Electric vehicle painting equipment intelligent scheduling control method based on big data

Abstract

The application belongs to the technical field of optical fiber loss regulation, and specifically relates to an intelligent scheduling control method for electric vehicle coating equipment based on big data, which comprises the following steps: collecting electric vehicle body data and coating equipment operation data; mapping the electric vehicle body into a flow water tank position according to the entering order, and predicting the time for the vehicle body in each flow water tank position to arrive at each coating equipment; generating corresponding equipment preparation actions; generating an equipment preparation phase table, and identifying the time conflict and phase arbitration of the equipment preparation actions in the equipment preparation phase table; and outputting an advance preparation control instruction to multiple systems; in the application, the flow water tank position is established, so that each coating equipment is always in a ready state accurately matched with the current vehicle body process demand; through the relaxation time calculation, hierarchical arbitration score and phase conflict resolution mechanism, the system can automatically coordinate the execution order of each preparation action, and preferentially ensure that the actions with higher risks of stop-line and quality defects are completed on time.

Description

Technical Field

[0001] This invention belongs to the field of optical fiber loss control technology, specifically a smart scheduling and control method for electric vehicle painting equipment based on big data. Background Technology

[0002] With the rapid and large-scale development of the new energy vehicle industry, the structure and materials of electric vehicle bodies and core components have undergone significant changes compared to traditional fuel vehicles. Lightweight new materials such as aluminum alloys and carbon fiber are widely used in vehicle bodies, while new dedicated painting components such as battery pack housings and motor housings have been added, continuously expanding the categories and coverage of vehicle painting. Automotive painting is one of the core processes in vehicle manufacturing. It has evolved from manual spraying and semi-automatic spraying to automated and intelligent equipment cluster operations. Currently, electric vehicle painting production lines integrate various specialized equipment such as electrophoresis equipment, intelligent spraying robots, constant temperature and humidity drying equipment, exhaust gas treatment equipment, and visual inspection equipment, forming an integrated painting operation system.

[0003] To adapt to the industry's production model of mixed production lines for multiple electric vehicle models and customized production, the painting workshop needs to achieve the coordinated operation of various workstation equipment such as painting robots, conveying equipment, temperature control equipment, and testing equipment. Relying on the scheduling and control system, production tasks are allocated in a coordinated manner, the operation rhythm of each process is coordinated, and material supply and equipment operating status are matched to achieve orderly connection and automated control of the entire painting process. This is the core support system for the stable operation of intelligent painting production lines.

[0004] Conventional electric vehicle painting equipment scheduling and control methods often rely on bypass buffering or vehicle reordering to cope with color and model changes in mixed-line production, resulting in complex conveyor system structures and significant cycle time losses. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention proposes an intelligent scheduling and control method for electric vehicle painting equipment based on big data. This method is mainly used to solve the problem that general scheduling and control methods for electric vehicle painting equipment often rely on bypass buffering or vehicle body reordering to deal with color and model switching in mixed-line production.

[0006] The technical solution adopted by this invention to solve its technical problem is: A big data-based intelligent scheduling and control method for electric vehicle painting equipment includes: S1: Collect vehicle body data and painting equipment operation data of electric vehicles entering the painting production line; S2: Map the electric vehicle body to the flow slot according to the entry order, and predict the time for the body in each flow slot to reach each painting equipment based on the cycle time of the painting production line and the position of each station. S3: Based on the body data of each vehicle body, the predicted time of arrival at each painting equipment, and the operating data of the painting equipment, generate corresponding equipment preparation actions, and determine the start time window, duration, priority, and resource usage information of each equipment preparation action. S4: Based on the preparation actions of each device, generate a device preparation phase table, and perform time conflict identification and phase arbitration on the device preparation actions in the device preparation phase table to obtain a conflict-resolved device preparation phase table. S5: Based on the equipment preparation phase table after conflict resolution, output advance preparation control commands to the paint supply system, spraying robot, spray booth environmental control system, drying room control system, VOC treatment system and detection system.

[0007] According to the intelligent scheduling and control method for electric vehicle painting equipment based on big data provided by the present invention, step S2, which predicts the time for the vehicle body in each flow trough to arrive at each painting equipment, includes: A series of flow tank positions are generated according to the order in which the car body enters the painting production line entrance; The water tank number is linked to the vehicle identification number, entry time, vehicle model, color, paint system, and robot program number; Based on the cycle time of the painting production line, the location of each workstation, and the length of the conveying path, calculate the theoretical arrival time of the car body corresponding to each flow trough to the painting equipment; Based on the conveyor speed fluctuations, equipment waiting status, and workstation occupancy status, the theoretical arrival time is corrected to obtain the predicted arrival time. The order of the flow slot numbers remains unchanged during the prediction process, and the vehicle sequence is not adjusted based on color, vehicle model, paint system, vehicle body heat capacity, or mass sensitivity level.

[0008] According to the intelligent scheduling and control method for electric vehicle painting equipment based on big data provided by the present invention, step S2, the step of correcting the theoretical arrival time includes: Acquire real-time speed of the conveyor chain, historical cycle deviation, downtime records, and real-time occupancy status of each workstation; Calculate the cumulative time deviation of each flow tank position relative to the standard cycle time; The cumulative time deviation is added to the theoretical arrival time of the corresponding water tank position to each coating equipment; When a local equipment wait or short pause is detected, only the predicted arrival time of the affected flow tank is corrected, without changing the order between the flow tanks. Generate a vehicle body arrival time series that is dynamically updated according to the production line's operating status.

[0009] According to the intelligent scheduling and control method for electric vehicle coating equipment based on big data provided by the present invention, step S3, the step of generating equipment preparation actions corresponding to each coating equipment, includes: Based on the color, paint system, vehicle model, body heat capacity, spraying area, robot program number, and quality sensitivity level of each vehicle body, determine the target preparation requirements of the paint supply system, spraying robot, spray booth, drying booth, VOC treatment system, and testing system for that vehicle body. Based on the difference between the target preparation requirements and the current equipment operating data, generate equipment preparation actions; Based on the predicted time when the vehicle body arrives at the corresponding painting equipment, the start time window for the equipment preparation action is calculated in reverse. Based on the execution time of the equipment preparation actions, the urgency of the actions, and the occupation of shared resources, determine the duration, priority, and resource occupation information of the equipment preparation actions.

[0010] According to the intelligent scheduling and control method for electric vehicle painting equipment based on big data provided by the present invention, in step S3, the equipment preparation actions corresponding to the paint supply system and the spraying robot include: Based on the differences in color and paint system between the current car body in the flow tank and the previous car body in the flow tank, generate actions such as color change, cleaning, pipeline switching, pressure stabilization, temperature adjustment or viscosity adjustment for the paint supply system. Based on the vehicle model, painting area, and robot program number of the current assembly line position, generate the painting robot's program preloading, trajectory verification, painting parameter distribution, and atomization parameter adjustment actions; Based on the vehicle body quality sensitivity level, increase the priority of corresponding paint supply parameter stabilization actions and robot parameter verification actions; Associate the equipment preparation actions of the paint supply system and the spraying robot with the corresponding flow tank position number.

[0011] According to the intelligent scheduling and control method for electric vehicle coating equipment based on big data provided by the present invention, in step S3, the equipment preparation actions corresponding to the spray booth, drying booth, VOC treatment system, and detection system include: Based on the paint system, spraying area, and quality sensitivity level of the current vehicle body in the production line, advance adjustments are made to the spray booth temperature, humidity, wind speed, and pressure difference. Based on the current heat capacity of the car body in the flow tank, the paint system and the spraying area, generate actions such as preheating of the drying room temperature zone, adjustment of temperature curve, adjustment of air volume and adjustment of heating power; Based on the coating system, spraying area, and predicted exhaust load, the VOC treatment system generates air volume adjustment, adsorption unit switching, or combustion unit pre-start actions. Based on vehicle model, color, and quality sensitivity level, the system generates actions such as loading the detection model, adjusting light source parameters, adjusting camera parameters, and preparing the detection task queue.

[0012] According to the intelligent scheduling and control method for electric vehicle painting equipment based on big data provided by the present invention, step S4, the step of generating the equipment preparation phase table, includes: Using the water tank position number as an index, the preparation actions of each piece of equipment corresponding to the same vehicle body are arranged according to the predicted arrival time; Write the start time window, duration, priority, and resource usage information of each device preparation action into the phase table; The preparatory actions that need to be completed in advance in the paint supply system, spraying robot, spray booth, drying booth, VOC treatment system and testing system are divided into different preparation phases; Establish the correspondence between the preparation phase and the flow tank position number, the target coating equipment, and the predicted arrival time; A device preparation phase table is created to describe the order and timing of preparation of multiple devices.

[0013] According to the intelligent scheduling and control method for electric vehicle painting equipment based on big data provided by the present invention, the step of identifying time conflicts in the equipment preparation phase table in step S4 includes: determining the time occupancy interval of each equipment preparation action based on the start time window and duration of the equipment preparation action; Based on resource usage information, determine whether there are resource conflicts between different equipment preparation actions, such as shared valve groups, paint supply pipelines, robot control channels, spray booth ventilation resources, drying room heating resources, VOC processing capacity, or detection task queues. Determine whether there is any overlap in the preparation actions of different devices within the time interval; When time overlaps and shared resources are occupied, the corresponding device preparation actions will be marked as conflicting actions; Write the conflicting action, its corresponding flow slot number, conflicting resource, and conflict time into the conflict list.

[0014] According to the intelligent scheduling and control method for electric vehicle painting equipment based on big data provided by the present invention, step S4, the step of performing phase arbitration for conflicting actions, includes: The arbitration priority of conflicting actions is calculated based on the predicted arrival time of the vehicle body corresponding to the conflicting action, the latest completion time of the action, the quality sensitivity level, the duration of the action, and the resource release time. Prioritize the preparation of equipment with the closest arrival time, the earliest latest completion time, higher quality sensitivity level, or stronger safety constraints; Low-priority equipment preparation actions can be advanced, postponed, split, or combined. When the preparation action of low-priority equipment is delayed, determine whether its adjusted completion time is earlier than the predicted time when the corresponding vehicle body arrives at the equipment. Only when the requirement of continuous line entry in a fixed sequence for the vehicle body is met is the equipment preparation phase table updated to obtain the equipment preparation phase table after conflict resolution.

[0015] According to the intelligent scheduling and control method for electric vehicle painting equipment based on big data provided by the present invention, step S5, the step of outputting advance preparation control instructions based on the equipment preparation phase table after conflict resolution, includes: According to the start time in the equipment preparation phase table after conflict resolution, advance preparation control instructions are issued to the corresponding paint supply system, spraying robot, spray booth environmental control system, drying room control system, VOC treatment system and detection system. Receive the execution status of the preparation actions from each coating equipment; when there is a delay in the execution of a certain preparation action, re-identify time conflicts and perform phase arbitration for the preparation actions of the equipment that has not yet started. During the re-arbitration process, the fixed order of the vehicles in the flow slot remains unchanged, and no vehicle reordering instructions, bypass buffer instructions, or candidate vehicle replacement instructions are generated.

[0016] This invention establishes a precise mapping between the flow tank position and the predicted arrival time, driving the paint supply system, painting robot, spray booth, drying booth, VOC treatment system, and testing system to complete all preparatory actions at the optimal phase before the vehicle body arrives. This achieves coordinated synchronization of multiple devices in the time dimension, ensuring that each painting device is always in a ready state precisely matched to the current vehicle body process requirements without affecting the production cycle. Through relaxation time calculation, hierarchical arbitration scoring, and phase conflict resolution mechanisms, the system can automatically coordinate the execution order of each preparatory action under limited equipment resources and time window constraints, prioritizing actions with higher risks of line stoppage and quality defects to be completed on time. It also provides early warnings of potential production interruptions to operators through a non-stoppage risk warning system. Furthermore, through a quality sensitivity level classification system, it further ensures that high-value vehicles and vehicles with complex processes receive priority in the competition for equipment preparation resources, reducing the probability of appearance quality defects. Attached Figure Description

[0017] The invention will now be further described with reference to the accompanying drawings.

[0018] Figure 1 This is a flowchart of the intelligent scheduling and control method for electric vehicle painting equipment based on big data in an embodiment of the present invention; Figure 2 This is a flowchart illustrating the time it takes for a car body in each flow channel to reach each painting device, according to an embodiment of the present invention. Figure 3This is a flowchart of phase arbitration for conflicting actions in an embodiment of the present invention. Detailed Implementation

[0019] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments. Example

[0020] like Figures 1 to 3 As shown, this embodiment provides an intelligent scheduling and control method for electric vehicle painting equipment based on big data, including: S1: Collect vehicle body data and painting equipment operation data entering the painting production line. The vehicle body data includes at least the vehicle model, color, paint system, body heat capacity, painting area, robot program number, and quality sensitivity level. The body heat capacity is obtained from pre-stored vehicle model BOM data. The painting equipment operation data includes at least the status of the paint supply system, the painting robot, the spray booth environment, the drying oven temperature zone, the VOC treatment system, and the detection system. S2: Map electric vehicle bodies to flow slots according to their entry sequence, and predict the time for each body in each flow slot to reach each painting equipment based on the painting production line cycle time and the position of each station. The order of the bodies in the flow slots is not adjusted due to color, model, paint system, heat capacity or quality risk. The establishment of the flow tray positions is based on the physical topology model of the painting production line. The system pre-creates a static topology map containing all painting stations, equipment locations, and conveyor paths, and uses the conveyor path length between stations and the standard cycle speed to calculate the standard travel time between adjacent stations as basic parameters. When the path from one station to the next contains a lift, turntable, or transfer point, the corresponding mechanism's action time is included in the standard travel time calculation. The flow tray position number is automatically assigned by the system when the vehicle body passes the entrance detection point, and once assigned, it remains unchanged throughout the entire painting production line's operating cycle.

[0021] The steps for predicting the arrival time of car bodies at each flow tank position to the respective painting equipment include: A series of sequential flow tank positions are generated according to the order in which the car body enters the painting production line entrance; the flow tank positions are represented as follows: , in, For the water trough position, For the i-th electric vehicle body. These represent the predicted time for the i-th electric vehicle body to arrive at different painting equipment or workstations; When the painting production line uses a fixed-cycle conveyor, the predicted time for the i-th electric vehicle body to reach the j-th painting equipment or station satisfies: , in, CT is the time when the first electric vehicle body arrives at the j-th painting equipment or station, and CT is the cycle time of the painting production line. The water tank number is linked to the vehicle identification number, entry time, vehicle model, color, paint system, and robot program number; Based on the cycle time of the painting production line, the location of each workstation, and the length of the conveying path, calculate the theoretical arrival time of the car body corresponding to each flow trough to the painting equipment; Based on conveyor speed fluctuations, equipment waiting status, and workstation occupancy status, the theoretical arrival time is corrected to obtain the predicted arrival time. The specific calculation method for the time correction model is as follows: , in, To predict arrival time, Let be the theoretical arrival time of the i-th car body to the j-th workstation under the standard cycle time. This refers to the cumulative deviation caused by fluctuations in the conveyor chain speed. To account for the deviation caused by waiting at workstations, the deviation is predicted based on the real-time occupancy status of each workstation. When it is detected that the j-th workstation is occupied by the previous vehicle body and the estimated release time is later than the theoretical arrival time of the i-th vehicle body, the difference between the two is included in the waiting deviation. The time correction is dynamically refreshed at a frequency of no less than once per cycle to ensure that the predicted arrival time series remains synchronized with the actual operating status of the production line.

[0022] The order of the flow slot numbers remains unchanged during the prediction process, and the vehicle sequence is not adjusted based on color, vehicle model, paint system, vehicle body heat capacity, or mass sensitivity level.

[0023] The steps to correct the theoretical arrival time include: Acquire real-time speed of the conveyor chain, historical cycle deviation, downtime records, and real-time occupancy status of each workstation; Calculate the cumulative time deviation of each flow tank position relative to the standard cycle time; The cumulative time deviation is added to the theoretical arrival time of the corresponding water tank position to each coating equipment; When a local equipment wait or short pause is detected, only the predicted arrival time of the affected flow tank is corrected, without changing the order between the flow tanks. Generate a vehicle body arrival time series that is dynamically updated according to the production line's operating status.

[0024] S3: Based on the body data of each vehicle body, the predicted time of arrival at each painting equipment, and the operating data of the painting equipment, generate equipment preparation actions corresponding to the paint supply system, spraying robot, spray booth, drying booth, VOC treatment system, and detection system, and determine the start time window, duration, priority, and resource usage information of each equipment preparation action. Step S3, which generates the equipment preparation actions corresponding to each coating device, includes: Based on the color, paint system, vehicle model, body heat capacity, painting area, robot program number, and quality sensitivity level of each vehicle body, determine the target preparation requirements for the paint supply system, painting robot, spray booth, drying booth, VOC treatment system, and testing system. The steps include: The scheduling system takes the attribute data of the car body at the current flow tank position as input, queries the process parameter database, retrieves the painting process specifications corresponding to the car body color, paint system and model, and extracts the target operating parameters of each painting equipment from the specifications, including the target color and viscosity of the paint supply system, the target program number and spraying parameters of the spraying robot, the target temperature, humidity and air volume of the spray booth, the target temperature curve and circulating air volume of the drying booth, the target processing capacity of the VOC treatment system and the target detection model of the detection system; The target operating parameters are compared with the current equipment operating data item by item, and corresponding equipment preparation actions are generated only for parameters that differ, thus avoiding the generation of unnecessary redundant actions.

[0025] Based on the difference between the target preparation requirements and the current equipment operation data, generate equipment preparation actions; the equipment preparation actions include at least two of the following: paint supply system color change action, paint supply pipeline cleaning action, paint supply circulation stabilization action, spraying robot program preloading action, spray booth temperature and humidity feedforward adjustment action, spray booth air supply and exhaust adjustment action, drying room temperature zone heat load compensation action, VOC treatment load adjustment action, and detection program switching action. Equipment preparation actions are represented as follows:

[0026] in, For painting equipment that performs the action, For action types, EST is the earliest start time, and LST is the latest start time. For the duration of the action, This provides a stability margin after the action is completed. Action priority, For resource usage information, For action mode, This field indicates whether an action is incompressible, uninterrupted, or cannot be postponed. The assignment rules for the Lock flag include: for actions such as paint supply cycle stabilization after color change, robot program validity verification, and P0-level safety interlock related actions, Lock is set to true, indicating that the action must be executed completely within the predetermined time window and cannot be interrupted or postponed due to resource conflicts; for actions with a certain degree of flexibility, such as spray booth temperature and humidity feedforward adjustment and VOC treatment load pre-adjustment, Lock is set to false, indicating that, under the constraint of completion before the vehicle body arrives, it is allowed to be moved forward or executed in segments within the time window. The Mode field records the optional execution mode of the action. For example, the paint supply and color change action can choose between a standard mode or a fast mode (suitable for shortened cleaning processes involving similar color changes). The scheduling system automatically selects the execution mode during the phase arbitration stage based on time constraints and the vehicle body quality sensitivity level.

[0027] Based on the predicted time when the vehicle body arrives at the corresponding painting equipment, the start time window for the equipment preparation action is calculated in reverse. In step S3, the startup time window includes the earliest startup time and the latest startup time; The latest startup time is calculated as follows: , in, The latest start time, The duration of the preparation action for the corresponding equipment. To ensure stability after the completion of corresponding equipment operations, such as achieving target viscosity and pressure stability in paint circulation after color change, achieving thermal balance in spray booth temperature and humidity control, and maintaining target temperature in the drying chamber, stability margin values ​​for all operations are pre-calibrated using process verification data. The value is determined based on the action type and the current state of the device, specifically including: For color changing operations in the paint supply system The color switching span (similar colors or color families), pipeline volume, and cleaning solvent usage are determined and initialized according to historical color change time statistics in the process parameter database. Adaptive corrections are then made based on actual execution feedback. For the heat load compensation action in the drying oven temperature zone, It is determined by the difference between the target temperature and the current temperature, the heating power margin, and the heat capacity of the temperature zone.

[0028] The earliest startup time is calculated as follows: , in, The earliest start time, For coating equipment Release time, Time required for the pre-processing of the upstream equipment. The maximum allowable lead time for the corresponding action type, based on the process constraints of the action type, specifically includes: If the pipeline circulation after paint color change is initiated too early, the paint may undergo shear degradation or sedimentation due to excessive circulation. Limited to the maximum cycle time for process validation; If the preheating process of the drying oven is initiated too early, the energy consumption of the drying oven during idle operation will increase significantly. Determined based on energy consumption optimization targets and the insulation performance of the drying room; The values ​​are stored in the process parameter database and associated with the action type code.

[0029] The relaxation time for the equipment preparation action is calculated using the following formula: , in, The relaxation time for equipment preparation is considered. If the relaxation time is less than zero, it is determined that there is a risk that the equipment preparation action will not be completed on time; when... When the scheduling system triggers a risk warning process: First, it checks the Lock flag of the action. If it is an incompressible action, it directly reports the risk warning to the scheduling control interface and initiates the non-stop risk warning logic; if it is a compressible action, it attempts to extend the action duration while ensuring the lower limit of process quality. Compress to the minimum allowable value by the process. The relaxation time is recalculated; if the relaxation time is still less than zero after compression, the action is included in the phase arbitration process for conflict resolution.

[0030] Based on the execution time of the equipment preparation actions, the urgency of the actions, and the occupation of shared resources, determine the duration, priority, and resource occupation information of the equipment preparation actions.

[0031] In step S3, the equipment preparation actions for the paint supply system and the spraying robot include: Based on the differences in color and paint system between the current vehicle body in the production line and the previous vehicle body in the production line, the paint supply system generates actions such as color changing, cleaning, pipeline switching, pressure stabilization, temperature adjustment, or viscosity adjustment. The specific execution parameters of the color changing action of the paint supply system are determined by the color switching matrix. The color switching matrix uses color codes as a two-dimensional index and records the number of cleaning steps, solvent usage, and minimum color changing time required for switching between any two colors. This matrix is ​​pre-calibrated by the painting process engineer based on process verification results and stored in the process parameter database. When the color code and paint system of the current vehicle body in the production line are the same, the paint supply system does not generate a color changing action, but only generates routine viscosity and pressure checks to save shared resources and reduce the action density in the equipment preparation phase table.

[0032] Based on the vehicle model, painting area, and robot program number of the current assembly line position, the following actions are generated for the painting robot: program preloading, trajectory verification, painting parameter distribution, and atomization parameter adjustment. The logic for generating the painting robot program preloading action is as follows: if the robot program number of the current assembly line position is different from the robot's currently loaded program number, a program preloading action is generated, including three sequentially dependent sub-steps: program file transfer, memory loading, and loading integrity verification. The dependencies between these sub-steps are determined by... The parameters are reflected in the EST calculation; if the program numbers are the same, only the spraying parameter verification action is generated to verify that the spraying pressure, flow rate and atomization parameters are consistent with the current vehicle model specifications; Based on the vehicle body quality sensitivity level, increase the priority of corresponding paint supply parameter stabilization actions and robot parameter verification actions; vehicle body quality sensitivity level The division method is expressed as follows: , in, For destination market factors, This is the coating process complexity factor. As a customer priority factor, For production process stage factors, The historical quality record factor is used, and the value range of each factor is normalized to [0,1]. to The corresponding weighting coefficients are pre-set by production management personnel based on the company's quality policy, and . The higher the value, the higher the quality sensitivity, after weighting. The value is in the range [0,1]. According to... The correspondence between the data and preset thresholds determines the quality sensitivity level, expressed as follows: , in, The threshold for grade classification is determined by process engineers in conjunction with on-site quality control requirements. When any dimension triggers the mandatory QS1 condition, the QS1 grade is directly assigned regardless of the overall evaluation score.

[0033] For vehicle bodies with a quality sensitivity level of QS1, a trajectory verification action is forcibly generated regardless of whether the program needs to be switched, in order to eliminate trajectory errors caused by robot joint wear or calibration drift accumulation.

[0034] For car bodies with a quality sensitivity level of QS2, a trajectory verification action is forcibly generated when the painting robot program is switched; it is not forcibly executed when the program is not switched; stability margin. An additional half-cycle of stable testing is added to the standard value; the testing system generates enhanced testing tasks, with a testing coverage rate of no less than 80% for key areas (front and rear fenders, roof, and outer door panels).

[0035] For vehicle bodies with a quality sensitivity level of QS3, the stability margin for each equipment preparation action is taken from the standard calibration value in the process parameter database, without any additional extension; the painting robot only generates program preloading and integrity verification actions when the program is switched, and generates routine verification actions for painting parameters when the program is not switched; the inspection system performs standard sampling inspection tasks, and the sampling ratio and coverage area are implemented in accordance with the production site quality control specifications.

[0036] When equipment resources are scarce, QS4 level allows for a suitable reduction in the stability margin of unnecessary preparatory actions within the lower limit of process quality constraints; the testing system performs a minimal sampling inspection task.

[0037] Associate the equipment preparation actions of the paint supply system and the spraying robot with the corresponding flow tank position number.

[0038] In step S3, the equipment preparation actions for the spray booth, drying room, VOC treatment system, and detection system include: Based on the paint system, spraying area, and quality sensitivity level of the vehicle body in the current assembly line, advance adjustments are made to the spray booth temperature, humidity, air velocity, and pressure differential. The parameters for generating the spray booth temperature and humidity feedforward adjustments are determined by the vehicle body paint system: for water-based paint systems, the target relative humidity range is 65%–75%, and the target temperature range is 23℃–25℃; the feedforward adjustment must complete at least one air exchange cycle in the spray booth before the vehicle body arrives. For solvent-based paint systems, the target relative humidity range is 50%–65%, and the temperature and air velocity parameters are determined based on the paint type and solvent evaporation rate. For vehicles with quality sensitivity levels QS1 and QS2, a stability margin for spray booth temperature and humidity is provided. An additional spray booth stability testing cycle is added to the standard values ​​to ensure that the vehicle body can only enter the spray booth after the environmental parameters have reached a convergent and stable state.

[0039] Based on the current vehicle body's heat capacity, coating system, and spraying area, the system generates actions for preheating the drying chamber temperature zones, adjusting the temperature profile, adjusting airflow, and adjusting heating power. The parameters for the drying chamber temperature zone heat load compensation actions are jointly determined by the vehicle body's heat capacity and coating system. The system predicts the actual heat load changes in each temperature zone of the drying chamber based on the vehicle body's baseline heat capacity and the initial temperature of the vehicle upon entering the drying chamber, and generates corresponding heating power compensation values. When multiple vehicles with significantly different heat capacities enter the drying chamber consecutively, the scheduling system generates a sequence of heat load compensation actions for each vehicle and temperature zone in advance to suppress the impact of temperature fluctuations in the drying chamber temperature zones on the coating curing quality. The drying chamber entrance cycle confirmation action is performed by the scheduling system before the predicted arrival of the vehicle at the drying chamber entrance. The time was sent in advance. The value is the sum of the thermal stability time of the drying oven temperature zone and the cycle time margin, which is pre-calibrated based on process verification data.

[0040] The generation of heat load compensation actions in the drying oven temperature zone is achieved using a thermodynamic calculation model based on the superposition of heat capacities from multiple vehicle bodies and a temperature zone temperature lag compensation algorithm. The steps include: At any time t, multiple car bodies exist simultaneously in the z-th temperature zone of the drying oven. Let S(z,t) be the set of car bodies located in this temperature zone. Then, the instantaneous total heat load of this temperature zone is... The formula for the sum of the instantaneous heat absorbed by each vehicle body is as follows: , Where h is the convective heat transfer coefficient of the hot air in the drying room. Let be the equivalent heat exchange area of ​​the i-th vehicle body. This represents the real-time temperature of the z-th temperature zone. Let be the real-time temperature of the i-th vehicle body at time t. The temperature change of a single vehicle body satisfies a lumped-parameter thermodynamic model: , in, Let be the rate of change of the temperature of the i-th vehicle body with respect to time. For the mass of vehicle body i, For its equivalent specific heat capacity, the product of the two This is the baseline value of the vehicle's heat capacity. The initial temperature of the vehicle entering the z-th temperature zone is taken from the actual measured value of the temperature sensor at the exit of the previous temperature zone, or recursively derived from the thermodynamic model of the previous temperature zone. The number and composition of vehicles in the zzz-th temperature zone at any given time are determined by predicting the arrival time series of the water tank positions. , in, and The predicted times for the i-th vehicle body to enter and leave the z-th temperature zone are respectively calculated from the predicted arrival time series of the water tank position, thus directly linking the time prediction results of the scheduling system with the thermodynamic calculation model.

[0041] Considering thermal coupling between adjacent temperature ranges, the temperature dynamic equation for the z-th temperature range is: , in, The total heat capacity of the z-th temperature zone includes the sum of the heat capacities of the air, furnace walls, and guide rail accessories within the zone; For heating power, The heat lost to the outside world and The thermal coupling coefficient between the temperature zone and the adjacent temperature zone. This represents the temperature difference between adjacent temperature ranges.

[0042] Due to the significant inertia of tunnel-type drying chambers, after the heating power adjustment command is issued, the temperature response in the z-th temperature zone exhibits a superposition effect of pure hysteresis and first-order inertial hysteresis. The temperature response transfer function of the z-th temperature zone is approximately: , in, Let be the transfer function of the z-th temperature zone with heating power as input and temperature zone as output, and s be the Laplace operator. For the temperature range static gain, For pure time delay, The thermal inertia time constant; these three parameters are pre-calibrated through step response identification tests and stored in the process parameter database, and periodically recalibrated as the equipment's operating status changes over a long period. Power compensation commands must be issued before the vehicle body reaches its designated temperature range. issue: , in, λ represents the minimum advance time required for the power compensation command of the z-th temperature zone to be issued relative to the time when the vehicle body reaches that temperature zone. λ is the inertia margin coefficient, with a value range of [2,3], which is calibrated by the process engineer based on the temperature fluctuation tolerance of the temperature zone. That is, the heat load compensation action of the drying room temperature zone is within the corresponding stability margin. The specific value of the time window is determined by this, thus directly incorporating the temperature hysteresis characteristic into the calculation system of the startup time window.

[0043] At the current time t, when the future is predicted... When a new vehicle body enters the z-th temperature zone within the time window, predict the set of vehicles within the temperature zone at the end of that window. Based on the thermodynamic models of each vehicle body, the predicted temperature of each vehicle body at that moment is calculated recursively. Thus, the predicted total heat load is obtained, expressed as: , in, To predict the total heat load, Set the target temperature for the z-th temperature zone. The feedforward compensation amount relative to the current steady-state heating power. for: , in, This is the current steady-state baseline value for the total heat load. The feedforward compensation command is issued at the current time t, causing the temperature zone to... Power adjustment is completed before the time zone to ensure that the vehicle body temperature has stabilized near the target value when it arrives at the designated temperature zone, thus achieving precise feedforward suppression of temperature fluctuations.

[0044] Because of thermal coupling between adjacent temperature zones, when applying feedforward power compensation to the z-th temperature zone, the coupling disturbance generated to adjacent temperature zones will be calculated simultaneously: , in, To predict in The temperature of the z-th temperature zone at time z For time t, the adjacent number of... Real-time temperature of the temperature zone.

[0045] Furthermore, the corresponding decoupling correction amount is superimposed on the compensation command of adjacent temperature zones to eliminate thermal crosstalk between temperature zones and ensure that the temperature of each temperature zone converges independently to its own target setpoint.

[0046] Based on the coating system, spraying area, and predicted exhaust load, the VOC treatment system generates airflow adjustment, adsorption unit switching, or combustion unit pre-start actions. The predicted exhaust load of the VOC treatment system is estimated based on the solvent content of the coating system, spraying area, and spraying amount per unit area. When the predicted exhaust load exceeds 80% of the rated processing capacity of the adsorption unit, the adsorption unit switching action or combustion unit pre-start action is generated in advance. When the predicted exhaust load of multiple vehicles exceeds the total processing capacity of the VOC treatment system within the same time period, the VOC treatment load adjustment action is included in the arbitration score calculation.

[0047] Based on vehicle model, color, and quality sensitivity level, the system generates actions for loading the detection model, adjusting light source parameters, adjusting camera parameters, and preparing the detection task queue. When multiple equipment preparation actions conflict in time, the equipment preparation action that retains the original phase is determined according to the arbitration score, as expressed by the formula: , in, Arbitration score for action a in preparing the equipment. This is the priority coefficient. For relaxation time, The probability of line stoppage due to equipment preparation actions not being completed on time. The probability of quality defects caused by the failure to complete equipment preparation actions on time. and The value is determined by the scheduling system based on historical production data and is continuously updated with actual production feedback. The preparation for this equipment involves vehicle delivery or key model weighting. It is a constant. to The initial value of the weighting coefficient is set by production management personnel based on their on-site experience and can be adjusted online in the scheduling and control system. This is the priority weight, and it takes the largest value to ensure that high-priority actions always dominate. The relaxation time is weighted; the shorter the relaxation time (i.e., the more urgent the action), the higher the score. and These are the downtime risk weight and the quality risk weight, which are set according to the relative impact of downtime losses and quality defects on the production site. As a delivery weight, it is used to ensure the painting quality of high-priority delivery vehicles or key models.

[0048] When there is an advance window for the equipment preparation action with a low arbitration score, the equipment preparation action with the low arbitration score will be moved forward. If moving forward is not feasible and there is a window for moving backward, the equipment preparation action with a low arbitration score will be postponed. When neither forward nor backward movement is feasible, select an alternative action mode that has been verified by the process, or combine the preparation actions of multiple devices with the same adjustment direction on the same controlled object into one phase action; If, after moving forward, moving backward, using alternative action modes, or merging phases, there are still P1-level continuous production necessary actions that cannot be completed before the corresponding vehicle body arrives, then an unstoppable production line risk warning will be triggered.

[0049] S4: Based on the start time window, duration, priority, and resource usage information of each equipment preparation action, generate an equipment preparation phase table. Then, identify time conflicts and arbitrate phases for the equipment preparation actions in the phase table to obtain a conflict-resolved equipment preparation phase table. The data structure of the equipment preparation phase table is designed as a two-dimensional matrix, with row indices representing the flow tank position number and column indices representing the combination code of the coating equipment and action type. Matrix elements store the corresponding equipment preparation action objects. It contains all its attribute fields; when a certain flow channel position of the car body does not need to perform a preparation action on a specific device, the corresponding matrix element is set to empty; the phase table also maintains a global action sequence arranged by the time axis for conflict detection and arbitration calculation.

[0050] Step S4, the step of generating the device preparation phase table includes: Using the water tank position number as an index, the preparation actions of each piece of equipment corresponding to the same vehicle body are arranged according to the predicted arrival time; Write the start time window, duration, priority, and resource usage information of each device preparation action into the phase table; The preparatory actions that need to be completed in advance in the paint supply system, spraying robot, spray booth, drying booth, VOC treatment system, and testing system are divided into different preparation phases, according to the following rules: Based on the predicted time of the vehicle body arriving at each painting equipment, the preparatory actions of each equipment are divided into near-term preparation phases (no more than 2 cycles from the arrival time of the vehicle body), medium-term preparation phases (2 to 5 cycles), and long-term preparation phases (more than 5 cycles) according to their urgency from the current time. Actions in the near-term preparation phase are given priority to enter the conflict identification and fine arbitration process and immediately trigger the issuance of control commands. Actions in the medium-term and long-term phases are rolled forward with the production process. When the phase enters the near-term range, fine arbitration and command issuance are triggered. Actions in the long-term phase can be pre-adjusted based on the latest equipment status data to improve the accuracy of action parameters.

[0051] Establish the correspondence between the preparation phase and the flow tank position number, the target coating equipment, and the predicted arrival time; A device preparation phase table is created to describe the order and timing of preparation of multiple devices.

[0052] Step S4, the step of preparing the phase table for time conflict identification includes: Based on the start time window and duration of the equipment preparation actions, determine the time interval occupied by each equipment preparation action; Based on resource usage information, determine whether there are resource conflicts between different equipment preparation actions, such as shared valve groups, paint supply pipelines, robot control channels, spray booth ventilation resources, drying room heating resources, VOC processing capacity, or detection task queues. Determine whether there is any overlap in the preparation actions of different devices within the time interval; When time overlaps and shared resources are occupied, the corresponding device preparation actions will be marked as conflicting actions; Write the conflicting action, its corresponding flow slot number, conflicting resource, and conflict time into the conflict list.

[0053] In step S4, absolute arbitration is first performed according to priority level, and then the scores are sorted within the same level. The priorities include: P0 level safety interlock actions include emergency stop, safety door, fire alarm, VOC over-limit protection, robot safety area interlock, and conveyor safety interlock; Essential actions for P1 level continuous production include paint supply and color change completion, effective loading of robot program, availability of spray gun, availability of clear coat station, and acceptance of drying room entrance. P2 level quality assurance actions include spray booth temperature and humidity feedforward, air supply and exhaust balance, drying room heat load compensation, circulation stability after color change, and key inspection of the first vehicle. P3 level energy consumption and VOC optimization actions include energy consumption peak shifting, VOC load smoothing, air conditioning energy-saving mode switching, and waste heat utilization in drying rooms. P4 level diagnostic and maintenance prompts include equipment health checks, spray gun status trend checks, and robot maintenance prompts. The priority classification is based on the following: P0-level safety interlock actions are executed independently by the safety control system and are not included in the scheduling of the equipment preparation phase table. No phase table action may occupy the control channels and execution resources required for P0-level safety interlock actions; P1-level continuous production necessary actions directly determine whether the car body can complete the corresponding process within the current cycle time. Failure to complete them on time will inevitably lead to line stoppage or missed processes, thus they have an absolute guarantee status in arbitration; P2-level quality assurance actions affect the appearance and performance of the coating, but have a certain time flexibility within the allowable range of the process; P3 and P4-level actions are of a production optimization nature and are executed when resources are sufficient. They can be postponed or canceled when resources are tight, without affecting production continuity and basic quality requirements; The above priority classification system ensures that under the condition of concurrent scheduling of multiple equipment and multiple actions, safety constraints and production continuity constraints are always given priority.

[0054] Step S4, the step of phase arbitration for conflicting actions includes: The arbitration priority of conflicting actions is calculated based on the predicted arrival time of the vehicle body corresponding to the conflicting action, the latest completion time of the action, the quality sensitivity level, the duration of the action, and the resource release time. Prioritize the preparation of equipment with the closest arrival time, the earliest latest completion time, higher quality sensitivity level, or stronger safety constraints; Low-priority equipment preparation actions are processed by advancing, delaying, splitting, or merging them. The specific rules for phase merging are as follows: when multiple equipment preparation actions with the same adjustment direction on the same controlled object (such as the same paint supply pipeline or the same spray booth blower) cannot be executed independently due to time conflicts, they are merged into one phase action. The duration of the merged action is the maximum of the durations of each sub-action, the target parameter of the merged action is the most stringent one of the target parameters of each sub-action (e.g., the highest target value for temperature and the maximum value for stability margin), the priority of the merged action is the highest priority among each sub-action, and the Lock flag of the merged action is the result of any one of the sub-actions being true. The merged phase action replaces the original sub-actions and is written into the phase table, and its start time window and arbitration score are recalculated.

[0055] When the preparation action of low-priority equipment is delayed, determine whether its adjusted completion time is earlier than the predicted time when the corresponding vehicle body arrives at the equipment. Only when the requirement of continuous line entry in a fixed sequence for the vehicle body is met is the equipment preparation phase table updated to obtain the equipment preparation phase table after conflict resolution.

[0056] S5: Based on the equipment preparation phase table after conflict resolution, output advance preparation control instructions to the paint supply system, spraying robot, spray booth environment control system, drying room control system, VOC treatment system and detection system, so that each painting equipment can complete phase synchronization preparation according to the vehicle body requirements of continuous entry in a fixed order without changing the fixed sequence of the vehicle body, without adding bypass buffers and without setting up candidate vehicle replacement.

[0057] Step S5, which involves outputting the advance control command based on the conflict-resolved device preparation phase table, includes: According to the start time in the equipment preparation phase table after conflict resolution, advance preparation control commands are issued to the corresponding paint supply system, spraying robot, spray booth environmental control system, drying oven control system, VOC treatment system, and detection system. After issuing each advance preparation control command, the scheduling control system waits for the corresponding equipment controller to return a command reception confirmation message. If no confirmation message is received within the preset timeout period, the scheduling control system automatically resends the command. If the number of resends exceeds the threshold, an equipment communication abnormality alarm is triggered, and the action is marked as pending confirmation in the phase table. After completing the corresponding preparation action, the equipment controller reports the action completion message to the scheduling control system. The message includes the actual completion time, execution result, and actual execution parameters. The scheduling control system compares the actual completion time with the planned completion time in the phase table. When the deviation exceeds the preset threshold, a local phase recalculation is triggered.

[0058] Preparing control commands in advance includes at least two of the following: Output color change start, cleaning start, and circulation stabilization commands to the paint supply system; Output program preloading, program verification, and spraying preparation confirmation instructions to the painting robot; Output temperature, humidity, air volume, pressure difference, and air supply and exhaust adjustment commands to the spray booth environmental control system; Output temperature zone compensation power, hot air circulation volume and inlet cycle confirmation commands to the drying oven control system; Output pre-adjustment commands for exhaust volume, treatment load, and peak load to the VOC treatment system; Output detection program switching and key detection area confirmation instructions to the detection system; Furthermore, the advance control commands do not include vehicle reconfiguration commands, bypass buffer commands, candidate vehicle replacement commands, or return transport commands.

[0059] Receive feedback on the execution status of preparation actions from each coating equipment; when a preparation action is delayed, re-identify time conflicts and perform phase arbitration for the preparation actions of equipment that have not yet started. Specifically, replace the planned completion time of the corresponding entry in the phase table with the actual completion time of the delayed action, and recalculate the earliest start time of the affected subsequent actions based on this. The affected action range is re-identified for time conflicts. If new conflicts exist, they are resolved according to the phase arbitration process. Only the affected flow tank positions and action entries are locally updated, while the phase table entries of the unaffected flow tank positions remain unchanged. The updated control commands are reissued to the corresponding equipment. The entire local recalculation and reissue process is required to be completed within one cycle time to ensure that the impact on the production cycle is minimized.

[0060] During the re-arbitration process, the fixed order of the vehicles in the flow slot remains unchanged, and no vehicle reordering instructions, bypass buffer instructions, or candidate vehicle replacement instructions are generated.

[0061] The scheduling and control system monitors the completion status of the preparation actions of each painting equipment in real time, and displays the execution progress, completion status, and risk warning information of the preparation actions corresponding to each production line position in a timeline format on the scheduling and control interface. When there is a risk that the necessary actions for continuous production at the P1 level will not be completed on time, the corresponding production line position and action item will be highlighted on the interface, and an alarm will be pushed to the relevant operators to assist in manual judgment on whether intervention measures are needed. The scheduling and control system does not output vehicle body reordering instructions, bypass buffering instructions, or candidate vehicle replacement instructions throughout the process, strictly ensuring that the phase synchronization preparation of multiple equipment is achieved under the constraint of continuous entry in a fixed sequence.

[0062] In summary, the intelligent scheduling and control method for electric vehicle painting equipment based on big data provided by this invention establishes a precise mapping between the flow tank positions and the predicted arrival time. This drives the paint supply system, painting robot, spray booth, drying booth, VOC treatment system, and detection system to complete all preparatory actions at the optimal phase before the vehicle body arrives. This achieves coordinated synchronization of multiple devices in the time dimension, ensuring that each painting device is always in a ready state precisely matched to the current vehicle body process requirements without affecting the production cycle. Simultaneously, by utilizing relaxation time calculation, hierarchical arbitration scoring, and phase conflict resolution mechanisms, the system can automatically coordinate the execution order of each preparatory action under limited equipment resources and time window constraints, prioritizing line stoppages. Actions with higher risks and quality defects are completed on time, and operators are notified in advance of potential production interruptions through non-stop risk warnings. The introduction of a quality sensitivity level classification system further ensures that high-value vehicles and vehicles with complex processes receive priority in the competition for equipment preparation resources, reducing the probability of appearance quality defects. In addition, the continuous archiving and statistical analysis of actual execution data of each piece of equipment forms an adaptive optimization closed loop based on big data feedback, which makes the prediction accuracy of action duration and stability margin continuously improve with production accumulation. Thus, the overall intelligent scheduling and control goal of high efficiency, high quality, and low downtime risk is achieved under the conditions of frequent color changes, mixed vehicle models, and fixed cycle time in the painting production line.

[0063] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent scheduling and control of electric vehicle painting equipment based on big data, characterized in that, include: S1: Collect vehicle body data and painting equipment operation data of electric vehicles entering the painting production line; S2: Map the electric vehicle body into a flow channel according to the entry order, and predict the time for the body in each flow channel to reach each painting equipment based on the cycle time of the painting production line and the position of each station. S3: Based on the body data of each vehicle body, the predicted arrival time of each painting equipment, and the operating data of the painting equipment, generate corresponding equipment preparation actions, and determine the start time window, duration, priority, and resource consumption information of each equipment preparation action. S4: Based on the preparation actions of each device, generate a device preparation phase table, and perform time conflict identification and phase arbitration on the device preparation actions in the device preparation phase table to obtain a conflict-resolved device preparation phase table. S5: Based on the equipment preparation phase table after conflict resolution, output advance preparation control commands to the paint supply system, spraying robot, spray booth environmental control system, drying room control system, VOC treatment system and detection system.

2. The intelligent scheduling and control method for electric vehicle painting equipment based on big data according to claim 1, characterized in that, Step S2, which predicts the time it takes for the car body in each flow trough to reach each painting device, includes: A series of flow tank positions are generated according to the order in which the car body enters the painting production line entrance; The water tank position number is linked to the vehicle identification number, entry time, vehicle model, color, paint system, and robot program number; Based on the cycle time of the painting production line, the location of each workstation, and the length of the conveying path, calculate the theoretical arrival time of the car body corresponding to each flow trough to the painting equipment; The theoretical arrival time is corrected based on the conveyor speed fluctuation, equipment waiting status, and workstation occupancy status to obtain the predicted arrival time; During the prediction process, the order of the water tank numbering remains unchanged, and the vehicle body order is not adjusted based on color, vehicle model, paint system, vehicle body heat capacity, or mass sensitivity level.

3. The intelligent scheduling and control method for electric vehicle painting equipment based on big data according to claim 2, characterized in that, Step S2, the step of correcting the theoretical arrival time includes: Acquire real-time speed of the conveyor chain, historical cycle deviation, downtime records, and real-time occupancy status of each workstation; Calculate the cumulative time deviation of each flow tank position relative to the standard cycle time; The cumulative time deviation is added to the theoretical arrival time of the corresponding water tank position to each coating equipment; When a local equipment wait or short pause is detected, only the predicted arrival time of the affected flow tank is corrected, without changing the order between the flow tanks. Generate a vehicle body arrival time series that is dynamically updated according to the production line's operating status.

4. The intelligent scheduling and control method for electric vehicle painting equipment based on big data according to claim 1, characterized in that, Step S3, which generates the equipment preparation actions corresponding to each coating device, includes: Based on the color, paint system, vehicle model, body heat capacity, spraying area, robot program number, and quality sensitivity level of each vehicle body, determine the target preparation requirements of the paint supply system, spraying robot, spray booth, drying booth, VOC treatment system, and testing system for that vehicle body. Based on the difference between the target preparation requirements and the current equipment operating data, generate equipment preparation actions; Based on the predicted time when the vehicle body arrives at the corresponding painting equipment, the start time window for the equipment's preparation action is calculated in reverse. The duration, priority, and resource usage information of the equipment preparation actions are determined based on the execution time, urgency, and shared resource usage of the actions.

5. The intelligent scheduling and control method for electric vehicle painting equipment based on big data according to claim 4, characterized in that, In step S3, the equipment preparation actions corresponding to the paint supply system and the spraying robot include: Based on the differences in color and paint system between the current car body in the flow tank and the previous car body in the flow tank, generate actions such as color change, cleaning, pipeline switching, pressure stabilization, temperature adjustment or viscosity adjustment for the paint supply system. Based on the vehicle model, painting area, and robot program number of the current assembly line position, generate the painting robot's program preloading, trajectory verification, painting parameter distribution, and atomization parameter adjustment actions; Based on the vehicle body quality sensitivity level, increase the priority of corresponding paint supply parameter stabilization actions and robot parameter verification actions; The equipment preparation actions of the paint supply system and the spraying robot are associated with the corresponding water tank position numbers.

6. The intelligent scheduling and control method for electric vehicle painting equipment based on big data according to claim 4, characterized in that, In step S3, the equipment preparation actions corresponding to the spray booth, drying room, VOC treatment system, and detection system include: Based on the paint system, spraying area, and quality sensitivity level of the current vehicle body in the production line, advance adjustments are made to the spray booth temperature, humidity, wind speed, and pressure difference. Based on the current heat capacity of the car body in the flow tank, the paint system and the spraying area, generate actions such as preheating of the drying room temperature zone, adjustment of temperature curve, adjustment of air volume and adjustment of heating power; Based on the coating system, spraying area, and predicted exhaust load, the VOC treatment system generates air volume adjustment, adsorption unit switching, or combustion unit pre-start actions. Based on vehicle model, color, and quality sensitivity level, the system generates actions such as loading the detection model, adjusting light source parameters, adjusting camera parameters, and preparing the detection task queue.

7. The intelligent scheduling and control method for electric vehicle painting equipment based on big data according to claim 1, characterized in that, Step S4, the step of generating the device preparation phase table includes: Using the water tank position number as an index, the preparation actions of each piece of equipment corresponding to the same vehicle body are arranged according to the predicted arrival time; Write the start time window, duration, priority, and resource usage information of each device preparation action into the phase table; The preparatory actions that need to be completed in advance in the paint supply system, spraying robot, spray booth, drying booth, VOC treatment system and testing system are divided into different preparation phases; Establish the correspondence between the prepared phase and the water tank position number, the target coating equipment, and the predicted arrival time; A device preparation phase table is created to describe the order and timing of preparation for multiple devices.

8. The intelligent scheduling and control method for electric vehicle painting equipment based on big data according to claim 1, characterized in that, Step S4, the step of preparing a phase table for time conflict identification in the device, includes: Based on the start time window and duration of the equipment preparation actions, determine the time interval occupied by each equipment preparation action; Based on resource usage information, determine whether there are resource conflicts between different equipment preparation actions, such as shared valve groups, paint supply pipelines, robot control channels, spray booth ventilation resources, drying room heating resources, VOC processing capacity, or detection task queues. Determine whether there is any overlap in the preparation actions of different devices within the time interval; When time overlaps and shared resources are occupied, the corresponding device preparation actions will be marked as conflicting actions; Write the conflicting action, its corresponding water tank number, conflicting resource, and conflicting time into the conflict list.

9. The intelligent scheduling and control method for electric vehicle painting equipment based on big data according to claim 8, characterized in that, Step S4, the step of performing phase arbitration on the conflicting actions, includes: The arbitration priority of conflicting actions is calculated based on the predicted arrival time of the vehicle body corresponding to the conflicting action, the latest completion time of the action, the quality sensitivity level, the duration of the action, and the resource release time. Prioritize the preparation of equipment with the closest arrival time, the earliest latest completion time, higher quality sensitivity level, or stronger safety constraints; Low-priority equipment preparation actions can be advanced, postponed, split, or combined. When the preparation action of low-priority equipment is delayed, determine whether its adjusted completion time is earlier than the predicted time when the corresponding vehicle body arrives at the equipment. The equipment preparation phase table is updated only if the requirement of continuous vehicle body entry in a fixed sequence is met, resulting in a conflict-resolved equipment preparation phase table.

10. The intelligent scheduling and control method for electric vehicle painting equipment based on big data according to claim 1, characterized in that, Step S5, which involves outputting the advance control command based on the conflict-resolved device preparation phase table, includes: According to the start time in the equipment preparation phase table after conflict resolution, advance preparation control instructions are issued to the corresponding paint supply system, spraying robot, spray booth environmental control system, drying room control system, VOC treatment system and detection system. Receive the execution status of the preparation actions from each coating equipment; when there is a delay in the execution of a certain preparation action, re-identify time conflicts and perform phase arbitration for the preparation actions of the equipment that has not yet started. During the re-arbitration process, the fixed order of the vehicles in the flow slot remains unchanged, and no vehicle reordering instructions, bypass buffer instructions, or candidate vehicle replacement instructions are generated.

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