Simulation analysis and optimization method for logistics distribution and electronic equipment

Abstract

The invention discloses a simulation analysis and optimization method for logistics distribution and electronic equipment, and the simulation analysis and optimization method for logistics distribution comprises the steps: taking original data as modeling parameters, building a basic simulation model through simulation software, setting parameters of the basic simulation model, simulating a real production scene to obtain production data, and carrying out the simulation analysis and optimization of the production data; the production data comprises an automatic guided vehicle utilization rate, an equipment utilization rate and productivity; raw materials required by daily work plans of all stations of the battery pack workshop are used as input conditions, original data are imported into the basic simulation model for simulation operation, and the utilization rate of the automatic guided vehicle and a productivity inspection result are obtained; according to the automated guided vehicle utilization rate and the productivity inspection result, logistics distribution information of each station is dynamically optimized, and the logistics distribution information comprises the number of automated guided vehicles, a logistics distribution route and task redistribution. According to the method, the completion degree of the production plan can be ensured, and the situation that the utilization rate of the automatic guided vehicle is insufficient or the load is reduced is greatly reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of lithium battery production, and more specifically, to a simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop, and an electronic device. Background Art

[0002] The assembly process of new energy vehicle power battery packs can be divided into module assembly and battery pack (PACK) assembly. The assembled parts include the transportation of materials such as brackets, casings, standard parts, busbars (BUSBARs), connectors, and wire harnesses. Usually, the raw material distribution methods are manual transportation (such as trailers and forklifts) and automated transportation (such as automated guided vehicles AGV, Automated Guided Vehicle). Among them, the raw materials for module assembly are centrally distributed manually; for the raw materials for PACK assembly, some are distributed manually, and the boxes and covers are distributed by automated guided vehicles. For the production workshop of battery packs, the material distribution is in the same area with many road intersections. If there are too many automated guided vehicles, there will easily be a large number of congestion phenomena and low utilization rate of automated guided vehicles; if the number of automated guided vehicles is small, they will operate under load, affecting production capacity and not having the characteristics of modern production. Therefore, it is a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0003] In view of this, the present invention provides a simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop, so as to solve the problem of insufficient utilization rate or reduced load of automated guided vehicles while ensuring the completion degree of the production plan.

[0004] In a first aspect, the present application provides a simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop, including the following steps:

[0005] Taking the original data as modeling parameters, using simulation software to construct a basic simulation model, and setting the parameters of the basic simulation model to simulate the real production scenario to obtain production data, where the production data includes the utilization rate of automated guided vehicles, equipment utilization rate, and production capacity;

[0006] Taking the raw materials required for the daily work plan of each station in the battery pack workshop as input conditions, importing the original data into the basic simulation model for simulation operation, and obtaining the utilization rate of automated guided vehicles and the production capacity inspection results;

[0007] According to the utilization rate of automated guided vehicles and the production capacity inspection results, dynamically optimize the logistics distribution information of each station, where the logistics distribution information includes the number of automated guided vehicles, logistics distribution routes, and task reallocation.

[0008] Optionally, the calculation formula for the utilization rate of automated guided vehicles is as follows:

[0009]

[0010] Wherein, P is the average utilization rate of the automatic guided vehicle, p is the utilization rate of a single automatic guided vehicle, and i is the number of automatic guided vehicles.

[0011] Optionally, the calculation formula for the utilization rate of a single automatic guided vehicle is as follows:

[0012]

[0013] Wherein, T 负荷 represents the running time of the automatic guided vehicle, and T 生产 represents the production time.

[0014] Optionally, the calculation formula for the production capacity is as follows:

[0015]

[0016] Wherein, N represents the production capacity, n represents a single product, and i represents the number of products.

[0017] Optionally, the dynamically optimizing the logistics distribution information of each site according to the utilization rate of the automatic guided vehicle and the production capacity inspection result includes:

[0018] Determine the physical distribution information of each site;

[0019] Analyze the utilization rate of the automatic guided vehicle to obtain the analysis result of the utilization rate of the automatic guided vehicle;

[0020] Adjust the logistics distribution information of each site according to the analysis result of the utilization rate of the automatic guided vehicle.

[0021] Optionally, before using the original data as modeling parameters, constructing a basic simulation model using simulation software, and setting the parameters of the basic simulation model to simulate the real production scenario to obtain production data, it includes:

[0022] Determine the production line evaluation indicators, where the production line evaluation indicators include the number of automatic guided vehicles, the utilization rate of the automatic guided vehicle, and the production capacity;

[0023] Obtain the basic data related to the logistics distribution of the automatic guided vehicle.

[0024] Optionally, determine the optimal number of automatic guided vehicles according to the utilization rate of the automatic guided vehicle and the production capacity output by the basic simulation model.

[0025] Optionally, the original data includes the equipment layout diagram, equipment parameters, material parameters, automatic guided vehicle parameters, and production plan.

[0026] In a second aspect, an electronic device includes:

[0027] One or more processors, and

[0028] a memory storing a computer program which, when executed by the one or more processors, causes the one or more processors to implement the above-mentioned simulation analysis and optimization method for the logistics distribution in the lithium battery pack workshop.

[0029] Compared with the prior art, the simulation analysis and optimization method for the logistics distribution in the lithium battery pack workshop provided by the present invention achieves at least the following beneficial effects:

[0030] The simulation analysis and optimization method for the logistics distribution in the lithium battery pack workshop provided by the present invention takes the original data as modeling parameters, builds a simulation model of the real scenario, and outputs reliable production data. Using the daily production plan as the input condition for simulation, it examines the completion of the production plan through the output production capacity and the utilization rate of the automatic guided vehicle (AGV). By comparing the utilization rates of AGVs at each station and optimizing, a feasible improvement plan is finally found, which can not only ensure the completion of the production plan, but also greatly reduce the situation of insufficient utilization rate or reduced load of the AGV.

[0031] Of course, it is not necessary for any product implementing the present invention to simultaneously achieve all the above-mentioned technical effects.

[0032] Through the following detailed description of the exemplary embodiments of the present invention with reference to the accompanying drawings, other features and advantages of the present invention will become clear. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings incorporated in and constituting a part of this specification illustrate embodiments of the present invention and, together with the description, are used to explain the principles of the present invention.

[0034] Figure 1 is a schematic flowchart of the simulation analysis and optimization method for the logistics distribution in the lithium battery pack workshop provided by the present invention;

[0035] Figure 2 is a schematic operation diagram of the simulation scenario of the current situation of the AGV logistics distribution in the lithium battery pack workshop provided by the present invention;

[0036] Figure 3 is a schematic diagram of the utilization rate of the AGV for the core material feeding provided by the prior art;

[0037] Figure 4 is a schematic diagram of the utilization rate of the AGV for the battery pack discharging provided by the prior art;

[0038] Figure 5Schematic diagram of the utilization rate of an automated guided vehicle for battery pack loading provided in the prior art;

[0039] Figure 6 Schematic diagram of the utilization rate of an automated guided vehicle for cell loading after optimizing the number of automated guided vehicles provided by the present invention;

[0040] Figure 7 Schematic diagram of the utilization rate of an automated guided vehicle for battery pack unloading after optimizing the number of automated guided vehicles provided by the present invention;

[0041] Figure 8 Schematic diagram of the utilization rate of an automated guided vehicle for battery pack loading after optimizing the number of automated guided vehicles provided by the present invention;

[0042] Figure 9 Schematic diagram of the utilization rate of an automated guided vehicle for cell loading after another optimization of the number of automated guided vehicles provided by the present invention;

[0043] Figure 10 Schematic diagram of the utilization rate of an automated guided vehicle for battery pack loading after another optimization of the number of automated guided vehicles provided by the present invention. Detailed implementation manners

[0044] Now, various exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be noted that: Unless otherwise specifically stated, the relative arrangements, numerical expressions, and numerical values of the components and steps set forth in these embodiments do not limit the scope of the present invention.

[0045] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present invention, its application, or its use.

[0046] Technologies, methods, and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, such technologies, methods, and devices should be regarded as part of the specification.

[0047] In all the examples shown and discussed herein, any specific values should be construed as merely exemplary and not as a limitation. Thus, other examples of the exemplary embodiments may have different values.

[0048] It should be noted that: Similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0049] Embodiment 1

[0050] Refer to Figure 1 - Figure 2 as shown Figure 1It is a schematic flow chart of the simulation analysis and optimization method for the logistics distribution in the lithium battery pack workshop provided by the present invention; Figure 2 It is an operation schematic diagram of the current simulation scenario of the automatic guided vehicle logistics distribution in the lithium battery pack workshop provided by the present invention, where Figure 2 In it, 1 is the electrical test area, 2 is the box body and box cover feeding area, 3 is the battery pack assembly area, 4 is the module area, and 5 is the battery cell feeding area; This embodiment provides a simulation analysis and optimization method for the logistics distribution in the lithium battery pack workshop, including the following steps:

[0051] Step 100: Use the original data as modeling parameters, build a basic simulation model with simulation software, and set the parameters of the basic simulation model to simulate the real production scenario to obtain production data, where the production data includes the utilization rate of automatic guided vehicles, the utilization rate of equipment, and the production capacity;

[0052] Specifically, the above original data includes the equipment layout diagram, equipment parameters, material parameters, automatic guided vehicle parameters, and production plan. The equipment layout diagram includes the positions of the equipment in the workshop, the paths of automatic guided vehicles, and the material storage areas; The equipment parameters include the processing time of each single equipment and abnormal states (such as failure rate and maintenance time); The material parameters include material sorting (battery pack box bodies and battery pack box covers) and distribution frequency; The automatic guided vehicle parameters include the number of automatic guided vehicles, the speed of automatic guided vehicles, and the driving paths of automatic guided vehicles; The production plan includes the daily production input, such as the number of battery packs to be produced daily.

[0053] The above simulation software can be Plant Simulation software. Plant Simulation software is a tool for simulating factory logistics and production systems, and supports the modeling and optimization of complex production systems.

[0054] Steps for building a basic simulation model:

[0055] 1) Import the equipment layout diagram: Import the equipment layout diagram of the lithium battery pack workshop into the Plant Simulation software as the basis of the basic simulation model; Draw the equipment, the driving paths of automatic guided vehicles, and the material storage areas in the Plant Simulation software.

[0056] 2) Create basic simulation model objects: Include equipment objects (such as processing equipment), logistics objects (such as automatic guided vehicles), and material objects (such as battery packs, raw materials, and semi-finished products);

[0057] 3) Define the logistics process: Simulate the driving paths of automatic guided vehicles; And set the scheduling rules of automatic guided vehicles (such as shortest path first, task priority, etc.);

[0058] 4) Define logical rules: the working logic of the equipment (such as processing time and failure rate) and the operation logic of the automated guided vehicle (such as path planning and task assignment of the automated guided vehicle).

[0059] Set the parameters of the basic simulation model based on the equipment layout diagram, equipment parameters, material parameters, automated guided vehicle parameters, and production plan.

[0060] Equipment parameters: processing time (such as the processing time of each piece of equipment) and abnormal states (failure rate and maintenance time of the automated guided vehicle).

[0061] Material parameters: material sorting rules (such as sorting by order and sorting by type) and distribution frequency (such as delivering once every 10 minutes).

[0062] Automated guided vehicle parameters: the number of automated guided vehicles (the total number of automated guided vehicles, such as 10 vehicles), the speed of the automated guided vehicle (the running speed of the automated guided vehicle, such as 1.5 m / s), and the driving path of the automated guided vehicle (fixed path or dynamic path planning of the automated guided vehicle);

[0063] Production plan: daily production input, such as the number of battery packs to be produced daily.

[0064] In the Plant Simulation software, set the parameters through the object property panel or scripting language (such as SimTalk).

[0065] For example, set the processing time of each piece of equipment to 120 seconds, the speed of the automated guided vehicle to 1.5 m / s, etc.

[0066] Real - scenario simulation: Simulate the logistics distribution and production process in the workshop through the Plant Simulation software to ensure that the basic simulation model can truly reflect the actual production scenario.

[0067] Simulation steps:

[0068] Run the simulation: Start the simulation and observe the processes such as material flow, operation of the automated guided vehicle, and equipment processing.

[0069] Monitor key indicators: Real - time monitor indicators such as the utilization rate of the automated guided vehicle, equipment utilization rate, production capacity, etc.

[0070] Adjust parameters: Adjust the parameters according to the simulation results to optimize the basic simulation model.

[0071] Real - scenario verification:

[0072] Compare with the actual production data to ensure the reliability of the simulation results.

[0073] For example, compare the production capacity output by the simulation with the production capacity of the actual workshop to verify the accuracy of the basic simulation model.

[0074] Output reliable production data

[0075] Production data: Key performance indicators output through simulation, including:

[0076] Automated guided vehicle utilization rate: The ratio of the actual running time of the automated guided vehicle during working hours.

[0077] Equipment utilization rate: The ratio of the actual working time of the equipment during the production process.

[0078] Production capacity: The number of battery packs that can be processed and completed by the production line within a single day.

[0079] Material turnover time: The total time for materials from warehousing to outbound.

[0080] Optionally, the calculation formula for the above production capacity is as follows:

[0081]

[0082] In the formula, N represents production capacity, n represents a single product, and i represents the number of products.

[0083] Using the above calculation formula for production capacity can obtain the production capacity, effectively evaluate the production capacity, check the completion of the production plan, identify production bottlenecks, optimize resource allocation, and provide data support for decision-making.

[0084] Bottleneck analysis: Identify bottleneck links in the production system (such as the utilization rate of a certain automated guided vehicle being too high resulting in production delays).

[0085] Data output method:

[0086] Use the statistical tools or report functions of Plant Simulation software to export the simulation results.

[0087] Generate charts or reports to visually display production data.

[0088] Simulation analysis and optimization method

[0089] Data analysis: Based on the production data output from the simulation results, analyze the performance bottlenecks of the current logistics distribution system.

[0090] For example: A too low utilization rate of the automated guided vehicle means that there are too many automated guided vehicles or the task allocation is unreasonable; a too low utilization rate of the automated guided vehicle means that the equipment layout or production rhythm needs to be optimized.

[0091] Optimization method:

[0092] Automated guided vehicle scheduling optimization: Adjust the path planning or task allocation rules of the automated guided vehicle to improve the utilization rate of the automated guided vehicle.

[0093] Production cycle optimization: Adjust the production time of each process to balance the production line cycle.

[0094] Resource allocation optimization: Increase or decrease the number of automated guided vehicles.

[0095] Iterative simulation: Re-enter the optimized parameters into the basic simulation model to verify the optimization effect until the expected goal is achieved.

[0096] Optionally, the calculation formula for the utilization rate of automated guided vehicles is as follows:

[0097]

[0098] In the formula, P is the average utilization rate of automated guided vehicles, p is the utilization rate of a single automated guided vehicle, and i is the number of automated guided vehicles.

[0099] Using the above calculation formula for the utilization rate of automated guided vehicles to obtain the utilization rate of automated guided vehicles can effectively evaluate the working efficiency of automated guided vehicles, identify resource waste or bottlenecks, optimize the number of automated guided vehicles, improve production efficiency, and provide data support for decision-making.

[0100] Optionally, the calculation formula for the utilization rate of a single automated guided vehicle is as follows:

[0101]

[0102] In the formula, T 负荷 represents the running time of the automated guided vehicle, and T 生产 represents the production time.

[0103] Using the above calculation formula for the utilization rate of a single automated guided vehicle can obtain the utilization rate of a single automated guided vehicle and provide data support for the utilization rate of automated guided vehicles.

[0104] Optionally, in step 101, after completing the construction of the basic simulation model, set the simulation time to 8:00, with two shifts per day, 12 hours per shift, and input quantitatively according to the daily production capacity plan. Stop the simulation after simulating for a period of time to obtain the output daily production capacity and the utilization rate of automated guided vehicles.

[0105] Step 102: Use the raw materials required for the daily work plan of each station in the battery pack workshop as input conditions, import the original data into the basic simulation model for simulation operation, and obtain the utilization rate of automated guided vehicles and the production capacity inspection results;

[0106] The above production capacity inspection results can be used to check the completion of the production plan for production capacity.

[0107] Input conditions: The raw materials required for the daily work plan of each station in the battery pack workshop are used as input conditions, such as the number of battery packs to be produced daily, the material requirements for each process, the production rhythm, etc.; according to the production plan, determine the types and quantities of raw materials required for each station.

[0108] Use the daily work plan and the required raw materials as the input of the basic simulation model to ensure that the basic simulation model can accurately reflect the actual production scenario.

[0109] Import the original data and simulate: Import the original data into the basic simulation model and run the simulation; during the simulation process, according to the input conditions and the model logic, dynamically generate the logistics distribution and production process.

[0110] Obtain the simulation results:

[0111] Automated guided vehicle utilization rate: The average utilization rate of automated guided vehicles at each station.

[0112] Production capacity: The number of battery packs that can be processed and completed by the production line within a single day;

[0113] Completion status of the production plan: By comparing the production capacity output by the simulation with the planned production capacity, verify the feasibility of the production plan. If the production capacity output by the simulation ≥ the planned production capacity, it means the production plan is feasible; if the production capacity output by the simulation < the planned production capacity, it means there are bottlenecks in the production plan and it needs to be optimized.

[0114] Step 104: Dynamically optimize the logistics distribution information of each station according to the automated guided vehicle utilization rate and the production capacity inspection results. Among them, the logistics distribution information includes the number of automated guided vehicles, the logistics distribution route, and the task reallocation.

[0115] Specifically, through the basic simulation model, the load of the automated guided vehicle within the simulation time can be calculated, so as to obtain the automated guided vehicle utilization rate. According to the automated guided vehicle utilization rate, the task volume of the overloaded automated guided vehicle is evenly distributed to the remaining automated guided vehicles, and the number of automated guided vehicles is determined under the condition of completing the set production capacity. By analyzing the situation of the automated guided vehicle utilization rate, such as whether the automated guided vehicle utilization rate is overloaded or unsaturated, adjust the number of automated guided vehicles or the logistics distribution route, change the original route, and improve the automated guided vehicle utilization rate.

[0116] Determine the number of automated guided vehicles at the station:

[0117] Calculate the load of the automated guided vehicle. Through the basic simulation model, calculate the load of the automated guided vehicle within the simulation time (such as the task volume and running time of the automated guided vehicle).

[0118] Obtain the automated guided vehicle utilization rate. The automated guided vehicle utilization rate refers to the average utilization rate of automated guided vehicles at each station.

[0119] Adjust the number of automated guided vehicles (AGVs): If the utilization rate of AGVs is too high (close to 100%), it indicates that the AGVs are overloaded and the number of AGVs needs to be increased. If the utilization rate of AGVs is too low (far below 100%), it means the AGVs are not saturated and the number of AGVs can be reduced.

[0120] Logistics distribution route: Adjust the driving path of AGVs to reduce the empty driving time and task conflicts; for example, adopt the shortest path algorithm or dynamic path planning to improve the operation efficiency of AGVs.

[0121] Task reallocation: Reallocate the task volume of overloaded AGVs to other AGVs. For example, evenly distribute the task volume of overloaded AGVs to the remaining AGVs to ensure a reasonable utilization rate for each AGV; for example, through a task scheduling algorithm, optimize the task allocation of AGVs and determine the optimal number of AGVs on the premise of meeting the set production capacity.

[0122] Analyze the utilization rate of AGVs as follows:

[0123] Analyze the utilization rate of AGVs: Through the simulation results, analyze the utilization rate of each AGV to determine whether there are overloaded or unsaturated AGVs.

[0124] Adjust the number of AGVs: If the AGVs are overloaded, increase the number of AGVs to share the task volume; if the AGVs are not saturated, reduce the number of AGVs to reduce costs.

[0125] Optimize the logistics distribution route: Adjust the driving path of AGVs to reduce the empty driving time and task conflicts. For example, adopt the shortest path algorithm or dynamic path planning to improve the operation efficiency of AGVs. Improve the utilization rate of AGVs:

[0126] Through optimizing the number of AGVs and the distribution route, make the utilization rate of AGVs reach a reasonable range (such as 70%-90%), neither overloaded nor idle.

[0127] Compared with the prior art, the simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop provided in this embodiment has at least achieved the following beneficial effects:

[0128] The simulation analysis and optimization method for logistics distribution in the lithium battery pack workshop provided in this embodiment uses the original data as modeling parameters to build a simulation model of the real scenario and output reliable production data. Taking the daily production plan as the input condition for simulation, it examines the completion of the production plan through the output production capacity and the utilization rate of the automatic guided vehicle (AGV). By comparing the utilization rates of AGVs at each station and optimizing, a feasible improvement plan is finally found, which can not only ensure the completion degree of the production plan, but also greatly reduce the situation of insufficient utilization rate or overloading of AGVs.

[0129] It should be noted that: This application mainly optimizes the number of AGVs based on the utilization rate of AGVs to avoid over-purchasing or equipment idleness and reduce the initial investment and long-term operation costs.

[0130] When problems occur in the PACK production line, digital means are used to simulate the production line, which can dynamically simulate the production process of the workshop. During this process, no physical resources need to be consumed. By simulating solutions, the simulation of AGV logistics distribution in the PACK workshop is an abstraction of the overall processing process from the perspective of logistics. A simulation model of AGV logistics distribution in the PACK workshop is established to dynamically display the simulation process of logistics distribution operations, evaluate specific executable solutions, provide methods and technical support for improving AGV logistics distribution problems, and at the same time provide quantitative decision-making basis for decision-makers. Therefore, there is a strong practical need and significance for the optimization and simulation analysis of AGV logistics distribution in the PACK workshop.

[0131] In an alternative embodiment, according to the utilization rate of AGVs and the inspection results of production capacity, dynamically optimizing the logistics distribution information of each station includes:

[0132] Determine the physical distribution information of each station;

[0133] Analyze the utilization rate of AGVs to obtain the analysis result of the utilization rate of AGVs;

[0134] According to the analysis result of the utilization rate of AGVs, adjust the logistics distribution information of each station.

[0135] Specifically, determine the total number of AGVs in the current workshop and calculate the utilization rate of each AGV.

[0136] Through the calculation formula of the utilization rate of automated guided vehicles (AGVs), the utilization rate of each AGV is obtained through calculation. Analyze the distribution of the utilization rate of AGVs at each station, and identify stations with too high or too low utilization rates of AGVs. For example, if the utilization rate of an AGV at a certain station is as high as 95%, there may be a risk of overloading. If the utilization rate of an AGV is too high (close to 100%), it means that the AGV is overloaded, resulting in a logistics bottleneck; if the utilization rate of an AGV is too low (far lower than 100%), it means that the AGV is not saturated and there is a waste of resources.

[0137] Adjustment objectives: Optimize the utilization rate of AGVs, ensure the balanced workload of AGVs, and improve the logistics distribution efficiency.

[0138] Adjust the number of AGVs: If the utilization rate of AGVs is too high, increase the number of AGVs to share the task volume. If the utilization rate of AGVs is too low, reduce the number of AGVs to reduce costs. For example, increase the number of AGVs from 5 to 6 to share the tasks of overloaded AGVs.

[0139] Optimize the distribution route:

[0140] Adjust the driving path of AGVs to reduce the empty driving time and task conflicts. For example, adopt a dynamic path planning algorithm to adjust the driving route of AGVs according to real-time tasks.

[0141] Reallocate tasks:

[0142] Reallocate the task volume of overloaded AGVs to other AGVs to ensure a reasonable utilization rate of each AGV.

[0143] For example, optimize the task allocation of AGVs through a task scheduling algorithm.

[0144] Adjust the distribution frequency:

[0145] Adjust the distribution frequency according to the material requirements of each station to avoid material shortages or overstocking.

[0146] For example, adjust the distribution frequency of a certain station from once every 10 minutes to once every 8 minutes.

[0147] Adopt the above solutions. By determining the physical distribution information of each station (such as the number of AGVs and the utilization rate of AGVs), analyzing the utilization rate of AGVs, and dynamically adjusting the logistics distribution information according to the analysis results, the logistics distribution efficiency of the lithium battery pack workshop can be effectively optimized. This method can ensure the balanced workload of AGVs, avoid waste of resources or overloading, and at the same time improve production efficiency and resource utilization rate.

[0148] In an alternative embodiment, before using the original data as modeling parameters, constructing a basic simulation model using simulation software, setting the parameters of the basic simulation model, and simulating the real production scenario to obtain production data, the following steps are included:

[0149] Determine the production line evaluation indicators, which include the number of automatic guided vehicles (AGVs), the utilization rate of AGVs, and production capacity; obtain the basic data related to AGV logistics distribution.

[0150] The above production line evaluation indicators can be AGV logistics evaluation indicators, taking the number of AGVs, the utilization rate of AGVs, and production capacity in AGV logistics distribution as the production line evaluation indicators.

[0151] The above number of AGVs: refers to the total number of AGVs at each station in the workshop, reflecting the resource allocation of the logistics distribution system.

[0152] The above original data includes the equipment layout diagram, equipment parameters, material parameters, AGV parameters, and production plan.

[0153] The above equipment layout diagram: the physical layout of equipment in the workshop, including equipment positions, AGV paths, material storage areas, etc. For example, Equipment A is located in the upper left corner of the workshop, Equipment B is located in the lower right corner, and the AGV path is a straight line from Equipment A to Equipment B.

[0154] The above equipment parameters: the processing time of each single equipment, abnormal states (such as failure rate and maintenance time); for example, the processing time of Equipment A is 120 seconds per piece, and the failure rate is 1%.

[0155] The above material parameters: material sorting (sorting of battery pack boxes and battery pack lids) and distribution frequency.

[0156] The above AGV parameters: the number of AGVs, the speed of AGVs, and the driving paths of AGVs.

[0157] The above production plan: the number of battery packs to be produced daily, the time arrangement for each process, the material distribution plan, etc.; for example: the daily production plan is 1000 battery packs, and the production cycle is 120 seconds per piece.

[0158] Adopting the above solution, taking the three items of the number of AGVs, AGV utilization rate, and production capacity in AGV logistics distribution as the production line evaluation indicators, and taking the basic parameters (equipment processing time, abnormal states, material sorting, distribution frequency, number of AGVs) as modeling parameters, it can provide a basis for subsequent simulation analysis and optimization, ensure that the model can accurately reflect the actual production scenario, and provide data support for optimizing logistics distribution and production efficiency.

[0159] Optionally, determine the optimal number of automated guided vehicles according to the utilization rate of the automated guided vehicles and the production capacity output by the basic simulation model.

[0160] Specifically, through the above basic simulation model, obtain the utilization rate of the automated guided vehicles and the production capacity; based on the basic simulation model, test the production capacity and the utilization rate of the automated guided vehicles under different numbers of automated guided vehicles; select the minimum number of automated guided vehicles that can meet the target production capacity, ensure that the utilization rate of the automated guided vehicles is within a reasonable range (such as 70%-90%), and avoid being too high (the automated guided vehicles are overloaded) or too low (the automated guided vehicles are idle); according to the changes in real-time production requirements, dynamically adjust the number of automated guided vehicles to ensure that the production capacity and the utilization rate of the automated guided vehicles are always in an optimal state, and improve the overall flexibility of the battery pack workshop. Avoid overloading the automated guided vehicles, reduce the equipment failure rate, and improve the system stability.

[0161] Refer to Figure 3 - Figure 10 as shown Figure 3 is a schematic diagram of the utilization rate of the automated guided vehicle for cell loading provided by the prior art; Figure 4 is a schematic diagram of the utilization rate of the automated guided vehicle for battery pack unloading provided by the prior art; Figure 5 is a schematic diagram of the utilization rate of the automated guided vehicle for battery pack loading provided by the prior art;

[0162] Figure 6 is a schematic diagram of the utilization rate of the automated guided vehicle for cell loading after optimizing the number of automated guided vehicles provided by the present invention; Figure 7 is a schematic diagram of the utilization rate of the automated guided vehicle for battery pack unloading after optimizing the number of automated guided vehicles provided by the present invention; Figure 8 is a schematic diagram of the utilization rate of the automated guided vehicle for battery pack loading after optimizing the number of automated guided vehicles provided by the present invention; Figure 9 is another schematic diagram of the utilization rate of the automated guided vehicle for cell loading after optimizing the number of automated guided vehicles provided by the present invention; Figure 10 is another schematic diagram of the utilization rate of the automated guided vehicle for battery pack loading after optimizing the number of automated guided vehicles provided by the present invention; Figure 3 - Figure 10 The small vehicle is the above-mentioned automated guided vehicle, the utilization rate of the small vehicle is the utilization rate of the above-mentioned automated guided vehicle, PACK loading is battery pack loading, PACK unloading is battery pack unloading. According to the WEB standard color table, the CSS color number of lime green is #01FF70, the CSS color number of grass green is #02ECC40, the CSS color number of olive green is #3D9970, the CSS color number of sky blue is #0074D9, and the CSS color number of dark blue is #00008B; Figure 3 - Figure 10Medium lime green means the order is occupied, olive green means the trolley is returned, referred to as returned, gray means the trolley is ready, referred to as ready; red means the trolley is faulty, which is represented by failure on the schematic diagram; dark blue means the trolley is paused, referred to as paused; azure blue means the trolley is not planned (the trolley's task is not scheduled in the initial plan), referred to as unplanned. Figure 10 The PACK loading trolley can be a forklift type trolley.

[0163] Taking the basic simulation model of the automated guided vehicle logistics distribution in a lithium battery PACK workshop as an example, four backpack AGVs are used to load lithium batteries, eight lurking AGVs are used to transport boxes and box covers, and six forklift AGVs are used to unload PACKs and send them to electrical testing. The automated guided vehicle AGVs that transport boxes and box covers need to pass through the electrical testing area on the way back, and the automated guided vehicle AGVs are empty on the way back. The basic simulation model runs for two days, one day as the warm-up time of the basic simulation model, and outputs the utilization rate and production capacity of the automated guided vehicle for one day. In the simulation process, 15% of the time has been used as the charging time and failure time of the automated guided vehicle. The basic simulation model and its running results are shown as follows. Figure 2 , Figure 3 - Figure 5 As shown, from Figure 2 It can be seen that there are a large number of AGVs, so that the AGVs cause less task allocation for each AGV during transportation; Figure 3 - Figure 6 After all the work plans for the day were completed, the utilization rate of the automatic guided vehicles at each station was as follows: there were 4 backpack AGVs, with an automatic guided vehicle utilization rate of 17.03%; there were 6 forklift AGVs, with an automatic guided vehicle utilization rate of 62.05%; there were 8 latent AGVs, with an automatic guided vehicle utilization rate of 57.79%. This shows that under the current conditions, the utilization rate of automatic guided vehicles has obviously not reached saturation, and the number of automatic guided vehicles is too large and needs to be reduced.

[0164] Optimization plan:

[0165] Solution 1: Reduce the number of automatic guided vehicles for lithium battery loading to 1, the number of automatic guided vehicles for box bodies and box covers to 4, and the number of automatic guided vehicles for PACK unloading and electrical testing to 4;

[0166] Solution 2: Reduce the number of lithium battery loading automatic guided vehicles to 1, use manual forklifts to transport box covers (10 box covers per trip), and share 6 forklift-type AGVs for box delivery and PACK unloading. That is, the PACK unloading automatic guided vehicle delivers the PACK to the electrical test, and then takes the box and delivers it to the box loading position, reducing the number of empty-load situations of the automatic guided vehicle.

[0167] Rerun the simulation model, see Figure 6 - Figure 8 As shown, Figure 6 - Figure 8Schematic diagram of the utilization rate of automatic guided vehicles corresponding to the first improvement plan simulation output. Among them, the number of back-mounted AGVs is 1, the utilization rate of automatic guided vehicles is 68.46%, the number of forklift AGV is 4, the utilization rate of automatic guided vehicles is 78.36%, and the number of latent AGVs is 4, the utilization rate of automatic guided vehicles is 76.41%. Compared with the prior art, on the premise of reducing the number of automatic guided vehicles, the utilization rate of automatic guided vehicles has been greatly improved, and it does not exceed the load of automatic guided vehicles; continue to refer to Figure 9 - Figure 10 corresponding to Figure 9 - Figure 10 is a schematic diagram of the utilization rate of automatic guided vehicles corresponding to the simulation output of the second improvement plan. After sharing the forklift AGV for PACK blanking and box loading, the utilization rate of automatic guided vehicles is fully utilized. Among them, the number of back-mounted AGVs is 1, the utilization rate of automatic guided vehicles is 67.96%, and the number of forklift AGVs is 6, the utilization rate of automatic guided vehicles is 81.63%.

[0168]

[0169] Table 1 is a table comparing the number of automatic guided vehicles, the utilization rate of automatic guided vehicles, and the production capacity in the prior art and this embodiment. Referring to Table 1, it can be clearly obtained that both Plan 1 and Plan 2 reduce the number of automatic guided vehicles. By evenly distributing the load of the subtracted automatic guided vehicles to other automatic guided vehicles, the utilization rate of automatic guided vehicles has been greatly improved, saving costs for the logistics distribution of automatic guided vehicles in the workshop and avoiding unnecessary waste.

[0170] Embodiment 2

[0171] This embodiment provides an electronic device, including:

[0172] One or more processors, and

[0173] A memory, on which a computer program is stored. When the computer program is executed by one or more processors, the one or more processors implement the above-mentioned simulation analysis and optimization method for the logistics distribution of the lithium battery pack workshop.

[0174] Examples of the electronic device may be a mobile phone, a smart phone, a personal digital assistant (PDA), a PDA phone, a laptop computer, or a tablet computer, etc. The electronic device may include one or more processors, and the processor may include, but is not limited to, a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processing (DSP) chip, a microprocessor (MCU), a programmable logic device (FPGA), a neural network processor (NPU), a tensor processor (TPU), an artificial intelligence (AI) type processor. The electronic device may also include a memory for storing data.

[0175] The memory can be used to store computer programs, such as the computer program corresponding to the simulation analysis and optimization method for the logistics distribution of the lithium - battery pack workshop based on the present invention. The processor executes various functional applications and data processing by running the computer program stored in the memory, that is, to implement the simulation analysis and optimization method for the logistics distribution of the lithium - battery pack workshop based on the present invention. The memory may include high - speed random access memory and may also include non - volatile memory, such as magnetic storage devices, flash memory, or other non - volatile solid - state memories. In some instances, the memory may further include memories remotely set relative to the processor, and these remote memories can be connected to the mobile terminal through a network. Examples of the above - mentioned network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network, and their combinations.

[0176] Although some specific embodiments of the present invention have been described in detail by way of examples, those skilled in the art should understand that the above examples are for illustration purposes only and not for limiting the scope of the present invention. Those skilled in the art should understand that the above - mentioned embodiments can be modified without departing from the scope and spirit of the present invention. The scope of the present invention is defined by the appended claims.

Claims

1. A simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop, characterized in that, Including the following steps: Taking the original data as modeling parameters, using simulation software to construct a basic simulation model, setting the parameters of the basic simulation model, and simulating the real production scenario to obtain production data, where the production data includes the utilization rate of automated guided vehicles, equipment utilization rate, and production capacity; Taking the raw materials required for the daily work plan of each station in the battery pack workshop as input conditions, importing the original data into the basic simulation model for simulation operation, and obtaining the utilization rate of automated guided vehicles and the production capacity inspection results; According to the utilization rate of automated guided vehicles and the production capacity inspection results, dynamically optimize the logistics distribution information of each station, where the logistics distribution information includes the number of automated guided vehicles, logistics distribution routes, and task reallocation.

2. The simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop according to claim 1, wherein, The calculation formula for the utilization rate of automated guided vehicles is as follows: In the formula, P is the average utilization rate of automated guided vehicles, p is the utilization rate of a single automated guided vehicle, and i is the number of automated guided vehicles.

3. The simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop according to claim 2, wherein, The calculation formula for the utilization rate of a single automated guided vehicle is as follows: Where, T 负荷 represents the running time of the automated guided vehicle, and T 生产 represents the production time.

4. The simulation analysis and optimization method for logistics distribution based on a lithium battery pack workshop according to claim 1, characterized in that The calculation formula for the production capacity is as follows: In the formula, N represents the production capacity, n represents a single product, and i represents the number of products.

5. The simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop according to claim 1, wherein The dynamically optimizing the logistics distribution information of each station according to the utilization rate of automated guided vehicles and the production capacity inspection results includes: Determining the physical distribution information of each station; Analyzing the utilization rate of automated guided vehicles to obtain the analysis result of the utilization rate of automated guided vehicles; Adjusting the logistics distribution information of each station according to the analysis result of the utilization rate of automated guided vehicles.

6. The simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop according to claim 1, wherein Before taking the original data as modeling parameters, using simulation software to construct a basic simulation model, setting the parameters of the basic simulation model, and simulating the real production scenario to obtain production data, it includes: Determining the production line evaluation indicators, where the production line evaluation indicators include the number of automated guided vehicles, the utilization rate of automated guided vehicles, and the production capacity; Obtaining the basic data related to the logistics distribution of automated guided vehicles.

7. The simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop according to claim 1, characterized in that Determining the optimal number of automated guided vehicles according to the utilization rate of automated guided vehicles and the production capacity output by the basic simulation model.

8. The simulation analysis and optimization method for logistics distribution in a lithium battery pack workshop according to claim 1, wherein The original data includes equipment layout diagrams, equipment parameters, material parameters, automated guided vehicle parameters, and production plans.

9. An electronic device, characterized in that, Including: One or more processors, and A memory storing a computer program, where the computer program, when executed by the one or more processors, causes the one or more processors to implement the simulation analysis and optimization method for logistics distribution based on a lithium battery pack workshop according to any one of claims 1 to 8.

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