A cross-feeding multi-axis linkage control system and method for a stamping production line

Abstract

The application belongs to the technical field of process control, and discloses a kind of transverse material feeding multi-axis linkage control system and method for stamping production line;Including: acquisition material feeding production data and standardization processing obtain standard material feeding production data;Obtain production line key operating parameter and establish state association, obtain controllable operating factor set;Extract the logical relationship of controllable operating factor set Design multi-axis linkage response condition;Calculate multi-axis control priority and carry out action instruction sorting, obtain ordered action sequence;Based on ordered action sequence, construct multi-axis linkage control strategy and execute;Real-time acquisition operating feedback data and carry out performance evaluation, obtain performance evaluation index;Based on performance evaluation index, parameter optimization is carried out to controllable operating factor set, and obtain optimized operating factor set;Based on optimized operating factor set, generate optimized linkage control strategy, and send to preset production line system database;Realize efficient control to production line multi-axis linkage.

Description

Technical Field

[0001] This invention relates to the field of process control technology, and more specifically, to a multi-axis linkage control system and method for transverse material output in a stamping production line. Background Technology

[0002] With the rapid development of intelligent manufacturing and industrial automation, in related industrial fields, especially in stamping production lines, the processes of material output, transmission, positioning and stamping require high coordination among multiple actuators to ensure cycle efficiency and motion accuracy. However, achieving high-precision multi-axis linkage control in traditional control processes remains challenging.

[0003] The transverse material ejection task in a stamping production line is characterized by its fast execution cycle and complex control path. Existing control technologies struggle to adapt to the high-frequency collaborative scheduling requirements. Furthermore, the lack of information exchange between actuators prevents real-time perception of the working status or linkage needs of other actuators, leading to the inability to identify motion conflicts in the control path. For example, after the sheet metal is heated and ejected from the furnace, the roller conveyor and gripping axis may not adjust their control logic in time, resulting in abnormal situations such as interlocking of two actuator axes or idling of the pusher axis. Most existing control strategies rely on fixed strategy templates, lacking the ability to capture key parameters and operational characteristics in the running data. This makes it difficult to adjust the control strategy in real-time based on actual working conditions, resulting in low control accuracy. For instance, when the robotic arm performs a gripping operation… Firstly, the failure to identify the weight of the material and thus pre-adjust the gripping force may lead to excessive vibration of the gripping axis during movement, causing material displacement, increasing wear on the actuator axis structure, and affecting subsequent alignment accuracy. Secondly, existing control technologies often prioritize actions based on static priorities set by task type, making it difficult to provide a more reasonable action sequence considering task complexity and response delays. This can easily lead to phenomena such as unreasonable actuator operation sequence and excessive control resource consumption, resulting in multi-axis blockage. Furthermore, in terms of feedback on strategy execution effects, existing control technologies lack semantic relationship modeling between control paths and control factors, making it difficult to improve parameter optimization accuracy and hindering long-term stable system operation.

[0004] In view of this, the present invention proposes a multi-axis linkage control system and method for transverse material discharge in stamping production lines to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of the prior art and to achieve the above objectives, the present invention provides the following technical solution: a multi-axis linkage control method for transverse material discharge in a stamping production line, comprising:

[0006] S1. Collect the output production data, standardize the output production data, and obtain standard output production data;

[0007] S2. Based on standard output production data, obtain key operating parameters of the production line, establish state relationships between key operating parameters of the production line, and obtain a set of controllable operating factors;

[0008] S3. Extract the logical relationships of the set of controllable operating factors, and design multi-axis linkage response conditions based on the logical relationships between the controllable operating factors;

[0009] S4. Calculate the multi-axis control priority based on the multi-axis linkage response conditions, and sort the action commands based on the multi-axis control priority to obtain an ordered action sequence;

[0010] S5. Construct a multi-axis linkage control strategy based on an ordered sequence of actions, and execute control commands based on the multi-axis linkage control strategy;

[0011] S6. Collect operational feedback data in real time, perform performance evaluation on the operational feedback data, and obtain performance evaluation indicators;

[0012] S7. Optimize the parameters of the controllable operating factor set based on performance evaluation indicators to obtain an optimized operating factor set; generate an optimized linkage control strategy based on the optimized operating factor set, and send the optimized linkage control strategy to the preset production line system database.

[0013] Furthermore, the methods for establishing state correlations between key operating parameters of the production line include:

[0014] An operating parameter state matrix is ​​constructed based on the key operating parameters of the production line. At the same time, a dynamic time-series sliding window is built. The operating parameter state matrix is ​​updated and adjusted in real time using the dynamic time-series sliding window to generate a parameter state change sequence. Feature extraction is performed on the parameter state change sequence to obtain a state feature change sequence.

[0015] Calculate the information transfer entropy between any two operating parameters in the state feature change sequence, construct a state influence weight map based on the information transfer entropy between operating parameters in the state feature change sequence and the temporal features; design state association rules, perform path filtering on the state influence weight map based on the state association rules to obtain state change paths; extract the operating parameters on all state change paths to obtain a set of controllable operating factors.

[0016] Furthermore, the method for designing multi-axis linkage response conditions based on the logical relationship between controllable operating factors includes:

[0017] Extract the state change paths corresponding to the set of controllable operating factors, which are the logical relationships; perform structural analysis on the state change paths to obtain the horizontal material discharge state change subset; mark the control areas of the horizontal material discharge state change subset, and classify the horizontal material discharge state change subset based on the markings to obtain the state path parameter group corresponding to each execution axis control unit; construct a multi-axis linkage control association table based on the state path parameter group.

[0018] A dynamic state change window is constructed and the multi-axis linkage control association table is traversed. The correspondence between each controllable operating factor and the execution axis control unit in each dynamic state change window is extracted. Based on the correspondence, a state behavior field is generated and used as a control condition. The control conditions of all execution axis control units are integrated to obtain the multi-axis linkage response condition.

[0019] Furthermore, the method for constructing the multi-axis linkage control association table based on the state path parameter group includes:

[0020] Each state path parameter group is encapsulated as a path parameter structure unit; the execution axis number, horizontal discharge control stage label, and action path corresponding to each path parameter structure unit are identified, and the corresponding fields of the execution axis number, horizontal discharge control stage label, and action path are embedded into the corresponding path parameter structure unit to obtain a structured linkage control group; the field set in the structured linkage control group is extended by task association to obtain an extended semantic linkage control group; the fields of the extended semantic linkage control group are reordered based on the timestamp in each extended semantic linkage control group to obtain the execution axis task control template; all execution axis task control templates are integrated to generate a multi-axis linkage control association table.

[0021] Furthermore, the method for calculating multi-axis control priority based on multi-axis linkage response conditions includes:

[0022] Extract task-related fields from each multi-axis linkage response condition, and evaluate the task level based on these fields, outputting task level evaluation indicators. Quantify the task complexity of the linkage control execution phase covered by each multi-axis linkage response condition, generating the task complexity factor for the corresponding execution axis. Extract the trigger time and start time of the multi-axis linkage response condition, and calculate the difference between the start time and trigger time to obtain the task execution response time of the corresponding execution axis. Set a time window and statistically analyze the task adjustment frequency of each execution axis within the time window. Construct a task priority function based on the task level evaluation indicators, task complexity factors, task execution response time, and task adjustment frequency, and use this function to calculate the task priority during the adjustment process of each execution axis.

[0023] By combining the task priority in the control process of each execution axis with the corresponding linkage control execution stage, overlapping tasks of different execution axes are identified; tasks with lower priority among overlapping tasks are delayed, and the priority of each task in the linkage control execution stage is adjusted to obtain the multi-axis control priority.

[0024] Furthermore, the method for constructing a multi-axis linkage control strategy based on an ordered sequence of actions includes:

[0025] Extract the action combinations of the corresponding execution axes in the ordered action sequence during the control process to obtain action combination units; classify all action combination units according to the execution logic of each action combination unit to obtain a set of serial task units and a set of parallel task units; establish control chain relationships for the action combination units in the set of serial task units, adjust the execution order of actions based on multi-axis control priority, and add scheduling constraints; add synchronization trigger flags to the set of parallel task units, and eliminate resource scheduling conflicts for different tasks in the parallel task units based on the synchronization trigger flags.

[0026] The system obtains real-time operating parameters from each action combination unit and dynamically adjusts these parameters to obtain a set of control strategy parameters. A control strategy is generated by combining the action combination unit of each execution axis with the set of control strategy parameters. A scheduling task chain is constructed based on the logical association attributes between any control strategy and other control strategies. The task nodes in the scheduling task chain are unified by time period based on timestamps to obtain a multi-axis linkage control strategy for any time period.

[0027] Furthermore, the method for dynamically adjusting the real-time parameters of the system operation includes:

[0028] The real-time operating parameters of the system include the shaft execution angle, shaft running speed, shaft force parameters, and shaft displacement parameters. A control threshold standard system corresponding to the real-time operating parameters of the system is constructed, and the real-time operating parameters of the system are adjusted based on the control threshold standard system. At the same time, strategy matching is performed on the corresponding execution shaft based on all the adjusted real-time operating parameters of the system to obtain a set of control strategy parameters.

[0029] The methods for matching strategies to the corresponding execution axes based on all adjusted real-time system operating parameters include:

[0030] Based on the adjusted shaft execution angle, control the range of angle changes in the corresponding execution axis running trajectory;

[0031] Based on the duration of the adjusted shaft running speed and the shaft speed control logic;

[0032] The intensity of the actuator's motion is controlled based on the adjusted shaft force parameters, and a dynamic buffer threshold is set based on the motion intensity.

[0033] Based on the adjusted shaft displacement parameters, the position error of the actuator shaft is corrected to obtain a precise displacement path;

[0034] Extract the wear degree parameters of the corresponding actuator axis, and design motion constraints based on the wear degree parameters; integrate the adjusted angle change range, motion duration, axis speed control logic, motion intensity, dynamic buffer threshold, precise displacement path and motion constraints into a set of control strategy parameters.

[0035] Furthermore, the methods for performance evaluation of the operational feedback data include:

[0036] Based on the multi-axis linkage control strategy, the operational feedback data is grouped according to the strategy category to obtain strategy group feedback data. A task feedback entity path is constructed based on each group of strategy group feedback data. Key action nodes in the task feedback entity path are identified and assigned weights. Each group of strategy group feedback data is time-series divided based on the time dimension to output the task scheduling cycle. A standard execution effect template is constructed, and the structural difference between the task feedback entity path and the standard execution effect template is calculated based on the key action weights. The structural difference corresponding to the strategy group feedback data within all task scheduling cycles is sorted to obtain a difference change sequence. Trend analysis is performed on the difference change sequence to identify abnormal change areas, and anomaly detection is performed on the strategy group feedback data corresponding to the abnormal change areas, outputting anomaly scores. Behavioral pattern analysis is performed on the task feedback entity path to generate behavioral pattern vectors. Multidimensional evaluation indicators are constructed based on the anomaly scores and behavioral pattern vectors, and the multidimensional evaluation indicators are compared with a preset performance evaluation system to obtain performance evaluation indicators.

[0037] Furthermore, the method for optimizing the parameters of the controllable operating factor set includes:

[0038] The correlation between performance evaluation indicators and the set of controllable operating factors is calculated to construct a performance-factor relationship matrix. Cluster analysis is performed on the performance-factor relationship matrix to obtain a set of candidate optimization factors. Operating factors related to abnormal change areas are extracted from the candidate optimization factor set and replaced with preset ideal operating factors to obtain a preliminary optimization factor set. Ideal strategy simulation is performed based on the preliminary optimization factor set and other controllable operating factor sets to determine the response coupling degree between the preliminary optimization factor set and the ideal strategy. Based on the response coupling degree, all factors in the preliminary optimization factor set are iteratively weighted and adjusted until the response coupling degree reaches a preset threshold, at which point the optimized operating factor set is obtained.

[0039] A multi-axis linkage control system for horizontal material output in a stamping production line, used to implement a multi-axis linkage control method for horizontal material output in a stamping production line, characterized in that it includes:

[0040] The data acquisition module is used to collect output production data and standardize the output production data to obtain standard output production data.

[0041] The parameter extraction module is used to obtain key operating parameters of the production line based on standard output production data, establish the state correlation between key operating parameters of the production line, and obtain a set of controllable operating factors.

[0042] The condition construction module is used to extract the logical relationships of the set of controllable operating factors and design multi-axis linkage response conditions based on the logical relationships between the controllable operating factors.

[0043] The instruction sorting module is used to calculate the multi-axis control priority based on the multi-axis linkage response conditions, and sort the action instructions based on the multi-axis control priority to obtain an ordered action sequence.

[0044] The strategy generation module is used to construct multi-axis linkage control strategies based on ordered action sequences and execute control commands based on the multi-axis linkage control strategies.

[0045] The performance feedback module is used to collect real-time operation feedback data, perform performance evaluation on the operation feedback data, and obtain performance evaluation indicators.

[0046] The parameter optimization module is used to optimize the set of controllable operating factors based on performance evaluation indicators to obtain an optimized set of operating factors; it generates an optimized linkage control strategy based on the optimized set of operating factors and sends the optimized linkage control strategy to a preset production line system database; the modules are connected to each other via wired and / or wireless means.

[0047] The technical effects and advantages of the multi-axis linkage control system and method for transverse material discharge in a stamping production line according to the present invention are as follows:

[0048] By extracting state change information from collected production data and constructing a path semantic structure, along with multi-axis resource scheduling and feedback parameter optimization, multi-axis linkage control for the transverse discharge system in a stamping production line was achieved. Compared to traditional control methods, this approach offers higher task adaptability and adjustment flexibility, maintaining efficient system operation under complex conditions or frequently changing production cycles. It provides early warnings for abnormal conditions and automatically corrects key control parameters, effectively reducing the probability of anomalies and improving the utilization rate of multi-axis control resources. Furthermore, it demonstrates excellent control performance in motion timing optimization, task cycle balancing, and strategy response stability, ensuring stable scheduling and precise positioning of stamping tasks in multi-axis linkage control scenarios. The overall method possesses good portability and engineering implementation value, significantly improving production cycle time, reducing equipment wear frequency, and extending the service life of key components, thereby enhancing production line operating efficiency and intelligent control levels. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of a multi-axis linkage control method for horizontal material discharge in a stamping production line according to the present invention.

[0050] Figure 2 This is a schematic diagram of a multi-axis linkage control system for horizontal material discharge in a stamping production line according to the present invention. Detailed Implementation

[0051] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] Example 1

[0053] Please see Figure 1 As shown in this embodiment, a multi-axis linkage control method for transverse material discharge in a stamping production line includes:

[0054] S1. Collect the output production data, standardize the output production data, and obtain standard output production data;

[0055] S2. Based on standard output production data, obtain key operating parameters of the production line, establish state relationships between key operating parameters of the production line, and obtain a set of controllable operating factors;

[0056] S3. Extract the logical relationships of the set of controllable operating factors, and design multi-axis linkage response conditions based on the logical relationships between the controllable operating factors;

[0057] S4. Calculate the multi-axis control priority based on the multi-axis linkage response conditions, and sort the action commands based on the multi-axis control priority to obtain an ordered action sequence;

[0058] S5. Construct a multi-axis linkage control strategy based on an ordered sequence of actions, and execute control commands based on the multi-axis linkage control strategy;

[0059] S6. Collect operational feedback data in real time, perform performance evaluation on the operational feedback data, and obtain performance evaluation indicators;

[0060] S7. Optimize the parameters of the controllable operating factor set based on performance evaluation indicators to obtain an optimized operating factor set; generate an optimized linkage control strategy based on the optimized operating factor set, and send the optimized linkage control strategy to the preset production line system database.

[0061] The production data is collected collaboratively by multiple types of sensors deployed on the production line system. This includes data such as sheet position data, sheet temperature data, furnace opening signal data, action trigger signal data, and data related to the operation of the robotic arm. Each type of data has a corresponding timestamp when it is collected. The production data is standardized by means of missing value handling, outlier detection, and normalization. The output standardized production data serves as the data basis for subsequent operations.

[0062] The ways to establish state relationships between key operating parameters of a production line include:

[0063] An operating parameter state matrix is ​​constructed based on the key operating parameters of the production line. At the same time, a dynamic time-series sliding window is built to update and adjust the operating parameter state matrix in real time, generating a parameter state change sequence. Each column in the operating parameter state matrix represents a type of data for a key operating parameter of the production line, each row represents the timestamp of the data collection, and each element represents the specific value of a key operating parameter of the production line.

[0064] Data is periodically collected by sensors and traversed using a dynamic time-series sliding window. New data is added to the operating parameter state matrix composed of older data, while the oldest collected data is deleted to ensure real-time data acquisition. The operating parameter state matrix is ​​continuously traversed through the dynamic time-series sliding window, and the data in each window is sorted according to timestamp order and concatenated across windows to generate a parameter state change sequence. The size of the dynamic time-series sliding window is determined based on the time required to complete one discharge cycle; for example, the maximum time required to complete one discharge cycle is [missing information]. If the window size is less than or equal to 1 second, then the maximum window size should be less than or equal to 1 second. Second.

[0065] Feature extraction is performed on the parameter state change sequence to obtain the state feature change sequence. The PCA principal component analysis algorithm is used to extract features from the parameter state change sequence, reduce the dimensionality of the parameter state change sequence and select principal components to simplify the data complexity. The state feature change sequence is generated based on the extracted principal components. This sequence includes various parameters such as velocity offset, angle offset, fluctuation frequency and vibration amplitude, which can reflect the characteristics of parameter change state.

[0066] The information transfer entropy between any two operating parameters in the state feature change sequence is calculated. Based on the information transfer entropy between operating parameters in the state feature change sequence and the temporal characteristics, a state influence weight map is constructed. By obtaining any two parameters with adjacent timestamps to form a parameter pair, the influence of the change of one parameter on the change of the other parameter in the parameter pair is evaluated using a joint distribution model, which is the information transfer entropy.

[0067] The formula for calculating the information transmission entropy is: ;in, express The parameters of time and Information transmission entropy of time parameters; express The time parameter is ,at the same time The time parameter is The joint probability; Indicates in The time parameter is Under the premise, The time parameter is The conditional probability; express The time parameter is The marginal probability; this calculation method is a commonly used statistical method, which can be understood by those skilled in the art by consulting existing materials; the temporal feature represents the time stamp relationship between parameters, the information transmission direction of each running parameter is determined based on the information transmission entropy, and the parameters are used as nodes. Based on the information transmission entropy and the time stamp relationship, a directed weighted edge is constructed to form a state influence weight graph.

[0068] Design state association rules, and use these rules to filter paths in the state influence weight graph to obtain state change paths. Extract the operating parameters on all state change paths to obtain a set of controllable operating factors. The state association rules include judgment conditions such as path length limits, node types of the start and end points, lower limit of path cumulative weight, and necessary key nodes. Evaluate the logic and effectiveness of each path in the graph based on the state association rules, remove low-weight paths and invalid control paths, and obtain the state change paths.

[0069] By screening effective paths and controllable operating factors through graph analysis, the accuracy of factor extraction was improved, and the semantic relationship between control logic and data was strengthened. The obtained set of controllable operating factors includes not only the values ​​of parameters, but also the logical semantic relationship between the parameters and the discharge process.

[0070] The methods for designing multi-axis linkage response conditions based on the logical relationships between controllable operating factors include:

[0071] Extracting the state change paths corresponding to the set of controllable operating factors yields the logical relationships. Each state change path represents the actual scheduling sequence or state change condition in the control logic of the production line system in this embodiment, serving as the logical basis for subsequent operations.

[0072] Structural analysis is performed on the state change path to obtain the subset of horizontal material discharge state changes. Since the production line system in this embodiment is based on the collaborative construction of multiple functional areas, such as the feeding section, buffer section, heating section, horizontal material discharge section, centering section, stamping section and unloading output section, only the data related to the horizontal material discharge section is extracted here, which is the subset of horizontal material discharge state changes.

[0073] The control area is marked for the subset of horizontal material discharge state changes, and the subset of horizontal material discharge state changes is classified based on the marking to obtain the state path parameter group corresponding to each execution axis control unit. The control area is identified based on the position parameters and other parameters related to a certain control stage in the horizontal material discharge process, such as the control area composed of roller conveyor, robotic arm or lifting device, and is marked with number.

[0074] The subset of horizontal material discharge state changes is classified based on the regions belonging to the same execution axis and the regions assisted by that axis. The execution axis control unit does not only represent a specific control device, but also a collaborative action object after functional division. The execution axis can represent a roller axis, a push axis, a gripping axis, a steering axis, or a lifting axis. Through the collaborative work of different execution axes, multi-axis linkage is realized.

[0075] A multi-axis linkage control association table is constructed based on the state path parameter group, which is transformed into a chain structure to reflect the parameter changes in the task. The multi-axis linkage control association table is used to express the scheduling order between multi-axis tasks and the linkage relationship between parameters.

[0076] A dynamic state change window is constructed and the multi-axis linkage control association table is traversed. The correspondence between each controllable operating factor and the execution axis control unit in each dynamic state change window is extracted. The dynamic state change window adjusts its size based on the horizontal discharge control cycle and moves according to the time axis direction. The correspondence is obtained by extracting the semantic relationship between the control process of the execution axis control unit and the controllable operating factor in each cycle in the multi-axis linkage control association table.

[0077] A state behavior field is generated based on the correspondence, and the state behavior field is used as a control condition. The control conditions of all execution axis control units are integrated to obtain the multi-axis linkage response condition. The state behavior field is constructed based on the matching relationship between the controllable operating factor and the execution axis. This field includes information such as trigger status label, scheduling cycle time, response action and operating constraints. The control conditions corresponding to each execution axis control unit are sorted in time sequence and aggregated into the multi-axis linkage response condition.

[0078] The methods for constructing a multi-axis linkage control association table based on state path parameter groups include:

[0079] Each state path parameter group is encapsulated as a path parameter structure unit. By binding the various parameters in the state path parameter group to the control node that it triggers, and combining and storing the information related to the control process into a data body of the same format, the path parameter structure unit is formed.

[0080] The execution axis number, horizontal discharge control stage label, and action path corresponding to each path parameter structure unit are identified. The corresponding fields of the execution axis number, horizontal discharge control stage label, and action path are embedded into the corresponding path parameter structure unit to obtain a structured linkage control group. By parsing the path execution semantics of the path parameter structure unit, the execution axis number, horizontal discharge control stage label, and action path corresponding to that unit are obtained, and this information is embedded into the path parameter structure unit to obtain a structured linkage control group. Since the horizontal discharge process also includes multiple stages, the horizontal discharge control stage label includes stage labels such as furnace exit stage, horizontal push stage, and positioning stage, which are used to clearly describe the horizontal discharge process.

[0081] The action path representation embodies the path structure of the specific control process of horizontal material discharge, including information such as parameter changes and specific drive trajectories. The field set in the structured linkage control group is extended by task association to obtain the extended semantic linkage control group. By identifying the task field set in the structured linkage control group and extracting other task fields that are performed at the same time in the entire horizontal material discharge process, it is determined whether there is a correlation between the two types of fields. If so, the interaction relationship between the two types of fields is established, thereby obtaining the extended semantic linkage control group. For example, in the furnace discharge stage, it is necessary to push the plate to the roller conveyor position, and at the same time, the roller conveyor needs to be displaced. The robotic arm also needs to complete the positioning in advance. Therefore, the fields related to these tasks constitute an interaction relationship.

[0082] The resulting extended semantic linkage control group can reflect the execution environment information and provides strong adaptability for subsequent control strategy construction. Based on the timestamp in each extended semantic linkage control group, the fields of the extended semantic linkage control group are reordered to obtain the execution axis task control template. The various fields in the extended semantic linkage control group are sorted according to the timestamp to obtain a combination of fields that can reflect the changes in task status.

[0083] Integrate all execution axis task control templates to generate a multi-axis linkage control association table. The multi-axis linkage control association table uses the horizontal material discharge control stage label as the main index dimension, and divides the horizontal material discharge process based on the control stage label, which can be directly called by the dynamic status change window.

[0084] Methods for calculating multi-axis control priority based on multi-axis linkage response conditions include:

[0085] Extract task-related fields from each multi-axis linkage response condition, evaluate the task level based on the task-related fields, and output the task level evaluation index. The task-related fields include information fields recorded in the multi-axis linkage response condition, such as task type field, execution stage field, and associated function field, which can reflect the semantic relationship of the control task in the process and the control resource requirements.

[0086] Task level assessment is conducted by constructing a task scoring model, with the analytic hierarchy process (AHP) as the algorithmic basis for the task scoring model. The task level assessment criteria are obtained by consulting existing data and embedded into the task scoring model. The task scoring model is then used to process task-related fields and output scores, which are the task level assessment indicators.

[0087] For each multi-axis linkage response condition, the task complexity of the linkage control execution stage is quantified to generate a task complexity factor for the execution axis corresponding to the multi-axis linkage response condition. Each multi-axis linkage response condition corresponds to a linkage control execution stage in the overall process. By weighted fusion of task-related data such as the motion path length, control signal interaction frequency, and control resource occupation time of the stage, a task complexity factor for the execution axis corresponding to the condition is obtained. The number of data dimensions for weighted fusion is the number of data dimensions involved in the specific control process, which varies in different cases.

[0088] Extract the trigger time of the multi-axis linkage response condition and the start time of the linkage control execution stage corresponding to the multi-axis linkage response condition. Calculate the difference between the start time and the trigger time to obtain the task execution response time of the corresponding execution axis. The task execution response time is used to determine the trigger delay and response scheduling interval of the task and to judge the urgency of the task from the time dimension. The smaller the value, the more timely the response, and vice versa.

[0089] A time window is set, and the task adjustment frequency of each execution axis within the time window is counted. In this embodiment, the time window is set to... The time window size can be adjusted based on specific working conditions; the task control frequency refers to the number of times a certain execution axis is configured to execute tasks within a unit time window. The larger the value, the greater the scheduling load of that execution axis.

[0090] A task priority function is constructed based on task level evaluation indicators, task complexity factors, task execution response time, and task adjustment frequency. This function is then used to calculate the task priority during the adjustment process of each execution axis. The formula for calculating the task priority function is as follows: ;in, Indicates task priority; Indicates the task level assessment indicators; This represents the task complexity factor; Indicates the task execution response time; Indicates the task adjustment frequency; where , , and They are respectively , , and The weight, in this embodiment , , , .

[0091] By combining the task priority and corresponding linkage control execution stage in the control process of each execution axis, overlapping tasks of actions on different execution axes can be identified. By binding the task priority and corresponding control stage in the control process of a certain execution axis, it can be used as the basis for subsequent identification of overlapping tasks.

[0092] The time period overlap detection algorithm is used to determine whether any two or more execution axes have concurrent conflicts in logic or control resources within the same execution phase. The action combination that has conflicts is the action overlap task. The task with lower priority in the action overlap task is delayed, and the priority of each task in the linkage control execution phase is adjusted to obtain the multi-axis control priority. In the case of action overlap, the lower priority task is delayed and reordered according to the logical correlation between the adjusted task sequences to obtain a new round of task sequence without task conflicts. The priority combination of all tasks of all execution axes is the multi-axis control priority.

[0093] Based on multi-axis control priority, the motion command signals transmitted in a production line system composed of multiple execution axes are sorted to ensure that there are no timing conflicts and that the control chain is complete.

[0094] Methods for constructing multi-axis linkage control strategies based on ordered action sequences include:

[0095] Extract the action combinations of the corresponding execution axis in the control process from the ordered action sequence to obtain action combination units. The continuous action combinations of each execution axis in the control process are identified based on information such as timestamp, control target and resource call. Action instructions that belong to the same control flow and have strong coherence are encapsulated into an action combination unit.

[0096] Based on the execution logic of each action combination unit, all action combination units are classified into sets of serial task units and sets of parallel task units. The dependence of the control process of each action combination unit on other units is analyzed to determine whether it belongs to a serial task that depends on the completion of a previous step or a parallel task that can be executed concurrently. Based on this logic, the action combination units are divided into sets of serial task units and sets of parallel task units.

[0097] A control chain relationship is established for the action combination units in the serial task unit set. The execution order of actions is adjusted based on multi-axis control priority. At the same time, scheduling constraints are added. For all serial task units, the order and dependency of the action instructions to be executed are defined to construct a chain-like control flow structure. At the same time, sorting optimization is performed based on multi-axis control priority to ensure that the execution order of serial tasks is more reasonable. Finally, scheduling constraints are added to ensure that the serial control chain can operate stably. The scheduling constraints include control synchronization signals, action time intervals, and interlocking conditions.

[0098] A synchronization trigger flag is added to the set of parallel task units, and resource scheduling conflicts are eliminated for different tasks in the parallel task units based on the synchronization trigger flag. The synchronization trigger flag is used to ensure that parallel tasks can start in a coordinated manner. When multiple parallel tasks have control resource occupation problems in the same control process, the lower priority task in the conflicting task group is delayed by a minimum execution cycle to ensure that the parallel tasks run efficiently without interfering with each other.

[0099] The system obtains real-time system operating parameters from each action combination unit and dynamically adjusts these parameters to obtain a set of control strategy parameters. By dynamically adjusting the real-time system operating parameters, the executed action instructions are made more consistent with the real-time system state. The system generates a control strategy by combining the action combination unit of each execution axis with the set of control strategy parameters. The generated control strategy includes information such as scheduling conditions, execution logic, real-time operating parameters, and control resource status. Each control strategy corresponds to a complete task flow for one execution axis and can be directly converted into a system-callable format.

[0100] A scheduling task chain is constructed based on the logical association attributes between any control strategy and other control strategies. The task nodes in the scheduling task chain are unified by time period based on timestamps to obtain the multi-axis linkage control strategy for any time period. The logical association attributes between control strategies refer to the semantic relationships such as dependence, mutual exclusion or synchronization between several control strategies in terms of time or control resources. For example, a certain control strategy can only be executed after another control strategy is completed, or there are several control strategies that cannot be performed at the same time.

[0101] Each control strategy is treated as a node, and logical association attributes are used as edges to form a linkage graph between control strategies. The scheduling task chain with logical dependencies is extracted from the graph structure. By aligning the tasks in the scheduling task chain in time, a multi-axis linkage control strategy for that time period is generated. Each time period represents a complete task execution process, and different scheduling task chains correspond to different multi-axis linkage control strategies.

[0102] Methods for dynamically adjusting real-time system operating parameters include:

[0103] The real-time operating parameters of the system include the axis execution angle, axis running speed, axis force parameters, and axis displacement parameters. These real-time operating parameters are parameters that are collected in real time and can reflect the state of the execution axis during the execution process. At the same time, the real-time operating parameters of the system include parameters of different execution axes and do not uniquely correspond to a single execution axis.

[0104] A control threshold standard system corresponding to the real-time parameters of system operation is constructed, and the real-time parameters of system operation are adjusted based on the control threshold standard system. At the same time, strategy matching is performed on the corresponding execution axes based on all the adjusted real-time parameters of system operation to obtain a set of control strategy parameters. The control threshold standard system is constructed by analyzing historical data, and the real-time parameters of system operation are adjusted based on this system to ensure that the parameter values ​​do not exceed the threshold. The adjusted real-time parameters of system operation are matched with the corresponding execution axes, and the operation status of the execution axes is adjusted based on the adjusted parameters. The various parameters and action constraints of the adjusted execution axes are integrated into the control strategy parameter set of the corresponding execution axes.

[0105] The methods for matching strategies to the corresponding execution axes based on all adjusted real-time system operating parameters include:

[0106] The range of angle changes in the running trajectory of the corresponding actuator axis is controlled based on the adjusted actuator angle.

[0107] Based on the duration of the adjusted shaft running speed adjustment action and the shaft speed control logic.

[0108] The intensity of the actuator's motion is controlled based on the adjusted shaft force parameters, and a dynamic buffer threshold is set based on the motion intensity.

[0109] Based on the adjusted shaft displacement parameters, the position error of the actuating shaft is corrected to obtain a precise displacement path. In the production line system required for control in this embodiment, the adjusted shaft actuating angle can correspond to the steering shaft, gripping shaft, and lifting shaft; the adjusted shaft running speed can correspond to the roller conveyor shaft, pushing shaft, and lifting shaft; the adjusted shaft force parameters can correspond to the gripping shaft, pushing shaft, and lifting shaft; and the adjusted shaft displacement parameters can correspond to all shafts.

[0110] The adjustment angle range refers to the difference between the real-time attitude of the actuator axis and the desired control angle. By adjusting the upper and lower limits of the angle range, the angle of several actuator axes can be corrected to ensure a stable attitude during operation.

[0111] Adjusting the duration of the action and the axis speed control logic refers to modifying the duration of the action based on the running speed of the axis under the task rhythm requirements of the task being executed. The axis speed control logic refers to the speed change law followed by the axis during the completion of a certain action. In this embodiment, a phased speed adjustment logic including an acceleration segment, a constant speed segment, and a deceleration segment is used. By adjusting the time proportion of the constant speed segment to match different task rhythms, the axis speed control logic can be adjusted.

[0112] Controlling the motion intensity of the actuator refers to controlling the output force, such as the pushing force of a sheet metal and the clamping force of a robotic arm. A dynamic buffer threshold is set based on the generated force. When a strong impact exceeding the dynamic buffer threshold or a drastic change in force occurs, timely buffering is performed to reduce structural wear and control risks of the actuator. The formula for calculating the dynamic buffer threshold is: ;in, Indicates the first Dynamic buffer threshold for each execution axis; Indicates the first Historical average force on each actuator axis; No. Historical standard deviation of force on each actuator axis; This represents an adjustment coefficient, which in this embodiment takes the value of Correcting the position error of the execution axis refers to comparing the running trajectory with the preset trajectory, identifying the offset, and performing offset compensation to correct the position error; the calculation formula for offset compensation is as follows: ;in, Indicates the offset compensation value; Indicates the actual displacement of the axis of execution; This indicates the standard displacement of the axis of execution.

[0113] Extract the wear degree parameters of the corresponding actuator axis, and design motion constraints based on the wear degree parameters. The wear degree parameters are quantitative indicators of information such as the running time, load level and temperature change of the actuator axis, which are used to reflect the wear condition of the axis.

[0114] Based on the wear parameters of the shaft, the corresponding actuator shaft can withstand the intensity of motion, continuous running time, and maximum load. Then, constraints such as the motion frequency threshold and buffer time are set for it, which are the motion constraints. This avoids overload fatigue of the actuator shaft and extends the shaft life. The adjusted angle change range, motion duration, shaft speed control logic, motion intensity, dynamic buffer threshold, precise displacement path, and motion constraints are integrated into a set of control strategy parameters.

[0115] Methods for evaluating performance based on runtime feedback data include:

[0116] Based on the strategy category of the executed multi-axis linkage control strategy, the operation feedback data is grouped to obtain strategy grouped feedback data. The strategy categories include several categories such as serial control strategy, parallel control strategy, and compensation control strategy.

[0117] Based on the feedback data of each policy group, a task feedback entity path is constructed. By extracting data such as action event sequences, timestamps, and control response information from the policy group feedback data, a path structure is constructed based on the task execution order corresponding to the executed policy. This path is the task feedback entity path, which maps the behavior trajectory of a certain execution axis under the policy drive.

[0118] Identify key action nodes in the task feedback entity path and assign weights to them. Key action nodes are those that can directly affect the task execution effect, such as centering, grasping, and positioning actions. Set the weight of the key action according to the degree of influence of the action on the task effect.

[0119] The feedback data of each strategy group is divided into time-series segments based on the time dimension to output the task scheduling cycle. Based on the pause interval between actions in the time dimension of the feedback data of each strategy group, several independent task cycle segments are obtained, which are the task scheduling cycles.

[0120] Construct a standard execution effect template, calculate the structural difference between the task feedback entity path and the standard execution effect template by combining the weights of key actions, and construct a standard execution effect template under ideal conditions based on historical data, including information such as standard time intervals, standard motion trajectories, and standard action node sequences.

[0121] The task feedback entity path is structurally aligned with the standard execution effect template, and the key action nodes in the task feedback entity path are weighted using key action weights. Difference indicators of the weighted task feedback entity path are designed, such as action offset, trajectory offset, and time interval difference.

[0122] The formula for calculating the structural difference is: ;in, This indicates the degree of structural difference between any weighted task feedback entity path and the standard effect execution effect template; This represents the number of nodes in any weighted task feedback entity path. This represents the critical action weight of critical action nodes in the weighted task feedback entity path. This value is used when the node is not a critical action node. ; This indicates the first element in the weighted task feedback entity path. In this embodiment, the difference index between the individual node and the corresponding node in the standard execution effect template is set as the action offset. This value is obtained by calculating the Euclidean distance between the two nodes. The appropriate difference index can be selected according to the specific working conditions. This value is used to quantify the degree of deviation between the entity path and the standard path.

[0123] The structural differences corresponding to the policy group feedback data within all task scheduling cycles are sorted to obtain the difference change sequence. The difference change sequence can reflect the performance fluctuation trend of the production line system after the policy is executed, which serves as the basis for subsequent analysis.

[0124] Trend analysis is performed on differential change sequences to identify anomalous regions. Anomaly detection is then performed on the strategy grouping feedback data corresponding to these anomalous regions, and anomaly scores are output. By identifying stable regions, high-fluctuation regions, and continuously changing regions in the differential change sequences (including continuously increasing and decreasing regions), high-fluctuation regions are identified as anomalous regions. An isolation forest model is used to detect anomalies in the strategy grouping feedback data corresponding to these anomalous regions and outputs anomaly scores. The isolation forest model is widely used in anomaly detection and calculates anomaly scores, which are then used by professionals in the relevant field to determine anomalies.

[0125] Behavioral pattern analysis is performed on the task feedback entity path to generate behavioral pattern vectors. Information such as continuous action combinations, change trends, and execution time intervals in the task feedback entity path are extracted as the behavioral patterns reflected by the path, and these data are mapped to a vector space to become behavioral pattern vectors. Multidimensional evaluation indicators are constructed based on anomaly scores and behavioral pattern vectors. The multidimensional evaluation indicators are compared with a preset performance evaluation system to obtain performance evaluation indicators. The anomaly scores and behavioral pattern vectors are concatenated to obtain the multidimensional evaluation indicators. The performance evaluation system includes multiple evaluation dimensions such as time, spatial displacement, response mechanism, and deviation trend changes. The multidimensional evaluation indicators are mapped to this system to obtain the evaluation score for each dimension and the ideal state data for the corresponding dimension. The original multidimensional evaluation indicators, corresponding scores, and ideal state data are integrated to obtain the performance evaluation indicators.

[0126] Methods for optimizing parameters of a set of controllable operating factors include:

[0127] The correlation between performance evaluation indicators and the set of controllable operating factors is calculated, and a performance-factor relationship matrix is ​​constructed. The correlation is quantified by calculating the Pearson correlation coefficient between the performance evaluation indicators and the set of controllable operating factors. This coefficient is the quantitative value of the correlation. The formula for calculating the Pearson correlation coefficient is as follows: ;in, Representing variables With variables The Pearson correlation coefficient; Representing variables With variables covariance; and Representing variables respectively Standard deviation and variable The standard deviation.

[0128] The performance-factor relationship matrix is ​​obtained by storing all the quantitative values ​​of the degree of correlation in matrix form. Cluster analysis is performed on the performance-factor relationship matrix to obtain a set of candidate optimization factors. In this embodiment, the clustering algorithm is used to screen out one or more combinations of factors with similar influence or high influence intensity from the performance-factor relationship matrix. These factors may be factors related to abnormal regions, or they may be multiple similar factors that also need to be optimized. Therefore, all these factors are taken as the set of candidate optimization factors.

[0129] The operation factors related to the abnormal change area are extracted from the candidate optimization factor set and replaced with the preset ideal operation factors to obtain the preliminary optimization factor set. The intersection analysis of the candidate optimization factor set and the abnormal change area is performed to obtain the operation factors whose change degree in the abnormal change area is greater than the preset change threshold as the adjustment target, and replaced with the ideal operation factors to realize the elimination of abnormal operation factors.

[0130] An ideal strategy simulation is performed based on a preliminary set of optimized factors and other controllable operating factors. The degree of response coupling between the preliminary set of optimized factors and the ideal strategy is determined. Based on the degree of response coupling, all factors in the preliminary set of optimized factors are iteratively weighted and adjusted until the degree of response coupling reaches a preset threshold. At this point, an optimized operating factor set is obtained. A simulation environment for the ideal strategy is constructed. The preliminary set of optimized factors and other controllable operating factors are combined to form an input sample. The degree of response coupling of the input sample in the simulation environment is calculated to measure the fit between the parameter combination of the current input sample and the ideal effect.

[0131] The formula for calculating the degree of response coupling is as follows: ;in, This indicates the degree of coupling between the simulation results obtained using input samples and the response of the ideal strategy; Indicates the number of input samples; Indicates the first The output value after simulation of an input sample, such as parameters like the speed or displacement of the actuator axis; Indicates the first The output value of each input sample in the ideal policy; This represents the sample mean of the output values ​​after the input sample has undergone simulation.

[0132] The parameter gradient fine-tuning method is used to adjust the influence of each operating factor in the input sample on the control task corresponding to the simulation environment based on the degree of response coupling. Through multiple rounds of iteration, the response coupling degree of the parameter combination in the simulation environment reaches a preset threshold. At this point, the iteration stops, and the corresponding optimized operating factor combination is the optimized operating factor set.

[0133] This embodiment extracts state change information from collected production data and constructs a path semantic structure. Simultaneously, it performs multi-axis resource scheduling and feedback parameter optimization to achieve multi-axis linkage control for the transverse discharge system in a stamping production line. Compared to traditional control methods, it offers higher task adaptability and adjustment flexibility, maintaining efficient system operation under complex conditions or frequently changing production cycles. It can provide early warnings for abnormal conditions and automatically correct key control parameters, effectively reducing the probability of anomalies and improving the utilization rate of multi-axis control resources. Furthermore, it demonstrates excellent control performance in motion timing optimization, task cycle balancing, and strategy response stability, ensuring stable scheduling and precise positioning of stamping tasks in multi-axis linkage control scenarios. The overall method possesses good portability and engineering implementation value, significantly improving production cycle time, reducing equipment wear frequency, and extending the service life of key components, thereby enhancing production line operating efficiency and intelligent control levels.

[0134] Example 2

[0135] Please see Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. A multi-axis linkage control system for horizontal material discharge in a stamping production line is provided, including:

[0136] The data acquisition module is used to collect output production data and standardize the output production data to obtain standard output production data.

[0137] The parameter extraction module is used to obtain key operating parameters of the production line based on standard output production data, establish the state correlation between key operating parameters of the production line, and obtain a set of controllable operating factors.

[0138] The condition construction module is used to extract the logical relationships of the set of controllable operating factors and design multi-axis linkage response conditions based on the logical relationships between the controllable operating factors.

[0139] The instruction sorting module is used to calculate the multi-axis control priority based on the multi-axis linkage response conditions, and sort the action instructions based on the multi-axis control priority to obtain an ordered action sequence.

[0140] The strategy generation module is used to construct multi-axis linkage control strategies based on ordered action sequences and execute control commands based on the multi-axis linkage control strategies.

[0141] The performance feedback module is used to collect real-time operation feedback data, perform performance evaluation on the operation feedback data, and obtain performance evaluation indicators.

[0142] The parameter optimization module is used to optimize the set of controllable operating factors based on performance evaluation indicators to obtain an optimized set of operating factors; it generates an optimized linkage control strategy based on the optimized set of operating factors and sends the optimized linkage control strategy to a preset production line system database; the modules are connected to each other via wired and / or wireless means.

[0143] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0144] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0145] In the description of this invention, it should be understood that the terms "first," "second," etc., are used only for distinguishing descriptions and should not be construed as indicating or implying relative importance.

[0146] In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0147] In the description of this invention, "several" means one or more, and "a large number" means two or more.

[0148] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0149] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0150] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A multi-axis linkage control method for transverse material discharge in a stamping production line, characterized in that, include: S1. Collect the output production data, standardize the output production data, and obtain standard output production data; S2. Methods for obtaining key operating parameters of the production line based on standard output production data and establishing state relationships between key operating parameters of the production line include: Based on the key operating parameters of the production line, an operating parameter state matrix is ​​constructed. At the same time, a dynamic time-series sliding window is built. The operating parameter state matrix is ​​updated and adjusted in real time using the dynamic time-series sliding window to generate a parameter state change sequence. Feature extraction is performed on the parameter state change sequence to obtain a state feature change sequence. Calculate the information transfer entropy between any two operating parameters in the state feature change sequence, and construct a state influence weight map based on the information transfer entropy between operating parameters and temporal features in the state feature change sequence; design state association rules, and perform path filtering on the state influence weight map based on the state association rules to obtain state change paths; extract the operating parameters on all state change paths to obtain a set of controllable operating factors. S3. Extract the logical relationships of the set of controllable operating factors, and design multi-axis linkage response conditions based on the logical relationships between the controllable operating factors; S4. Methods for calculating multi-axis control priority based on multi-axis linkage response conditions include: Extract task-related fields from each multi-axis linkage response condition, and evaluate the task level based on these fields, outputting task level evaluation indicators. Quantify the task complexity of the linkage control execution phase covered by each multi-axis linkage response condition, generating the task complexity factor for the corresponding execution axis. Extract the trigger time and start time of the multi-axis linkage response condition, and calculate the difference between the start time and trigger time to obtain the task execution response time of the corresponding execution axis. Set a time window and statistically analyze the task adjustment frequency of each execution axis within the time window. Construct a task priority function based on the task level evaluation indicators, task complexity factors, task execution response time, and task adjustment frequency, and use this function to calculate the task priority during the adjustment process of each execution axis. By combining the task priority in the control process of each execution axis with the corresponding linkage control execution stage, overlapping tasks of different execution axes are identified; tasks with lower priority among overlapping tasks are delayed, and the priority of each task in the linkage control execution stage is adjusted to obtain the multi-axis control priority. Action commands are sorted based on multi-axis control priority to obtain an ordered action sequence; S5. Construct a multi-axis linkage control strategy based on an ordered sequence of actions, and execute control commands based on the multi-axis linkage control strategy; S6. Collect operational feedback data in real time, perform performance evaluation on the operational feedback data, and obtain performance evaluation indicators; S7. Optimize the parameters of the controllable operating factor set based on performance evaluation indicators to obtain an optimized operating factor set; generate an optimized linkage control strategy based on the optimized operating factor set, and send the optimized linkage control strategy to the preset production line system database.

2. The multi-axis linkage control method for transverse material discharge in a stamping production line according to claim 1, characterized in that, The method for designing multi-axis linkage response conditions based on the logical relationship between controllable operating factors includes: Extract the state change paths corresponding to the set of controllable operating factors, which are the logical relationships; perform structural analysis on the state change paths to obtain the horizontal material discharge state change subset; mark the control areas of the horizontal material discharge state change subset, and classify the horizontal material discharge state change subset based on the markings to obtain the state path parameter group corresponding to each execution axis control unit; construct a multi-axis linkage control association table based on the state path parameter group. A dynamic state change window is constructed and the multi-axis linkage control association table is traversed. The correspondence between each controllable operating factor and the execution axis control unit in each dynamic state change window is extracted. Based on the correspondence, a state behavior field is generated and used as a control condition. The control conditions of all execution axis control units are integrated to obtain the multi-axis linkage response condition.

3. The multi-axis linkage control method for transverse material discharge in a stamping production line according to claim 2, characterized in that, The method of constructing a multi-axis linkage control association table based on the state path parameter group includes: Each state path parameter group is encapsulated as a path parameter structure unit; the execution axis number, horizontal discharge control stage label, and action path corresponding to each path parameter structure unit are identified, and the corresponding fields of the execution axis number, horizontal discharge control stage label, and action path are embedded into the corresponding path parameter structure unit to obtain a structured linkage control group; the field set in the structured linkage control group is extended by task association to obtain an extended semantic linkage control group; the fields of the extended semantic linkage control group are reordered based on the timestamp in each extended semantic linkage control group to obtain the execution axis task control template; all execution axis task control templates are integrated to generate a multi-axis linkage control association table.

4. The multi-axis linkage control method for transverse material discharge in a stamping production line according to claim 3, characterized in that, The method of constructing a multi-axis linkage control strategy based on an ordered sequence of actions includes: Extract the action combinations of the corresponding execution axes in the ordered action sequence during the control process to obtain action combination units; classify all action combination units according to the execution logic of each action combination unit to obtain a set of serial task units and a set of parallel task units; establish control chain relationships for the action combination units in the set of serial task units, adjust the execution order of actions based on multi-axis control priority, and add scheduling constraints; add synchronization trigger flags to the set of parallel task units, and eliminate resource scheduling conflicts for different tasks in the parallel task units based on the synchronization trigger flags. The system obtains real-time operating parameters from each action combination unit and dynamically adjusts these parameters to obtain a set of control strategy parameters. A control strategy is generated by combining the action combination unit of each execution axis with the set of control strategy parameters. A scheduling task chain is constructed based on the logical association attributes between any control strategy and other control strategies. The task nodes in the scheduling task chain are unified by time period based on timestamps to obtain a multi-axis linkage control strategy for any time period.

5. A multi-axis linkage control method for transverse material discharge in a stamping production line according to claim 4, characterized in that, The methods for dynamically adjusting the real-time parameters of system operation include: The real-time operating parameters of the system include the shaft execution angle, shaft running speed, shaft force parameters, and shaft displacement parameters. A control threshold standard system corresponding to the real-time operating parameters of the system is constructed, and the real-time operating parameters of the system are adjusted based on the control threshold standard system. At the same time, strategy matching is performed on the corresponding execution shaft based on all the adjusted real-time operating parameters of the system to obtain a set of control strategy parameters. The methods for matching strategies to the corresponding execution axes based on all adjusted real-time system operating parameters include: Based on the adjusted shaft execution angle, control the range of angle changes in the corresponding execution axis running trajectory; Based on the duration of the adjusted shaft running speed and the shaft speed control logic; The intensity of the actuator's motion is controlled based on the adjusted shaft force parameters, and a dynamic buffer threshold is set based on the motion intensity. Based on the adjusted shaft displacement parameters, the position error of the actuator shaft is corrected to obtain a precise displacement path; Extract the wear degree parameters of the corresponding actuator axis, and design motion constraints based on the wear degree parameters; integrate the adjusted angle change range, motion duration, axis speed control logic, motion intensity, dynamic buffer threshold, precise displacement path and motion constraints into a set of control strategy parameters.

6. The multi-axis linkage control method for transverse material discharge in a stamping production line according to claim 5, characterized in that, The methods for evaluating the performance of operational feedback data include: Based on the multi-axis linkage control strategy, the operational feedback data is grouped according to the strategy category to obtain strategy group feedback data. A task feedback entity path is constructed based on each group of strategy group feedback data. Key action nodes in the task feedback entity path are identified and assigned weights. Each group of strategy group feedback data is time-series divided based on the time dimension to output the task scheduling cycle. A standard execution effect template is constructed, and the structural difference between the task feedback entity path and the standard execution effect template is calculated based on the key action weights. The structural difference corresponding to the strategy group feedback data within all task scheduling cycles is sorted to obtain a difference change sequence. Trend analysis is performed on the difference change sequence to identify abnormal change areas, and anomaly detection is performed on the strategy group feedback data corresponding to the abnormal change areas, outputting anomaly scores. Behavioral pattern analysis is performed on the task feedback entity path to generate behavioral pattern vectors. Multidimensional evaluation indicators are constructed based on the anomaly scores and behavioral pattern vectors, and the multidimensional evaluation indicators are compared with a preset performance evaluation system to obtain performance evaluation indicators.

7. A multi-axis linkage control method for transverse material discharge in a stamping production line according to claim 6, characterized in that, The methods for optimizing the set of controllable operating factors include: The correlation between performance evaluation indicators and the set of controllable operating factors is calculated to construct a performance-factor relationship matrix. Cluster analysis is performed on the performance-factor relationship matrix to obtain a set of candidate optimization factors. Operating factors related to abnormal change areas are extracted from the candidate optimization factor set and replaced with preset ideal operating factors to obtain a preliminary optimization factor set. Ideal strategy simulation is performed based on the preliminary optimization factor set and other controllable operating factor sets to determine the response coupling degree between the preliminary optimization factor set and the ideal strategy. Based on the response coupling degree, all factors in the preliminary optimization factor set are iteratively weighted and adjusted until the response coupling degree reaches a preset threshold, at which point the optimized operating factor set is obtained.

8. A multi-axis linkage control system for horizontal material discharge in a stamping production line, used to implement the multi-axis linkage control method for horizontal material discharge in a stamping production line as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect output production data and standardize the output production data to obtain standard output production data. The parameter extraction module is used to obtain key operating parameters of the production line based on standard output production data, establish the state correlation between key operating parameters of the production line, and obtain a set of controllable operating factors. The condition construction module is used to extract the logical relationships of the set of controllable operating factors and design multi-axis linkage response conditions based on the logical relationships between the controllable operating factors. The instruction sorting module is used to calculate the multi-axis control priority based on the multi-axis linkage response conditions, and sort the action instructions based on the multi-axis control priority to obtain an ordered action sequence. The strategy generation module is used to construct multi-axis linkage control strategies based on ordered action sequences and execute control commands based on the multi-axis linkage control strategies. The performance feedback module is used to collect real-time operation feedback data, perform performance evaluation on the operation feedback data, and obtain performance evaluation indicators. The parameter optimization module is used to optimize the set of controllable operating factors based on performance evaluation indicators to obtain an optimized set of operating factors. An optimized linkage control strategy is generated based on the optimized set of operating factors, and the optimized linkage control strategy is sent to the preset production line system database; the modules are connected to each other via wired and / or wireless means.

Images