine explosion time and the priority of the production process; matching the most suitable AGV according to the machine priority and the AGV priority, and issuing the task to the AGV for execution; according to the method, the problem that the traditional scheduling method of the photovoltaic cell production workshop is difficult to meet the high-efficiency and real-time requirements at present is solved, the production efficiency can be effectively improved, the production cost is reduced, the safety is enhanced, and the flexible response of an enterprise in a dynamic environment is supported.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse management and logistics distribution, and in particular to an AGV scheduling method, system, equipment and storage medium in a photovoltaic cell workshop. Background Art

[0002] Automated Guided Vehicles (AGVs) are widely used in manufacturing, logistics, warehousing, and medical fields to realize automatic material handling. During the application process, AGVs need to dynamically adjust their work plans according to real-time tasks and environmental changes.

[0003] The photovoltaic cell production and manufacturing workshop realizes automated operation based on the AGV's movable carrier handling system. The handling system includes a handling server, a movable carrier and an AGV. The handling server is responsible for assigning tasks to the AGV, scheduling the AGV to transport the designated movable carrier from the upstream to the downstream of the process equipment, or from the cache (storage station) of the corresponding process to the process equipment of the process, so as to realize the flow of materials between the process processing equipment and cache of different processes, and complete the processing of photovoltaic cells on different process equipment.

[0004] The raw silicon wafers need to be processed into solar cells through multiple processes, and there are multiple similar processing equipment in each process. At present, with the increase in the number of AGVs and the complexity of tasks, the traditional scheduling method of increasing the number of AGVs and routes to meet scheduling needs faces challenges and is difficult to meet the requirements of efficiency and real-time. Due to equipment failures and downtime, it is easy to cause the production capacity between upstream and downstream processes to be difficult to connect and match. Traditional scheduling methods are often based on fixed tasks and paths for planning, lacking the ability to dynamically respond to real-time changes, resulting in local shortages, process equipment explosions, and serious buffer stacking, which affects the production capacity of photovoltaic cell production workshops.

[0005] Therefore, when the workload, priority or environmental conditions change, traditional methods often cannot be adjusted in time, resulting in reduced efficiency. At the same time, in many traditional scheduling systems, information transmission is not smooth enough, resulting in a lack of coordination between various parts. The information isolation between different AGVs and between AGVs and upper-level management systems will reduce the overall scheduling efficiency.

[0006] Based on the problems in the prior art, the present invention provides an AGV scheduling method, system, equipment and storage medium for a photovoltaic cell workshop. Summary of the invention

[0007] The purpose of the present invention is to provide an AGV scheduling method, system, equipment and storage medium for a photovoltaic cell workshop to solve the technical problem in the prior art that planning is based on fixed tasks and paths and lacks the ability to dynamically respond to real-time changes.

[0008] The technical solution of the present invention is: an AGV scheduling method for a photovoltaic cell workshop, comprising:

[0009] Obtain AGV operation status information based on historical data;

[0010] Based on the travel distance, average speed, and docking time in the AGV operation status information, estimate the potential completion time of the AGV task;

[0011] The potential completion time of AGVs is sorted by the bubbling algorithm to obtain the basic priority order of AGVs, and the order is updated regularly to perform priority rolling optimization.

[0012] Monitor and obtain the number of flower baskets at the machine's automated interface and the production time of a single flower basket, as well as the time when the computer is out of stock;

[0013] The bubbling algorithm is used to sort the length of the machine's explosion time, obtain the basic priority order of the machine's explosion time, and regularly update the sorting to perform priority rolling optimization;

[0014] Based on the priority order of machines and AGVs, a corresponding AGV is matched for each machine, and the task is sent to the AGV for execution.

[0015] Preferably, the process of obtaining the operating status information of the AGV according to the historical data includes:

[0016] By identifying the key nodes of AGV, determining the feasible path and drawing directed line segments to connect them, marking the driving direction and weight information, and drawing the AGV driving map;

[0017] According to the AGV driving map, the Dijkstra shortest path algorithm is used to calculate the AGV's driving distance, thereby finding the shortest distance from the AGV starting point to all nodes; the AGV average speed and AGV docking time are calculated through the sensors and data acquisition system equipped by the AGV.

[0018] Preferably, based on the AGV's travel distance, the AGV's average speed, the AGV's docking time, the AGV's previous task completion time, and the AGV's travel time from the AGV's previous task end point to the current task demand point, the AGV's potential completion time is estimated and calculated as follows:

[0019] Set the time model of idle AGV arriving at the task machine: T = S / V + t,

[0020] AGV potential completion time: T total =T process +T travel ;

[0021] Among them, T represents the AGV task execution time, S represents the AGV travel distance, V represents the AGV average speed, t represents the AGV docking time, and T total represents the potential completion time of AGV, T process Indicates the completion time of the AGV's predecessor task, T travel Indicates the travel time from the end point of the AGV's previous task to the current task's demand point.

[0022] Preferably, the potential completion time will be calculated The bubble algorithm is used for sorting, where if T total1 ≤T total2 , then the order of the two does not change, if T total1 >T total2 , then the order of the two is swapped. And so on, total1 、T total2 ……T totaln Perform n-1 rounds of swaps, sort the potential completion times from smallest to largest, and find T totalmax ;

[0023] After completing the small-to-large order exchange, the basic priority ranking of AGVs is obtained.

[0024] Preferably, the machine burst time is calculated by the number of flower baskets automatically docked by the machine and the production time of a single flower basket, and the method is as follows:

[0025] The counter records the number of flower baskets in real time, and the timer records the start time of each flower basket production. 开始 and end time t 结束 , thus obtaining the number of automated docking baskets and the production time t of a single basket, t = t 结束 -t 开始 ;

[0026] The machine burst time can be calculated by the number of flower baskets automatically connected to the machine and the production time of a single flower basket. Set: T 爆仓 =(ma)×t 生产 ;

[0027] Among them, T 爆仓 represents the machine burst time, m represents the number of flower baskets, a represents the number of flower baskets that the machine can automatically connect to, and t 生产 Indicates the production time of a single flower basket.

[0028] Preferably, the length of the machine explosion time is sorted by a bubbling algorithm to obtain the basic priority order of the machine explosion time;

[0029] If there are cases where the potential completion time is equal during the sorting, several machines with the same potential completion time will be compared separately, sorted according to the machine yield rate, and the sorted equipment order will be applied to the basic priority order.

[0030] Preferably, the AGVs involved in the sorting are in dynamic change, and a bubble sorting update is performed at regular intervals. A batch of equipment after the dynamic change is sorted by the bubble algorithm, and a priority rolling optimization sorting is performed.

[0031] An AGV scheduling system for a photovoltaic cell workshop, used to implement the above-mentioned AGV scheduling method for a photovoltaic cell workshop, comprising:

[0032] The AGV is equipped with a unit that identifies the key nodes of the vehicle's running trajectory, collects position information through the configured sensors, and records the corresponding time;

[0033] The first information processing unit draws an AGV driving map according to the identified key nodes, and obtains the driving distance based on the AGV driving map; based on the collected position information and the response time, processes and obtains the average speed, docking time, and potential completion time of the AGV;

[0034] The first rolling sorting unit performs rolling sorting on the potential completion time of the AGV to obtain a priority order;

[0035] A monitoring unit, including a timer and a counter, records the number of flower baskets, and the start and end time of production of each flower basket in real time;

[0036] The second information processing unit calculates the time when the warehouse is full according to the recorded number of flower baskets and the production time of each flower basket;

[0037] The second rolling sorting unit performs rolling sorting on the machine explosion time to obtain the priority sorting of the machine;

[0038] The task issuing unit matches the machine with the AGV with the shortest response time in order according to the AGV priority sorting and the machine burst time priority, and issues the task to the AGV for execution.

[0039] An electronic device comprises a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the AGV scheduling method for a photovoltaic cell workshop.

[0040] A computer-readable storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the AGV scheduling method for a photovoltaic cell workshop.

[0041] Compared with the prior art, the advantages of the present invention are:

[0042] The AGV trolley scheduling method based on the task rolling optimization algorithm provided by the present invention draws an AGV driving map based on historical information, obtains AGV operating status information, calculates the potential completion time of the AGV based on the AGV operating status information, and uses a bubbling algorithm to prioritize and continuously optimize the AGV potential completion time. At the same time, the number of automated interface flower baskets and the production time of a single flower basket are obtained through machine status monitoring to calculate the machine burst time, which is also prioritized and continuously optimized using the bubbling algorithm. According to the expected burst time priority, the most suitable AGV is matched to each task machine and the task is issued to the AGV for execution.

[0043] At regular intervals, the priority order of machine equipment and the priority order of AGV are scrolled and sorted, and the information is updated and correlated in a timely manner. The two are matched according to the new sorting status. At the upper management system level, the information between the workshop vehicles, material status, and machine equipment status is interconnected and coordinated to improve the overall scheduling efficiency.

[0044] It realizes the intelligent and efficient AGV scheduling, solves the problems of low efficiency, insufficient resource utilization and insufficient response capability in the current traditional scheduling method AGV scheduling process, and is difficult to meet high efficiency and real-time requirements. It can effectively improve production efficiency, reduce production costs, and enhance safety. It supports enterprises to respond flexibly in dynamic environments and provides more efficient solutions for modern manufacturing and workshop logistics systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The present invention will be further described below in conjunction with the accompanying drawings and embodiments:

[0046] Figure 1 It is a schematic diagram of the flow chart of the embodiment of the invention described in the present invention;

[0047] Figure 2 This is a schematic diagram of a process of calculating the driving distance of an AGV using the Dijkstra shortest path algorithm according to an AGV driving map according to the present invention;

[0048] Figure 3 The schematic diagram of the rolling optimization sorting matching of the machine and AGV of the present invention;

[0049] Figure 4 This is a system block diagram of the AGV dispatching system for the photovoltaic cell workshop of the present invention;

[0050] Figure 5 The figure is a schematic diagram of the structure of an electronic device described in the present invention. DETAILED DESCRIPTION

[0051] The present invention is further described in detail below in conjunction with specific embodiments:

[0052] The present invention relates to the field of warehouse management and logistics distribution, and is particularly suitable for material handling and logistics distribution between production lines. Figure 1 , which shows a flow chart of the AGV scheduling method for a photovoltaic cell workshop provided by an embodiment of the present invention. This specification provides method operation steps as shown in the embodiment or flow chart, but it may include more or fewer operation steps based on conventional or non-creative labor. The order of steps listed in the embodiment is only one way of executing the steps among many, and does not represent the only execution order. When the actual system or server product is executed, it can be executed in the order of the method shown in the embodiment or the accompanying drawings. Specifically, Figure 1 As shown, the information acquisition and planning are mainly carried out for the AGV trolley part and the machine part. The AGV scheduling method of the photovoltaic cell workshop includes:

[0053] The planning contents for the AGV car are as follows:

[0054] Step 1: Draw the AGV driving map.

[0055] By identifying key locations such as the starting point, end point, intersection, and turning point as nodes, the feasible paths between the nodes are determined, and directed edges are established between the nodes. The directed edges (line segments) represent the direction of movement of the vehicle, and weights can also be assigned to the directed line segments to represent the cost required to complete the segment path. All nodes are connected by directed line segments, and finally the paths are visualized and optimized to draw the AGV driving map.

[0056] Step 2: According to the AGV driving map, use the Dijkstra shortest path algorithm to calculate the AGV driving distance. The calculation process is shown in the attached figure. Figure 2 shown.

[0057] First, initialize, add the starting point a to the set S, and mark the node.

[0058] Then find the nodes adjacent to the starting point a in the set X, select the nearest point b among the adjacent nodes as a mark, and add it to the set S.

[0059] Next, take b as the new starting point and search for point c in set X that is adjacent to b and has the shortest distance. If DIST[c]>DIST[b]+Z(b,c), then update the value of DIST[c] to DIST[b]+Z(b,c) and add point c to set S.

[0060] Repeat the above steps n-1 times to find the shortest distance from the starting point a to all nodes, thereby calculating the driving distance of the AGV.

[0061] Among them, set S represents the set of nodes whose shortest paths have been determined, set X represents the set of nodes whose shortest paths have not been determined, DIST[x] is defined as an array, which represents the distance between any node x and the starting point a, and Z(b,c) represents the distance between any adjacent node b and node c.

[0062] Due to the failure and maintenance of some equipment in the workshop process, each machine has mechanical errors and the yield rate of processed materials varies. In this embodiment, equipment with high finished product rate, small fluctuation of yield rate and high output efficiency is defined as high-quality machine equipment.

[0063] AGV gives priority to supplying materials to high-quality equipment, and efficiently processes more high-quality materials, which helps to improve the quality and output of workshop products.

[0064] The nodes in the planned driving map are the high-quality machines and equipment in each process, and the planned routes are the shortest paths for the AGV to run from the starting point to the high-quality equipment in each process.

[0065] Step 3: Calculate the average AGV speed and AGV docking time.

[0066] The distance to the obstacle (or target) is measured by the sensor equipped on the AGV, and the location information of the AGV at different time points is obtained. Each time the location data is collected, the corresponding time is recorded. The average speed is calculated: V = Δx / Δt;

[0067] Among them, Δx represents the displacement of the AGV between two time points (obtained by the AGV's sensor), and Δt represents the time difference between the two time points.

[0068] Through the data acquisition system, such as the AGV's driving recorder, the time point t when the AGV arrives at the docking position is obtained 到达 And the time point t when AGV docking is completed 完成 , calculate the AGV docking time t = t 完成 -t 到达 .

[0069] Step 4: Calculate the potential completion time of the AGV task.

[0070] Assume that the time model for the idle AGV to reach the task machine is: T = S / V + t

[0071] AGV potential completion time: T total =T process +T travel ;

[0072] Among them, T represents the AGV task execution time, S represents the AGV travel distance, V represents the AGV average speed, t represents the AGV docking time, and T total Indicates the potential completion time of the AGV, T process Indicates the completion time of the AGV's predecessor task, T travel Indicates the travel time from the end point of the AGV's previous task to the current task's demand point.

[0073] Step 5: Prioritize AGVs by potential completion time and perform rolling optimization.

[0074] The potential completion time will be calculated The bubble algorithm is used for sorting, where if The order of the two does not change. Then the order of the two is swapped. And so on. Perform n-1 rounds of swaps and sort the potential completion times from smallest to largest to obtain

[0075] After the order exchange from small to large is completed, the above method is used to update the sorting at regular intervals, and n-1 cycles are performed each time to perform priority rolling optimization sorting on a group of AGVs that have changed dynamically.

[0076] The planning contents for the task machine are as follows:

[0077] Step 1: monitor the number of flower baskets at the interface of the machine and the production time of a single flower basket.

[0078] Step 2: The time of liquidation on the computer.

[0079] The production counter records the number of flower baskets in real time, and the timer records the start time of each flower basket production. 开始 and end time t 结束 The collected information is transmitted to the machine control processor, thereby obtaining the number of automated interface baskets and the production time of a single basket t = t 结束 -t 开始 .

[0080] Among them, the docking basket is a carrier that holds the photovoltaic cells to be processed, and the production time of a single basket is specifically the production and processing time of the cells in a single basket (carrier).

[0081] Setting, T 爆仓 =(ma)×t 生产 ;

[0082] Among them, T 爆仓represents the machine burst time, m represents the number of flower baskets, a represents the number of flower baskets that the machine can automatically connect to, and t 生产 Indicates the production time of a single flower basket.

[0083] For example, when the machine is double-track, T 爆仓 =(10-a)×t 生产 ;

[0084] When the machine is a single track, T 爆仓 =(5-a)×t 生产 .

[0085] Among them, when the machine is double-track, the corresponding number of flower baskets is 10, and when the machine is single-track, the corresponding number of flower baskets is 5.

[0086] Step 3: Prioritize the machines according to the length of machine overflow time and production process priority and perform rolling optimization.

[0087] The calculated machine liquidation time T total1 、T total2 ……T totaln Use the bubble algorithm to sort and roll optimization. If T total1 ≤T total2 , then the order of the two does not change, if T total1 >T total2 , then the order of the two is swapped, and so on. total1 、T total2 ……T totaln Perform n-1 rounds of exchanges, sort the machine liquidation time from small to large, and obtain the basic priority order of the machine liquidation time.

[0088] If there are equal potential completion times in the sort after bubble sorting by time, several equipment with equal potential completion times will be compared separately, sorted by the equipment yield, and the sorted equipment order will be applied to the basic priority order to obtain a complete priority sort order.

[0089] Since some equipment in the workshop process is faulty or under maintenance, each time the bubble algorithm is used to sort the machine priorities, the equipment and quantity in each batch will change dynamically, depending on the specific situation.

[0090] Therefore, after completing the order exchange from small to large, the above method is used to update the sorting again at regular intervals, and n-1 cycles are performed each time to perform priority rolling optimization sorting on a batch of machine equipment after dynamic changes, and to schedule them in accordance with the latest operating status of the equipment in the workshop.

[0091] According to the machine priority and AGV priority of the task, the most suitable AGV is matched to the machine, so that each machine has its corresponding AGV optimal order, thus matching the AGV with the shortest response time, and sending the task to the AGV for execution. Figure 3 shown.

[0092] The embodiment of the present invention prioritizes AGVs and machine equipment respectively, and performs AGV scheduling and feeding for the equipment that is expected to be out of stock first in the process, and so on, so as to configure an AGV with the shortest response time for each machine equipment, thereby realizing intelligent and efficient AGV scheduling, solving the problems of low efficiency, insufficient resource utilization and insufficient response capability in the AGV scheduling process of the current traditional scheduling method, and difficulty in meeting high efficiency and real-time requirements, which can effectively improve production efficiency, reduce production costs, enhance safety, support enterprises to respond flexibly in a dynamic environment, and provide a more efficient solution for modern manufacturing and logistics systems.

[0093] The embodiment of the present invention provides an AGV scheduling system for a photovoltaic cell workshop, which is used to implement the above-mentioned AGV scheduling method for a photovoltaic cell workshop. Figure 4 The system block diagram provided includes:

[0094] The AGV is equipped with a unit to identify the key nodes of the vehicle's running trajectory, collect position information through configured sensors and record the corresponding time.

[0095] The first information processing unit draws an AGV driving map according to the identified key nodes, and obtains the driving distance based on the AGV driving map; based on the collected position information and response time, it processes and obtains the average speed of the AGV, docking time, and potential completion time.

[0096] The first rolling sorting unit performs rolling sorting on the potential completion time of the AGV to obtain a priority order;

[0097] The monitoring unit, including a timer and a counter, records the number of flower baskets, and the start time and end time of production of each flower basket in real time.

[0098] The second information processing unit calculates the time when the warehouse is full according to the recorded number of flower baskets and the production time of each flower basket.

[0099] The second rolling sorting unit performs rolling sorting on the machine explosion time to obtain the priority sorting of the machines.

[0100] The task issuing unit matches the machine with the AGV with the shortest response time in order according to the AGV priority sorting and the machine burst time priority, and issues the task to the AGV for execution.

[0101] The AGV scheduling method for a photovoltaic cell workshop provided by the present invention draws an AGV driving map through the graph theory principles of nodes and directed line segments, then calculates the driving distance of the AGV based on the AGV driving map using the Dijkstra shortest path algorithm, calculates the potential completion time of the AGV after obtaining the average speed and docking time of the AGV by monitoring the running status of the AGV trolley, and uses the bubbling algorithm to prioritize and continuously optimize it, and at the same time, obtains the number of automated docking interface flower baskets and the production time of a single flower basket through machine status monitoring to calculate the machine burst time, and uses the bubbling algorithm to prioritize and continuously optimize it, and finally matches the most suitable AGV to the task machine according to the priority and sends the task to the AGV for execution, thereby solving the problem that the current traditional scheduling method is difficult to meet the requirements of high efficiency and real-time, effectively improving production efficiency, reducing production costs, enhancing safety, and supporting enterprises to respond flexibly in dynamic environments.

[0102] An embodiment of the present invention also provides an electronic device, which includes a processor and a memory; the memory stores one or more instructions, and the one or more instructions are suitable for the processor to load and execute to implement the AGV scheduling method for a photovoltaic cell workshop as in the above method embodiment.

[0103] The memory can be used to store software programs and modules. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store the operating system, application programs required for the function, etc.; the data storage area can store data created according to the use of the device, etc. In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as at least one disk storage device, a flash memory device or other volatile solid-state storage device. Accordingly, the memory can also include a memory controller to provide the processor with access to the memory.

[0104] Figure 5 The schematic diagram of the structure of an electronic device provided in an embodiment of the present invention, the internal structure of the electronic device may include but is not limited to: a processor, a memory and a communication interface, wherein the processor, the memory and the communication interface in the electronic device may be connected through a bus or other means, and the embodiment shown in this specification Figure 5 The example of connecting through bus is taken in the following.

[0105] Among them, the processor (or CPU, Central Processing Unit, central processing unit) is the computing core and control core of the electronic device. The communication interface is used for communication between the memory and the processor. The memory is used to store programs and data. It can be understood that the memory here can be a high-speed RAM storage device, or a non-volatile memory device (non-volatile memory), such as at least one disk storage device; optionally, it can also be at least one storage device located away from the aforementioned processor. The memory provides a storage space, which stores the operating system of the electronic device, which may include but is not limited to: Windows system (an operating system), Linux system (an operating system), etc., and the present invention is not limited to this; and, a computer program (including program code) suitable for being loaded and executed by the processor is also stored in the storage space. In the embodiment of this specification, the processor loads and executes the computer program stored in the memory to implement the AGV scheduling method for the photovoltaic cell workshop provided in the above method embodiment.

[0106] An embodiment of the present invention also provides a computer-readable storage medium, which can be set in an electronic device to store at least one instruction, at least one program, code set or instruction set related to the AGV scheduling method for the photovoltaic cell workshop in the method embodiment. The at least one instruction, at least one program, code set or instruction set can be loaded and executed by the processor of the electronic device to implement the AGV scheduling method for the photovoltaic cell workshop provided in the above method embodiment.

[0107] Optionally, in this embodiment, the above-mentioned storage medium may include but is not limited to: a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk, and other media that can store program codes.

[0108] It should be noted that the sequence of the embodiments of the present invention described above is for description only and does not represent the advantages and disadvantages of the embodiments. The above describes specific embodiments of this specification, and other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0109] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0110] Those skilled in the art will appreciate that all or part of the steps to implement the above embodiments may be accomplished by hardware, or by programs instructing related hardware to accomplish the steps. The programs may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk, or an optical disk, etc.

[0111] The above disclosure is only a preferred embodiment of the present invention, which certainly cannot be used to limit the scope of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. The AGV scheduling method for a photovoltaic cell workshop is characterized by: include: Obtain AGV operation status information based on historical data; Based on the travel distance, average speed, and docking time in the AGV operation status information, estimate the potential completion time of the AGV task; The potential completion time of AGVs is sorted by the bubbling algorithm to obtain the basic priority order of AGVs, and the order is updated regularly to perform priority rolling optimization. Monitor and obtain the number of flower baskets at the machine's automated interface and the production time of a single flower basket, as well as the time when the computer is out of stock; The bubbling algorithm is used to sort the length of the machine's explosion time, obtain the basic priority order of the machine's explosion time, and regularly update the sorting to perform priority rolling optimization; Based on the priority order of machines and AGVs, a corresponding AGV is matched for each machine, and the task is sent to the AGV for execution.

2. The AGV scheduling method for a photovoltaic cell workshop according to claim 1, characterized in that: Based on historical data, the process of obtaining the operating status information of AGV includes: By identifying the key nodes of AGV, determining the feasible path and drawing directed line segments to connect them, marking the driving direction and weight information, and drawing the AGV driving map; According to the AGV driving map, the Dijkstra shortest path algorithm is used to calculate the AGV's driving distance, so as to find the shortest distance from the AGV starting point to all nodes. The AGV average speed and AGV docking time are calculated through the sensors and data acquisition system equipped by the AGV.

3. The AGV scheduling method for a photovoltaic cell workshop according to claim 2, characterized in that: Based on the AGV's driving distance, the AGV's average speed, the AGV's docking time, the AGV's previous task completion time, and the AGV's driving time from the AGV's previous task end point to the current task demand point, the AGV's potential completion time is estimated. The process is as follows: Set the time model of idle AGV arriving at the task machine: T = S / V + t, AGV potential completion time: T total =T process +T travel ; Among them, T represents the AGV task execution time, S represents the AGV travel distance, V represents the AGV average speed, t represents the AGV docking time, and T total represents the potential completion time of AGV, T process Indicates the completion time of the AGV's predecessor task, T travel Indicates the travel time from the end point of the AGV's previous task to the current task's demand point.

4. The AGV scheduling method for a photovoltaic cell workshop according to claim 3, characterized in that: The calculated potential completion time T total1 , T total2 ……T totaln The bubble algorithm is used for sorting, where if T total1 ≤T total2 , then the order of the two does not change, if T total1 >T total2 , then the order of the two is swapped. And so on, total1 , T total2 ……T totaln Perform n-1 rounds of swaps, sort the potential completion times from smallest to largest, and find T totalmax ; After completing the small-to-large order exchange, the basic priority ranking of AGVs is obtained.

5. The AGV scheduling method for a photovoltaic cell workshop according to claim 4, characterized in that: The machine burst time can be calculated by the number of flower baskets automatically connected to the machine and the production time of a single flower basket. The method is as follows: The counter records the number of flower baskets in real time, and the timer records the start time of each flower basket production. 开始 and end time t 结束 , so as to obtain the number of automated docking baskets and the production time t of a single basket 生产 , t 生产 =t 结束 -t 开始 ; The machine burst time can be calculated by the number of flower baskets automatically connected to the machine and the production time of a single flower basket. Set: T 爆仓 =(ma)×t 生产 ; Among them, T 爆仓 represents the machine burst time, m represents the number of flower baskets, a represents the number of flower baskets that the machine can automatically connect to, and t 生产 Indicates the production time of a single flower basket.

6. The AGV scheduling method for a photovoltaic cell workshop according to claim 5, characterized in that: Sort the machine explosion time lengths by bubbling algorithm to obtain the basic priority order of the machine explosion time; If there are cases where the potential completion time is equal during the sorting, several machines with the same potential completion time will be compared separately, sorted according to the machine yield rate, and the sorted equipment order will be applied to the basic priority order.

7. The AGV scheduling method for a photovoltaic cell workshop according to claim 2, characterized in that: The AGVs involved in the sorting are in dynamic change. A bubble sort update is performed at regular intervals. The batch of equipment after dynamic change is sorted by the bubble algorithm to perform priority rolling optimization sorting.

8. An AGV scheduling system for a photovoltaic cell workshop, used to implement the AGV scheduling method for a photovoltaic cell workshop as described in any one of claims 1 to 7, characterized in that: include: The AGV is equipped with a unit that identifies the key nodes of the vehicle's running trajectory, collects position information through the configured sensors, and records the corresponding time; The first information processing unit draws an AGV driving map according to the identified key nodes, and obtains the driving distance based on the AGV driving map; based on the collected position information and the response time, processes and obtains the average speed, docking time, and potential completion time of the AGV; The first rolling sorting unit performs rolling sorting on the potential completion time of the AGV to obtain a priority order; A monitoring unit, including a timer and a counter, records the number of flower baskets, and the start and end time of production of each flower basket in real time; The second information processing unit calculates the time when the warehouse is full according to the recorded number of flower baskets and the production time of each flower basket; The second rolling sorting unit performs rolling sorting on the machine explosion time to obtain the priority sorting of the machine; The task issuing unit matches the machine with the AGV with the shortest response time in order according to the AGV priority sorting and the machine burst time priority, and issues the task to the AGV for execution.

9. An electronic device, characterized in that: The device includes a processor and a memory, wherein the memory stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by the processor to implement the AGV scheduling method for a photovoltaic cell workshop as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The storage medium stores at least one instruction or at least one program, and the at least one instruction or the at least one program is loaded and executed by the processor to implement the AGV scheduling method for a photovoltaic cell workshop as described in any one of claims 1-7.

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