Simulation method and device for AGV logistics scheduling

Abstract

The invention belongs to the field of vehicle scheduling, and discloses a simulation method and device for AGV logistics scheduling. The method comprises the following steps: importing an on-site transportation map into preset simulation software, creating a dynamic three-dimensional entity model, and importing a production shift, a plurality of production times and a working condition of a guide vehicle AGV into the dynamic three-dimensional entity model; in the dynamic three-dimensional entity model, setting task actuators of a plurality of AGVs, and creating a plurality of logistics transportation paths of the AGVs; setting a plurality of control points in the dynamic three-dimensional entity model, setting an on-line sequence for each AGV, and establishing a simulation model; according to a preset production shift, a preset AGV number, a preset control point number and the simulation model, simulation material scheduling is carried out, the simulation model outputs simulation full-load efficiency, simulation discharging point waiting duration, simulation feeding point waiting duration and simulation production line feeding efficiency, and material scheduling optimization is carried out. According to the invention, the efficiency of AGV material scheduling can be optimized.

Description

Technical Field

[0001] The present application relates to the technical field of vehicle scheduling, and in particular to a simulation method and device for AGV logistics scheduling. Background Art

[0002] At present, with the development of industrial automation, AGV (Automated Guided Vehicle) is an important part of modern intelligent logistics and is widely used in the fields of production, manufacturing, warehousing and logistics. On highly automated production lines, AGV is responsible for material transportation. In the existing AGV logistics scheduling, the number of loading and unloading points is limited. When AGV is waiting in line, a lot of time will be wasted, which affects the efficiency of material circulation. At the same time, when the material time is uncertain and there is a sudden transportation demand, the existing logistics scheduling cannot cope with the transportation demand, which affects the efficiency of logistics scheduling. Summary of the invention

[0003] Based on this, it is necessary to provide a simulation method and device for AGV logistics scheduling to address the above technical problems.

[0004] In a first aspect, a simulation method for AGV logistics scheduling is provided, the method comprising:

[0005] The on-site transportation map imported into the preset simulation software is used to create a dynamic three-dimensional solid model, and the production shifts, multiple production times and working conditions of the automatic guided vehicle AGV are imported into the dynamic three-dimensional solid model;

[0006] In the dynamic three-dimensional entity model, multiple AGV task executors are set, and multiple logistics transportation paths of the AGV are created;

[0007] In the dynamic three-dimensional entity model, multiple control points are set, and priorities are configured for each of the control points. The control points include loading points, unloading points, buffer areas, and charging points, and the order of going online is set for each AGV to establish a simulation model of AGV transporting materials;

[0008] According to the preset production shifts, the preset number of AGVs, the preset number of control points and the simulation model, simulated material scheduling is performed, and the simulation model outputs the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency, and material scheduling optimization is performed based on the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency.

[0009] As an optional implementation, the simulated material scheduling is performed according to the preset production shift, the preset number of AGVs, the preset number of control points and the simulation model to obtain the simulated full load efficiency, the simulated unloading point waiting time, the simulated loading point waiting time and the simulated production line feeding efficiency, including:

[0010] In the simulated transportation process, the preset production shift, the preset number of AGVs, and the preset number of control points are input into the simulation model, and the generation time of the materials to be transported is determined based on the normal distribution. After the materials to be transported are generated, the AGVs are controlled to go online according to the preset online sequence, and the AGVs after going online are controlled to travel to the high-priority and idle loading points according to the logistics transportation path;

[0011] After the AGV reaches the loading point, it scans the code to obtain the order number, loads the materials according to the order number, and dispatches the AGV to the buffer area;

[0012] According to the material demand of the unloading point, the AGV is dispatched to the unloading point in the order of the order number to unload the material;

[0013] After the preset production shift ends, the simulation model outputs the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency.

[0014] As an optional implementation, the method further includes:

[0015] During the simulated transportation process, after the AGV unloads the material, the current power level of the AGV is detected;

[0016] If the current power level is greater than or equal to the preset power threshold, control the AGV to return to the online area;

[0017] If the current power level is less than the preset power threshold, the AGV is scheduled to travel to an idle charging point for charging; if there is no idle charging point, the AGV waits until there is an idle charging point;

[0018] If the current power levels of multiple AGVs are all less than the preset power threshold, the AGV with the lowest current power level is dispatched to an idle charging point for charging.

[0019] As an optional implementation, the method further includes:

[0020] According to the preset production shift, the preset number of AGVs, and the preset number of control points, the actual production line is controlled to perform material scheduling, and the actual full load efficiency of the AGV, the actual waiting time at the unloading point, the actual waiting time at the feeding point, and the actual feeding efficiency of the production line are obtained;

[0021] The simulated full load efficiency, the simulated unloading point waiting time, the simulated loading point waiting time and the simulated production line feeding efficiency are compared one-to-one with the actual full load efficiency, the actual unloading point waiting time, the actual loading point waiting time and the actual production line feeding efficiency to obtain an error value. If the error value is less than the preset error threshold, the simulation model is determined to be accurate.

[0022] As an optional implementation, the material scheduling optimization based on the simulated full load efficiency, the simulated unloading point waiting time, the simulated loading point waiting time and the simulated production line feeding efficiency includes:

[0023] According to the simulated full load efficiency, the simulated waiting time of the unloading point, the simulated waiting time of the loading point and the simulated production line feeding efficiency, the number of AGVs, the buffer area positions and the number of control points are adjusted.

[0024] As an optional implementation, the method further includes:

[0025] Based on historical production data and production plans, a material demand forecasting model is established through big data analysis;

[0026] Predicting material demand according to the established material demand prediction model, and dispatching a preset number of AGVs to travel to a loading point or a buffer area according to the material demand;

[0027] When changes in logistics demand are detected, the AGV scheduling strategy and the number of buffer areas are adjusted in real time.

[0028] As an optional implementation, the AGV is provided with a sensor device and a positioning device, and the method further comprises:

[0029] The operating status and real-time position of the AGV are acquired through the sensor device and the positioning device, and self-diagnosis is performed according to the operating status. If a problem is diagnosed, an alarm is issued.

[0030] As an optional implementation, the method further includes:

[0031] According to the preset grouping number, multiple AGVs are divided into multiple vehicle groups, and the online order of each vehicle group is set according to the preset online time rules.

[0032] As an optional implementation manner, configuring a priority for each of the control points includes:

[0033] Establish multiple loading point storage location groups, wherein the loading point storage location group includes multiple loading point storage locations, and the loading point storage location is the storage location when the AGV loads materials at the loading point, and one loading point corresponds to one loading point storage location;

[0034] The priority of each control point is obtained by assigning priorities to the multiple loading point storage locations in each loading point storage location group.

[0035] In a second aspect, a simulation device for AGV logistics scheduling is provided, the device comprising:

[0036] A creation module is used to create a dynamic three-dimensional solid model from the on-site transportation map imported into the preset simulation software, and to import production shifts, multiple production times and working conditions of the automatic guided vehicle AGV into the dynamic three-dimensional solid model;

[0037] A first setting module is used to set multiple AGV task executors in the dynamic three-dimensional entity model and create multiple logistics transportation paths for the AGV;

[0038] The second setting module is used to set a plurality of control points in the dynamic three-dimensional solid model and configure a priority for each of the control points, wherein the control points include a loading point, an unloading point, a buffer area and a charging point, and set an online sequence for each AGV to establish a simulation model of AGV transporting materials;

[0039] The first scheduling module is used to perform simulated material scheduling according to preset production shifts, a preset number of AGVs, a preset number of control points and the simulation model. The simulation model outputs simulated full load efficiency, simulated waiting time at the unloading point, simulated waiting time at the loading point and simulated production line feeding efficiency, and material scheduling optimization is performed based on the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency.

[0040] As an optional implementation manner, the first scheduling module is specifically used to:

[0041] In the simulated transportation process, the preset production shift, the preset number of AGVs, and the preset number of control points are input into the simulation model, and the generation time of the materials to be transported is determined based on the normal distribution. After the materials to be transported are generated, the AGVs are controlled to go online according to the preset online sequence, and the AGVs after going online are controlled to travel to the high-priority and idle loading points according to the logistics transportation path;

[0042] After the AGV reaches the loading point, it scans the code to obtain the order number, loads the materials according to the order number, and dispatches the AGV to the buffer area;

[0043] According to the material demand of the unloading point, the AGV is dispatched to the unloading point in the order of the order number to unload the material;

[0044] After the preset production shift ends, the simulation model outputs the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency.

[0045] As an optional implementation, the device further includes:

[0046] A detection module, used to detect the current power of the AGV after the AGV unloads the material during the simulated transportation process;

[0047] A control module, configured to control the AGV to return to the online area if the current power level is greater than or equal to a preset power threshold;

[0048] A charging module, configured to dispatch the AGV to an idle charging point for charging if the current power level is less than the preset power level threshold, and to wait until an idle charging point is available if there is no idle charging point;

[0049] If the current power levels of multiple AGVs are all less than the preset power threshold, the AGV with the lowest current power level is dispatched to an idle charging point for charging.

[0050] As an optional implementation, the device further includes:

[0051] The second scheduling module is used to control the actual production line to perform material scheduling according to the preset production shift, the preset number of AGVs, and the preset number of control points, and obtain the actual full load efficiency of the AGV, the actual waiting time of the unloading point, the actual waiting time of the feeding point, and the actual feeding efficiency of the production line;

[0052] A comparison module is used to compare the simulated full load efficiency, the simulated unloading point waiting time, the simulated loading point waiting time and the simulated production line feeding efficiency with the actual full load efficiency, the actual unloading point waiting time, the actual loading point waiting time and the actual production line feeding efficiency one-to-one to obtain an error value. If the error value is less than a preset error threshold, the simulation model is determined to be accurate.

[0053] As an optional implementation manner, the first scheduling module is specifically used to:

[0054] According to the simulated full load efficiency, the simulated waiting time of the unloading point, the simulated waiting time of the loading point and the simulated production line feeding efficiency, the number of AGVs, the buffer area positions and the number of control points are adjusted.

[0055] As an optional implementation, the device further includes:

[0056] Establish a module for building a material demand forecasting model through big data analysis based on historical production data and production plans;

[0057] A third scheduling module is used to predict material demand according to the established material demand prediction model, and schedule a preset number of AGVs to travel to a loading point or a buffer area according to the material demand;

[0058] The adjustment module is used to adjust the AGV scheduling strategy and the number of buffer areas in real time when changes in logistics demand are detected.

[0059] As an optional implementation, the AGV is provided with a sensor device and a positioning device, and the device further comprises:

[0060] The alarm module is used to obtain the operating status and real-time position of the AGV through the sensor device and the positioning device, and to perform self-diagnosis according to the operating status. If a problem is diagnosed, an alarm is issued.

[0061] As an optional implementation, the device further includes:

[0062] The division module is used to divide multiple AGVs into multiple vehicle groups according to a preset number of groups, and set the online order of each vehicle group according to a preset online time rule.

[0063] As an optional implementation manner, the second setting module is specifically used to:

[0064] Establish multiple loading point storage location groups, wherein the loading point storage location group includes multiple loading point storage locations, and the loading point storage location is the storage location when the AGV loads materials at the loading point, and one loading point corresponds to one loading point storage location;

[0065] The priority of each control point is obtained by assigning priorities to the multiple loading point storage locations in each loading point storage location group.

[0066] In a third aspect, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program executable on the processor, and when the processor executes the computer program, the method steps described in the first aspect are implemented.

[0067] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method steps described in the first aspect are implemented.

[0068] The present application provides a simulation method and device for AGV logistics scheduling. The technical solution provided by the embodiments of the present application brings at least the following beneficial effects: by reasonably setting the loading, unloading and buffering locations, the waiting time of AGV is reduced, the material flow efficiency is improved, and the AGV scheduling efficiency is improved. The arrival time of materials is simulated according to the normal distribution to ensure the continuity of material supply on the production line, reduce production interruptions caused by material backlog or shortage, and reduce the risk of material backlog. According to the simulation results, the number of AGVs and the allocation of resources such as buffer areas and charging points are optimized to achieve optimal resource utilization and optimize resource allocation. A reasonable charging scheduling plan effectively manages the power of AGVs, avoids frequent charging and excessive discharge, extends the service life of AGV batteries, and extends the working life of AGVs.

[0069] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0071] Figure 1 A flowchart of a simulation method for AGV logistics scheduling provided in an embodiment of the present application;

[0072] Figure 2 A schematic diagram of the structure of a simulation device for AGV logistics scheduling provided in an embodiment of the present application;

[0073] Figure 3 A schematic diagram of the structure of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0074] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. 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.

[0075] The following will describe in detail a simulation method for AGV logistics scheduling provided by an embodiment of the present application in combination with a specific implementation method. Figure 1 A flowchart of a simulation method for AGV logistics scheduling provided in an embodiment of the present application, such as Figure 1 As shown, the specific steps are as follows:

[0076] Step 101, a dynamic three-dimensional solid model is created by importing a site transportation map into a preset simulation software, and a production shift and multiple production times and working conditions of multiple automatic guided vehicles (AGVs) are imported into the dynamic three-dimensional solid model.

[0077] In practice, with the development of industrial automation, AGV, as an important part of modern intelligent logistics, is widely used in the fields of production, manufacturing, warehousing and logistics. On highly automated production lines, AGV undertakes the task of material transportation. In the existing technology, the number of loading and unloading points is limited, and AGV will waste a lot of time when waiting in line, which affects the efficiency of material circulation. At the same time, when the material time is uncertain and there is a sudden transportation demand, the existing logistics scheduling cannot cope with the transportation demand, which affects the efficiency of logistics scheduling. In order to improve the efficiency of logistics scheduling, a simulation model of AGV transporting materials can be established, and the logistics scheduling of several AGVs can be simulated through the simulation model, and the scheduling process of AGV can be gradually optimized through simulation. An efficient three-dimensional solid model is established through FlexSim simulation software to optimize and analyze the production line. At the same time, through virtual modeling, it is possible to better understand and improve on-site logistics and operation processes, improve production efficiency, reduce costs, and thus improve the operating efficiency of the logistics system. The on-site transportation map imported in the preset simulation software can be used to create a dynamic three-dimensional solid model. Among them, the preset simulation software can be FlexSim simulation software. The on-site transportation map is imported into the FlexSim simulation software to create a three-dimensional solid model with animation effects to truly reproduce the production scene. The production shifts and multiple production times and working conditions of multiple AGVs are imported into the dynamic three-dimensional solid model to ensure that the simulation data results are highly consistent with the actual production line. The working conditions of AGV can be failures and line stoppages. Among them, the dynamic three-dimensional solid model design is highly flexible and can quickly adjust the structure to adapt to diverse solutions and optimization needs.

[0078] Step 102, in the dynamic three-dimensional entity model, multiple AGV task executors are set, and multiple logistics transportation paths of the AGV are created.

[0079] In the implementation, in the dynamic 3D entity model, a preset number of AGVs are set as task executors to be responsible for logistics transportation, and a logistics transportation path is created using 3D entities to ensure transportation efficiency and smoothness. Therefore, in the dynamic 3D entity model, multiple AGV task executors are set, and multiple AGV logistics transportation paths are created. The preset number can be set according to actual conditions and is not limited here.

[0080] Furthermore, a forklift model can be designed in the dynamic three-dimensional solid model to simulate the loading process of the forklift to achieve efficient transfer of materials.

[0081] Step 103, in the dynamic three-dimensional solid model, multiple control points are set, and a priority is configured for each control point. The control points include loading points, unloading points, buffer areas and charging points, and the online sequence is set for each AGV to establish a simulation model of AGV transporting materials.

[0082] During implementation, multiple control points are determined in the dynamic three-dimensional solid model, including loading points, buffer areas, unloading points and charging points, to ensure the smoothness of the logistics process. Set multiple control points and configure priorities for each control point. The control points include loading points, unloading points, buffer areas and charging points, as well as setting the online order for each AGV. By establishing a storage location group for the loading point, the priority sorting of the control points can be configured to optimize the efficiency of the loading process. The same applies to other storage locations to ensure that the operation order of each storage location is reasonable. Furthermore, multiple AGVs can be divided into trolley groups, and a preset number of trolleys can be divided into trolley groups, which are sorted according to custom online time rules to improve the flexibility and response speed of scheduling. In this way, a simulation model of AGV transporting materials is established.

[0083] Specifically, the execution steps for configuring the priority of each control point are as follows:

[0084] Step 1) establish multiple loading point storage location groups, the loading point storage location group includes multiple loading point storage locations, the loading point storage location is the storage location when the AGV loads materials at the loading point, and one loading point corresponds to one loading point storage location.

[0085] In implementation, there are multiple control points in the production line. In order to simulate the actual production line, multiple control points are also designed in the simulation model. Control points include loading points, unloading points, buffer areas and charging points. In the process of AGV logistics transportation, in order to ensure the smoothness of the logistics process, the control points can be prioritized. Priority can be divided based on the loading point location in the control point. Among them, the loading point location is the location when the AGV loads at the loading point, and one loading point corresponds to one loading point location. In order to facilitate the prioritization of multiple loading point locations, multiple loading point location groups can be established. The subsequent steps are to prioritize the loading point locations in each loading point location group.

[0086] Step 2) Prioritize multiple loading point locations in each loading point location group to obtain the priority of each control point.

[0087] In implementation, the priority of each control point is obtained based on the priority of multiple loading point locations in each loading point location group. Therefore, by establishing a loading point location group and configuring the priority sorting of the control points, the efficiency of the loading process is optimized. The other locations are analogous to each other to ensure that the operation sequence of each loading point location is reasonable.

[0088] Furthermore, the plurality of AGVs are divided into a plurality of vehicle groups according to a preset number of groups, and the online order of each vehicle group is set according to a preset online time rule.

[0089] In implementation, during the logistics transportation process, there are multiple AGVs. In order to ensure the flexibility and response speed of logistics scheduling, multiple AGVs can be divided into multiple vehicle groups according to the preset number of groups, and the online order of each vehicle group can be set according to the preset online time rule. Among them, the preset online time rule can be to set different online time nodes for each vehicle group, and when the online time node arrives, the corresponding vehicle group goes online. The preset online time rule can be set according to the actual situation and is not limited here. The preset number of groups can be set according to the actual situation and is not limited here.

[0090] Step 104, performs simulated material scheduling according to the preset production shifts, the preset number of AGVs, the preset number of control points and the simulation model. The simulation model outputs the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency, and optimizes the material scheduling based on the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency.

[0091] In implementation, if you want to simulate the material transportation of AGV through the simulation model, you need to simulate the material transportation according to the preset production shift, the preset number of AGVs and the preset number of control points. Therefore, it is necessary to simulate material scheduling according to the preset production shift, the preset number of AGVs, the preset number of control points and the simulation model. The simulation model outputs the simulated full load efficiency, the simulated waiting time of the unloading point, the simulated waiting time of the loading point and the simulated production line feeding efficiency. According to the production shift setting, the simulation model simulates and analyzes the simulated full load efficiency of the AGV. By gradually adjusting the number of AGV carts, buffer areas, charging points and loading points, the optimal AGV scheduling plan is obtained, and the configuration of the number of carts, buffer locations and loading points is further optimized to improve the operating efficiency of the logistics system. Therefore, material scheduling optimization can be performed based on the simulated full load efficiency, the simulated waiting time of the unloading point, the simulated waiting time of the loading point and the simulated production line feeding efficiency.

[0092] Specifically, the process of executing step 104 is as follows:

[0093] Step 1: During the simulated transportation process, the preset production shifts, the preset number of AGVs, and the preset number of control points are input into the simulation model, and the generation time of the materials to be transported is determined based on the normal distribution. After the materials to be transported are generated, the AGV is controlled to go online according to the preset online sequence, and the AGV after going online is controlled to travel to a high-priority and idle loading point according to the logistics transportation route.

[0094] In implementation, when simulating material transportation through a simulation model, the preset production shift, the preset number of AGVs, and the preset number of control points can be input into the simulation model, and the simulation model starts to simulate transportation. The material generation logic in the simulation model can be to use a normal distribution function to simulate the arrival time of the materials to be transported, forming a random material generation pattern. The generated materials to be transported are piled up in the inventory location, providing a demand source for the subsequent loading tasks of the AGV. Therefore, the generation time of the materials to be transported can be determined based on the normal distribution. After the materials to be transported are generated, several AGVs are on standby in the online area. When the system detects that one of the several loading points is idle, the AGV is scheduled to queue up to enter the loading point and wait for the forklift to load the material onto the trolley. Therefore, after the materials to be transported are generated, the AGV is controlled to go online according to the preset online order, and the AGV after being controlled to go online is driven to a high priority and idle loading point according to the logistics transportation path.

[0095] Step 2: After the AGV arrives at the loading point, it scans the code to obtain the order number, loads the materials according to the order number, and dispatches the AGV to the buffer area.

[0096] In practice, after the AGV reaches the loading point, it scans the code to obtain the order number and sorts according to the order number. The smallest order number is prioritized to simulate the loading process of the forklift for loading. After loading, check the idle status of the storage location in the buffer area, select the idle location to arrange the AGV to wait, so as to unload the material efficiently later. Therefore, the AGV is dispatched to the buffer area. In this way, the orderliness of logistics scheduling can be ensured.

[0097] Step 3: According to the material demand of the unloading point, dispatch the AGV to the unloading point in the order of the order number to unload the materials.

[0098] In practice, when the unloading point needs materials, the unloading point logistics demand will be generated, and then the materials will be called in the order of the order number, and the AGV that meets the requirements will be dispatched from the buffer area to the unloading point. After arriving at the unloading point, the materials will be unloaded. Furthermore, after returning empty, the AGV enters the loading area and waits for the loading point to be free before loading.

[0099] Step 4: After the preset production shift ends, the simulation model outputs the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency.

[0100] In implementation, after the preset production shift is completed, the simulation model outputs the simulated full load efficiency, simulated waiting time at the unloading point, simulated waiting time at the loading point, and simulated production line feeding efficiency. This allows the simulation model to optimize the scheduling of AGVs after completing the simulated transportation to improve the operating efficiency of the logistics system.

[0101] Step 5: Adjust the number of AGVs, buffer area locations, and control points based on the simulated full load efficiency, simulated unloading point waiting time, simulated loading point waiting time, and simulated production line feeding efficiency.

[0102] During implementation, according to the production shift settings, the simulation model simulates and analyzes the AGV's simulated full-load efficiency, simulated unloading point waiting time, simulated loading point waiting time, and simulated production line feeding efficiency. By gradually adjusting the number of AGV carts, buffer areas, charging points, and loading points, the optimal AGV scheduling solution is obtained, and the number of AGV carts, buffer positions, and loading point configurations are further optimized to improve the operating efficiency of the logistics system. Therefore, material scheduling optimization can be performed based on the simulated full-load efficiency, simulated unloading point waiting time, simulated loading point waiting time, and simulated production line feeding efficiency. Therefore, according to the simulated full-load efficiency, simulated unloading point waiting time, simulated loading point waiting time, and simulated production line feeding efficiency, the number of AGVs, buffer area positions, and control points are adjusted. In this way, targeted optimization suggestions are put forward based on the simulation results to help improve overall production efficiency.

[0103] Furthermore, through multi-shift simulation operation, key data of the production line can be obtained and the effects of different solutions can be evaluated.

[0104] For example, Example 1: AGV scheduling under standard production shift conditions, simulation conditions: the production shift is 8 hours, the number of AGVs is a preset number, there are 3 loading points, 10 cache locations, 1 unloading point and 3 unloading cache areas, and 1 charging point. Simulation process: the materials to be transported are generated according to the normal distribution, a preset number of AGVs are on standby in the online area, and the 3 loading points alternately load the AGVs. Each AGV scans the code to load materials in the order of the order number and goes to the cache area to wait. According to the material demand at the unloading point, the AGV is dispatched to the unloading point in the order of the order number. When the current power of the AGV is lower than the set power threshold, the AGV is dispatched to the idle charging point for charging. Simulation results: During the simulation operation, the average full load efficiency of the AGV reached 85%, the waiting time at the unloading point was significantly reduced, and the feeding efficiency of the production line was increased by 20%. Example 2: AGV scheduling optimization under peak production demand, simulation conditions: the production shift is 10 hours, the number of AGVs is 20, 4 loading points, 12 cache locations, 1 unloading point and 4 unloading cache areas, and 2 charging points. Simulation process: In order to cope with peak demand, 5 AGVs were added, and 1 loading point, 2 cache locations and 1 charging point were added to alleviate the transportation pressure during peak periods. AGVs are dynamically scheduled according to the real-time demand of the unloading point based on the logistics transportation needs, and the charging points take turns to charge AGVs with lower power. Simulation results: The full load efficiency is increased to 92%, which reduces the material accumulation problem caused by peak demand, further reduces the material waiting time, and improves the overall efficiency of the production line. Example 3: Optimization of AGV scheduling and charging strategy under low power conditions, simulation conditions: the production shift is 12 hours, the number of AGVs is a preset number, the low power threshold is set to 30%, and there is 1 charging point. Simulation process: After the AGV completes the unloading, it determines whether the current power is less than 30%. If it is less than 30%, it enters the charging process, otherwise it enters the online area on standby. When the charging point is occupied, the AGV with lower power is charged first, and the AGV with higher power continues to standby. Simulation results: Through reasonable charging priority scheduling, the continuous operation of the AGV is guaranteed, while the loss of the battery due to frequent charging is reduced, and the battery life and the overall working time of the AGV are improved. Example 4: Optimization experiment with different numbers of cache areas, simulation conditions: the production shift is 8 hours, the number of AGVs is the preset number, 10 to 15 cache locations, and 1 unloading point. Simulation process: Through simulation analysis of the impact of the number of cache areas on the scheduling efficiency of AGV, the cache locations are gradually increased from 10 to 15. Analyze the impact of different numbers of cache locations on the waiting time of AGV and the continuity of material supply at the unloading point. Simulation results: When the number of cache areas is 12, the AGV has the highest full load efficiency, the material supply at the unloading point is relatively stable, and the AGV waiting time is the shortest. Therefore, 12 cache locations are selected as the optimal configuration to achieve the optimal scheduling effect. In this way, through multi-shift simulation operation, key data of the production line can be obtained and the effects of different plans can be evaluated.Then, the number of carts, buffer areas, charging points and loading points is gradually adjusted to obtain the optimal AGV scheduling plan, further optimize the number of carts, buffer locations and loading points, and improve the operating efficiency of the logistics system.

[0105] Furthermore, during the simulated transportation and actual transportation process, it is also necessary to detect the power of the AGV and determine whether to enter the charging process based on the power status. The specific charging process is as follows:

[0106] Step A: During the simulated transportation process, after the AGV unloads the material, the current power of the AGV is detected.

[0107] In the implementation, during the simulated transportation process, when the AGV unloads the material, the current power of the AGV is detected to determine whether the current power is sufficient to support the AGV to continue transporting the material. If not, charging is required.

[0108] Step B: If the current power level is greater than or equal to the preset power threshold, the AGV is controlled to return to the online area.

[0109] In implementation, the current power of the AGV is compared with the preset power threshold. If the current power is greater than or equal to the preset power threshold, it means that the current circuit of the AGV is sufficient to support the AGV to continue transporting materials and no charging is required. Then the AGV is controlled to return to the online area for scheduling and standby. The preset power threshold can be 30%.

[0110] Step C: if the current power level is less than the preset power threshold, the AGV is scheduled to travel to an idle charging point for charging. If there is no idle charging point, the AGV waits until there is an idle charging point.

[0111] In implementation, the current power of the AGV is compared with the preset power threshold. If the current power is less than the preset power threshold, it means that the current circuit of the AGV is insufficient to support the AGV to continue transporting materials, and the AGV is dispatched to an idle charging point for charging. It is determined whether there is an idle charging point at present. If there is, the AGV is dispatched to the idle charging point for charging. If there is no idle charging point, wait until there is an idle charging point, and then dispatch the AGV to the idle charging point for charging.

[0112] Step D: If the current power levels of multiple AGVs are all less than the preset power threshold, the AGV with the lowest current power level is dispatched to an idle charging point for charging.

[0113] In practice, when the current power of multiple AGVs is less than the preset power threshold, that is, multiple AGVs need to be charged. At this time, it is necessary to compare the current power of multiple AGVs to determine which AGV has the lowest current power. The AGV with the lowest current power is preferentially dispatched to an idle charging point for charging, while the others wait. Multiple AGVs whose current power is less than the preset power threshold are charged in order from low to high.

[0114] Furthermore, it is also necessary to compare the simulation data generated by the simulation model with the actual production data to ensure the accuracy and reliability of the simulation model and provide a scientific basis for production decisions. The specific steps are as follows:

[0115] Step E, according to the preset production shift, the preset number of AGVs, and the preset number of control points, controls the actual production line to perform material scheduling, and obtains the actual full load efficiency of the AGV, the actual waiting time at the unloading point, the actual waiting time at the loading point, and the actual production line feeding efficiency.

[0116] In implementation, after simulating material transportation through the simulation model, it is also necessary to perform logistics scheduling of the actual production line based on the same simulation parameters to ensure the accuracy and reliability of the model and provide a scientific basis for production decisions. Therefore, it is also necessary to control the actual production line for material scheduling based on the preset production shifts, the preset number of AGVs, and the preset number of control points to obtain the actual full load efficiency of the AGV, the actual waiting time at the unloading point, the actual waiting time at the feeding point, and the actual production line feeding efficiency.

[0117] Step F, compare the simulated full load efficiency, simulated unloading point waiting time, simulated loading point waiting time and simulated production line feeding efficiency with the actual full load efficiency, actual unloading point waiting time, actual loading point waiting time and actual production line feeding efficiency one-to-one, and obtain an error value. If the error value is less than a preset error threshold, the simulation model is determined to be accurate.

[0118] In implementation, after the material scheduling of the actual production line, the simulated full load efficiency, the simulated unloading point waiting time, the simulated loading point waiting time and the simulated production line feeding efficiency are compared one-to-one with the actual full load efficiency, the actual unloading point waiting time, the actual loading point waiting time and the actual production line feeding efficiency to obtain the error value. It is also determined whether the error value is within an acceptable range. The error value is compared with the preset error threshold. If the error value is less than the preset error threshold, the simulation model is determined to be accurate. Further, if the error value is greater than or equal to the preset error threshold, it means that there are defects in the construction of the simulation model, and an alarm is issued to prompt the technician to make corrections. The preset error threshold can be set according to the actual situation, and there is no restriction here. In this way, compared with the actual production data, the accuracy and reliability of the model can be ensured, providing a scientific basis for production decisions.

[0119] Furthermore, the AGV logistics scheduling simulation solution can be further expanded according to the changes in different production environments and demands. The specific process is as follows:

[0120] Step a: Based on historical production data and production plans, a material demand forecasting model is established through big data analysis.

[0121] In practice, a material demand forecasting model can be established through big data analysis based on historical production data and production plans. The material demand forecasting model can be used to predict the material demand for AGV logistics transportation, so as to prepare for it in advance, so that when the logistics demand is actually received, the scheduling time can be reduced and the transportation efficiency can be improved.

[0122] Step b: predicting material demand based on the established material demand prediction model, and dispatching a preset number of AGVs to travel to the loading point or buffer area based on the material demand.

[0123] In implementation, the material demand is predicted based on the established material demand prediction model, and a preset number of AGVs are dispatched to the loading point or buffer area according to the material demand. In this way, the AGVs are dispatched to the loading point or buffer area in advance, which reduces the dispatch time and improves the transportation efficiency.

[0124] Step c: when changes in logistics demand are detected, the AGV scheduling strategy and the number of buffer areas are adjusted in real time.

[0125] In implementation, the AGV scheduling strategy and the number of buffers are adjusted in real time according to the predicted changes in logistics demand. The number of AGVs or buffers can be reduced during off-peak hours to save energy. Therefore, when changes in logistics demand are detected, the AGV scheduling strategy and the number of buffers are adjusted in real time.

[0126] Furthermore, data is continuously accumulated during the simulation process to optimize the quantity configuration of AGVs, the arrangement of loading and unloading points, etc., so as to realize adaptive simulation optimization and achieve continuous simulation optimization.

[0127] Furthermore, the AGV is provided with sensor equipment and positioning equipment: the operating status and real-time position of the AGV are acquired through the sensor equipment and positioning equipment, and self-diagnosis is performed according to the operating status. If a problem is diagnosed, an alarm is issued.

[0128] In the implementation, AGV is equipped with sensor equipment and positioning equipment to monitor the operating status, position and speed of AGV in real time, locate and track in real time to avoid congestion and delays. It can also perform self-diagnosis according to the operating status, and if a problem is diagnosed, an alarm will be issued. In this way, problems can be discovered and alarms can be issued in time to reduce logistics interruptions caused by downtime. In this way, the real-time status of AGV and production line can be collected through sensor equipment to improve real-time monitoring capabilities and fault self-diagnosis and early warning. Furthermore, by monitoring the production rhythm and material flow rate of the production line, the system automatically adjusts the scheduling frequency and speed of AGV to make logistics more in line with the production rhythm.

[0129] The embodiment of the present application provides a simulation method for AGV logistics scheduling, which reduces the waiting time of AGV, improves the material flow efficiency, and improves the AGV scheduling efficiency by reasonably setting the loading, unloading and buffering locations. The arrival time of materials is simulated according to the normal distribution to ensure the continuity of material supply on the production line, reduce production interruptions caused by material backlog or shortage, and reduce the risk of material backlog. According to the simulation results, the number of AGVs and the allocation of resources such as buffer areas and charging points are optimized to achieve optimal resource utilization and optimize resource allocation. A reasonable charging scheduling plan effectively manages the power of the AGV, avoids frequent charging and excessive discharge, prolongs the battery life of the AGV, and prolongs the working life of the AGV.

[0130] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily performed at the same time, but can be performed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed in turn or alternately with other steps or at least part of the steps or stages in other steps.

[0131] It can be understood that the same / similar parts between the various embodiments of the above method in this specification can refer to each other, and each embodiment focuses on the differences from other embodiments. For related points, please refer to the description of other method embodiments.

[0132] The present application also provides a 2-device, such as Figure 2 As shown, the device comprises:

[0133] A creation module 201 is used to create a dynamic three-dimensional entity model from the on-site transportation map imported into the preset simulation software, and to import production shifts, multiple production times and working conditions of the automatic guided vehicle AGV into the dynamic three-dimensional entity model;

[0134] A first setting module 202 is used to set multiple AGV task executors in the dynamic three-dimensional entity model and create multiple logistics transportation paths for the AGV;

[0135] The second setting module 203 is used to set a plurality of control points in the dynamic three-dimensional solid model, and configure a priority for each of the control points, wherein the control points include a loading point, an unloading point, a buffer area and a charging point, and set an online sequence for each AGV, so as to establish a simulation model of AGV transporting materials;

[0136] The first scheduling module 204 is used to perform simulated material scheduling according to a preset production shift, a preset number of AGVs, a preset number of control points and the simulation model. The simulation model outputs a simulated full load efficiency, a simulated unloading point waiting time, a simulated loading point waiting time and a simulated production line feeding efficiency, and material scheduling optimization is performed based on the simulated full load efficiency, the simulated unloading point waiting time, the simulated loading point waiting time and the simulated production line feeding efficiency.

[0137] As an optional implementation manner, the first scheduling module 204 is specifically configured to:

[0138] In the simulated transportation process, the preset production shift, the preset number of AGVs, and the preset number of control points are input into the simulation model, and the generation time of the materials to be transported is determined based on the normal distribution. After the materials to be transported are generated, the AGVs are controlled to go online according to the preset online sequence, and the AGVs after going online are controlled to travel to the high-priority and idle loading points according to the logistics transportation path;

[0139] After the AGV reaches the loading point, it scans the code to obtain the order number, loads the materials according to the order number, and dispatches the AGV to the buffer area;

[0140] According to the material demand of the unloading point, the AGV is dispatched to the unloading point in the order of the order number to unload the material;

[0141] After the preset production shift ends, the simulation model outputs the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency.

[0142] As an optional implementation, the device further includes:

[0143] A detection module, used to detect the current power of the AGV after the AGV unloads the material during the simulated transportation process;

[0144] A control module, configured to control the AGV to return to the online area if the current power level is greater than or equal to a preset power threshold;

[0145] A charging module, configured to dispatch the AGV to an idle charging point for charging if the current power level is less than the preset power level threshold, and to wait until an idle charging point is available if there is no idle charging point;

[0146] If the current power levels of multiple AGVs are all less than the preset power threshold, the AGV with the lowest current power level is dispatched to an idle charging point for charging.

[0147] As an optional implementation, the device further includes:

[0148] The second scheduling module is used to control the actual production line to perform material scheduling according to the preset production shift, the preset number of AGVs, and the preset number of control points, and obtain the actual full load efficiency of the AGV, the actual waiting time of the unloading point, the actual waiting time of the feeding point, and the actual feeding efficiency of the production line;

[0149] A comparison module is used to compare the simulated full load efficiency, the simulated unloading point waiting time, the simulated loading point waiting time and the simulated production line feeding efficiency with the actual full load efficiency, the actual unloading point waiting time, the actual loading point waiting time and the actual production line feeding efficiency one-to-one to obtain an error value. If the error value is less than a preset error threshold, the simulation model is determined to be accurate.

[0150] As an optional implementation manner, the first scheduling module 204 is specifically configured to:

[0151] According to the simulated full load efficiency, the simulated waiting time of the unloading point, the simulated waiting time of the loading point and the simulated production line feeding efficiency, the number of AGVs, the buffer area positions and the number of control points are adjusted.

[0152] As an optional implementation, the device further includes:

[0153] Establish a module for building a material demand forecasting model through big data analysis based on historical production data and production plans;

[0154] A third scheduling module is used to predict material demand according to the established material demand prediction model, and schedule a preset number of AGVs to travel to a loading point or a buffer area according to the material demand;

[0155] The adjustment module is used to adjust the AGV scheduling strategy and the number of buffer areas in real time when changes in logistics demand are detected.

[0156] As an optional implementation, the AGV is provided with a sensor device and a positioning device, and the device further comprises:

[0157] The alarm module is used to obtain the operating status and real-time position of the AGV through the sensor device and the positioning device, and to perform self-diagnosis according to the operating status. If a problem is diagnosed, an alarm is issued.

[0158] As an optional implementation, the device further includes:

[0159] The division module is used to divide multiple AGVs into multiple vehicle groups according to a preset number of groups, and set the online order of each vehicle group according to a preset online time rule.

[0160] As an optional implementation manner, the second setting module is specifically used to:

[0161] Establish multiple loading point storage location groups, wherein the loading point storage location group includes multiple loading point storage locations, and the loading point storage location is the storage location when the AGV loads materials at the loading point, and one loading point corresponds to one loading point storage location;

[0162] The priority of each control point is obtained by assigning priorities to the multiple loading point storage locations in each loading point storage location group.

[0163] The embodiment of the present application provides a simulation device for AGV logistics scheduling, which reduces the waiting time of AGV, improves the material flow efficiency, and improves the AGV scheduling efficiency by reasonably setting the loading, unloading and buffering locations. The arrival time of materials is simulated according to the normal distribution to ensure the continuity of material supply on the production line, reduce production interruptions caused by material backlog or shortage, and reduce the risk of material backlog. According to the simulation results, the number of AGVs and the allocation of resources such as buffer areas and charging points are optimized to achieve optimal resource utilization and optimize resource allocation. A reasonable charging scheduling plan effectively manages the power of the AGV, avoids frequent charging and excessive discharge, prolongs the battery life of the AGV, and prolongs the working life of the AGV.

[0164] For the specific definition of the simulation device for AGV logistics scheduling, please refer to the definition of the simulation method for AGV logistics scheduling above, which will not be repeated here. Each module in the above-mentioned simulation device for AGV logistics scheduling can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0165] In one embodiment, a computer device is provided, such as Figure 3As shown, it includes a memory and a processor, the memory stores a computer program that can be run on the processor, and the processor implements the simulation method steps of the above-mentioned AGV logistics scheduling when executing the computer program.

[0166] In one embodiment, a computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the above-mentioned simulation method for AGV logistics scheduling.

[0167] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0168] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0169] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0170] Each embodiment in this specification is described in a related 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 system 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.

[0171] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0172] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A simulation method for AGV logistics scheduling, characterized in that: The method comprises: The on-site transportation map imported into the preset simulation software is used to create a dynamic three-dimensional solid model, and the production shifts, multiple production times and working conditions of the automatic guided vehicle AGV are imported into the dynamic three-dimensional solid model; In the dynamic three-dimensional entity model, multiple AGV task executors are set, and multiple logistics transportation paths of the AGV are created; In the dynamic three-dimensional entity model, multiple control points are set, and priorities are configured for each of the control points. The control points include loading points, unloading points, buffer areas, and charging points, and the order of going online is set for each AGV to establish a simulation model of AGV transporting materials; According to the preset production shifts, the preset number of AGVs, the preset number of control points and the simulation model, simulated material scheduling is performed, and the simulation model outputs the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency, and material scheduling optimization is performed based on the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency.

2. The method according to claim 1, characterized in that The simulated material scheduling is performed according to the preset production shift, the preset number of AGVs, the preset number of control points and the simulation model to obtain the simulated full load efficiency, the simulated unloading point waiting time, the simulated loading point waiting time and the simulated production line feeding efficiency, including: In the simulated transportation process, the preset production shift, the preset number of AGVs, and the preset number of control points are input into the simulation model, and the generation time of the materials to be transported is determined based on the normal distribution. After the materials to be transported are generated, the AGVs are controlled to go online according to the preset online sequence, and the AGVs after going online are controlled to travel to the high-priority and idle loading points according to the logistics transportation path; After the AGV reaches the loading point, it scans the code to obtain the order number, loads the materials according to the order number, and dispatches the AGV to the buffer area; According to the material demand of the unloading point, the AGV is dispatched to the unloading point in the order of the order number to unload the material; After the preset production shift ends, the simulation model outputs the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency.

3. The method according to claim 1, characterized in that The method further comprises: During the simulated transportation process, after the AGV unloads the material, the current power level of the AGV is detected; If the current power level is greater than or equal to the preset power threshold, control the AGV to return to the online area; If the current power level is less than the preset power threshold, the AGV is scheduled to travel to an idle charging point for charging; if there is no idle charging point, the AGV waits until there is an idle charging point; If the current power levels of multiple AGVs are all less than the preset power threshold, the AGV with the lowest current power level is dispatched to an idle charging point for charging.

4. The method according to claim 2, characterized in that: The method further comprises: According to the preset production shift, the preset number of AGVs, and the preset number of control points, the actual production line is controlled to perform material scheduling, and the actual full load efficiency of the AGV, the actual waiting time at the unloading point, the actual waiting time at the feeding point, and the actual feeding efficiency of the production line are obtained; The simulated full load efficiency, the simulated unloading point waiting time, the simulated loading point waiting time and the simulated production line feeding efficiency are compared one-to-one with the actual full load efficiency, the actual unloading point waiting time, the actual loading point waiting time and the actual production line feeding efficiency to obtain an error value. If the error value is less than the preset error threshold, the simulation model is determined to be accurate.

5. The method according to claim 1, characterized in that The material scheduling optimization based on the simulated full load efficiency, the simulated unloading point waiting time, the simulated loading point waiting time and the simulated production line feeding efficiency includes: According to the simulated full load efficiency, the simulated waiting time of the unloading point, the simulated waiting time of the loading point and the simulated production line feeding efficiency, the number of AGVs, the buffer area positions and the number of control points are adjusted.

6. The method according to claim 1, characterized in that The method further comprises: Based on historical production data and production plans, a material demand forecasting model is established through big data analysis; Predicting material demand according to the established material demand prediction model, and dispatching a preset number of AGVs to travel to a loading point or a buffer area according to the material demand; When changes in logistics demand are detected, the AGV scheduling strategy and the number of buffer areas are adjusted in real time.

7. The method according to claim 1, characterized in that The AGV is provided with a sensor device and a positioning device, and the method further comprises: The operating status and real-time position of the AGV are acquired through the sensor device and the positioning device, and self-diagnosis is performed according to the operating status. If a problem is diagnosed, an alarm is issued.

8. The method according to claim 1, characterized in that The method further comprises: According to the preset grouping number, multiple AGVs are divided into multiple vehicle groups, and the online order of each vehicle group is set according to the preset online time rules.

9. The method according to claim 1, characterized in that: The configuring a priority for each of the control points includes: Establish multiple loading point storage location groups, wherein the loading point storage location group includes multiple loading point storage locations, and the loading point storage location is the storage location when the AGV loads materials at the loading point, and one loading point corresponds to one loading point storage location; The priority of each control point is obtained by assigning priorities to the multiple loading point storage locations in each loading point storage location group.

10. A simulation device for AGV logistics scheduling, characterized in that: The device comprises: A creation module is used to create a dynamic three-dimensional solid model from the on-site transportation map imported into the preset simulation software, and to import production shifts, multiple production times and working conditions of multiple automatic guided vehicles (AGVs) into the dynamic three-dimensional solid model; A first setting module is used to set multiple AGV task executors in the dynamic three-dimensional entity model and create multiple logistics transportation paths for the AGV; The second setting module is used to set a plurality of control points in the dynamic three-dimensional solid model and configure a priority for each of the control points, wherein the control points include a loading point, an unloading point, a buffer area and a charging point, and set an online sequence for each AGV to establish a simulation model of AGV transporting materials; The first scheduling module is used to perform simulated material scheduling according to preset production shifts, a preset number of AGVs, a preset number of control points and the simulation model. The simulation model outputs simulated full load efficiency, simulated waiting time at the unloading point, simulated waiting time at the loading point and simulated production line feeding efficiency, and material scheduling optimization is performed based on the simulated full load efficiency, the simulated waiting time at the unloading point, the simulated waiting time at the loading point and the simulated production line feeding efficiency.

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