A production line intelligent scheduling method and scheduling system

Abstract

The invention relates to the technical field of intelligent scheduling of production lines, and discloses an intelligent scheduling method and a scheduling system for a production line, wherein the method comprises: performing parameter configuration and communication initialization on an equipment control system to obtain system initialization data; scanning and position detection on batteries of a conveyor line based on the system initialization data to obtain a feeding data packet; dynamically scoring and calculating the position status of a drying furnace according to the feeding data packet to obtain a position allocation strategy; performing path planning and collision detection on the motion trajectory of a scheduling robot based on the position allocation strategy to obtain scheduling instruction data; performing real-time monitoring and data collection on the temperature and vacuum degree of a baking process according to the scheduling instruction data to obtain a process monitoring data packet; and classifying and transferring material trays that have completed baking based on the process monitoring data packet to obtain classification result data. The method ensures precise control of the baking process and improves product yield and production efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent scheduling of production lines, and in particular to an intelligent scheduling method and scheduling system for a production line. Background Art

[0002] The traditional drying equipment scheduling system has problems such as excessive manual intervention and low scheduling decision-making efficiency, which makes it difficult to meet the needs of large-scale production.

[0003] The existing drying equipment scheduling system generally has the following problems: in the loading link, due to the lack of real-time detection and intelligent allocation mechanism, the efficiency of battery entry is low; in the process of furnace allocation, only a single factor is considered for decision-making, and the multi-dimensional factors such as moving distance, furnace status, and production rhythm are not comprehensively considered, resulting in uneven resource utilization; in the robot scheduling process, there is a lack of effective path planning and collision detection mechanism, which increases the safety risks of equipment. In addition, the existing technology also has deficiencies in the baking process monitoring and product classification links, mainly manifested in: the real-time monitoring accuracy of temperature and vacuum degree is not high, and abnormal working conditions cannot be discovered in time; there is a lack of intelligent secondary baking mechanism for the treatment of over-standard products, which affects the product yield; the classification and transfer process of the unloading link is inefficient, which restricts the improvement of overall production efficiency. Summary of the invention

[0004] The present invention provides a production line intelligent scheduling method and scheduling system, which ensures accurate control of a baking process and improves product yield and production efficiency.

[0005] In a first aspect, the present invention provides a production line intelligent scheduling method, the production line intelligent scheduling method comprising:

[0006] Perform parameter configuration and communication initialization on the equipment control system to obtain system initialization data;

[0007] Scan the battery of the conveyor line and detect the position based on the system initialization data to obtain a loading data packet;

[0008] Perform dynamic scoring calculation on the drying furnace position status according to the loading data packet to obtain a furnace position allocation strategy;

[0009] Based on the furnace allocation strategy, path planning and collision detection are performed on the motion trajectory of the scheduling robot to obtain scheduling instruction data;

[0010] Perform real-time monitoring and data collection on the temperature and vacuum degree of the baking process according to the scheduling instruction data to obtain a process monitoring data packet;

[0011] Based on the process monitoring data packet, the trays that have completed baking are classified and transferred to obtain classification result data.

[0012] In a second aspect, the present invention provides a production line intelligent scheduling system, the production line intelligent scheduling system comprising:

[0013] The initialization module is used to configure parameters and initialize communication of the equipment control system to obtain system initialization data;

[0014] A detection module, used to scan and detect the position of the batteries on the conveyor line based on the system initialization data to obtain a loading data packet;

[0015] A calculation module, used for dynamically scoring and calculating the furnace position status of the drying furnace according to the feeding data packet to obtain a furnace position allocation strategy;

[0016] A planning module, used to perform path planning and collision detection on the motion trajectory of the scheduling robot based on the furnace allocation strategy to obtain scheduling instruction data;

[0017] A monitoring module, used to perform real-time monitoring and data collection on the temperature and vacuum degree of the baking process according to the scheduling instruction data, and obtain a process monitoring data packet;

[0018] The processing module is used to classify and transfer the baking trays based on the process monitoring data packet to obtain classification result data.

[0019] In the technical solution provided by the present invention, a complete system initialization system is established by configuring parameters and initializing communication of the control system, which ensures the coordinated operation of various functional modules and significantly reduces the failure rate at system startup; a real-time scanning and analysis mechanism based on the IO signal status is adopted, combined with the comparison and verification of MES data, to build an efficient loading data management system and improve the efficiency of battery entry; a multi-dimensional scoring mechanism is introduced to dynamically evaluate the furnace status, and the optimal allocation plan is obtained through weighted calculation to solve the problem of uneven resource allocation; a scheduling robot control scheme including fixture leveling detection and path safety verification is designed to achieve accurate planning of motion trajectory and collision prevention; a closed-loop control system for multi-point temperature acquisition and vacuum monitoring is established, combined with real-time feedback of moisture detection to ensure accurate control of the baking process; an intelligent classification mechanism and secondary baking process based on moisture content are constructed, and an efficient material transfer strategy is used to significantly improve product yield and production efficiency.

[0020] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A schematic diagram of an embodiment of a production line intelligent scheduling method in an embodiment of the present invention;

[0023] Figure 2 It is a schematic diagram of an embodiment of the intelligent scheduling system of a production line in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0025] The terms "including" and "having" and any variations thereof mentioned in the embodiments of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or device end including a series of steps or units is not limited to the listed steps or units, but may optionally include other steps or units that are not listed, or may optionally include other steps or units that are inherent to these processes, methods, products, or device ends.

[0026] To facilitate understanding of this embodiment, a production line intelligent scheduling method disclosed in an embodiment of the present invention is first described in detail. Figure 1 As shown, the method comprises the following steps:

[0027] 101. Perform parameter configuration and communication initialization on the equipment control system to obtain system initialization data;

[0028] It is understandable that the execution subject of the present invention may be a production line intelligent scheduling system, or a terminal or a server, which is not limited here. The embodiment of the present invention is described by taking a server as the execution subject as an example.

[0029] Specifically, the motor and sensor of the equipment control system are powered on for self-test to check whether the motor and sensor can start normally and perform their specified functions, so as to ensure the stability and reliability of the hardware components. During the self-test, the IO signal status of the motor and sensor and the equipment interlocking status are collected to form hardware detection data. The hardware detection data contains the working status and interaction signals of all the equipment in the system. The hardware detection data is input into the system parameter configuration module. The module sets the key process parameters in the baking process according to the detection results. The thresholds of important parameters such as baking temperature, preheating time and vacuum degree are set according to the actual status of the equipment. The setting of these parameters is based on the hardware capabilities of the equipment, the requirements of the production process and the safety standards, so as to ensure that all equipment and processes can operate stably and safely during the production process. After completing this step, a set of process parameter configuration data is obtained. After the process parameter configuration is completed, the system establishes a communication connection with the production execution system (MES) to obtain the production work order information. By docking with the MES, the production parameter data contained in the production work order is obtained, which includes the batch information of the product, the process requirements, etc. At the same time, through synchronization with the MES system, the production work order information will help the system refine the production scheduling and improve the production efficiency and accuracy. Based on the production parameter data, the equipment control system performs the next step, namely, calibrating the coordinate system of the robot execution unit and the origin calibration to ensure that the robot can accurately perform the task. The calibration process adjusts the angle and position of each joint of the robot according to the initial position and working environment of the robot to ensure that the robot can accurately reach each working point and complete the predetermined action. The origin calibration is achieved by calibrating the robot's motion path, so that the robot can perform tasks from the set "zero point" to ensure the accuracy and stability of the motion. Through this step, the robot motion control parameters are obtained. The robot motion control parameters are configured with safety boundaries and interlocking logic. The full boundary setting is to prevent the robot from accidentally colliding or exceeding the predetermined range during the movement, including limiting the robot's movement range, speed and acceleration. The interlocking logic ensures the coordinated work between the various control systems to prevent equipment failure or safety accidents caused by mutual interference between equipment. Through these configurations, a set of safety parameter data including limit conditions and emergency stop strategies is generated. The safety parameter data, process parameter configuration data and production parameter data are mapped to the database storage to obtain the system initialization data.

[0030] 102. Scan the battery of the conveyor line and detect the position based on the system initialization data to obtain the loading data packet;

[0031] Specifically, according to the IO signal status in the system initialization data, the photoelectric sensors and proximity switches on the conveyor line are polled and scanned according to the preset sampling cycle. Through polling scanning, the sensor returns high and low level signals and analog signals. These signals are processed by the A / D converter, converted into digital signals and transmitted to the controller through the 485 bus. After receiving these digital signals, the controller obtains the conveying status data containing the battery status and quantity. The conveying status data is analyzed in real time, and the start and stop rhythm of the conveyor line is controlled by analyzing the data to ensure that the battery can be smoothly conveyed according to the set rhythm, while avoiding production delays or equipment overloads caused by improper rhythm. By analyzing the obtained battery conveying interval control parameters, the running speed and stop timing of the conveyor line are adjusted to ensure that the conveying distance and time of each battery can be accurately controlled. The barcode scanner is triggered to read the battery barcode based on the battery conveying interval control parameters. The unique identification of the battery is scanned and recorded for subsequent tracking and management. After the barcode scanner reads the barcode, the format of the battery barcode is verified to ensure the correctness and integrity of the read data. Through format verification, valid battery barcodes are screened out and converted into battery batch identification data. Compare and verify the battery batch identification data with the data in the MES system. The MES system contains key information such as production plans, material requirements, and process requirements. Comparison and verification of battery batch identification data and MES data is a necessary step to ensure the stable operation of the production line. By comparison, verify whether the battery meets the requirements of the current production batch and whether it has the conditions to enter the next process. The comparison results provide a basis for the battery's entry permit status to ensure that only batteries that meet the requirements can enter the production process. After verifying the battery entry permit status, the number of currently available material trays and the feeding cycle time are calculated based on the battery verification results. This calculation is based on the battery batch and entry permit status to dynamically adjust the material allocation in the current production process. The dynamic allocation parameters obtained by calculation ensure the accurate delivery of materials during the feeding process, thereby avoiding production imbalances caused by too much or too little material. The dynamic allocation parameters are associated with the battery batch identification data to ensure that the production line can accurately grasp the batch information, verification results, and related feeding parameters of each battery. After this information is stored, it forms a feeding data packet.

[0032] 103. Perform dynamic scoring calculation on the furnace position status of the drying furnace according to the loading data packet to obtain the furnace position allocation strategy;

[0033] Specifically, the real-time data collection of the furnace temperature, vacuum degree, baking time and furnace door status of the drying furnace is carried out to obtain the status monitoring data containing the physical parameters of each furnace position. The status monitoring data is input into the scoring system. The scoring system calculates the weight of each furnace position according to the idle time, maintenance status and whether the furnace temperature meets the standard. The idle time and the compliance of the furnace temperature ensure the stability of the baking process, while the maintenance status is related to the reliability and failure rate of the equipment. Through the weight calculation of these parameters, a furnace position status score is generated for each furnace position, reflecting the current working condition and applicability of the furnace position. Based on the loading data packet, the shortest path distance from each furnace position to the loading station is calculated. This calculation takes into account the layout of the conveyor line and the relative position between the furnace positions. Through the optimization calculation of the path, the moving distance score of each furnace position is obtained. This score helps the system evaluate the transportation efficiency from the loading station to each furnace position, avoiding the problem of wasting time and resources due to too long a path. The historical baking records of each furnace position are analyzed based on the status monitoring data. Through the analysis of historical data, the equipment utilization rate and failure rate of each furnace position are calculated to obtain the historical record score. The equipment utilization rate reflects the frequency of use of the furnace, while the failure rate reflects the reliability of the equipment. The weighted sum of the furnace status score, moving distance score and historical record score is used to obtain the comprehensive score data of each furnace. The weight of the furnace status score is the first target value and is the most important evaluation factor, because the current state of the furnace directly affects the stability and effect of the entire baking process; the weight of the moving distance score is used as the second target value to optimize the efficiency during the transportation process; and the weight of the historical record score is used as the third target value to help evaluate the stability and reliability of the furnace from the perspective of long-term use. By weighted summing these three scores, the state of the furnace, transportation efficiency and historical performance are comprehensively considered to obtain the comprehensive score data that best meets production needs. According to the comprehensive score data, the furnaces are sorted from the highest score to the lowest score, and the furnace with the highest score is selected as the target furnace to obtain the furnace allocation strategy.

[0034] 104. Perform path planning and collision detection on the motion trajectory of the dispatching robot based on the furnace allocation strategy to obtain dispatching instruction data;

[0035] Specifically, according to the target furnace position determined by the furnace position allocation strategy, the motion trajectory of the scheduling robot is calculated, mainly including the position of the fork, the height of the material picking and placing, and the spatial coordinates of the moving path. This process involves the angle calculation of the robot joints, the optimization of the horizontal moving path, and the precise control of the height adjustment to ensure that the robot can reach the target furnace position in the shortest time, efficiently and safely, and complete the material handling task. While calculating the robot's motion trajectory, the fixture leveling detection setting is performed to ensure that the fixture is always in a horizontal state during the material picking and placing process. The horizontality of the fixture is detected by the built-in sensor, and the fixture posture data is fed back in real time. If the fixture fails to remain horizontal, it will cause the battery to slip or the picking and placing to fail. Therefore, the robot joint angle is adjusted according to the feedback data to make the fixture reach the optimal horizontal state, thereby ensuring the stability and safety of the material during the handling process. While determining the fixture posture, the path safety verification is performed. During the operation of the scheduling robot, it will encounter obstacles such as other robots, manual operators or equipment. The photoelectric sensor is used to monitor the obstacles on the robot's motion path in real time and obtain the path safety status data. If other robots or obstacles are detected on the motion path, the robot's motion trajectory is adjusted immediately, or the robot's action is paused to avoid collision and interference. In this process, combined with the robot's dynamic planning algorithm, the optimal obstacle avoidance strategy is calculated based on the path safety status data, so that the robot can safely avoid obstacles while ensuring the efficient execution of the material picking and placing tasks. After the path safety status is confirmed to be correct, the robot is controlled and scheduled to perform the material picking action. This process includes several key steps. The first is positioning. The robot accurately adjusts the position according to the spatial coordinates of the target furnace position; then alignment, that is, adjusting the angle and position of the fork so that it is completely aligned with the clamping point of the target tray; followed by clamping, by controlling the closing force of the clamp to ensure that the battery is stably clamped; finally, lifting, the robot takes the battery out of the current tray according to the set lifting height and prepares for subsequent material placement operations. These operation steps form a complete robot material picking execution data and provide a basis for the subsequent furnace door linkage control. The robot material picking execution data is linked with the furnace door control system. The drying furnace needs to break the vacuum and open the door before unloading. Therefore, the system will control the furnace door to complete the vacuum breaking process in a preset order to ensure that the pressure inside and outside the furnace chamber is balanced before executing the door opening command to avoid mechanical jamming or material damage due to the pressure difference between the inside and outside of the furnace chamber. In this process, the furnace door status feedback data is used to monitor the opening and closing of the furnace door in real time, and ensure that the robot does not perform the unloading action before the furnace door is fully opened to prevent collision or misoperation. The furnace door status feedback data and the robot material collection execution data are time-controlled to ensure that the robot can perform the unloading action after the furnace door is fully opened and the vacuum is broken.The material unloading action instruction sequence needs to be executed strictly in chronological order, including adjusting the angle of the robot fork, slowly moving to the unloading position, gradually loosening the clamp, and withdrawing the fork after confirming that the battery is stably placed. These operations require precise system control to ensure the accuracy and stability of unloading. The unloading action instruction sequence is integrated into the scheduling instruction data and sent to the scheduling robot for execution to complete the entire material handling process.

[0036] 105. Perform real-time monitoring and data collection on the temperature and vacuum degree of the baking process according to the scheduling instruction data to obtain a process monitoring data packet;

[0037] Specifically, when the scheduling instruction data confirms the target furnace position and material status, the baking program is started, temperature sensors are arranged at multiple temperature measuring points in the drying furnace cavity, and the data of these sensors are continuously collected to obtain the trend of temperature change. Since the temperature in the furnace cavity is different at different positions, in order to ensure the uniformity of temperature and optimize the heat transfer effect, the collected temperature data is calculated using the curve fitting method to obtain the temperature distribution data inside the entire furnace cavity. The temperature distribution data is gradient analyzed to identify the temperature differences in various areas inside the furnace cavity, and the power output of the heater is adjusted accordingly. By setting a predetermined temperature curve, it is ensured that the temperature at different positions can meet the process requirements in a short time, and the temperature is dynamically adjusted by accurately controlling the power of the heater. The calculation process of the temperature control parameters takes into account the current temperature distribution and combines the baking time, the thermal characteristics of the material, and the response speed of the heater to ensure the stability and efficiency of the temperature regulation. When the heating system is running, the temperature gradient will be continuously corrected to ensure the uniformity of the temperature inside the cavity so that the battery baking can achieve the best effect. At the same time, based on the temperature control parameters, the vacuum system is pressure monitored, and the air pressure inside the furnace cavity is kept within the set range through the continuous operation of the vacuum pump. During the operation of the vacuum pump, the exhaust rate is adjusted to prevent the baking effect from being affected or the material from being damaged due to excessive pressure changes. The stability of the vacuum data directly affects the water evaporation rate, so this data is used as a key monitoring parameter in the baking process. When the vacuum inside the furnace cavity reaches the set range, the vacuum data is input into the detection module, triggering the moisture detection device to measure the moisture content of the battery in the tray. The moisture detection device uses a high-precision sensor to detect the residual moisture on the surface of the battery in a non-contact or low-loss manner and generate moisture detection data. The results of the moisture detection can intuitively reflect whether the current baking process is effective and provide a basis for subsequent adjustments. Since different batches of batteries have different initial moisture contents, the current process parameters are fine-tuned in combination with these detection data to ensure that each batch of batteries can meet the final drying requirements. After the moisture detection data is generated, it is analyzed and compared, and the set moisture threshold is used to determine whether the current baking time is sufficient. If the moisture content is still higher than the set safety range, the system automatically calculates the baking time that needs to be supplemented and generates supplementary baking time parameters. The calculation of the supplementary baking time takes into account the current moisture detection data and combines the temperature control curve, vacuum degree change trend and battery material characteristics to ensure that the supplementary baking time will neither waste excessive time nor affect the drying effect due to insufficient time. While calculating the supplementary baking time, the system adjusts the working status of the heater and vacuum pump to optimize the energy consumption and efficiency of the supplementary baking stage. The supplementary baking time parameters, temperature distribution data and vacuum degree data are fused to form a complete process monitoring data package.

[0038] 106. Based on the process monitoring data packet, the baking trays are classified and transferred to obtain classification result data.

[0039] Specifically, the moisture detection data is extracted from the process monitoring data packet, and the data is subjected to threshold comparison analysis. Each tray is tested according to the preset moisture content standard. If the moisture content of the battery in a tray is less than the standard value, the tray is marked as a qualified product, and if the moisture content is greater than the standard value, the tray is marked as an over-standard product. All trays are automatically classified and classification marking data is generated. After the classification marking is completed, a targeted processing strategy is executed according to the over-standard product information recorded in the classification marking data. Because the over-standard product tray fails to meet the process requirements, it needs to be baked again. Therefore, the system controls the dispatching robot or conveying equipment to transfer the over-standard product tray from the current furnace cavity to the cache position to prepare for the subsequent secondary baking. In this process, the over-standard product processing parameters are generated, which include the number of the over-standard tray, the storage location, the expected time of the secondary baking and the temperature compensation value, so as to ensure that the secondary baking can be carried out according to the optimized process conditions, so that the moisture content of the over-standard product can be reduced to within the qualified range. At the same time, avoid repeated processing of trays that have been baked twice but are still unqualified, and adopt different processing strategies according to the degree of excess, such as extending the baking time or adjusting the vacuum degree, to maximize the production qualification rate. At the same time, based on the qualified product information in the classification mark data, start the unloading process, control the unloading robot to take the battery out of the qualified tray, and place it in an orderly manner on the unloading conveyor line. When performing this operation, ensure that the unloading robot accurately grabs the battery to avoid battery falling or damage due to improper operation, and complete the material picking action of all qualified products according to the set beat. In this process, the qualified product unloading data is generated, which records the battery number, corresponding tray number, grabbing time and placement position of all completed unloading batteries, providing data support for subsequent traceability and quality management. The qualified product unloading data is analyzed, and the unloading conveyor line is started after all qualified batteries are successfully taken out. The conveyor line will transport the battery to the next process, such as battery packaging, testing or assembly process, according to the established process flow. During the conveying process, the conveying status is monitored in real time, and the sensor feedback data is used to ensure that the battery enters the next process smoothly according to the set route. After this step is completed, the unloading completion status data is generated, which is used to record the execution of this unloading process. At the same time, the empty trays are managed reasonably to ensure the continuity of the production process. After the unloading operation of the battery is completed, all empty trays are allocated to the tray buffer area for continued use in subsequent production. This process is based on the processing parameters of over-standard products, and special management is performed on trays that require secondary baking, and all empty trays are ensured to be stored in appropriate locations according to the optimized path, thereby avoiding stagnation of the production line due to improper tray allocation. To this end, tray allocation data is generated, which contains information such as tray number, storage location, and subsequent use plan, and ensures the automation and efficiency of tray management. Based on the tray allocation data and unloading completion status data, the data of the entire process is fused to generate classification result data.

[0040] In the embodiment of the present invention, a complete system initialization system is established by performing parameter configuration and communication initialization on the control system, thereby ensuring the coordinated operation of various functional modules and significantly reducing the failure rate at system startup; a real-time scanning and analysis mechanism based on the IO signal status is adopted, combined with the comparison and verification of MES data, to construct an efficient loading data management system and improve the efficiency of battery entry into the station; a multi-dimensional scoring mechanism is introduced to dynamically evaluate the furnace status, and the optimal allocation plan is obtained through weighted calculation, thereby solving the problem of uneven resource allocation; a scheduling robot control scheme including fixture leveling detection and path safety verification is designed, thereby achieving accurate planning of motion trajectories and collision prevention; a closed-loop control system for multi-point temperature acquisition and vacuum monitoring is established, combined with real-time feedback from moisture detection, thereby ensuring accurate control of the baking process; an intelligent classification mechanism and secondary baking process based on moisture content are constructed, and combined with an efficient material transfer strategy, thereby significantly improving product yield and production efficiency.

[0041] In a specific embodiment, the process of executing step 101 may specifically include the following steps:

[0042] Perform power-on self-test on the motors and sensors of the equipment control system to obtain hardware detection data including IO signal status and equipment interlocking status;

[0043] Input the hardware detection data into the system parameter configuration module, set the thresholds for the baking temperature, preheating time and vacuum degree, and obtain the process parameter configuration data;

[0044] Establish MES communication connection based on process parameter configuration data, obtain production work order information, and obtain production parameter data including product batch and process requirements;

[0045] The coordinate system of the robot execution unit is calibrated and the origin is calibrated according to the production parameter data to obtain the robot motion control parameters;

[0046] Configure the safety boundary and interlocking logic of the robot motion control parameters to obtain safety parameter data including limit conditions and emergency stop strategies;

[0047] The safety parameter data, process parameter configuration data and production parameter data are mapped to the database storage to obtain the system initialization data.

[0048] Specifically, the motors and sensors of the equipment control system are powered on for self-test to obtain hardware test data including IO signal status and equipment interlock status. The power-on self-test process involves gradually activating the drive signals of all motors and judging the operating status of the equipment through the current, voltage and position signals fed back by the sensors. Set the input signal and output signal The detection formula is:

[0049] ;

[0050] in, Represents the feedback function of the device system, Represents the power state of the motor, Represents the data fed back by the sensor. During the detection process, if With expected value If they do not match, it means that the device has an interlock anomaly or hardware failure and needs to be calibrated or repaired. Input the hardware detection data into the system parameter configuration module to ensure that the subsequent production parameters can be set correctly. Set thresholds for the baking temperature, preheating time, and vacuum degree to ensure that the drying process meets the production process requirements. Assume that the baking temperature is set to , the ambient temperature is , then the heating power The calculation method is expressed as:

[0051] ;

[0052] in, is the adjustment coefficient of the temperature control system, which is determined by the heat transfer characteristics of the equipment. Similarly, the preheating time By target temperature Thermal inertia of the device Related:

[0053] ;

[0054] Vacuum degree Need to stay within the set range To meet the requirements of air pressure control during baking:

[0055] ;

[0056] in, is the operating time of the vacuum pump, is the volume of the drying furnace chamber. The system adjusts the temperature, preheating time and vacuum degree in real time according to the set threshold value to obtain complete process parameter configuration data. After obtaining the process parameter configuration data, the system establishes a communication connection with the MES (manufacturing execution system) to obtain the production work order information and obtain the production parameter data including product batches and process requirements. The MES system stores the production plan, process flow and bill of materials, and the number of each batch of products. And the corresponding process parameters It is expressed as:

[0057] ;

[0058] in, represents the target baking temperature, Represents the vacuum degree required by the process, Represents the baking cycle time. By connecting with MES, it automatically matches the process requirements of the current production batch to ensure that all parameters meet the set requirements. After obtaining the production parameter data, the coordinate system of the robot execution unit is calibrated and the origin is calibrated to ensure that the robot can accurately complete the task of picking and placing materials. In this process, the position of the robot end effector Determined by multi-point calibration:

[0059] ;

[0060] in, represents the base coordinate system of the robot, Represents the location of the target placement point, Represents the calibration offset. The origin calibration process ensures that the zero deviation of all robot axes is minimized by repeatedly performing the zero return action, so as to obtain accurate robot motion control parameters. After obtaining the robot motion control parameters, the robot is configured with safety boundaries and interlocking logic to prevent collision or malfunction of the robot during operation. Safety range of robot movement Determined by limited space constraints:

[0061] ;

[0062] in, Represents the range of motion of the X axis, Represents the range of motion of the Y axis, Represents the range of motion of the Z axis. Set the emergency stop strategy, that is, when the robot moves at a speed of Exceeding safety limit When the system automatically triggers an emergency stop:

[0063] ;

[0064] Ensure that the robot can stop moving immediately when it exceeds the speed limit or deviates from the safe track to avoid safety accidents. All safety parameter data, process parameter configuration data and production parameter data are stored and mapped in the database to form complete system initialization data. These data are stored and used for automatic scheduling of the production line, and can be traced back and adjusted when necessary.

[0065] In a specific embodiment, the process of executing step 102 may specifically include the following steps:

[0066] According to the IO signal status in the system initialization data, the photoelectric sensors and proximity switches on the conveyor line are polled and scanned at a preset sampling period, and the high and low level signals and analog signals returned by the sensors are converted into A / D signals, and transmitted to the controller through the 485 bus to obtain the conveying status data including the battery status and quantity;

[0067] Analyze the conveying status data in real time, control the start and stop rhythm of the conveying line, and obtain the battery conveying interval control parameters;

[0068] Based on the battery conveying interval control parameters, the barcode scanner is triggered to read the battery barcode, and the format of the battery barcode is checked to obtain the battery batch identification data;

[0069] Compare and verify the battery batch identification data with the MES data to obtain the battery verification results including the entry permission status, and calculate the current available material tray quantity and loading cycle time based on the battery verification results to obtain the dynamic allocation parameters;

[0070] The dynamic allocation parameters are associated with the battery batch identification data and stored to obtain a loading data packet.

[0071] Specifically, based on the IO signal status in the system initialization data, the photoelectric sensors and proximity switches installed on the conveyor line are polled and scanned at a preset sampling period to ensure real-time monitoring of the conveyor status and data update. Set the sampling period , and read the return signals of the photoelectric sensor and proximity switch according to the cycle polling, these signals include high and low level signals and analog signals ,in Indicates binary status, such as whether a battery is in place, and Represents the position accuracy or angle information of the battery on the conveyor line. In order to allow the analog signal to be processed on the digital controller, it is converted to A / D, that is:

[0072] ;

[0073] in, is the converted digital signal, is the input voltage of the sensor, is the A / D conversion reference voltage, is the number of bits of A / D conversion. The converted data is transmitted to the controller via the 485 bus to obtain the delivery status data including the battery status and quantity, which is expressed as:

[0074] ;

[0075] in, It is a mapping function for sensor data processing, which converts the signals collected by the sensor into effective conveying status data. When the conveying status data is received by the controller, the data is analyzed in real time, and the start and stop status of the conveying line is determined by calculating the running rhythm of the conveying line. Conveying interval Battery position feedback from sensor and the speed of the conveyor line calculate:

[0076] ;

[0077] in, It is the target conveying position. If it is detected that the battery has reached the target conveying position, the system will issue a stop conveying command to pause the conveying line until the scanning and verification process is completed. After the conveying line stops, the barcode scanner is triggered based on the calculated battery conveying interval control parameters to read the battery barcode. The scanner obtains the batch information of the battery by identifying the barcode or QR code, and performs format verification on the information to ensure that the barcode conforms to the standard format, for example, the barcode length Whether the preset standards are met :

[0078] ;

[0079] If the verification fails, an alarm signal is triggered and manual intervention is required. If the verification succeeds, the battery batch identification data is obtained. The battery batch identification data is compared with the information in the MES database to verify whether the battery meets the entry permission status of the current work order. The MES database stores production work order information, including batch number, production requirements, loading priority, etc. The matching result of the battery is expressed as:

[0080] ;

[0081] If the battery batch does not match, the battery may be an abnormal product, triggering the abnormal processing logic. If the match is successful, the current available tray quantity is calculated based on the MES data. And the feeding cycle time :

[0082] ;

[0083] ;

[0084] in, is the inventory status data, Responsible for calculating the number of currently available trays, Calculate the feeding cycle time. In order to ensure the continuity and accuracy of the entire feeding process, the parameters will be dynamically allocated. Associated with the battery batch identification data for storage to generate a complete loading data package:

[0085] ;

[0086] The data packet records the identity information of each battery and contains key parameters for loading, such as entry permission status, corresponding material tray quantity and conveying cycle time.

[0087] In a specific embodiment, the process of executing step 103 may specifically include the following steps:

[0088] Collect real-time data on the drying furnace temperature, vacuum degree, baking time and furnace door status to obtain status monitoring data including physical parameters of each furnace position;

[0089] The status monitoring data is input into the scoring system, and the weight calculation is performed based on the idle time, maintenance status and temperature compliance of each furnace to obtain the furnace status score;

[0090] According to the loading data packet, calculate the shortest path distance from each furnace position to the loading station and obtain the moving distance score;

[0091] Analyze the historical baking records of each furnace based on the condition monitoring data, calculate the equipment utilization rate and failure rate, and obtain the historical record score;

[0092] The furnace status score, moving distance score and historical record score are weighted and summed, wherein the furnace status weight is the first target value, the moving distance weight is the second target value, and the historical record weight is the third target value, to obtain comprehensive score data;

[0093] The comprehensive scoring data are sorted from high to low, and the furnace with the highest score is selected as the target furnace to obtain the furnace allocation strategy.

[0094] Specifically, real-time data collection is performed on the drying furnace temperature, vacuum degree, baking time and furnace door status to obtain the status information of each furnace position. The temperature distribution data is obtained by measuring multiple temperature sensors in the furnace cavity after curve fitting:

[0095] ;

[0096] in, Indicates the furnace cavity The temperature measured by the sensor, is the number of sensors. Vacuum degree Measured by the pressure sensor and meets the set process range :

[0097] ;

[0098] if If it exceeds this range, it indicates that the vacuum state of the oven cavity is abnormal and maintenance is required. The record is the time that the current batch of batteries has been baked in the oven cavity, and needs to be compared with the set standard baking time To compare:

[0099] ;

[0100] in, is the current time, It is the time when the oven cavity starts baking. Detected by the sensor, the value is 1 for the furnace door to be open, and 0 for the furnace door to be closed. By fusing the above parameters, the status monitoring data containing the physical parameters of each furnace position is formed. The furnace position status is scored to determine the priority of each furnace position. The scoring system calculates the weight based on the idle time, maintenance status and furnace temperature compliance of the furnace position to obtain the furnace position status score. . Free time Calculated from the time the oven cavity was last used to the present time:

[0101] ;

[0102] If the idle time is longer, it means that the furnace is rarely used and can be allocated first. Determined by the maintenance record, if the furnace is currently in maintenance mode, then the value is assigned ,otherwise The furnace temperature compliance is determined by Is it close to the set temperature? calculate:

[0103] ;

[0104] like Less than a certain threshold , the furnace temperature is considered to be up to standard. Combining these factors, the furnace status score is expressed as:

[0105] ;

[0106] in, It is an empirical weight parameter to ensure the rationality of the score. While calculating the furnace status score, the shortest path distance from each furnace to the loading station is calculated according to the loading data packet to obtain the moving distance score. Assuming furnace position The straight-line distance to the loading station is , then the moving time The robot travel speed calculate:

[0107] ;

[0108] In order to give higher priority to furnaces with shorter distances, the travel distance score is defined as:

[0109] ;

[0110] in, The weight coefficient ensures that the scoring results can reasonably distinguish different furnaces. The historical baking records of each furnace are analyzed based on the status monitoring data to calculate the equipment utilization rate and failure rate to obtain the historical record score. . Utilization rate Indicates the utilization rate of the furnace, through the furnace operation time and total recording time calculate:

[0111] ;

[0112] Failure rate By historical failure times Total number of runs calculate:

[0113] ;

[0114] History Scoring It is expressed as:

[0115] ;

[0116] in, It is an adjustment factor used to balance the impact of utilization rate and failure rate. The furnace status score, moving distance score and historical record score are weighted and summed to obtain the final comprehensive score data. :

[0117] ;

[0118] in, They are weight parameters for furnace status, moving distance and historical records, ensuring that the influence of different factors is reasonably balanced. The furnaces are sorted from high to low according to their scores, and the furnace with the highest score is selected as the target furnace to generate a furnace allocation strategy.

[0119] In a specific embodiment, the process of executing step 104 may specifically include the following steps:

[0120] According to the target furnace position in the furnace allocation strategy, the spatial coordinates of the fork position, material picking and placing height and moving path of the dispatching robot are calculated to obtain the robot motion trajectory parameters;

[0121] The robot motion trajectory parameters are set to detect whether the fixture is placed horizontally, and the fixture posture data is obtained by using sensors to detect whether the fixture is placed horizontally;

[0122] Path safety verification is performed based on the fixture posture data. Photoelectric sensors are used to monitor whether there are other robots or obstacles on the motion path to obtain path safety status data.

[0123] According to the path safety status data, the robot is controlled and dispatched to perform the material picking action, including the positioning, alignment, clamping and lifting action sequence, and the robot material picking execution data is obtained;

[0124] The robot's material-picking execution data is used to control the furnace door linkage, control the drying furnace to break the vacuum and open the door, and obtain the furnace door status feedback data;

[0125] The furnace door status feedback data and the robot's material picking execution data are time-controlled to generate a material discharge action instruction sequence, and the material discharge action instruction sequence is used as the scheduling instruction data.

[0126] Specifically, according to the target furnace position The three-dimensional space coordinates of , combined with the robot's current position And the working range inside the furnace cavity , calculate the final positioning point of the robot fork :

[0127] ;

[0128] in, It is a path planning function that ensures that the robot can move to the target furnace position along the optimal trajectory. According to the height of the tray Distance from furnace bottom Perform the calculation:

[0129] ;

[0130] The robot can accurately perform pick-and-place operations at the appropriate height without the problem of being too high or too low. During the robot's movement, the fixture is ensured to be in a horizontal state, so the system performs a fixture leveling test. Fixture Angle The angle is measured in real time by the angle sensor and compared with the set horizontal reference angle. For comparison:

[0131] ;

[0132] if Out of tolerance range , the system will issue an adjustment command to return the fixture to a horizontal position to ensure that the battery will not tilt or slide during transportation. Provided by multiple sensors, including accelerometers, gyroscopes, and contact sensors, the gripper's horizontal state is ensured to remain stable during robot operation. After the gripper is leveled, the robot's motion trajectory is verified for path safety to avoid collisions with other robots or obstacles. Path Safety Status The photoelectric sensor detects obstacles in the robot's motion path and calculates the shortest obstacle avoidance path. is the shortest distance from the robot to the obstacle, is the safety threshold, then the path safety status is defined as:

[0133] ;

[0134] if , replan the robot path to ensure that the robot can safely avoid obstacles without affecting the production process. After the path safety status is confirmed, the system starts to control the dispatching robot to perform the material picking action. During the material picking process, the robot is accurately positioned and adjusted to the target point. , and then align, correcting the position error between the robot and the target material through a visual system or laser sensor. The clamping action involves the clamp closing pressure Control:

[0135] ;

[0136] in, is the clamping force constant, is the current fixture opening distance, The robot performs a lifting action to slowly lift the tray from the furnace chamber to a safe transport height. After the robot successfully completes the material removal, the furnace door is controlled to ensure that the furnace door can be opened correctly and allow the robot to remove the tray. The furnace chamber performs a vacuum breaking operation to return the internal pressure to normal. Vacuum degree Controlled by a vacuum pump and maintained within a set safety range:

[0137] ;

[0138] When the furnace chamber reaches the safe pressure, the system controls the furnace door to execute the door opening command and monitors the furnace door status. :

[0139] ;

[0140] Only when The robot will only perform the material picking operation when the furnace door is fully opened to ensure that there will be no misoperation before the furnace door is fully opened to prevent equipment damage or safety accidents. The furnace door status feedback data and the robot material picking execution data are time-controlled to generate a material discharge action instruction sequence. Determined by the coordinates of the unloading station and optimized in combination with the material transportation path:

[0141] ;

[0142] in, It is the charging furnace position. It is the unloading station. During the unloading process, the robot needs to slowly lower the fork to ensure that the tray is placed securely and release the clamping force. :

[0143] ;

[0144] After ensuring that the material is safely placed, the robot retreats to its initial position and completes the entire material placement process. The material placement action instruction sequence is stored as scheduling instruction data and used to control subsequent robot tasks, so that the production process can proceed smoothly according to the established scheduling strategy.

[0145] In this embodiment, before the temperature and vacuum degree of the baking process are monitored in real time and data is collected according to the scheduling instruction data, the scheduling instruction data is also subjected to delay compensation processing, specifically including: establishing a frequency domain model of the temperature and vacuum degree control signal based on the scheduling instruction data to obtain frequency domain characteristic data including amplitude and phase; performing model-free predictive analysis on the frequency domain characteristic data to calculate the delay time in the signal transmission process to obtain delay characteristic parameters; inputting the delay characteristic parameters into the compensator, preprocessing the signal through the ideal transformer method to obtain compensation coefficient data; and performing frequency correlation analysis on the control signal based on the compensation coefficient data. The signal amplitude variation trend is calculated and the amplitude correction factor is obtained; a normalization processing model is established according to the amplitude correction factor, and the signal amplitude is dynamically adjusted to obtain the normalization parameter; the normalization parameter and the delay characteristic parameter are coupled to generate a real-time compensation matrix to obtain the compensation matrix data; the temperature and vacuum control signals are corrected in real time according to the compensation matrix data, the optimal control quantity is calculated and the corrected control parameters are obtained; the corrected control parameters are fused with the scheduling instruction data to generate the compensated control instruction and obtain the optimized control data; the temperature and vacuum degree of the baking process are monitored in real time based on the optimized control data.

[0146] Among them, before the motion control of the scheduling robot is carried out according to the furnace allocation strategy, it also includes: encrypting the identity information of the scheduling robot based on Chebyshev chaotic mapping to obtain robot identity authentication data; inputting the robot identity authentication data into the authentication module, generating a dynamic verification code based on a timestamp, and obtaining a temporary authentication key; encrypting the temporary authentication key in segments, generating an authentication signature through hash function calculation, and obtaining session authentication data; establishing a secure communication channel based on the session authentication data, encrypting and transmitting the scheduling instructions, and obtaining encrypted control instructions; decrypting the encrypted control instructions and matching and verifying them with a preset instruction template to obtain an instruction verification result; judging whether there is abnormal access based on the instruction verification result, generating abnormal warning information, and obtaining abnormal monitoring data; performing real-time analysis on the abnormal monitoring data, establishing an access blacklist, and obtaining an access control strategy; performing security filtering on subsequent scheduling instructions based on the access control strategy to ensure the security of instruction execution and obtain a secure authentication result.

[0147] Among them, according to the loading data packet, the dynamic scoring calculation of the drying furnace position status is performed to obtain the furnace position allocation strategy; based on the furnace position allocation strategy, the path planning and collision detection of the motion trajectory of the scheduling robot are performed to obtain the scheduling instruction data, and it also includes: based on the battery batch information in the loading data packet, the coordinate sensitivity of each furnace position is differentiated and calculated to obtain the coordinate sensitivity matrix; the coordinate sensitivity matrix is ​​optimized by Laplace distribution parameters, and the noise addition weight of each coordinate point is calculated to obtain the coordinate noise distribution parameters; the furnace position allocation strategy is compensated for independent non-identical distribution noise according to the coordinate noise distribution parameters to obtain the compensated furnace position scoring data; the compensated The final furnace position scoring data is input into the trajectory planning module for coordinate space mapping conversion to obtain the initial data of the robot's motion trajectory; the path optimization iterative calculation is performed based on the initial data of the robot's motion trajectory, and the optimal path parameters are obtained by minimizing the weighted mean square error; the robot's motion trajectory is dynamically corrected according to the optimal path parameters, and a coordinate compensation feedback mechanism is established to obtain compensation optimization data; the compensation optimization data is fused with the furnace position allocation strategy to generate the robot motion control instructions to obtain the optimized scheduling strategy; the optimized scheduling strategy is safety checked and executed predicted, and the trajectory simulation verification is used to ensure that there is no collision risk to obtain the scheduling instruction data.

[0148] In a specific embodiment, the process of executing step 105 may specifically include the following steps:

[0149] Start the baking program according to the scheduling instruction data, collect and curve fit the data of multiple temperature sensors in the furnace cavity, and obtain the temperature distribution data;

[0150] Perform gradient analysis on the temperature distribution data, control the heater to adjust the power output according to the preset temperature curve, and obtain the temperature control parameters;

[0151] The vacuum system pressure is monitored based on the temperature control parameters, and the cavity pressure is maintained within the set range by the continuous operation of the vacuum pump to obtain the vacuum degree data;

[0152] The vacuum degree data is input into the detection module, and the moisture detection device is triggered to collect the moisture content of the material tray to obtain the moisture detection data;

[0153] Analyze and compare the moisture detection data, determine whether additional baking time is needed by setting a threshold, and obtain the additional baking time parameter;

[0154] The baking time parameters, temperature distribution data and vacuum degree data are fused and processed to obtain a process monitoring data package.

[0155] Specifically, the baking program is started according to the scheduling instruction data, and the data of the multi-point temperature sensors in the furnace cavity are collected to build the temperature distribution model of the furnace cavity. The real-time measured temperature of each sensor is recorded as , then the overall temperature distribution of the furnace chamber is expressed as:

[0156] ;

[0157] The measured data are then curve-fitted using a polynomial regression method to fit the function Estimate the temperature at various locations inside the furnace cavity:

[0158] ;

[0159] in, is the internal coordinate point of the furnace cavity, The fitting coefficient is obtained by the least square method to obtain the optimal solution and the temperature distribution data in the furnace cavity. The temperature distribution data is subjected to gradient analysis to calculate the change trend of the temperature field and control the heater power output so that the temperature gradually reaches the set preheating curve. Calculated from partial derivatives:

[0160] ;

[0161] If the temperature gradient in a certain area is too large, it means that the area is heating up too fast or too slow, and the heater power needs to be adjusted. The heating power is adjusted by the PID (proportional-integral-differential) control algorithm:

[0162] ;

[0163] in, To set the temperature, is the current temperature, They are PID control parameters. Through PID adjustment, the furnace chamber temperature can be stably close to the set value without overshoot or oscillation, and accurate temperature control parameters can be obtained. While controlling the temperature, the vacuum degree of the furnace chamber is monitored in real time to ensure that the air pressure remains within the set range. The pressure inside the furnace chamber It is measured by a vacuum sensor and needs to satisfy the following constraints:

[0164] ;

[0165] if If it exceeds this range, adjust the operating state of the vacuum pump to increase or decrease the pumping rate. :

[0166] ;

[0167] in, is the vacuum control gain coefficient, The target pressure is maintained by the continuous operation of the vacuum pump to maintain a stable pressure inside the furnace chamber, thereby improving the baking efficiency and ensuring that the battery moisture can be effectively evaporated. After the vacuum degree is stable, the moisture detection device is triggered to measure the moisture content of the battery in the tray. Moisture content The moisture content is measured by an infrared moisture sensor or a coulometric moisture meter and compared with the set moisture threshold. For comparison:

[0168] ;

[0169] if , indicating that the moisture content exceeds the standard, and the baking time needs to be extended to ensure that the moisture content is further reduced. Calculated by empirical formula:

[0170] ;

[0171] in, is the water evaporation rate constant. , no additional baking is required, and the battery can directly enter the cooling stage. The additional baking time parameters, temperature distribution data and vacuum degree data are fused to form a complete process monitoring data package. The data fusion method adopts weighted average:

[0172] ;

[0173] in, are the importance weights of temperature, vacuum degree and moisture content respectively.

[0174] Among them, the temperature and vacuum degree of the baking process are monitored and collected in real time according to the scheduling instruction data to obtain a process monitoring data packet. Before the baking trays are classified and transferred, it also includes: constructing a time series data set according to historical baking data, and standardizing the data to obtain training sample data; inputting the training sample data into the encoder for feature extraction and time step encoding to obtain a time series feature vector; performing time attention calculation on the time series feature vector, assigning weight coefficients based on similarity, and obtaining attention weight data; inputting the attention weight data into the decoder for prediction calculation to generate prediction sequence data; calculating the attention loss according to the prediction sequence data and the actual monitoring data, recalibrating the attention weight, and obtaining calibration weight data; dynamically predicting the temperature and vacuum degree of the current baking process based on the calibration weight data to obtain prediction parameter data; comparing the prediction parameter data with the process standard in real time, generating an adjustment strategy, and obtaining process compensation data; feeding back the process compensation data to the baking control system, performing closed-loop adjustment on the heating power and vacuum degree, and obtaining an updated process monitoring data packet.

[0175] In a specific embodiment, the process of executing step 106 may specifically include the following steps:

[0176] Perform threshold comparison analysis on the moisture detection data in the process monitoring data package, mark the trays with moisture content less than the standard value as qualified products, and mark the trays with moisture content greater than the standard value as out-of-standard products, and obtain classified marking data;

[0177] According to the information of the over-standard products in the classification mark data, the over-standard product tray is transferred to the cache position for secondary baking to obtain the over-standard product processing parameters;

[0178] Based on the qualified product information in the classified mark data, the unloading robot is controlled to take the battery out of the tray and place it on the unloading conveyor line to obtain qualified product unloading data;

[0179] Analyze the qualified product unloading data and start the unloading conveyor line to transport the batteries to the next process and obtain the unloading completion status data;

[0180] The empty trays corresponding to the out-of-standard product processing parameters are allocated to the tray buffer area to obtain the tray allocation data, and the classification result data is generated based on the tray allocation data and the material unloading completion status data.

[0181] Specifically, the moisture detection data in the process monitoring data package is compared and analyzed by threshold value to determine whether the batteries in the tray meet the drying requirements. The moisture sensor collects the moisture in real time and compares it with the set moisture standard value. For comparison:

[0182] ;

[0183] in, Represents the maximum moisture content allowed after the battery is baked. , it means that the battery moisture content meets the requirements and the tray is marked as qualified, otherwise the tray is marked as exceeding the standard. The system stores this information in the classification mark data :

[0184] ;

[0185] in, Representative Tray number, The status of the product is (1 for qualified products, 0 for substandard products). After the classification is completed, the substandard product trays are subjected to secondary baking. The system instructs the dispatching robot to transfer the tray of over-standard products to the cache position so that it can enter the secondary baking process. The storage location of the tray of over-standard products is determined by the cache position coordinates. The decision is made, and its transfer path is calculated by the scheduling algorithm:

[0186] ;

[0187] in, Represents the path planning function, ensuring that the robot moves the tray from the current furnace cavity position in the shortest path Transfer to cache location At the same time, calculate the secondary baking parameters required for products that exceed the standard , i.e. additional baking time:

[0188] ;

[0189] in, is an empirical constant, which indicates the dependence of water evaporation rate on time. Contains tray number, cache location coordinates and additional baking time:

[0190] ;

[0191] At the same time, the qualified trays are processed and the unloading robot is controlled to take the batteries out of the trays and place them on the unloading conveyor line for the next step. Status in The pick-and-place trajectory of the unloading robot is calculated by the path optimization algorithm:

[0192] ;

[0193] in, Represents the placement position on the unloading conveyor line, To ensure that the robot can perform pick-and-place operations accurately, set the clamping force to ensure that the battery is not damaged by excessive or insufficient clamping force. :

[0194] ;

[0195] in, is the clamping force adjustment coefficient, is the current fixture opening width, is the target clamping width. When the unloading robot completes the battery pick-up and placement, it analyzes the qualified unloading data and starts the unloading conveyor line to allow the battery to enter the next process smoothly. The running speed of the conveyor line By conveying distance and target time Decide:

[0196] ;

[0197] If the current conveying speed Below the set value , the system adjusts the conveyor belt motor power to achieve the desired speed. After the conveying is completed, the unloading completion status data is generated:

[0198] ;

[0199] At the same time, the empty trays corresponding to the processing parameters of the products exceeding the standard are managed and allocated to the tray buffer area so that they can re-enter the production process. Determined by the cache management system and calculated in conjunction with the scheduling algorithm:

[0200] ;

[0201] The system then updates the tray allocation data , which contains the numbers of all empty trays and their storage locations:

[0202] ;

[0203] Fusion of tray allocation data and unloading completion status data to generate classification result data :

[0204] ;

[0205] This data package records the final status of all trays, including the unloading information of qualified products, the secondary baking arrangement of substandard products, and the storage information of empty trays.

[0206] The above describes the intelligent scheduling method of the production line in the embodiment of the present invention. The following describes the intelligent scheduling system of the production line in the embodiment of the present invention. Figure 2 , an embodiment of the production line intelligent scheduling system in the embodiment of the present invention includes:

[0207] Initialization module 201, used to perform parameter configuration and communication initialization on the device control system to obtain system initialization data;

[0208] The detection module 202 is used to scan and detect the position of the battery of the conveyor line based on the system initialization data to obtain a loading data packet;

[0209] The calculation module 203 is used to dynamically score and calculate the furnace position status of the drying furnace according to the loading data packet to obtain a furnace position allocation strategy;

[0210] The planning module 204 is used to perform path planning and collision detection on the motion trajectory of the scheduling robot based on the furnace allocation strategy to obtain scheduling instruction data;

[0211] The monitoring module 205 is used to perform real-time monitoring and data collection on the temperature and vacuum degree of the baking process according to the scheduling instruction data to obtain a process monitoring data packet;

[0212] The processing module 206 is used to classify and transfer the baking trays based on the process monitoring data packets to obtain classification result data.

[0213] Through the coordinated cooperation of the above-mentioned components, a complete system initialization system was established by configuring the parameters of the control system and initializing the communication, which ensured the coordinated operation of various functional modules and significantly reduced the failure rate at system startup; a real-time scanning and analysis mechanism based on the IO signal status was adopted, combined with the comparison and verification of MES data, to build an efficient loading data management system and improve the efficiency of battery entry; a multi-dimensional scoring mechanism was introduced to dynamically evaluate the furnace status, and the optimal allocation plan was obtained through weighted calculation to solve the problem of uneven resource allocation; a scheduling robot control solution including fixture leveling detection and path safety verification was designed to achieve accurate planning of motion trajectories and collision prevention; a closed-loop control system for multi-point temperature acquisition and vacuum monitoring was established, combined with real-time feedback from moisture detection to ensure accurate control of the baking process; an intelligent classification mechanism and secondary baking process based on moisture content were constructed, and with the efficient unloading and transfer strategy, the product yield and production efficiency were significantly improved.

[0214] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0215] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0216] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A production line intelligent scheduling method, characterized in that: The method comprises: Perform parameter configuration and communication initialization on the equipment control system to obtain system initialization data; Based on the system initialization data, the battery of the conveyor line is scanned and the position is detected to obtain a loading data packet; specifically, the following steps are performed: according to the IO signal state in the system initialization data, the photoelectric sensor and the proximity switch on the conveyor line are polled and scanned at a preset sampling period, the high and low level signals and analog signals returned by the sensor are A / D converted, and transmitted to the controller through the 485 bus to obtain the conveying status data including the battery in place status and quantity; the conveying status data is analyzed in real time to control the start and stop rhythm of the conveyor line to obtain the battery conveying interval control parameters; based on the battery conveying interval control parameters, the barcode scanner is triggered to read the battery barcode, and the battery barcode is formatted to obtain the battery batch identification data; the battery batch identification data is compared and verified with the MES data to obtain the battery verification result including the station entry permission status, and the current available material tray quantity and the loading rhythm time are calculated according to the battery verification result to obtain the dynamic allocation parameters; the dynamic allocation parameters are associated with the battery batch identification data and stored to obtain the loading data packet; According to the loading data packet, the furnace position status of the drying furnace is dynamically scored and calculated to obtain a furnace position allocation strategy; specifically, the following steps are performed: real-time data collection is performed on the furnace temperature, vacuum degree, baking time and furnace door status of the drying furnace to obtain status monitoring data containing physical parameters of each furnace position; the status monitoring data is input into the scoring system, and weight calculation is performed based on the idle time, maintenance status and furnace temperature compliance of each furnace position to obtain a furnace position status score; according to the loading data packet, the shortest path distance from each furnace position to the loading station is calculated to obtain a moving distance score; based on the status monitoring data, the historical baking records of each furnace position are analyzed, the equipment utilization rate and failure rate are calculated, and a historical record score is obtained; the furnace position status score, the moving distance score and the historical record score are weighted and summed, wherein the furnace position status weight is the first target value, the moving distance weight is the second target value, and the historical record weight is the third target value, to obtain comprehensive scoring data; the comprehensive scoring data is sorted from high to low according to the score, and the furnace position with the highest score is selected as the target furnace position to obtain a furnace position allocation strategy; Based on the furnace allocation strategy, path planning and collision detection are performed on the motion trajectory of the scheduling robot to obtain scheduling instruction data; Perform real-time monitoring and data collection on the temperature and vacuum degree of the baking process according to the scheduling instruction data to obtain a process monitoring data packet; Based on the process monitoring data packet, the trays that have completed baking are classified and transferred to obtain classification result data.

2. The production line intelligent scheduling method according to claim 1 is characterized in that: The device control system is configured with parameters and communication is initialized to obtain system initialization data, including: Perform power-on self-test on the motors and sensors of the equipment control system to obtain hardware detection data including IO signal status and equipment interlocking status; Input the hardware detection data into the system parameter configuration module, set the thresholds for the baking temperature, preheating time and vacuum degree, and obtain the process parameter configuration data; Establishing an MES communication connection based on the process parameter configuration data, acquiring production work order information, and obtaining production parameter data including product batches and process requirements; Calibrate the coordinate system and origin of the robot execution unit according to the production parameter data to obtain the robot motion control parameters; Performing safety boundary and interlocking logic configuration on the robot motion control parameters to obtain safety parameter data including limit conditions and emergency stop strategies; The safety parameter data, the process parameter configuration data and the production parameter data are mapped to database storage to obtain system initialization data.

3. The intelligent scheduling method for production lines according to claim 1, characterized in that: The path planning and collision detection of the motion trajectory of the scheduling robot based on the furnace allocation strategy to obtain scheduling instruction data includes: According to the target furnace position in the furnace position allocation strategy, the spatial coordinates of the fork position, the height of picking and placing materials, and the moving path of the dispatching robot are calculated to obtain the robot motion trajectory parameters; The robot motion trajectory parameters are set to detect whether the fixture is placed horizontally by using a sensor to obtain fixture posture data; Perform path safety verification based on the fixture posture data, monitor whether there are other robots or obstacles on the motion path through a photoelectric sensor, and obtain path safety status data; Controlling the dispatching robot to perform material picking actions according to the path safety status data, including positioning, alignment, clamping and lifting action sequences, and obtaining robot material picking execution data; The robot material taking execution data is used to perform furnace door linkage control, and the drying furnace is controlled to perform vacuum breaking and door opening actions, and furnace door status feedback data is obtained; The furnace door state feedback data and the robot material picking execution data are time-sequentially controlled to generate a material discharge action instruction sequence, and the material discharge action instruction sequence is used as scheduling instruction data.

4. The production line intelligent scheduling method according to claim 1, characterized in that: The temperature and vacuum degree of the baking process are monitored and collected in real time according to the scheduling instruction data to obtain a process monitoring data packet, including: Start the baking program according to the scheduling instruction data, collect and curve fit the data of multiple temperature sensors in the furnace cavity, and obtain temperature distribution data; Performing gradient analysis on the temperature distribution data, controlling the heater to adjust power output according to a preset temperature curve, and obtaining temperature control parameters; Based on the temperature control parameters, the vacuum system is pressure-monitored, and the cavity pressure is maintained within a set range by continuously operating the vacuum pump to obtain vacuum degree data; The vacuum degree data is input into the detection module, and the moisture detection device is triggered to collect the moisture content of the material tray to obtain moisture detection data; Analyze and compare the moisture detection data, determine whether additional baking time is needed by setting a threshold, and obtain additional baking time parameters; The supplementary baking time parameter, the temperature distribution data and the vacuum degree data are subjected to data fusion processing to obtain a process monitoring data packet.

5. The production line intelligent scheduling method according to claim 1, characterized in that: The method of classifying and transferring the baking trays based on the process monitoring data packet to obtain classification result data includes: Performing threshold comparison analysis on the moisture detection data in the process monitoring data packet, marking the trays with moisture content less than the standard value as qualified products, and marking the trays with moisture content greater than the standard value as out-of-standard products, to obtain classification marking data; According to the information of the out-of-standard products in the classification mark data, the out-of-standard product tray is transferred to the cache position for secondary baking to obtain the out-of-standard product processing parameters; Based on the qualified product information in the classification mark data, the unloading robot is controlled to take the battery out of the material tray and place it on the unloading conveyor line to obtain qualified product unloading data; Analyze the qualified product unloading data and start the unloading conveyor line to convey the battery to the next process to obtain unloading completion status data; The empty trays corresponding to the out-of-standard product processing parameters are allocated to the tray buffer area to obtain tray allocation data, and classification result data is generated based on the tray allocation data and the material unloading completion status data.

6. A production line intelligent scheduling system, characterized in that: The system is used to execute the production line intelligent scheduling method according to any one of claims 1 to 5, comprising: The initialization module is used to configure parameters and initialize communication of the equipment control system to obtain system initialization data; A detection module, used to scan and detect the position of the batteries on the conveyor line based on the system initialization data to obtain a loading data packet; A calculation module, used for dynamically scoring and calculating the furnace position status of the drying furnace according to the feeding data packet to obtain a furnace position allocation strategy; A planning module, used to perform path planning and collision detection on the motion trajectory of the scheduling robot based on the furnace allocation strategy to obtain scheduling instruction data; A monitoring module, used to perform real-time monitoring and data collection on the temperature and vacuum degree of the baking process according to the scheduling instruction data, and obtain a process monitoring data packet; The processing module is used to classify and transfer the baking trays based on the process monitoring data packet to obtain classification result data.

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