A control method and system of a trackless die-changing vehicle, an intelligent terminal and a storage medium

Abstract

The application relates to the technical field of automatic guided vehicle control and industrial robots, in particular to a control method and system of a trackless mold changing vehicle, an intelligent terminal and a storage medium, which method comprises the following steps: acquiring real-time mechanical parameters in a mold core dragging process; based on a preset mold core-vehicle body dynamic coupling model, calculating a vehicle body stability prediction value; when the preset stability threshold is exceeded, dynamically adjusting a support pressure distribution and synchronously correcting a traction force control curve; in the mold core dragging process, real-time monitoring of a relative position error is carried out, and based on the relative position error and the corrected traction force control curve, a cooperative compensation control is executed to maintain the mold changing operation accuracy. The application has the effects of improving the cooperative control accuracy of the vehicle body stability and the traction force of the trackless mold changing vehicle in the process of dragging a heavy mold core, and improving the position error compensation capability between the mold changing track and the docking track.

Description

Technical Field

[0001] This application relates to the technical field of automated guided vehicle control and industrial robots, and in particular to a control method, system, intelligent terminal and storage medium for a trackless mold changing vehicle. Background Technology

[0002] Molds are core production tools in industrial manufacturing fields such as injection molding, stamping, and die casting. The mold core, located within the mold base, is a key component that directly determines the shape of the product. To adapt to the production needs of different products, mold cores need to be frequently replaced. Therefore, efficiently and accurately completing the disassembly, assembly, and transfer of mold cores is a crucial link in ensuring production line efficiency, reducing downtime, and achieving flexible production. Trackless mold changing carts, as a type of autonomously moving automated handling equipment, are used to carry and pull heavy mold cores between the mold base and storage area, replacing traditional manual or semi-mechanized mold changing operations, thereby improving the automation level of the mold changing process.

[0003] In related technologies, for the replacement of mold cores in large or heavy molds, the industry mainly uses dedicated track-type mold changing trolleys. These trolleys require pre-embedded, fixed metal guide rails in the ground, and the trolley is constrained to move linearly back and forth on these rails. During operation, the operator fixes the mold core to be replaced onto the trolley and, through manual or simple mechanical drive, guides it along the preset track to and from the mold installation station. Another type is the trackless automated guided mold changing trolley. This type eliminates the reliance on pre-embedded ground guide rails, achieving autonomous movement through its own steering mechanism. It utilizes its onboard mold changing track to align and connect with the docking track on the mold base, and a towing mechanism completes the pushing and pulling of the mold core.

[0004] Regarding the aforementioned technologies, on the one hand, track-based solutions lack flexibility, have fixed paths, and cannot adapt to changes in factory layout. Their low versatility necessitates the use of dedicated tracks for different molds. On the other hand, while existing trackless mold-changing vehicles achieve autonomous trackless movement and alignment in their mechanical structure, their control mechanisms remain relatively rudimentary. For example, during the dragging of heavy mold cores, it is difficult to perceive and coordinate the dynamic changes in vehicle stability and traction force in real time, leading to positional errors between the mold-changing track and the docking track, affecting operational accuracy. Furthermore, control strategies typically employ fixed parameters, failing to adapt to different mold types or operating environments, and there is room for improvement in areas such as energy efficiency management, self-optimization of alignment accuracy, and fault prevention. Summary of the Invention

[0005] To improve the coordinated control accuracy of vehicle stability and traction force during the dragging of heavy mold cores by trackless mold changing vehicles, and to enhance the positional error compensation capability between the mold changing track and the docking track, this application provides a control method, system, intelligent terminal, and storage medium for trackless mold changing vehicles.

[0006] Firstly, this application provides a control method for a trackless mold-changing vehicle, employing the following technical solution: A control method for a trackless mold-changing vehicle includes: The real-time mechanical parameters during the core dragging process are obtained, including at least the force value of the drag hook mechanism, the vibration amplitude of the vehicle body, and the deformation of the mold changing track. Based on the preset dynamic mapping relationship between the core motion parameters and vehicle stability, the core-vehicle dynamic coupling model calculates the predicted value of vehicle stability according to real-time mechanical parameters. When the predicted stability value of the vehicle body exceeds the preset stability threshold, the support pressure distribution of the lifting auxiliary component is dynamically adjusted, and the traction control curve of the tow hook mechanism is corrected simultaneously. During the core dragging process, the relative position error between the mold changing track and the mold base docking track is monitored in real time. Based on the relative position error and the corrected traction control curve, collaborative compensation control is performed to maintain the accuracy of the mold changing operation.

[0007] By adopting the above technical solution, the mechanical parameters during the mold core dragging process are acquired in real time. Combined with the dynamic coupling model of the mold core and the car body, the stability of the car body is predicted, enabling proactive perception of the car body's state. When the predicted stability value exceeds the limit, the support pressure distribution and traction control curve are adjusted simultaneously to achieve coordinated regulation of car body stability and traction. The coordinated compensation control mechanism can correct the positional error between tracks in real time, maintain the accuracy of mold changing operations, improve the reliability of heavy mold core changing operations, and reduce the risk of track docking misalignment caused by car body instability.

[0008] Optionally, the steps for dynamically adjusting the support pressure distribution of the lifting auxiliary components include: Calculate the target support pressure of each lifting auxiliary component based on the core weight data and the current dragging speed; The actual support pressure of each lifting auxiliary component is obtained in real time through pressure sensors; Adjust the opening of the flow control valve of the hydraulic system based on the deviation between the actual support pressure and the target support pressure. When a deviation in unilateral support pressure is detected to exceed the preset allowable range, the pressure equalization adjustment program is activated to redistribute the support pressure of each lifting auxiliary component; If the support pressure deviation is detected to exceed the preset allowable range for a preset number of consecutive times, it is determined that there is an abnormality in the support system, and the emergency braking process is triggered.

[0009] By adopting the above technical solution, the target support pressure is dynamically calculated based on the core weight and dragging speed, and closed-loop control is achieved through pressure sensor feedback. The pressure equalization adjustment program automatically redistributes the support pressure when the pressure deviation on one side exceeds the standard, avoiding excessive force on one side of the vehicle body. The anomaly detection mechanism continuously monitors the support pressure deviation, promptly identifies faults and triggers emergency braking, enhancing the vehicle body's anti-overturning ability and improving the reliability and safety of the support system.

[0010] Optionally, the method further includes multimodal alignment accuracy self-enhancing control, the specific steps of which include: Fusion positioning technology is used to position the AGV body to the docking area of ​​the mold base to obtain the first positioning accuracy; The visual guidance system is activated to identify the docking marks of the mold base, and closed-loop control is performed in combination with the force feedback sensor to improve the positioning accuracy to the second positioning accuracy. The contact state between the mold changing track and the docking track is detected by a contact sensor, and adaptive compensation is performed based on the track gap data to optimize the positioning accuracy to the third positioning accuracy. If the standard deviation of the position error fluctuation exceeds the preset fluctuation threshold during the positioning process, the system will automatically revert to the initial positioning stage and re-execute until the standard deviation of the error fluctuation is less than the preset convergence threshold.

[0011] By adopting the above technical solutions, coarse positioning of the AGV body is achieved using fusion positioning technology, while the visual guidance system combined with force feedback sensors improves positioning accuracy. Contact sensors detect the track contact state for adaptive compensation. An error fluctuation detection mechanism automatically backs up and retryes when positioning is unstable, ensuring the reliability of the alignment process. Multi-sensor fusion and hierarchical control strategies improve track alignment accuracy, reduce manual intervention, and enhance the automation level of mold changing operations.

[0012] Optionally, the method further includes support-traction coordinated energy optimization control, the specific steps of which include: Construct an energy optimization objective function, which includes at least a weighted combination of hydraulic system energy consumption, electric motor system energy consumption, and vehicle body stability index; Based on the core weight, track friction coefficient, and environmental parameters, the optimal energy distribution ratio between the support system and the traction system is calculated. During the descent of the support system, the energy recovery device is activated to convert potential energy into electrical energy and store it. During the acceleration phase of the traction system, the stored electrical energy is used first to power the traction motor; When the energy recovery efficiency is detected to be lower than the preset efficiency threshold, the system automatically switches to the backup power supply mode and records the abnormal energy recovery event.

[0013] By adopting the above technical solution, an objective function incorporating energy consumption and stability indicators is constructed to achieve coordinated energy allocation between the support and traction systems. The energy recovery device converts potential energy into electrical energy during the support's descent and stores it; during traction acceleration, the stored electrical energy is prioritized for use. The anomaly detection function automatically switches to standby mode when recovery efficiency is insufficient, reducing total system energy consumption, extending equipment runtime, and achieving the dual goals of energy saving and stability.

[0014] Optionally, the method further includes embedded control of mold changing process knowledge, the specific steps of which include: Establish a mold change process knowledge base containing process parameter sets for different mold types. The process parameter sets should include at least material property parameters, temperature compensation parameters, and historical failure mode data. Before the mold change operation begins, the current mold type is automatically identified, and the corresponding set of process parameters is retrieved from the mold change process knowledge base; The control parameters are dynamically adjusted based on the process parameter set. The control parameters include at least the traction speed curve, the support pressure threshold, and the alignment accuracy tolerance. During operation, the system analyzes the matching degree between equipment operating data and historical fault modes in real time. When the matching degree exceeds the preset matching threshold, preventive protection measures are automatically activated. If the matching degree of the same type of failure mode exceeds the preset severity threshold in a series of preset operations, process optimization suggestions will be automatically generated.

[0015] By adopting the above technical solutions, a process knowledge base is established, including material properties, temperature compensation, and historical fault data. This base automatically identifies mold types and retrieves corresponding process parameters. Control parameters such as traction speed, support pressure, and alignment tolerance are dynamically adjusted. Real-time analysis of the matching degree between operating data and historical fault modes allows for the early activation of preventative protection measures. Deep integration of expert experience with equipment control improves the adaptability of mold change operations, reduces manual debugging time, and extends equipment lifespan.

[0016] Optionally, the multimodal alignment accuracy self-enhancement control also includes an accuracy self-enhancement mechanism, the specific steps of which include: Record the actual position error data for each alignment operation and build an error history database; Based on the historical error database, systematic deviations at different positioning stages are analyzed; When a systematic deviation is detected to exceed a preset deviation threshold and continues for a preset number of operations, the target position parameters and sensor calibration parameters of each positioning stage are automatically corrected. Based on the error correction amount for each operation, the control parameters for each positioning stage are continuously optimized; Generate an alignment accuracy trend chart, and trigger a manual review process when the accuracy improvement is less than a preset improvement threshold.

[0017] By adopting the above technical solution, historical alignment error data is recorded to construct an error database, systematic deviations are analyzed, and positioning parameters are automatically corrected. The control strategy is continuously optimized based on the error correction amount for each operation, generating an accuracy trend chart. When accuracy improvement stagnates, manual review is triggered, establishing a closed-loop mechanism for error analysis and parameter correction. This ensures that alignment accuracy continuously improves over time, reducing the need for periodic maintenance and guaranteeing long-term positioning reliability.

[0018] Optionally, the steps for constructing the energy optimization objective function include: Define stability constraints, which include minimum support pressure constraints, track levelness error constraints, and vehicle vibration amplitude constraints. Solve for the optimal combination of control parameters that satisfies the stability constraints; Monitor the deviation between actual energy consumption and predicted energy consumption online, and calculate the energy consumption deviation rate; When the energy consumption deviation rate exceeds a preset deviation rate threshold for a preset number of consecutive cycles, the model parameter update mechanism is triggered to retrain the dynamic coupling model. Generate a visualization interface for energy optimization effects, and display a warning sign when the stability index is lower than the preset stability threshold.

[0019] By adopting the above technical solution, stability constraints are defined to ensure that energy optimization does not affect operational safety, and the optimal control parameters that satisfy the constraints are solved. The deviation between actual and predicted energy consumption is monitored online, triggering model updates when anomalies occur. A visualization interface for the optimization effect is generated, displaying warnings when stability is insufficient. An energy optimization framework under stability constraints is established to balance energy-saving needs and operational safety. Online model updates adapt to changes in operating conditions, improving the robustness of energy optimization.

[0020] Secondly, this application provides a control method system for a trackless mold-changing vehicle, which adopts the following technical solution: A control system for a trackless mold-changing vehicle includes: The acquisition module is used to acquire real-time mechanical parameters and the actual support pressure of each lifting auxiliary component during the core dragging process; A memory for storing a program for the control method of a trackless mold-changing vehicle as described above; The processor and the program in the memory can be loaded and executed by the processor to implement the control method of the trackless mold changing vehicle as described above.

[0021] By adopting the above technical solution, the system integrates the acquisition module, memory, and processor using a modular architecture, enabling real-time acquisition of mechanical parameters and efficient execution of control algorithms. The acquisition module synchronously monitors the mechanical parameters and support pressure data during the mold core dragging process, providing complete input for control decisions. The memory stores the optimized control program, and the processor quickly calls and executes the dynamically coupled model calculation and collaborative compensation control. The system architecture design ensures the synergy of data acquisition, storage, and processing, improving control response speed and calculation accuracy. The modular design facilitates functional expansion and maintenance upgrades, providing a stable and reliable control platform for the trackless mold changing vehicle, ensuring the continuity and safety of mold changing operations under complex working conditions.

[0022] Thirdly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described above.

[0023] Fourthly, this application provides a computer storage medium capable of storing corresponding programs, employing the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the above-described control methods for trackless mold-changing vehicles.

[0024] In summary, this application includes at least one of the following beneficial technical effects: This application realizes dynamic coordinated control of vehicle body stability and traction force. The vehicle body stability state is predicted in real time through the dynamic coupling model of mold core and vehicle body. When the stability exceeds the standard, the support pressure distribution and traction force control curve are adjusted synchronously. Combined with the coordinated compensation mechanism of track position error, the problem of vehicle body instability and track misalignment during the dragging of heavy mold core is effectively solved, and the reliability and accuracy of mold changing operation are significantly improved. This application establishes a multimodal alignment accuracy self-enhancement mechanism, employing a hierarchical control strategy that integrates positioning, visual guidance, and contact sensing. Combined with error history data analysis and parameter self-correction functions, it continuously optimizes track docking accuracy over time. Error fluctuation detection and automatic backoff mechanisms ensure the reliability of the alignment process, significantly reducing the need for manual intervention and improving the automation level and adaptability of mold-changing operations. This application achieves coordinated energy optimization of the support-traction system, constructs an optimization objective function that includes energy consumption and stability constraints, realizes potential energy conversion and storage through an energy recovery device, and prioritizes the use of recovered electrical energy during the traction acceleration phase. Online energy consumption monitoring and model update mechanisms adapt to changes in operating conditions, significantly reducing the total system energy consumption and extending equipment endurance while ensuring operational safety, thus achieving a balance between energy saving and stability. Attached Figure Description

[0025] Figure 1 This is a flowchart of a control method for a trackless mold changing vehicle according to an embodiment of this application.

[0026] Figure 2 This is a flowchart of the steps for dynamically adjusting the support pressure distribution of the lifting auxiliary component according to an embodiment of this application.

[0027] Figure 3 This is a flowchart of the multimodal alignment accuracy self-enhancing control steps in an embodiment of this application.

[0028] Figure 4 This is a flowchart of the support-traction coordinated energy optimization control steps in the embodiments of this application.

[0029] Figure 5 This is a flowchart of the embedded control steps for the mold changing process in an embodiment of this application.

[0030] Figure 6 This application embodiment of the multimodal alignment accuracy self-enhancement control also includes a flowchart of the accuracy self-enhancement mechanism steps.

[0031] Figure 7 This is a flowchart of the steps for constructing the energy optimization objective function in an embodiment of this application.

[0032] Figure 8 This is a block diagram of a control method for a trackless mold-changing vehicle according to an embodiment of this application. Detailed Implementation

[0033] The present application will be further described in detail below with reference to the accompanying drawings.

[0034] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0035] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the appendices in the embodiments of this application will be described below. Figure 1-8 The technical solutions in the embodiments of this application are clearly and completely described. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0036] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0037] This application discloses a control method for a trackless mold-changing vehicle. (Refer to...) Figure 1 The control methods for trackless mold changing vehicles include: Step S100: Obtain real-time mechanical parameters during the core dragging process. The mechanical parameters include at least the force value of the drag hook mechanism, the vibration amplitude of the vehicle body, and the deformation of the mold changing track.

[0038] Among them, real-time mechanical parameters refer to physical quantities reflecting the stress state of the system and environmental deformation during the mold core dragging operation. The hook mechanism force value refers to the tensile force borne at the connection between the hook and the mold core. Vehicle body vibration amplitude refers to the acceleration or displacement amplitude generated by the mold changing vehicle body during movement. Mold changing track deformation refers to the local deflection of the ground-laid mold changing track under heavy load.

[0039] The general process is as follows: First, a sensing and acquisition system is constructed. Tension and compression sensors are installed at the hook mechanism of the mold-changing vehicle to collect the force signal F of the hook in real time. hook Vibration sensors are installed on the vehicle chassis to acquire vehicle vibration acceleration signals and convert them into vibration amplitude A. body A laser rangefinder is deployed along the vehicle's direction of travel to scan the surface of the mold-changing track and acquire the track deformation D in real time. track .

[0040] Subsequently, the above data is filtered and converted from analog to digital. The processed force values ​​of the hook mechanism, the vibration amplitude of the vehicle body, and the deformation of the mold-changing track are packaged into a real-time mechanical parameter set P. real ={F hook A body D track The data is transmitted to the central controller via the vehicle bus, providing a data foundation for subsequent stability prediction.

[0041] Step S101: Based on the preset dynamic mapping relationship between the core motion parameters and the vehicle stability of the core, the predicted value of vehicle stability is calculated according to the real-time mechanical parameters of the core-vehicle dynamic coupling model.

[0042] The mold core-vehicle dynamic coupling model is a pre-established mathematical model used to describe the nonlinear mapping relationship between the kinematic parameters and the dynamic response of the mold-changing vehicle during the mold core dragging process. The vehicle stability prediction value is a quantitative indicator output by the model calculation, characterizing whether the vehicle is currently in a safe operating state.

[0043] The general process is described as follows: The central controller receives the real-time mechanical parameter set P. real The dynamic coupling model S between the core and the vehicle body, stored in the controller, is invoked. pred =f(P real ), where P real ={F hook A body D track} represents the real-time mechanical parameter vector. During model calculations, these real-time parameters are substituted into the model formulas. For example, the model is based on the current track deformation D. track and the force value F of the tow hook hook Calculate the offset of the vehicle's center of gravity. The model outputs the predicted vehicle stability value S. pred This value reflects the vehicle's ability to maintain balance under the current mechanical parameters. pred The higher the value, the more stable the vehicle body; if S pred If the value approaches the critical value, it indicates that the vehicle body is in an unstable edge state.

[0044] Step S102: When the predicted value of vehicle stability exceeds the preset stability threshold, the support pressure distribution of the lifting auxiliary component is dynamically adjusted, and the traction control curve of the tow hook mechanism is corrected simultaneously.

[0045] Among them, the preset stability threshold refers to the pre-set limit range S of the stability prediction value. threshold The lifting auxiliary assembly refers to the hydraulic or electric support legs installed at the bottom of the mold-changing vehicle body. The traction control curve refers to the reference curve F showing the change in output torque of the tow hook mechanism over time. curve .

[0046] The general process is as follows: The central controller compares the vehicle stability prediction Spred with the preset stability threshold S. threshold Compare them.

[0047] If S pred threshold If the risk of vehicle instability is deemed too high, the controller immediately triggers a collaborative control strategy: 1. Adjusting the support pressure distribution, the controller sends instructions to the lifting auxiliary components, based on a fuzzy control algorithm, according to the vehicle vibration amplitude A. body Dynamically adjust the hydraulic support pressure P of each outrigger suppor ​t. For example, increase the support pressure on the sinking side to bring the vehicle's center of gravity back to a safe area. 2. Correct the traction control curve, and the controller intervenes in the tow hook drive system. Based on the current force value F hook For the preset traction force curve F curve The target traction force peak value is reduced by smoothing and order reduction, and a corrected traction force control curve F is generated. curve-new This is to reduce the impact of sudden loads on the vehicle body.

[0048] Action 1 and Action 2 are executed simultaneously, using the principle of torque balance to counteract external disturbances and restore vehicle stability.

[0049] Step S103: During the mold core dragging process, the relative position error between the mold changing track and the mold base docking track is monitored in real time. Based on the relative position error and the corrected traction force control curve, collaborative compensation control is performed to maintain the accuracy of the mold changing operation.

[0050] The relative position error refers to the deviation vector ΔX between the current actual position of the mold-changing vehicle and the theoretical position of the mold base docking track. Cooperative compensation control is a composite control strategy that combines position closed-loop and force control closed-loop control.

[0051] The general process is described as follows: After the vehicle's stability is restored, the system enters the high-precision docking phase. Using the positioning system, the current relative position error ΔX is calculated in real time. The controller constructs a cooperative compensation controller, inputting the relative position error ΔX plus the corrected traction control curve F. curve-new The operation employs an impedance control strategy. If the lateral deviation ΔX exceeds the allowable range, the controller generates a corrective torque τ. corr This drives the mold-changing vehicle to correct its trajectory. The final motor drive command V is then output. motor .

[0052] When performing correction actions, the controller always refers to the corrected traction control curve F. curve-new This limits acceleration during the correction process, preventing the vehicle body from repeatedly entering an unstable state due to rapid correction. Through a hybrid position-force control system, it ensures both the precise sliding of the mold core into the mold base docking track and maintains stability throughout the entire dragging process.

[0053] Reference Figure 2 The steps for dynamically adjusting the support pressure distribution of the lifting auxiliary components include: Step S200: Calculate the target support pressure of each lifting auxiliary component based on the core weight data and the current dragging speed.

[0054] Among them, the mold core weight data refers to the mold core mass information pre-entered into the system or obtained through real-time weighing. The current towing speed refers to the real-time speed of the mold changing vehicle on the track. The target support pressure refers to the theoretically required hydraulic support force benchmark value of each lifting auxiliary component to maintain the balance of the vehicle body under the current load and motion state.

[0055] The general process is described as follows: The central controller reads the preset mold core weight data M and the current towing speed V of the mold changing vehicle. Based on the static uniformly distributed load model of the vehicle body, combined with the dynamic impact coefficient, the target support pressure is calculated. First, the basic support pressure P is calculated by dividing the sum of the mold core weight M and the vehicle body weight G by the number of lifting auxiliary components N. base Then, a speed correction coefficient k is introduced. Considering that the greater the towing speed V, the greater the inertial impact during vehicle start-up / stop, the target support pressure needs to be appropriately increased to counteract the overturning moment. The speed correction coefficient k is set to be positively correlated with the speed V. The target support pressure P of each lifting auxiliary component is calculated. target =(P base +ΔP bias )×(1+k⋅V). Where, ΔP bias This is the static off-center load compensation amount preset based on the vehicle's center of gravity position. The controller will calculate the target support pressure P. target As a control benchmark, it is sent to the hydraulic control systems of each lifting auxiliary component.

[0056] Step S201: The actual support pressure of each lifting auxiliary component is obtained in real time through pressure sensors.

[0057] The pressure sensor is a detection element integrated into the hydraulic circuit of the lifting auxiliary component. The actual support pressure refers to the real force value currently fed back by the hydraulic system, used to reflect the real-time balance between ground reaction force and hydraulic thrust.

[0058] The general process is described as follows: At the hydraulic cylinder inlet of each lifting auxiliary component, pressure sensors collect oil pressure signals at millisecond-level frequencies. The sensors convert the collected analog signals into digital signals and transmit them to the central controller via the vehicle bus. The controller performs noise reduction processing on the received signals, eliminating high-frequency interference caused by hydraulic pulsation, to obtain smooth actual support pressure data P. actual The actual support pressure data P actual With target support pressure P target The comparison is performed to generate a pressure deviation signal ΔP, which provides a basis for subsequent closed-loop regulation.

[0059] Step S202: Based on the deviation between the actual support pressure and the target support pressure, adjust the opening of the flow control valve of the hydraulic system.

[0060] Here, deviation refers to the difference between the target support pressure and the actual support pressure. The flow control valve is a proportional flow valve; its opening directly determines the flow rate of the hydraulic oil, thereby controlling the lifting speed and force of the support cylinder.

[0061] The general process is described as follows: The controller calculates the current pressure deviation ΔP = P target -P actual The flow control valve is regulated using proportional control logic (P control). The pressure deviation ΔP is multiplied by a preset proportional gain coefficient K. p The valve opening control quantity U=K is obtained. p ×ΔP. Based on the sign and magnitude of the control quantity U, a corresponding PWM signal is output to the electromagnet of the flow control valve. If ΔP>0, meaning the actual support pressure is less than the target value, the valve opening is increased to increase the oil inlet and raise the support pressure; if ΔP<0, meaning the actual support pressure is greater than the target value, the valve opening is decreased or the return oil circuit is opened to release pressure. Through real-time adjustment, the actual support pressure P is adjusted to achieve the desired effect. actual Rapidly converges to the target support / resistance P target Within the allowable error range, force control closed loop is achieved.

[0062] Step S203: When the deviation of the support pressure on one side exceeds the preset allowable range, the pressure equalization adjustment program is started to redistribute the support pressure of each lifting auxiliary component.

[0063] Among them, unilateral support pressure deviation refers to the situation where the sum of the actual forces on the lifting auxiliary components on the left or right side of the mold changing vehicle exceeds a threshold compared to the theoretical uniform distribution value. The pressure equalization adjustment program is an active torque balancing strategy.

[0064] The general process is described as follows: The controller monitors the lateral force balance of the vehicle body in real time. It calculates the sum of the actual pressures P of the left-side lifting auxiliary components. left The sum of the actual pressures on the right, P right Calculate the pressure difference ΔP between the left and right sides. lr =∣P left -P right |. If ΔP lr Greater than the preset allowable range threshold Th balance The system determines that there is a risk of unilateral tilting of the vehicle body. This triggers the pressure equalization adjustment procedure, and the controller no longer simply executes the single-point PID control in step S202, but instead activates the differential adjustment mode.

[0065] Specifically, this involves appropriately reducing the target opening of the flow control valve on the overpressure side while simultaneously increasing the target opening of the flow control valve on the underpressure side. For example, calculating the equalization compensation amount ΔPeq for the target pressure P on the left side. target-left Subtract ΔPeq from the target pressure P on the right side. target-rightAdd ΔPeq. Through reverse coupling adjustment, the support torques on the left and right sides are forcibly balanced, eliminating torsional stress on the car body and restoring the mold-changing car to a horizontal posture.

[0066] Step S204: If the support pressure deviation is detected to exceed the preset allowable range for a preset number of consecutive times, it is determined that there is an abnormality in the support system and the emergency braking process is triggered.

[0067] The preset number of attempts refers to the fault confirmation count threshold set by the system, used to filter transient interference signals. Support system anomaly refers to situations where hydraulic system failure, sensor malfunction, or severe track deformation prevents the maintenance of normal support. The emergency braking procedure refers to the highest level of safety protection action.

[0068] The general process is described as follows: The system initializes the fault counter Cnt=0. Within each control cycle, if the support pressure deviation |ΔP| of any lifting auxiliary component continuously exceeds the preset limit range Th... fault If the fault counter Cnt is positive, the fault counter Cnt will be incremented by 1; otherwise, the fault counter Cnt will be cleared.

[0069] When the fault counter Cnt is greater than or equal to the preset number N, for example, if it exceeds the limit three times consecutively, the controller determines that there is an unrecoverable abnormality in the support system. An emergency braking procedure is immediately triggered: first, the hydraulic system is locked by sending a full-close command to lock all flow control valves of the lifting auxiliary components, maintaining the existing support force. Then, the power is cut off, immediately cutting off the travel drive power and tow hook traction of the mold-changing vehicle. Finally, an audible and visual alarm is activated, the warning device is activated, and the specific fault code, such as "E02: Abnormal right-side support pressure," is displayed on the human-machine interface. The system is prohibited from restarting until manual intervention is performed to troubleshoot and reset the fault, ensuring the safety of the mold-changing operation.

[0070] Reference Figure 3 The method further includes multimodal alignment accuracy self-enhancing control, the specific steps of which include: In step S300, the AGV vehicle body is positioned to the docking area of ​​the mold base using fusion positioning technology to obtain the first positioning accuracy.

[0071] Among them, fusion positioning technology refers to a positioning method that combines laser navigation and inertial measurement unit data. The first positioning accuracy refers to the initial stopping accuracy of the AGV before visual intervention. This accuracy is determined by the resolution of the laser sensor and is typically within the range of ±20mm.

[0072] The general process is described as follows: The AGV control system calls up pre-built map data and controls the vehicle to travel to the vicinity of the mold docking area. When the AGV reaches the preset first stopping point, the vehicle decelerates and brakes. At this time, the laser sensor feeds back the actual coordinates P of the vehicle. curr With the target theoretical coordinates Pgoal There is a first position error E rough The controller records this error E. rough It then determines that the vehicle body has reached the limit of the fusion positioning capability and triggers the next stage of the visual guidance process.

[0073] Step S301: Activate the vision guidance system to identify the docking mark of the mold base, and combine it with the force feedback sensor to perform closed-loop control, thereby improving the positioning accuracy to the second positioning accuracy.

[0074] The visual guidance system refers to the industrial camera installed at the rear of the AGV. The mold base docking mark refers to the high-contrast positioning mark set on the mold base. The force feedback sensor refers to the current detection module of the drive wheel motor, used to indirectly reflect the force on the vehicle body. The second positioning accuracy refers to the accuracy after visual correction, typically within the range of ±2mm.

[0075] The general process is described as follows: The controller activates the industrial camera to capture an image of the docking mark on the mold base. The current visual deviation value E of the AGV is calculated using an image matching algorithm. vision If the deviation value E vision If the error exceeds the visual accuracy threshold, the controller generates a correction command to control the AGV to make fine adjustments.

[0076] During the process, the system monitors the value F of the force feedback sensor in real time. real If F real If the preset safety force threshold is exceeded, indicating that the vehicle body may have touched the mold or jammed, the micro-adjustment should be immediately paused. Only when the force feedback value F... real Visual fine-tuning is only permitted when the visual deviation value E is within a safe range. vision If the accuracy is less than the second positioning accuracy threshold, fine positioning is complete.

[0077] Step S302: The contact state between the mold changing track and the docking track is detected by a contact sensor, and adaptive compensation is performed based on the track gap data to optimize the positioning accuracy to the third positioning accuracy.

[0078] Among them, contact sensors refer to mechanical limit switches or photoelectric sensors installed at the end of the mold changing track. Track clearance data refers to the physical gap D between the end of the mold changing track and the mold base mating track. gap The third positioning accuracy refers to the final accuracy after physical contact compensation, which is usually less than ±0.5mm.

[0079] The general process is described as follows: After visual positioning is completed, the AGV controls the vehicle to move towards the mold base at a creeping speed. When the mold changing track comes into contact with the mold base docking track, the contact sensor generates a trigger signal. The controller determines the track gap data D based on the trigger signal. gapThe state. If D is detected. gap If the value is greater than zero, indicating the presence of a step or gap, the controller calculates the compensation distance L. comp Control the AGV's reverse movement distance L comp Alternatively, the vehicle body lifting mechanism can be activated to fill the height difference. Through physical contact retraction-compensation logic, mechanical gaps are forcibly eliminated, achieving a smooth physical transition between the mold changing track and the docking track, thus achieving the third positioning accuracy.

[0080] Step S303: If the standard deviation of position error fluctuation exceeds the preset fluctuation threshold during the positioning process, the process will automatically revert to the initial positioning stage and re-execute until the standard deviation of error fluctuation is less than the preset convergence threshold.

[0081] Wherein, position error refers to the positioning deviation value E at the current moment. curr The preset fluctuation threshold refers to the critical value T used to determine system oscillation. shake The preset convergence threshold refers to the stable value T used to determine successful localization. stable .

[0082] The general process is described as follows: During the execution of steps S300 to S302, the controller monitors the change in position error in real time. If, within a continuous sampling period, the current error value E... curr Error value E from the previous period prev The absolute value of the difference |E curr -E prev | continuously greater than the preset fluctuation threshold T shake For example, if the error exceeds 5mm three times consecutively, the AGV is determined to be in an unstable oscillation state, possibly caused by slippery ground or load shaking. In this case, the controller does not perform complex algorithm corrections but directly executes the safety strategy, controlling the AGV to exit the docking area and return to its initial standby position. After system reset, the full-process positioning is executed again starting from step S300. Only when the position error E... curr Stabilizes at the preset convergence threshold T stable If the positioning is within 1mm and maintained for the set time, the positioning is confirmed as successful, and the mold change operation is allowed.

[0083] Reference Figure 4 The method further includes support-traction coordinated energy optimization control, the specific steps of which include: Step S400: Construct an energy optimization objective function. The objective function includes at least a weighted combination of hydraulic system energy consumption, motor system energy consumption, and vehicle stability index.

[0084] The energy optimization objective function, denoted as J, is an evaluation index used to assess the comprehensive energy consumption and stability of the mold-changing vehicle during its operating cycle. The hydraulic system energy consumption refers to the energy value E consumed by the lifting auxiliary components during support and adjustment.h Motor system energy consumption refers to the energy value E consumed by the drive system and hook mechanism. m The vehicle stability index refers to the predicted vehicle stability value S calculated using step S101. pred To maintain consistency with the formula used in this step, S will be referred to as such in the following text. pred Let it be S p .

[0085] The general process is described as follows: The central controller calls the energy management strategy module and sets the objective function J to the hydraulic system energy consumption E. h Motor system energy consumption E m With vehicle stability index S p The weighted sum. Since the objective function J is solved in the direction of "minimization", that is, minimizing the total energy consumption, and S... p The stability represented by Spred is "maximized," meaning the larger the better. To mathematically achieve "better stability with lower overall cost," the formula uses a specific value for S. p Negative weighting is used.

[0086] J=K1×E h +K2×E m -K3×S p Where K1, K2, and K3 are the corresponding weight coefficients. Since S p The preceding sign is negative, which means that when the vehicle stability index S... p As the load increases, the value of the objective function J decreases accordingly, indicating that this operating condition is superior in terms of both energy and stability. The controller uses this function to calculate the optimal control objective under the current operating condition, minimizing total energy consumption while ensuring vehicle stability.

[0087] Step S401: Calculate the optimal energy distribution ratio between the support system and the traction system based on the core weight, track friction coefficient, and environmental parameters.

[0088] Here, core weight refers to the mass parameter M of the currently operating core. Track friction coefficient refers to the frictional resistance coefficient U of the mold-changing track surface. Environmental parameters refer to external operating conditions, including ground slope A and temperature. Optimal energy distribution ratio refers to the ideal ratio R between the energy required by the support system and the energy required by the traction system.

[0089] The general process is described as follows: First, the controller reads the core weight M, the track friction coefficient U, and the ground slope A. Based on the physical model, calculations are performed to determine the minimum hydraulic pressure P that the support system needs to maintain, according to the core weight M and the ground slope A. s Based on the core weight M and the track friction coefficient U, calculate the resistance F that the traction system needs to overcome. fThe power requirement corresponding to the hydraulic pressure Ps is compared with the traction resistance F. f The initial energy allocation ratio R is obtained by calculating the ratio of the corresponding power demand. base Subsequently, adjustments are made based on the effect of ambient temperature on hydraulic oil viscosity, and the final optimal energy distribution ratio R is output as the benchmark for energy coordinated control.

[0090] In step S402, during the descent of the support system, the energy recovery device is activated to convert potential energy into electrical energy and store it.

[0091] The descent process of the support system refers to the stage in which the lifting auxiliary components, carrying the vehicle body and mold core, move downwards when unloading or adjusting their height. The energy recovery device refers to a generator or hydraulic motor combination installed on the hydraulic circuit or drive shaft. Potential energy refers to the gravitational potential energy E possessed by the vehicle body and mold core when in a high position. p .

[0092] The general process is as follows: When the central controller issues a command to lower the support system, it checks the charge status of the onboard energy storage unit. If the charge is not full, the controller activates the energy recovery mode. During the retraction of the support cylinder, the gravitational potential energy of the vehicle body and mold core drives the hydraulic oil to flow in the reverse direction, pushing the hydraulic motor to rotate, which in turn drives the generator. The controller rectifies the AC power generated by the generator into DC power and stores it in the onboard supercapacitor or battery pack. The controller monitors the recovery voltage and current in real time and calculates the cumulative recovered energy E. rec And update the power information of the energy storage unit.

[0093] Step S403: During the acceleration phase of the traction system, the stored electrical energy is preferentially used to power the traction motor.

[0094] The acceleration phase of the traction system refers to the stage where the mold-changing vehicle accelerates from a standstill or low speed to the target speed. The stored electrical energy refers to the electrical energy recovered and stored in the energy storage unit in step S402.

[0095] The general process is as follows: When the controller detects a traction command requiring acceleration (i.e., the target acceleration is greater than 0), it initiates a cooperative power supply strategy. The controller first checks the energy storage unit's charge level. If the charge is sufficient, it controls the bidirectional DC-DC converter to prioritize using the energy from the energy storage unit to power the traction motor driver. During acceleration, the peak power required by the traction motor is shared by the energy storage unit and the external power grid. Prioritizing the use of recovered energy reduces the instantaneous load on the external power grid, achieving efficient energy utilization.

[0096] Step S404: When the energy recovery efficiency is detected to be lower than the preset efficiency threshold, automatically switch to the backup power supply mode and record the abnormal energy recovery event.

[0097] Energy recovery efficiency refers to the ratio η of actual recovered electrical energy to theoretically recoverable mechanical energy. The preset efficiency threshold is the minimum standard η for determining whether a recovery system is effective. set Backup power mode refers to the conventional mode where power is supplied entirely by the external power grid. Energy recovery anomaly events refer to log entries that record fault information.

[0098] The general process is as follows: During energy recovery, the controller calculates the current recovery efficiency η in real time. If η is detected to be less than the preset efficiency threshold η... set If the energy recovery rate falls below 20%, the controller determines that there is an anomaly in the energy recovery circuit, such as a pipeline leak or generator failure. In this case, the controller immediately shuts off the energy recovery device and automatically switches the mold-changing vehicle to standby power supply mode, directly powered by the external power grid. Simultaneously, the controller generates an energy recovery anomaly event log, recording the time of the fault and the fault code, and stores it in non-volatile memory until manually reset.

[0099] Reference Figure 5 The method also includes embedded control of mold changing process knowledge, and the specific steps include: Step S500: Establish a mold change process knowledge base containing process parameter sets for different mold types. The process parameter sets shall include at least material property parameters, temperature compensation parameters, and historical failure mode data.

[0100] The mold change process knowledge base refers to a database used to store mold-specific operational data. The process parameter set refers to a pre-calibrated combination of control data for a specific mold. Material property parameters refer to the physical properties of the mold material, including material hardness and coefficient of thermal expansion. Temperature compensation parameters refer to the correction coefficients for the influence of mold temperature on mechanical dimensions. Historical failure mode data refers to the sensor data characteristics of the mold during past mold change operations when jamming, uneven loading, or overloading occurred.

[0101] The general process is described as follows: First, the molds are classified and coded into three categories: heavy-duty molds, precision molds, and ordinary molds, and each is assigned a unique ID number. Then, basic process parameters are entered, and for each type of mold, material property parameters are entered, such as setting the coefficient of thermal expansion of steel molds to K. heat and standard operating parameters, such as standard traction force F std Finally, historical fault data is stored, and the characteristics of abnormalities that occurred in the mold's past operations are stored in the database. For example, when the mold guide rail is corroded, the characteristic of the traction force fluctuation P at the moment of startup is stored. rust These data constitute the foundational data layer of the mold-changing process knowledge base.

[0102] Step S501: Before the mold change operation begins, the current mold type is automatically identified, and the corresponding set of process parameters is retrieved from the mold change process knowledge base.

[0103] Automatic identification refers to the process of obtaining mold identity information using a code-reading device installed on the mold-changing vehicle. The process parameter set refers to a set of configuration parameters used to initialize the control system, which matches the current mold ID.

[0104] The general process is as follows: When the mold-changing vehicle approaches the mold core to be moved, the identification process is initiated. The barcode reader scans the QR code or RFID tag on the mold to obtain the current mold's ID information.

[0105] After receiving the ID information, the central controller searches and matches it in the mold-changing process knowledge base. If a match is found, the controller reads the process parameter set Pmold corresponding to the mold.

[0106] The read process parameter set Pmold is loaded into the controller's runtime cache as the basic configuration for this mold change operation, replacing the default general parameters.

[0107] Step S502: Dynamically adjust control parameters based on the process parameter set. The control parameters include at least the traction speed curve, support pressure threshold, and alignment accuracy tolerance.

[0108] Here, the process parameter set refers to the collection of material, temperature, and historical data retrieved in step S501. Control parameters refer to the command parameters directly used to drive the motor and hydraulic valves. The traction speed curve is a baseline curve showing the speed of the tow hook mechanism changing over time during the towing process. The support pressure threshold refers to the upper alarm limit of the pressure sensor of the lifting auxiliary component. The alignment accuracy tolerance refers to the allowable error range for the automatic alignment system to determine successful alignment.

[0109] The general process is described as follows: The central controller loads the process parameter set P... mold Dynamic mapping and adjustment of the underlying control parameters. 1. Adjust the traction speed curve. If the material property parameters indicate that the mold material is brittle, such as cast iron, the controller will automatically adjust the initial traction speed v. start 1. Reduce the traction speed by 20% to generate a smooth traction speed curve and prevent excessive impact force from damaging the mold. 2. Adjust the support pressure threshold. Based on the temperature compensation parameters, if a high mold surface temperature is detected, the controller predicts that the track may expand and appropriately increases the support pressure threshold P of the lifting auxiliary component. limit To prevent the support legs from misjudging overload and stopping due to track deformation. 3. Adjust the alignment accuracy tolerance. If the retrieved process parameter set is for a precision mold, the controller automatically tightens the alignment accuracy tolerance δ. align (For example, tightening from ±5mm to ±1mm), and activating the high-precision visual alignment mode.

[0110] Step S503: During the operation, the matching degree between the equipment operation data and historical fault modes is analyzed in real time. When the matching degree exceeds the preset matching threshold, preventive protection measures are automatically activated.

[0111] Among them, equipment operation data refers to the sensor values ​​collected in real time by the mold changing vehicle, including real-time traction force F. t Vehicle body vibration A v and positional deviation E x Historical failure modes refer to the waveform characteristics of data from past failures of this mold stored in the knowledge base. Matching degree refers to the similarity between real-time data waveforms and historical failure waveforms. The preset matching threshold is the critical value T at which a high-risk condition is determined. risk Preventative protective measures refer to actions taken to proactively reduce risk, including slowing down and increasing pressure.

[0112] The general process is described as follows: During the mold-changing vehicle's operation, the controller uses the real-time collected equipment operation data to construct the current feature vector V. cur The current feature vector V cur Compared with the historical failure mode data V of this mold in the knowledge base hist A comparison is performed. The comparison logic uses a simple Euclidean distance to calculate the similarity S = √((Ft - Fhist)). 2 +(Av-Ahist) 2 If the calculated similarity S is greater than the preset matching threshold T risk， If the waveforms highly overlap, it is determined that the current operating state is highly likely to repeat the historical fault. The controller immediately executes preventative protection measures, forcibly reducing the traction speed to the creeping speed v. crawl It also instructed the lifting auxiliary components to increase the support pressure by 10% to stabilize the vehicle body.

[0113] Step S504: If the matching degree of the same type of fault mode exceeds the preset severity threshold in a series of preset number of operations, process optimization suggestions are automatically generated.

[0114] Here, the preset frequency refers to the statistical counter N used to confirm frequent fault occurrences. The preset severity threshold refers to a risk assessment value T that is higher than a normal warning. severe Process optimization suggestions refer to corrections to control parameters.

[0115] The general process is described as follows: The system initializes a counter C. Whenever a match score exceeding a preset severity threshold T is detected in a job... severe When the operation is completed normally, counter C increments by 1; if the operation is completed normally, counter C is reset to zero.

[0116] If counter C accumulates to a preset number of times, such as N=3 times, the controller determines that the mold has a fixed process defect. At this point, the controller generates process optimization suggestions based on the deviation between the current operating data and standard parameters. For example, if the traction force exceeds the limit three times consecutively at a certain location, the system generates a suggestion: "It is recommended to reduce the upper limit of the traction force F of the mold." max A permanent 15% increase is recommended, or lubrication maintenance of the mold guide rail is suggested. Optimization suggestions should be written into the HMI or maintenance log for engineers to reference and update the knowledge base, enabling the control strategy to evolve automatically.

[0117] Reference Figure 6 Multimodal alignment accuracy self-enhancement control also includes an accuracy self-enhancement mechanism, the specific steps of which include: Step S600: Record the actual position error data for each alignment operation and build an error history database.

[0118] The actual position error data refers to the final residual position deviation value recorded by the system after each mold change operation completes the physical alignment. It includes lateral and longitudinal deviations, reflecting the final accuracy result of this alignment operation. The error history database refers to a data table stored in the non-volatile memory of the central controller, used to store alignment error data for the same mold in chronological order for a preset number of times, such as the most recent 20 times.

[0119] The general process is described as follows: After the mold-changing carriage completes the physical contact compensation in step S302 and confirms a smooth transition between the mold-changing track and the mold base docking track, the central controller reads the current final position coordinates. The controller compares these final position coordinates with the standard theoretical docking coordinates pre-stored in the knowledge base of S500 for the mold, calculates the difference between the two, and obtains the actual position error data for this operation. The actual position error data, along with the timestamp of this operation and the mold ID number, is written into a new record in the error history database. If the number of data entries stored in the database reaches the preset limit (e.g., 20 entries), the oldest record is automatically overwritten, ensuring that the database always stores the latest operation error data, providing a data foundation for subsequent analysis.

[0120] Step S601: Based on the error history database, analyze the systematic deviations at different positioning stages.

[0121] The different positioning stages refer to the coarse positioning stage, fine positioning stage, and fine adjustment stage in the mold changing machine alignment process. Systematic deviation refers to the average error component with a fixed direction and fixed amplitude that appears in the error history database during multiple repetitive operations. The deviation is usually caused by sensor zero drift, mechanical installation gaps, or environmental temperature drift, and is different from the random error of a single operation.

[0122] The general process is as follows: The central controller retrieves data from the historical error database and extracts all historical error records for the current mold ID. The controller calculates the arithmetic mean of these historical error records as the current systematic deviation value. Further analysis of the main factors contributing to this systematic deviation value reveals that if the systematic deviation is large and consistent in direction, primarily occurring during the coarse positioning stage, it is determined to be a global coordinate system offset of the laser navigation system. If the systematic deviation mainly manifests as small, fixed deviations, it is determined to be a camera calibration offset of the vision guidance system or a mechanical reference offset of the contact sensor. Through the above statistical analysis, the inherent error sources of the current positioning system are identified.

[0123] Step S602: When the absolute value of a systematic deviation exceeds a preset deviation threshold and continues for a preset number of operations, the target position parameters and sensor calibration parameters of each positioning stage are automatically corrected.

[0124] Among these, the preset deviation threshold refers to a pre-set error limit value used to determine whether a systematic deviation has become too large to affect normal operation. The preset number of occurrences refers to a statistical counting threshold used to confirm the stable existence of the deviation, such as three consecutive occurrences. The target position parameter refers to the coordinate command value that the controller attempts to achieve at each positioning stage. The sensor calibration parameter refers to the internal reference values ​​used to correct the original sensor readings, including the coordinate compensation value of the LiDAR and the pixel offset of the vision camera.

[0125] The general process is as follows: The central controller compares the systematic deviation value with a preset deviation threshold. If the absolute value of the systematic deviation value is greater than the preset deviation threshold, the controller starts a counter to count. If the counter count reaches a preset number of times, it indicates that the deviation is not a random fluctuation but a stable systematic error, and the controller determines that parameter correction is needed. The controller calculates the correction compensation amount, which is equal to the negative systematic deviation value.

[0126] The system performs automatic correction actions to adjust the target position parameters. During subsequent alignment operations for the mold, the controller automatically adds this correction compensation to the target coordinates of the generated motion commands. Sensor calibration parameters are also corrected. If a vision system deviation is identified, the controller automatically fine-tunes the pixel center point coordinates of the vision guidance system; if a laser navigation deviation is identified, the global coordinate compensation value of the laser navigation is updated. Through automatic calibration of hardware and software parameters, inherent positioning errors in the system are eliminated.

[0127] Step S603: Based on the error correction amount for each operation, continuously optimize the control parameters for each positioning stage.

[0128] The error correction amount refers to the numerical value used to offset systematic deviations. Control parameters refer to the algorithm parameters that affect the dynamic response of positioning, including the proportional coefficient of visual servo control, the operating speed during fine-tuning, and the data weighting ratio during fusion positioning.

[0129] The general process is as follows: The central controller establishes a simple lookup table mapping relationship, mapping the magnitude of the error correction to the adjustment coefficient of the control parameters. If the error correction of this operation is large, it indicates that the cumulative error of the current system is large. The controller appropriately increases the proportional coefficient of the visual servo control to improve the correction response speed and prevent oscillation. If the error correction of this operation is small, it indicates that the system accuracy is high. The controller reduces the running speed of the fine-tuning stage and increases the weight of historical data during fusion positioning to improve positioning stability. The controller applies the adjusted control parameters to the next alignment operation, ensuring that the positioning system is always in an optimal dynamic response state.

[0130] Step S604: Generate alignment accuracy trend chart. When the accuracy improvement is less than the preset improvement threshold, trigger the manual review process.

[0131] The alignment accuracy trend graph is a line chart displayed on the human-machine interface, showing historical error data, systematic deviations, and corrected residuals. The accuracy improvement margin refers to the percentage reduction in the systematic deviation value before and after parameter correction. The preset improvement threshold is a pre-set minimum value, such as 10%, used to determine whether the automatic correction function has failed.

[0132] The general process is described as follows: The central controller generates a visualized data stream from the key data processed by S600 to S603, sends it to the human-machine interface, and plots it as a positioning accuracy trend graph, displaying the evolution of accuracy in real time. Simultaneously, the controller calculates the accuracy improvement achieved in this automatic correction.

[0133] If the calculated accuracy improvement is less than the preset improvement threshold, and the systematic deviation value still exceeds the allowable range, the controller determines that there is a non-parametric fault such as hardware wear or mechanical deformation, and the automatic correction function has reached its limit. At this time, the controller triggers the manual review process, locks the automatic correction function, activates the audible and visual alarm, and displays specific fault prompts on the human-machine interface, such as "E05: Visual calibration failure, please manually calibrate the camera." The system can only continue to operate after manual intervention to troubleshoot and repair the hardware fault.

[0134] Reference Figure 7 The steps for constructing the energy optimization objective function include: Step S700: Define stability constraints, which include minimum support pressure constraints, track levelness error constraints, and vehicle vibration amplitude constraints.

[0135] Among them, stability constraints refer to the safety boundaries that must be followed during energy optimization calculations to prevent vehicle rollover caused by attempts to save power. Minimum support pressure constraints refer to the minimum pressure value P that the hydraulic outriggers must apply. min The track levelness error constraint refers to the maximum allowable tilt angle (Angle) of the mold-changing track. max Vehicle vibration amplitude constraint refers to the upper limit of the vehicle body sway amplitude (Amp). max .

[0136] The general process is described as follows: Before starting optimization, the central controller first reads the current safety rule base. For the minimum support pressure constraint, the controller calculates the theoretical support force based on the current core weight M and the vehicle body weight, and multiplies it by a safety factor, such as 1.2, to obtain the minimum support pressure constraint P. min The P min This setting is to ensure that the predicted vehicle stability value S calculated in step S101 is accurate. pred It can maintain a level above the safety threshold. Regarding track levelness error constraints, the controller reads data from the attitude sensor and sets a threshold, Angle. max For example, a 3-degree slope ensures that high-energy-consuming acceleration operations are not performed on excessively steep inclines. A vibration acceleration threshold, Amp, is set to constrain vehicle vibration amplitude. max Package these conditions into a constraint set (Constraint). set The result is passed to the optimization solution module, and during the optimization process, it must satisfy S. pred ≥S threshold .

[0137] Step S701: Solve for the optimal combination of control parameters that satisfies the stability constraints.

[0138] The optimal control parameter combination refers to a set of control commands that maximizes system energy efficiency without violating the safety boundaries in step S700. The control parameters include the target pressure P of the lifting auxiliary component. target Target speed V of the traction motor target and the opening degree K of the hydraulic valve valve .

[0139] The general process is as follows: The controller calls the optimization solver, with the input objective being to minimize energy consumption E, and the input variables being the aforementioned control parameters. The solver first generates a set of initial parameters and then simulates the vehicle body state under these parameters. If the simulation results show that the support pressure is lower than P... min Or the vibration exceeds Amp max The solver will automatically adjust these parameters (e.g., reduce the speed V). target(Or increase the support force). After multiple iterations, the solver finds a set of parameter combinations Param that satisfies all safety constraints and has the lowest energy consumption. optimal This process essentially involves finding the minimum value of the objective function J in step S400, while ensuring that S... p Not less than the preset stability threshold S threshold The controller sends this set of parameters to the actuator, for example, setting the motor speed to V. opt Set the outrigger pressure to P. opt .

[0140] Step S702: Monitor the deviation between actual energy consumption and predicted energy consumption online, and calculate the energy consumption deviation rate.

[0141] Online monitoring refers to real-time data collection during the mold-changing vehicle's operation. Actual energy consumption refers to the actual power consumption E measured by an electricity meter or flow meter. real Predicted energy consumption refers to the theoretical power consumption E calculated based on the model in step S701. cal Energy consumption deviation rate refers to the error ratio η between the two.

[0142] The general process is described as follows: During vehicle operation, the controller simultaneously records two data points: 1. Actual value: acquiring the current and voltage of the drive motor and calculating the actual energy consumption E. real 2. Predicted value: Based on the current control parameters (speed, pressure), look up the table or calculate the theoretical predicted energy consumption E. cal .

[0143] Calculate the energy consumption deviation rate η = (∣E) real -E cal ∣) / E cal For example, if the theoretical power consumption is 1 kWh, but the actual power consumption is 1.2 kWh, then the deviation rate η is 20%. The deviation rate η is stored in the cache for subsequent judgment.

[0144] Step S703: When the energy consumption deviation rate exceeds a preset deviation rate threshold for a preset number of consecutive cycles, the model parameter update mechanism is triggered to retrain the dynamic coupling model.

[0145] Here, the preset number of cycles refers to the counter N used to confirm the persistence of the deviation. The preset deviation rate threshold refers to the critical value η for determining model failure. th For example, 30%. The model parameter update mechanism refers to the process of correcting the dynamic coupling model of the core-vehicle body in step S101.

[0146] The general process is described as follows: The controller determines the state of the deviation rate η in real time. A counter Cnt is initialized; if η > η in the current cycle... thIf η increases, then Cnt is incremented by 1; if η returns to the normal range, then Cnt is cleared to zero. When Cnt accumulates to a preset value, such as 5 times consecutively, the controller determines that the current dynamic coupling model can no longer accurately describe the vehicle body state, for example, the track has become slippery or the center of gravity of the model core has changed, and then triggers the update mechanism.

[0147] The update process is as follows: 1. Collect samples and lock the real-time mechanical parameters at the current moment, including F. hook A body 1. Use the new data as new training data. 2. Parameter correction: Use this new data to fine-tune the friction coefficient and inertia parameters in the dynamic coupling model, so that the predicted values ​​output by the model are closer to the actual values. 3. Model replacement: Save the corrected model parameters, replace the old model, and complete the adaptive learning.

[0148] Step S704: Generate an energy optimization effect visualization interface and display a warning sign when the stability index is lower than the preset stability threshold.

[0149] The energy optimization effect visualization interface refers to the screen displayed on the touchscreen installed on the vehicle body. The stability index refers to the predicted vehicle stability value S calculated in step S101. pred The preset stability threshold refers to the safety line S. th Warning indicators refer to color changes or flashing icons on the screen.

[0150] The general process is as follows: The controller packages key data from the optimization process, such as current energy consumption, target parameters, and stability index, and sends them to the display screen. The display screen generates an intuitive monitoring interface, for example, showing the current energy saving percentage using a bar chart and displaying the stability index using a dashboard. The system continuously compares S... pred and S th Normally, the screen displays a green background. Once S is detected... pred th When the vehicle body approaches instability, the display screen immediately switches to a red background and pops up a "Stability Risk" warning box, while simultaneously issuing an audible and visual alarm to prompt operator intervention or automatic system deceleration.

[0151] Reference Figure 8 Based on the same inventive concept, embodiments of this application provide a control system for a trackless mold-changing vehicle, comprising: The acquisition module is used to acquire real-time mechanical parameters and the actual support pressure of each lifting auxiliary component during the core dragging process; A memory for storing a program for the control method of a trackless mold-changing vehicle as described above; The processor and the program in the memory can be loaded and executed by the processor to implement the control method of the trackless mold changing vehicle as described above. ​

[0152] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0153] This application provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed to control a trackless mold-changing vehicle.

[0154] Computer storage media include, for example, USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media that can store program code.

[0155] Based on the same inventive concept, embodiments of this application provide a smart terminal, including a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed to control a trackless mold-changing vehicle.

[0156] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. The specific working process of the system, device, and unit described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0157] The above are all preferred embodiments of this application and are not intended to limit the scope of protection of this application. Any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features unless specifically stated otherwise. That is, unless specifically stated otherwise, each feature is only one example of a series of equivalent or similar features.

Claims

1. A control method for a trackless mold-changing vehicle, characterized in that, include: The real-time mechanical parameters during the core dragging process are obtained, including at least the force value of the drag hook mechanism, the vibration amplitude of the vehicle body, and the deformation of the mold changing track. Based on the preset dynamic mapping relationship between the core motion parameters and vehicle stability, the core-vehicle dynamic coupling model calculates the predicted value of vehicle stability according to real-time mechanical parameters. When the predicted stability value of the vehicle body exceeds the preset stability threshold, the support pressure distribution of the lifting auxiliary component is dynamically adjusted, and the traction control curve of the tow hook mechanism is corrected simultaneously. During the core dragging process, the relative position error between the mold changing track and the mold base docking track is monitored in real time. Based on the relative position error and the corrected traction control curve, collaborative compensation control is performed to maintain the accuracy of the mold changing operation.

2. The control method for a trackless mold-changing vehicle according to claim 1, characterized in that, The steps for dynamically adjusting the support pressure distribution of the lifting auxiliary components include: Calculate the target support pressure of each lifting auxiliary component based on the core weight data and the current dragging speed; The actual support pressure of each lifting auxiliary component is obtained in real time through pressure sensors; Adjust the opening of the flow control valve of the hydraulic system based on the deviation between the actual support pressure and the target support pressure. When a deviation in unilateral support pressure is detected to exceed the preset allowable range, the pressure equalization adjustment program is activated to redistribute the support pressure of each lifting auxiliary component; If the support pressure deviation is detected to exceed the preset allowable range for a preset number of consecutive times, it is determined that there is an abnormality in the support system, and the emergency braking process is triggered.

3. The control method for a trackless mold-changing vehicle according to claim 1, characterized in that, The method also includes multimodal alignment accuracy self-enhancing control, the specific steps of which include: Fusion positioning technology is used to position the AGV body to the docking area of ​​the mold base to obtain the first positioning accuracy; The visual guidance system is activated to identify the docking marks of the mold base, and closed-loop control is performed in combination with the force feedback sensor to improve the positioning accuracy to the second positioning accuracy. The contact state between the mold changing track and the docking track is detected by a contact sensor, and adaptive compensation is performed based on the track gap data to optimize the positioning accuracy to the third positioning accuracy. If the standard deviation of the position error fluctuation exceeds the preset fluctuation threshold during the positioning process, the system will automatically revert to the initial positioning stage and re-execute until the standard deviation of the error fluctuation is less than the preset convergence threshold.

4. The control method for a trackless mold-changing vehicle according to claim 1, characterized in that, The method also includes support-traction coordinated energy optimization control, the specific steps of which include: Construct an energy optimization objective function, which includes at least a weighted combination of hydraulic system energy consumption, electric motor system energy consumption, and vehicle body stability index; Based on the core weight, track friction coefficient, and environmental parameters, the optimal energy distribution ratio between the support system and the traction system is calculated. During the descent of the support system, the energy recovery device is activated to convert potential energy into electrical energy and store it. During the acceleration phase of the traction system, the stored electrical energy is used first to power the traction motor; When the energy recovery efficiency is detected to be lower than the preset efficiency threshold, the system automatically switches to the backup power supply mode and records the abnormal energy recovery event.

5. The control method for a trackless mold-changing vehicle according to claim 1, characterized in that, The method also includes embedded control of mold changing process knowledge, and the specific steps include: Establish a mold change process knowledge base containing process parameter sets for different mold types. The process parameter sets should include at least material property parameters, temperature compensation parameters, and historical failure mode data. Before the mold change operation begins, the current mold type is automatically identified, and the corresponding set of process parameters is retrieved from the mold change process knowledge base; The control parameters are dynamically adjusted based on the process parameter set. The control parameters include at least the traction speed curve, the support pressure threshold, and the alignment accuracy tolerance. During operation, the system analyzes the matching degree between equipment operating data and historical fault modes in real time. When the matching degree exceeds the preset matching threshold, preventive protection measures are automatically activated. If the matching degree of the same type of failure mode exceeds the preset severity threshold in a series of preset operations, process optimization suggestions will be automatically generated.

6. The control method for a trackless mold-changing vehicle according to claim 3, characterized in that, Multimodal alignment accuracy self-enhancement control also includes an accuracy self-enhancement mechanism, the specific steps of which include: Record the actual position error data for each alignment operation and build an error history database; Based on the historical error database, systematic deviations at different positioning stages are analyzed; When a systematic deviation is detected to exceed a preset deviation threshold and continues for a preset number of operations, the target position parameters and sensor calibration parameters of each positioning stage are automatically corrected. Based on the error correction amount for each operation, the control parameters for each positioning stage are continuously optimized; Generate an alignment accuracy trend chart, and trigger a manual review process when the accuracy improvement is less than a preset improvement threshold.

7. The control method for a trackless mold-changing vehicle according to claim 4, characterized in that, The steps to construct the energy optimization objective function include: Define stability constraints, which include minimum support pressure constraints, track levelness error constraints, and vehicle vibration amplitude constraints. Solve for the optimal combination of control parameters that satisfies the stability constraints; Monitor the deviation between actual energy consumption and predicted energy consumption online, and calculate the energy consumption deviation rate; When the energy consumption deviation rate exceeds a preset deviation rate threshold for a preset number of consecutive cycles, the model parameter update mechanism is triggered to retrain the dynamic coupling model. Generate a visualization interface for energy optimization effects, and display a warning sign when the stability index is lower than the preset stability threshold.

8. A control system for a trackless mold-changing vehicle, characterized in that, include: The acquisition module is used to acquire real-time mechanical parameters and the actual support pressure of each lifting auxiliary component during the core dragging process; A memory for storing a program for controlling the trackless mold-changing vehicle as described in any one of claims 1 to 7; The processor and the program in the memory can be loaded and executed by the processor to implement the control method of the trackless mold changing vehicle as described in any one of claims 1 to 7.

9. A smart terminal, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that can be loaded by the processor and executed as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer program is stored that can be loaded by a processor and executed as described in any one of claims 1 to 7.

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