A fully automatic series switching process control method and system

Abstract

This invention relates to the technical field of industrial automation control, and provides a fully automatic series switching process control method and system. The method includes: acquiring motion process information of actuators related to series switching operations in industrial automated production; determining the performance deviation degree of the actuators based on the motion process information; acquiring the current operating condition information of the actuators; determining adjustment values ​​for the switching timing based on the performance deviation degree and the current operating condition information; and adjusting and controlling the series switching operation based on the performance deviation degree, the adjustment value, and preset interlocking conditions. This invention improves the intelligence level and operational stability of industrial automated production.

Description

Technical Field

[0001] This invention relates to the technical field of industrial automation control, specifically to a fully automatic serial switching process control method and system. Background Technology

[0002] In industrial automated production, multiple machines need to be connected and switched in a precise sequence to ensure production continuity and product quality. However, with long-term operation of the production line, as well as subtle differences between batches of raw materials and frequent adjustments to production speed, critical actuators (such as solenoid valves and relays) gradually experience performance degradation, manifesting as intermittent response delays or decreased motion accuracy. These minute performance changes are often difficult to detect by the fixed-threshold fault diagnosis mechanisms of existing automation systems. For example, the actual opening time of a solenoid valve may be several milliseconds slower than the theoretical value, or the contact closing time of a relay may be slightly prolonged. These minute deviations accumulate during long-term, high-frequency connected and switched operations, causing some critical actuators to be subjected to instantaneous impact loads or non-uniform wear exceeding their design expectations with each switch, thus accelerating performance degradation.

[0003] Existing automation systems, with their lists of equipment fault codes and corresponding countermeasures, and diagnostic procedures based on fixed logic, are primarily designed for well-defined and repeatable fault modes. Faced with intermittent and ambiguous performance degradation, the system cannot identify it as a specific fault, potentially triggering only general alarms such as "abnormal equipment operation," or worse, misreporting it as other unrelated faults, leading to low diagnostic efficiency. Maintenance personnel often need to spend a significant amount of time troubleshooting after receiving alarms, but due to the irregular nature of the problem, it is difficult to reproduce and accurately locate the fault source on-site.

[0004] To ensure stable production operations, engineering teams typically employ a system-level "fault-tolerant" strategy: by modifying the main control program, they globally extend the "timeout waiting time" and "action confirmation delay" parameters for multiple critical switching steps. The mechanism behind this approach is that by providing the system with longer waiting times, it passively adapts to slower-responding actuators, thereby avoiding production interruptions due to timeouts.

[0005] However, while this global modification of delay parameters successfully masked some actuator response delays in low-speed production modes, in high-speed production modes or when handling specific processes, these "legally" extended waiting times created new conflicts with the normal timing of other processes. These new problems caused by the "patch"—logic conflicts and process stalls—are more difficult to predict and diagnose than the original intermittent delays. This is because the occurrence of conflicts depends on specific production speeds, process flows, and the cumulative effect of multiple delay parameters; their manifestations are complex and varied, no longer merely a performance issue of a single component.

[0006] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0007] This application discloses a fully automatic serial switching process control method and system, which aims to solve the problems in industrial automated production where existing automation systems have difficulty capturing the intermittent and ambiguous performance degradation of key actuators, as well as the logical conflicts and process stalls caused by the global extension of timeout waiting time.

[0008] The technical solution of this application is as follows:

[0009] In a first aspect, this application discloses a fully automated tandem switching process control method, comprising:

[0010] To obtain information on the motion process of actuators related to series switching operations in industrial automated production;

[0011] Based on the motion process information, determine the degree of performance deviation of the actuator;

[0012] Obtain the current operating status information of the actuator;

[0013] Based on the degree of performance deviation and current operating condition information, determine the adjustment value of the switching timing;

[0014] Based on the degree of performance deviation, adjustment value, and preset interlocking conditions, the series switching operation is adjusted and controlled.

[0015] Through this technical solution, this application can dynamically adjust the switching sequence based on the actual performance deviation of the actuator and the current working condition, avoiding the limitations of traditional fixed threshold diagnosis and solving the logical conflict caused by global delay, thereby improving the stability and efficiency of production.

[0016] Secondly, this application also discloses a fully automatic series switching process control system for performing fully automatic series switching process control, including:

[0017] The process information acquisition module is used to acquire the motion process information of the actuators related to the series switching operation in industrial automated production;

[0018] The deviation determination module is used to determine the degree of performance deviation of the actuator based on the motion process information;

[0019] The operating condition information acquisition module is used to acquire the current operating condition information of the actuator;

[0020] The adjustment value determination module is used to determine the adjustment value of the switching sequence based on the degree of performance deviation and current operating condition information;

[0021] The series switching control module is used to adjust and control the series switching operation based on the degree of performance deviation, adjustment value, and preset interlocking conditions.

[0022] This application provides a system for a fully automated series switching process control method. Through modular design, the system realizes real-time monitoring of the performance of the actuator, accurate assessment of the degree of deviation, dynamic adjustment of the switching sequence, and final control execution, thereby effectively improving the intelligence level and operational stability of industrial automated production.

[0023] Beneficial Effects: This application discloses a fully automatic cascade switching process control method. It acquires the motion process information of actuators related to cascade switching operations in industrial automated production and determines the degree of performance deviation of the actuators based on this information. Simultaneously, it acquires the current operating condition information of the actuators. Then, based on the performance deviation and the current operating condition information, it determines the adjustment value for the switching timing. Finally, based on the performance deviation, the adjustment value, and preset interlocking conditions, it adjusts and controls the cascade switching operation.

[0024] This method effectively solves the problem in existing technologies where automated systems struggle to detect intermittent and subtle performance degradation in critical actuators. Traditional systems primarily rely on fixed thresholds for fault diagnosis, failing to identify minute response delays or decreases in motion accuracy, leading to inefficient fault diagnosis and even false alarms. This application, by acquiring motion process information in real time and quantifying the degree of performance deviation of the actuators, can accurately identify these subtle performance degradations.

[0025] Furthermore, this application overcomes the logical conflicts and process delays caused by the global extension of timeout waiting time adopted in existing technologies to ensure stable production. While traditional methods can passively adapt to slower-responding actuators, in high-speed production modes, these delays can conflict with the normal timing of other processes, leading to new and more difficult-to-predict and diagnose problems. This application achieves refined and adaptive control of the switching timing by dynamically calculating adjustment values ​​based on the actual performance deviation and current operating condition information. This dynamic adjustment avoids the negative impact of global delays, ensuring that the system maintains optimal operating conditions under different production speeds and processes, thereby significantly improving production continuity, product quality, and overall operating efficiency. Attached Figure Description

[0026] Figure 1 This is a flowchart of a fully automatic series switching process control method according to one embodiment of the present invention;

[0027] Figure 2 This is a flowchart of a fully automatic series switching process control method according to another embodiment of the present invention;

[0028] Figure 3 This is a system block diagram of a fully automatic series switching process control system according to another embodiment of the present invention;

[0029] Explanation of reference numerals in the attached figures:

[0030] 1. Fully automatic series switching process control system; 11. Process information acquisition module; 12. Deviation degree determination module; 13. Operating condition information acquisition module; 14. Adjustment value determination module; 15. Series switching control module. Detailed Implementation

[0031] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0032] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0033] This application proposes a fully automatic series switching process control method, combining... Figure 1 As shown, it includes:

[0034] S1, acquire the motion process information of the actuators related to the series switching operation in industrial automated production;

[0035] S2, based on the motion process information, determines the degree of performance deviation of the actuator;

[0036] S3, obtain the current operating status information of the actuator;

[0037] S4, determine the adjustment value of the switching timing based on the degree of performance deviation and current operating condition information;

[0038] S5 adjusts and controls the series switching operation based on the degree of performance deviation, adjustment value, and preset interlock conditions.

[0039] In order to make the technical solutions proposed in this application easier and clearer to understand, it is necessary to explain the key terms involved and their corresponding implementation environments, so that those skilled in the art can accurately understand and implement the technical content of this application in specific engineering applications.

[0040] "Series switching operation" refers to an operational method in industrial automated production where multiple devices, execution units, or actuators sequentially complete start-up, stop, or switching actions according to a pre-set order and time constraints. This method is typically used to achieve continuous, collaborative production processes with strict timing requirements. In practical applications, the result of the action of the preceding actuator often constitutes a prerequisite for the start-up or switching of the subsequent actuator. For example, on an automated assembly line, the fixture can only perform clamping or positioning actions after the robotic arm completes its grasping action and reaches the designated position, thus ensuring the continuity and safety of the production process.

[0041] An "actuator" refers to a functional unit in an industrial automation control system that directly receives control commands and performs physical actions, including but not limited to solenoid valves, relays, motors, cylinders, and servo drives. The response speed, operational stability, and repeatability of the actuator directly determine the overall timing accuracy and system efficiency of the series switching operation. When the performance of the actuator changes due to wear, aging, or changes in external operating conditions, using a fixed switching sequence can easily lead to action conflicts, cycle mismatch, and even safety risks.

[0042] "Action process information" refers to the process data that can be collected, recorded, and analyzed by the actuator during the completion of one or more actions. This information includes not only the start and end times of the action, but also the duration of the action, the trajectory of displacement or position change, velocity change characteristics, and current or voltage changes. This action process information can reflect the actual operating status of the actuator from multiple dimensions and is an important data foundation for judging its current performance status and trends.

[0043] "Performance deviation" refers to the degree of difference between the actual operating performance of an actuator and its design, calibrated, or standard performance, obtained by analyzing the actuator's motion process information. This deviation can manifest as prolonged action time, delayed response, unstable action, or increased fluctuations. The causes may include wear of mechanical components, drift in electrical parameters, changes in ambient temperature, or variations in load conditions.

[0044] "Current operating condition information" refers to a comprehensive description of the operating environment and working conditions of the actuator at a specific moment, including production cycle time, load level, material type, ambient temperature, humidity, and overall system operating mode. Different operating conditions will have varying degrees of impact on the actuator's motion characteristics. Therefore, when adjusting the switching sequence, a comprehensive judgment must be made in conjunction with the current operating conditions to avoid static corrections based solely on a single performance deviation parameter.

[0045] "Adjustment value of switching sequence" refers to the time correction amount introduced to the original series switching operation sequence to compensate for the performance deviation of the actuator when it is identified. This correction amount can be manifested as the advance or delay of the start command, so that the overall series switching process can still maintain coordination and consistency under dynamic conditions.

[0046] "Interlock conditions" refer to pre-set control constraints during series switching operations to prevent logical conflicts, mechanical interference, or safety hazards between actuators. For example, before a subsequent actuator is allowed to start, it must be confirmed that the previous actuator has completed its action and is in a safe state. Such interlock conditions are usually implemented through control logic, status confirmation signals, or safety modules.

[0047] The control method proposed in this application can be deployed in industrial control systems, such as programmable logic controllers (PLCs), distributed control systems, or as a standalone edge computing device working in conjunction with existing control systems. It acquires real-time information on the actuator's motion and operating conditions through a sensor network, and uses a computing unit to perform analysis and decision-making, thereby achieving dynamic optimization control of the series switching process.

[0048] In its implementation, this application provides a fully automatic series switching process control method, the core of which lies in the continuous evaluation of the performance status of the actuators and the adaptive adjustment of the switching timing. First, the system acquires the motion process information of the actuators related to the series switching operation. This acquisition can be achieved by deploying position sensors, current sensors, voltage sensors, or speed sensors on the actuators, or by directly reading the status signals and historical operating data of the actuators from the control system via an industrial communication bus. This motion process information can be collected and stored in real time, providing a reliable data foundation for subsequent analysis.

[0049] After acquiring the motion process information, the system determines the degree of performance deviation of the actuator based on the information. This determination process can be achieved by comparing the actual motion time, response curve, or electrical characteristics with preset standard parameters, thereby quantifying the performance changes of the actuator in the current state. The obtained performance deviation can be used to characterize the decline or changing trend of the actuator's response capability, providing a basis for subsequent timing adjustments.

[0050] Subsequently, the system further acquires the current operating condition information of the actuators. This information can be obtained through environmental sensors, production management systems, or control system status parameters, and reflects the impact of current production rhythm, load conditions, and environmental factors on the actuator performance. Based on this, the system combines the performance deviation degree with the current operating condition information to determine the adjustment value for the switching sequence. This adjustment value is used to compensate for performance changes in the actuators, ensuring that the cascade switching operation can still meet timing coordination and safety requirements under dynamic conditions.

[0051] Finally, under the premise of meeting the preset interlocking conditions, the system applies the determined adjustment values ​​to the actual series switching control process to correct the timing of the actuator's start or switch commands. By introducing the adjusted timing parameters into the control logic and performing real-time verification of the interlocking conditions, the system ensures that the entire series switching process can still operate safely and stably even if the actuator's performance changes, thereby effectively improving the reliability, adaptability, and overall operating efficiency of the industrial automation production process.

[0052] Optional, combined Figure 2As shown, the steps for determining the degree of performance deviation of the actuator based on the motion process information include:

[0053] A1, Obtain ambient temperature information around the actuator;

[0054] A2, extracting multi-dimensional behavioral features from action process information;

[0055] A3, based on multidimensional behavioral characteristics and ambient temperature information, as well as preset attribution rules, determines whether the performance deviation is caused by environmental factors or by the performance degradation of the actuator itself, and obtains the deviation judgment result;

[0056] A4. Based on the deviation judgment results, quantify the degree of each deviation source to determine the degree of performance deviation of the actuator.

[0057] Specifically, acquiring ambient temperature information around the actuator refers to collecting temperature data of the operating environment in real time or periodically through environmental sensors deployed near the actuator. This ambient temperature information can serve as an important indicator for assessing the impact of the external environment on the actuator's performance. Extracting multi-dimensional behavioral features from the motion process information can be understood as conducting in-depth analysis of various data generated by the actuator during the execution of series switching operations to identify its inherent operating modes and states. These multi-dimensional behavioral features may include, but are not limited to, the actuator's motion speed, acceleration, torque, vibration frequency, response time, and energy consumption changes. These features can reflect the actuator's dynamic performance and potential performance changes under different operating conditions.

[0058] In practical applications, based on multidimensional behavioral characteristics and ambient temperature information, as well as pre-defined attribution rules, it is determined whether performance deviation is caused by environmental factors or by the performance degradation of the actuator itself, thus obtaining the deviation judgment result. This can be achieved, for example, by establishing a machine learning model or expert system. The pre-defined attribution rules can be trained and defined based on historical data and domain knowledge to identify the correlation between specific behavioral characteristic patterns and changes in ambient temperature, as well as the correlation between them and performance degradation phenomena such as wear and aging within the actuator. For example, if the actuator's response time is significantly prolonged when the ambient temperature rises significantly, and other internal characteristics are not significantly abnormal, it may be attributed to environmental factors; if the actuator's vibration frequency continues to rise or energy consumption increases abnormally when the ambient temperature is stable, it may be attributed to its own performance degradation. Furthermore, based on the deviation judgment result, the degree of each deviation source is quantified to determine the extent of the actuator's performance deviation. This means that after determining the source of the performance deviation, it is necessary to numerically evaluate the contribution of each source (environmental factors or performance degradation) to the total deviation. For example, weights or influencing factors can be assigned to environmental factors and inherent performance degradation, and combined with their respective deviation indicators, a comprehensive performance deviation value can be calculated. The aim is to provide a more refined and instructive performance evaluation result, so that subsequent switching timing adjustments can be more targeted.

[0059] In some preferred embodiments, a specific example is given below. Suppose that in a high-temperature workshop, a robotic arm (actuator) used for material gripping and placement experiences a slight decrease in motion accuracy and response speed during a series cutting operation.

[0060] First, the system acquires ambient temperature information around the robotic arm, such as detecting a local workshop temperature of 40°C using an infrared sensor. Simultaneously, it extracts multi-dimensional behavioral characteristics from the robotic arm's movement data, including the current and speed of the joint motors, vibration sensor data, and the gripping force feedback from the end effector.

[0061] Next, the system analyzes this data according to the preset attribution rules. For example, the attribution rules may be set as follows: if the ambient temperature exceeds 35°C, and the joint motor current and speed are within the normal range but the response time is slightly increased, it tends to judge that the performance deviation is mainly caused by the high ambient temperature; if the ambient temperature is normal, but the joint vibration frequency is abnormally increased and the gripping force feedback is unstable, it tends to judge that the performance deviation is mainly caused by the wear and tear of the robotic arm's own components.

[0062] In this example, the system determines that the performance deviation is mainly caused by high ambient temperature, thus obtaining a deviation assessment result. Subsequently, based on this deviation assessment result, the system quantifies the contribution of environmental factors to the performance deviation. For example, it assesses that the high ambient temperature caused a 10% decrease in the robotic arm's response speed and includes this as part of the actuator's performance deviation. Based on this more accurate performance deviation assessment, subsequent adjustments to the switching timing can more reasonably consider environmental impacts. For example, during high-temperature periods, the switching interval can be appropriately extended or the motion path adjusted to ensure operational stability and reliability.

[0063] Optionally, the steps for determining the adjustment values ​​for the switching timing based on the degree of performance deviation and current operating condition information include:

[0064] Continuously captures images of the manipulated material after it detaches from the actuator;

[0065] Based on continuous images, the position and orientation of the corresponding material in space are tracked to obtain the displacement velocity and amplitude of the corresponding material.

[0066] Based on the displacement velocity and amplitude of the corresponding material, determine whether the corresponding material has reached a stable state;

[0067] If it is determined that the corresponding material has reached a stable state, then record the time point when the material reaches a stable state.

[0068] Obtain the time point at which the action of the executing agency is completed;

[0069] The material stabilization delay time is obtained by calculating the time difference between the point when the material reaches a stable state and the point when the actuator completes its action.

[0070] The total effective delay time is obtained by superimposing the performance deviation of the actuator with the material stabilization delay time.

[0071] Calculate the adjustment value for the switching timing based on the total effective delay time and current operating condition information.

[0072] Specifically, continuously capturing images of the manipulated material after it detaches from the actuator refers to using high-speed cameras, industrial cameras, or other vision sensors to continuously and in real-time acquire image or video data of the material as it completes its interaction with the actuator and begins to move freely or under inertial influence. The purpose is to obtain visual information about the material's movement in space and its eventual stabilization process.

[0073] The process of tracking the position and orientation of materials in space based on continuous images, and obtaining their displacement velocity and amplitude, can be understood as using image processing and computer vision techniques, such as target detection, feature point tracking, and motion estimation algorithms, to extract the geometric center, key points, or contour information of the materials from a continuous image sequence. This allows for the calculation of their real-time coordinate changes in three-dimensional space, and further derivation of their instantaneous displacement velocity and amplitude. The aim is to quantify the dynamic behavior of materials.

[0074] In practical applications, determining whether a material has reached a stable state based on its displacement velocity and amplitude involves setting a series of preset stability thresholds. When the material's displacement velocity and amplitude remain below these thresholds for a period of time, it is considered that the material has stopped shaking or oscillating and has reached a physically stable state where further operations can proceed. The purpose is to accurately identify the moment when the material reaches physical stability.

[0075] If it is determined that the material has reached a stable state, the time point at which the material reaches a stable state is recorded. This means that the system immediately records the current timestamp after recognizing the material's stable state.

[0076] Obtaining the time point at which the actuator completes its action refers to recording the precise time when the actuator completes its predetermined action (e.g., releasing material, completing a push, etc.).

[0077] The material stabilization delay time is obtained by calculating the time difference between the point when the material reaches a steady state and the point when the actuator completes its action. This means that the material stabilization delay time is quantified by the difference between the two time points, which is the extra time required for the material to go from the completion of the actuator's action to its own physical stability.

[0078] The total effective delay time is obtained by superimposing the performance deviation of the actuator with the material stabilization delay time. This means combining the performance changes of the actuator itself (such as a slowdown in response speed) with the physical stability characteristics of the material to form a comprehensive delay index.

[0079] The adjustment value for the switching sequence is calculated based on the total effective delay time and current operating conditions. This means that the specific value for adjusting the original switching sequence is calculated by comprehensively considering the actual stability of the material, the performance changes of the actuator, and the current production environment conditions, through a preset algorithm model (e.g., a rule-based expert system, a machine learning model, or a dynamic programming algorithm).

[0080] Optionally, the steps for determining whether a material has reached a steady state based on its displacement velocity and amplitude include:

[0081] Identify key areas or internal movable parts of the corresponding material from continuous images;

[0082] Track the position and orientation of each key area or internal movable component in space;

[0083] Based on the position and orientation of the movable parts in the key area or inside, calculate the displacement velocity and amplitude of the movable parts in the key area or inside.

[0084] When the displacement velocity and amplitude of all critical areas or internal movable parts are lower than their respective preset minimum thresholds in multiple consecutive frames of images, the material is judged to have reached a stable state.

[0085] Specifically, identifying key regions or internal movable parts of a material from continuous images refers to using image processing and computer vision techniques, such as object detection, semantic segmentation, or feature point matching, to accurately locate and distinguish specific representative parts of the material that reflect its motion state within captured continuous images. These key regions can be the geometric center of the material, edge feature points, specific markers, or internal components that undergo relative displacement during movement, such as joints in a robotic arm or specific carriers on a conveyor belt. The aim is to decompose the overall motion of the material into the motion of multiple local features, thereby capturing the dynamic behavior of the material more precisely.

[0086] Tracking the position and orientation of each key region or internal movable component in space can be understood as continuously tracking the identified key regions or internal movable components across consecutive image frames. This can be achieved using techniques such as Kalman filtering, particle filtering, and deep learning tracking algorithms. Through tracking, the coordinate changes of each key region or internal movable component in three-dimensional space, as well as its rotational changes relative to its own coordinate system, can be obtained, thus comprehensively describing its motion trajectory and orientation changes. The purpose is to provide accurate raw data for subsequent displacement velocity and amplitude calculations.

[0087] In practical applications, based on the position and orientation of key areas or internal movable components in space, the displacement velocity and amplitude of these components are calculated. Specifically, the instantaneous displacement velocity is calculated by dividing the positional change of the key area or internal movable component in consecutive frames by the time interval, and the amplitude is calculated based on its maximum displacement range or oscillation amplitude over a period of time. For example, the velocity components of each key area or internal movable component in the X, Y, and Z directions can be calculated, and their resultant velocity can be synthesized. Simultaneously, its maximum and minimum positions within a certain time window are recorded to determine the amplitude. The purpose is to quantify the intensity and stability of local material motion.

[0088] Furthermore, the material is considered to have reached a stable state when the displacement velocity and amplitude of all key areas or internal moving parts are below their respective preset minimum thresholds in multiple consecutive frames of images. This means that a truly stable state can only be considered achieved when all moving parts or key feature points of the material have ceased significant movement or oscillation, and this static state persists for a certain period of time (i.e., multiple consecutive frames of images). The preset minimum thresholds are set according to the actual application scenario and the stability requirements; for example, they can be set to displacement velocities at the millimeter / second level and amplitudes at the micrometer level. The purpose is to avoid misjudgments caused by instantaneous jitter or measurement errors, ensuring high reliability in the determination of material stability.

[0089] Optionally, the step of identifying key areas or internal moving parts of the corresponding material from continuous images includes:

[0090] A multi-view image sequence is obtained by simultaneously acquiring a continuous sequence of images of the material from at least two different perspectives.

[0091] Spatial calibration and image registration are performed on multi-view image sequences to establish geometric correspondences between images from different viewpoints;

[0092] Identify the corresponding key regions or internal movable parts in each single-view image of a multi-view image sequence;

[0093] When a key area or internal movable part is occluded or the recognition confidence is lower than a preset threshold, the corresponding key area or internal movable part is identified and confirmed from other viewpoint images in the multi-view image sequence by using geometric correspondence.

[0094] Specifically, simultaneously acquiring a continuous sequence of images of a material from at least two different perspectives refers to deploying multiple image acquisition devices (such as industrial cameras) in an industrial automated production environment. These devices are configured at different spatial positions and angles to ensure that real-time images of the material being handled can be captured from multiple sides or directions. Simultaneous acquisition aims to ensure temporal alignment of images from different perspectives for subsequent accurate spatial calibration and image registration. This results in a multi-view image sequence containing multi-dimensional visual information about the material.

[0095] Spatial calibration and image registration of multi-view image sequences aim to eliminate positional and pose differences and lens distortion between different cameras, thereby establishing pixel-level geometric correspondences between images from different viewpoints. For example, camera intrinsic and extrinsic parameters can be calibrated using a calibration board, and image registration can be performed using techniques such as feature point matching, enabling the accurate association of the projection positions of the same spatial point in images from different viewpoints. This geometric correspondence forms the basis for subsequent multi-view collaborative recognition.

[0096] In practical applications, identifying the corresponding key regions or internal moving parts in each single-view image of a multi-view image sequence refers to using image processing and machine learning algorithms (such as deep learning-based object detection models) to perform preliminary analysis on the images from each independent viewpoint in an attempt to identify the key regions or internal moving parts of the material. These key regions or internal moving parts can be specific geometric features, connection points, moving parts, etc., of the material, and their motion state can reflect the overall stability of the material.

[0097] Furthermore, when key regions or internal movable parts are occluded or the recognition confidence score is lower than a preset threshold, geometric correspondence is used to identify and confirm the corresponding key regions or internal movable parts from other viewpoints in the multi-view image sequence. This means that when the recognition result from a certain viewpoint is not ideal (for example, some areas are occluded, leading to incomplete recognition, or the confidence score given by the recognition algorithm is lower than a preset reliability threshold), the system will not abandon it directly. Instead, it will use the previously established geometric correspondence to map the information recognized (or not recognized) from the current viewpoint to other viewpoints. In other viewpoints, due to different viewing angles, the occluded parts may become visible, or the recognition confidence score may be higher. By integrating the recognition results from multiple viewpoints, key regions or internal movable parts can be more comprehensively and accurately identified and confirmed, thereby improving the robustness of the recognition.

[0098] Optionally, when the key area or internal movable part is occluded or the recognition confidence level is lower than a preset threshold, a step is taken to identify and confirm the corresponding key area or internal movable part from other viewpoint images in the multi-view image sequence using geometric correspondence. This step improves the robustness and accuracy of recognition by systematically integrating multi-view information and performing confidence evaluation. This step includes:

[0099] Identify key areas that are occluded or the visible parts of internal movable components from the current viewpoint;

[0100] Project the visible portion onto other viewpoint images in a multi-view image sequence;

[0101] In images from other perspectives, identify the corresponding key areas or supplementary visible parts of the internal movable components within the projected area;

[0102] The visible and supplementary visible parts identified from all perspectives are stitched together and merged to construct a complete image of the corresponding key area or internal movable part;

[0103] When the overall recognition confidence of the complete image reaches a preset threshold, the key area or internal movable part is identified.

[0104] Specifically, identifying the visible parts of key areas or internal movable parts that are occluded from the current viewpoint means, from a specific viewpoint, first using image processing and pattern recognition techniques to identify the unoccluded and clearly visible parts of the key areas or internal movable parts. Although this information is incomplete, it provides initial clues and local features for subsequent cross-view information integration.

[0105] Projecting the visible portion onto other viewpoints in a multi-view image sequence can be understood as using pre-established geometric correspondences between different viewpoints (e.g., through camera calibration and 3D reconstruction techniques) to map the position of the visible portion identified at the current viewpoint in 3D space onto the 2D plane of other viewpoints. The aim is to provide a precise search range and reference position for finding corresponding parts in other viewpoints, thereby improving search efficiency and accuracy.

[0106] In practical applications, finding corresponding key areas or supplementary visible parts of internal movable components within a projection area in images from other viewpoints refers to identifying, through further image analysis and feature matching, supplementary visible parts that correspond to the visible parts in the current viewpoint but may be occluded in the current viewpoint. This helps to obtain more comprehensive component information from multiple angles, compensating for information gaps in a single viewpoint.

[0107] Furthermore, stitching and fusing the visible and supplementary visible portions identified from all perspectives to construct a complete image of the corresponding key region or internal movable component involves spatially aligning, stitching, and fusing the image content of geometrically corrected visible and supplementary visible portions obtained from different perspectives. This process aims to reconstruct a complete image of the key region or internal movable component, overcoming the limitations of incomplete information from a single perspective and forming a more comprehensive and accurate representation of the component.

[0108] Specifically, identifying key regions or internal movable parts when the overall recognition confidence of the complete image reaches a preset threshold refers to evaluating the recognition of the stitched and fused complete image and calculating its overall recognition reliability index. Only when this confidence reaches the preset reliability threshold is the recognition result of the key region or internal movable part finally confirmed to be accurate and reliable, thereby avoiding misjudgments caused by insufficient local information or recognition errors.

[0109] Optionally, the step of stitching and fusing the visible and supplementary visible portions identified from all perspectives to construct a complete image of the corresponding key region or internal movable part includes:

[0110] Before stitching and fusion, local geometric corrections are performed on the visible and supplementary visible parts identified from all viewpoints to eliminate the effects of minor deformations and perspective distortions.

[0111] Identify the overlapping area between all visible parts and supplementary visible parts;

[0112] Calculate the similarity of image content within overlapping regions;

[0113] Adjust the fusion weight of overlapping regions based on the degree of similarity;

[0114] The visible and supplementary visible parts after local geometric correction are stitched and fused according to the fusion weight to construct a complete image of the key region or internal movable parts.

[0115] Specifically, local geometric correction aims to correct minor geometric deviations between images from different viewpoints caused by camera parameters, installation errors, or environmental factors. For example, feature-point-based image registration algorithms, such as SIFT, SURF, or ORB, can be used to identify corresponding feature points in different images and perform local transformations on the images by calculating homography matrices or affine transformation matrices, thereby eliminating the effects of minor deformations and perspective distortions. Its purpose is to ensure that the image blocks to be stitched are geometrically aligned, laying the foundation for subsequent seamless fusion. Overlapping regions refer to image areas where the visible and supplementary visible parts identified from different viewpoints spatially overlap. Identifying these overlapping regions is a key step in smooth fusion and can be determined through image registration results or a pre-defined camera model. In practical applications, the similarity of image content within overlapping regions can be calculated using various image similarity metrics, such as Structural Similarity Index (SSIM), mutual information, and normalized cross-correlation. Its purpose is to evaluate the consistency of image content (such as brightness, color, and texture) in overlapping regions from different viewpoints, providing a basis for subsequent fusion weight adjustments. Furthermore, adjusting the fusion weights of overlapping regions based on similarity means that within overlapping regions, image content with higher similarity should be assigned greater weight, while regions with lower similarity should be assigned less weight, or a smooth transition can be achieved through gradual weighting. For example, techniques such as linear weighting, Gaussian weighting, or Poisson fusion can be used to dynamically adjust the contribution of each pixel based on similarity, ensuring a natural transition in the fused region and avoiding obvious stitching artifacts. Thus, the visible and supplementary visible parts after local geometric correction are stitched and fused according to the fusion weights, ultimately constructing a complete image of the key region or internal movable parts. This process ensures precise geometric alignment and a smooth visual transition, thereby improving the quality of the complete image and the accuracy of subsequent recognition.

[0116] Optionally, the step of calculating the similarity of image content within overlapping regions may include:

[0117] Local brightness equalization is performed on the image content within the overlapping area to eliminate the effects of uneven local lighting, reflections, or shadows.

[0118] Edge contour extraction is performed on the image content after local brightness equalization to obtain the structural features of the image content;

[0119] Based on the structural features of image content, compare the degree of matching of edge contours of overlapping areas under different viewpoints;

[0120] Based on the degree of matching, the similarity of image content within the overlapping regions is determined.

[0121] Local brightness equalization refers to adjusting the brightness of local areas of an image to achieve a similar brightness distribution across different regions. For example, algorithms such as Adaptive Histogram Equalization (AHE) or Contrast Limited Adaptive Histogram Equalization (CLAHE) can be used to remap pixel values ​​within overlapping areas. This effectively reduces brightness differences caused by changes in ambient lighting, surface reflections, or shadows, providing a more consistent image foundation for subsequent feature extraction.

[0122] Specifically, edge contour extraction aims to identify the boundaries of regions in an image where brightness or color changes significantly. These boundaries typically represent the shape, structure, or texture of an object. For example, classic edge detection algorithms such as the Canny operator, Sobel operator, Prewitt operator, or Laplacian operator can be used to process images that have undergone brightness equalization, thereby accurately delineating the edges and contours of objects within overlapping areas. These edges and contours are considered structural features of the image content, and they are crucial for describing the geometry and spatial layout of objects.

[0123] The comparison of edge contour matching refers to evaluating the similarity or correspondence of structural features (i.e., edge contours) extracted from overlapping regions of images from different viewpoints. For example, feature point matching methods, such as SIFT (Scale Invariant Feature Transform), SURF (Speed-Up Robust Feature Transform), or ORB (Oriented Fast and Rotation Invariant Feature Transform), can be used to detect and match feature points on edge contours from different viewpoints. The matching degree is then quantified by calculating the number, distribution density, or geometric consistency of the matching points. Alternatively, the geometry of the contours can be directly compared, for example, using shape context or Fourier descriptors to evaluate the similarity between contours.

[0124] In practical applications, the degree of matching can directly or indirectly reflect the similarity of image content. For example, when the number of matching points on the edge contour is greater, the distribution is more uniform, and the geometric consistency is higher, it can be considered that the image content of the overlapping areas under different viewpoints has a higher degree of similarity. A function or mapping relationship can be set to convert the degree of matching (e.g., a matching score or percentage) into a similarity value, which is usually between 0 and 1, where 1 represents complete similarity and 0 represents complete dissimilarity.

[0125] Optionally, the steps for comparing the matching degree of edge contours of overlapping regions under different viewpoints based on the structural features of image content include:

[0126] Local adaptive sampling is performed on the edge contour of the overlapping area under each viewpoint to generate a set of uniformly distributed contour points;

[0127] Calculate the local shape descriptor for each contour point;

[0128] Preliminary matching is performed based on the local shape descriptor of the contour points to identify candidate matching point pairs that meet the preset similarity conditions;

[0129] When the number of candidate matching point pairs is insufficient or the distribution is uneven, the predicted position of the unmatched contour point in another view is predicted based on the spatial relative position relationship of the matched point pairs.

[0130] Within the local area of ​​the predicted location, supplementary matching is performed by searching and comparing local shape descriptors to obtain the updated contour matching result, which is output as the matching degree.

[0131] Specifically, local adaptive sampling of the edge contours of overlapping regions under each viewpoint generates a set of uniformly distributed contour points. This means that based on information such as the curvature and gradient changes of the contour, the sampling density can be increased in areas of drastic change and decreased in areas of flatness to more effectively capture the key geometric features of the contour, avoid errors caused by uneven sampling, and provide a foundation for subsequent descriptor calculation. The calculation of the local shape descriptor for each contour point can employ descriptors based on histogram of gradient orientation (HOG), scale-invariant feature transform (SIFT) descriptors, or accelerated robust feature transform (SURF) descriptors. These descriptors can capture the local texture and geometric information around the contour point and have a certain robustness to illumination, scale, and rotation. In practical applications, preliminary matching is performed based on the local shape descriptors of the contour points to identify candidate matching point pairs that meet preset similarity conditions. Nearest neighbor matching algorithms can be used, combined with ratio tests (e.g., the ratio of the nearest neighbor distance to the second nearest neighbor distance is less than a preset threshold) to filter out candidate matching point pairs with high confidence, thereby reducing false matches. When the number of candidate matching point pairs is insufficient or their distribution is uneven, the predicted position of the unmatched contour point in another viewpoint is predicted based on the spatial relative positional relationship of the matched point pairs. This means that the geometric transformation model (e.g., affine transformation or perspective transformation) between the two viewpoints can be calculated using robust estimation methods such as the RANSAC (Random Sample Consensus) algorithm, utilizing the matched point pairs. Then, the unmatched contour point is projected onto the other viewpoint through this transformation model to obtain its predicted position. Further, within the local range of the predicted position, supplementary matching is performed by searching and comparing local shape descriptors to obtain an updated contour matching result as the matching degree output. This means that within the local neighborhood of the predicted position, a smaller search window can be set to compare and match local shape descriptors again. This process can employ more relaxed similarity conditions to find as many effective matching points as possible, thereby obtaining a more comprehensive and accurate contour matching result.

[0132] In some preferred embodiments, a specific example is given below. Suppose that on an industrial automated production line, it is necessary to acquire multi-view images of an irregularly shaped casting with a complex surface texture to identify its internal minute defects. Due to uneven reflection and partial self-occlusion on the casting surface, traditional methods often only find a small number of unevenly distributed matching points when performing edge contour matching between images from different viewpoints. This makes it difficult to accurately assess the similarity of overlapping areas, affecting the accuracy of defect identification.

[0133] The specific implementation of the scheme in this application is as follows:

[0134] First, local adaptive sampling is performed on the casting edge contours obtained from different perspectives. For example, in areas with large curvature changes, such as sharp corners and hole edges of the casting, the sampling point density is automatically increased, while it is appropriately reduced in flat areas to ensure that key geometric features are fully captured.

[0135] Next, a local shape descriptor is calculated for each sampling point, for example, using an improved SIFT descriptor that can effectively handle variations in lighting and texture differences on the casting surface.

[0136] Then, preliminary matching is performed based on these descriptors to identify candidate matching pairs that meet preset similarity criteria. For example, a ratio test is used to filter out the initial 20-30 high-confidence matching points.

[0137] When the number of these initial matching points is insufficient (e.g., less than 50) or the distribution is too concentrated, the system will use these 20-30 matched point pairs to estimate the perspective transformation matrix between the two viewpoint images using the RANSAC algorithm.

[0138] Subsequently, all unmatched contour points in the first view are projected onto the second view using the perspective transformation matrix to obtain their predicted positions.

[0139] Finally, within a local area of ​​each predicted location (e.g., a 10x10 pixel window), local shape descriptors are searched and compared again for supplementary matching. In this way, even with sparse initial matching, an additional 50-80 valid matching points can be found, resulting in a more evenly distributed and comprehensive contour matching result with 80-110 matching points.

[0140] As a result, the degree of edge contour matching of overlapping areas in images of castings from different perspectives is more accurately determined, which in turn makes the splicing and fusion of multi-view images more precise, ultimately improving the accuracy and reliability of identifying minute defects inside the casting.

[0141] This application also discloses a fully automatic series switching process control system for performing fully automatic series switching process control, combined with... Figure 3 As shown, the fully automatic tandem switching process control system 1 includes:

[0142] Process information acquisition module 11 is used to acquire the action process information of the actuators related to the series switching operation in industrial automated production;

[0143] The deviation determination module 12 is used to determine the degree of performance deviation of the actuator based on the motion process information;

[0144] The operating condition information acquisition module 13 is used to acquire the current operating condition information of the actuator;

[0145] The adjustment value determination module 14 is used to determine the adjustment value of the switching sequence based on the degree of performance deviation and the current operating condition information;

[0146] The series switching control module 15 is used to adjust and control the series switching operation based on the degree of performance deviation, adjustment value and preset interlock conditions.

[0147] Through its modular structural design, this system enables continuous sensing, dynamic evaluation, and intelligent adjustment of the series switching sequence of actuators during industrial automated production. By decoupling and coordinating the functions of data acquisition, performance analysis, condition sensing, decision calculation, and control execution, the system can maintain the timing coordination and operational stability of series switching operations even when the performance of the actuators changes.

[0148] The process information acquisition module is used for real-time monitoring and data collection of the actuator actions related to series switching operations in industrial automated production. This module continuously acquires process information such as the start and end times of actions, duration of actions, response delays, current changes, and position signals during actual operation, providing a foundational data source for subsequent performance evaluation. Through this module's real-time acquisition mechanism, the system can avoid relying solely on static configuration parameters or historical experience to determine the actuator's status, thereby improving the accuracy of performance change perception.

[0149] The deviation determination module analyzes and processes the actual operating performance of the actuator based on the motion process information collected by the process information acquisition module, in order to quantify its deviation from the design or calibration performance. This module generates quantitative results reflecting the performance degradation or response changes of the actuator through comparative analysis of motion time, response characteristics, or statistical features. This transforms the previously difficult-to-observe "performance changes" into calculable and comparable deviation indicators, thus providing a clear basis for subsequent control decisions.

[0150] The operating condition information acquisition module is used to acquire operating condition information related to the current operating status of the actuator, including production cycle time, load conditions, environmental parameters, and the actuator's operating history. By introducing operating condition information, the system can distinguish between normal performance fluctuations caused by changes in external operating conditions and genuine performance degradation, avoiding unnecessary or excessive timing compensation for the actuator when operating conditions change, thereby improving the pertinence and rationality of adjustment decisions.

[0151] After receiving the performance deviation level output from the deviation level determination module and the current operating condition information provided by the operating condition information acquisition module, the adjustment value determination module comprehensively analyzes the above information and calculates the timing adjustment value used to correct the series switching operation. This adjustment value reflects the amount of time correction required to maintain the consistency of the series switching operation under the current performance state and operating conditions. Through the dynamic calculation mechanism of this module, the system can avoid using fixed delays or uniform compensation strategies, thereby achieving fine-grained timing adjustments for specific actuators and specific operating conditions.

[0152] The cascade switching control module, as the core of the system's execution, applies the adjustment values ​​output by the adjustment value determination module to the actual cascade switching control process, while strictly adhering to preset interlock conditions. This module generates control commands based on the adjusted timing parameters and sends start, switch, or stop commands to the corresponding actuators. Simultaneously, it continuously monitors whether the interlock conditions are met, ensuring that the preceding actuator has completed its action and is in a safe state before any actuator acts. Through this control mechanism, the system can still guarantee the safety and logical integrity of the production process while introducing dynamic timing adjustments.

[0153] The aforementioned modules form a collaborative working mechanism through clear functional division and data interaction, enabling the system to continuously perform adaptive optimization control on the series switching operation even when the performance of the actuators gradually declines, fluctuates intermittently, or changes in operating conditions. This system architecture effectively avoids the switching mismatch problem caused by the decline in the responsiveness of the actuators in traditional automation systems.

[0154] In traditional automated production systems, a common approach to handling slow response or performance degradation of actuators is to globally extend the timeout or action confirmation delay parameters of critical switching steps to increase the system's tolerance for performance fluctuations. However, this type of fixed-delay strategy cannot differentiate between the performance differences of different actuators, nor can it be adjusted based on real-time operating conditions. In high-speed production modes or under complex process conditions, the overall extended waiting time often creates new conflicts with other switching logic, leading to process stalls, reduced cycle time, and even logical conflicts and abnormal shutdowns.

[0155] In contrast, this application, through a modular system design, achieves dynamic evaluation of the performance deviation of the actuator and makes targeted adjustments to the switching sequence based on current operating condition information. The process information acquisition module and the deviation determination module enable real-time perception and quantification of actuator performance changes. The operating condition information acquisition module provides the necessary operational background for adjustment decisions, and the adjustment value determination module generates precise timing corrections based on this. In this way, the system introduces appropriate switching adjustments only when truly necessary, avoiding unnecessary accumulation of waiting time and effectively preventing logical conflicts and process blockages in high-speed production modes.

[0156] Meanwhile, the series switching control module always adheres to preset interlocking conditions when implementing adjustments, ensuring that the production process still meets safety constraints and control logic requirements even with the introduction of dynamic timing corrections. This control mechanism, which combines performance evaluation, condition awareness, intelligent decision-making, and interlocking control, enables series switching operations to maintain high precision, high stability, and high safety even in complex industrial environments.

[0157] In summary, the core innovation of the fully automated cascade switching process control system proposed in this application lies in its ability to dynamically generate precise switching timing adjustment values ​​by real-time monitoring and quantification of the performance deviation of the actuators, combined with current operating condition information for adaptive decision-making. This system effectively overcomes the limitations of traditional fixed-delay strategies in dealing with performance degradation and complex operating conditions. It not only significantly improves the stability and operating efficiency of industrial automated production processes but also demonstrates outstanding technical effects and practical application value in reducing the risk of logical conflicts, minimizing unnecessary downtime, and extending equipment lifespan.

[0158] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A fully automatic series switching process control method, characterized in that, include: To obtain information on the motion process of actuators related to series switching operations in industrial automated production; Based on the action process information, determine the degree of performance deviation of the actuator; Obtain the current operating status information of the actuator; Based on the performance deviation and current operating condition information, determine the adjustment value for the switching timing; Based on the performance deviation, the adjustment value, and the preset interlocking conditions, the series switching operation is adjusted and controlled.

2. The fully automatic series switching process control method according to claim 1, characterized in that, The step of determining the degree of performance deviation of the actuator based on the action process information includes: Obtain ambient temperature information around the actuator; Extract multidimensional behavioral features from the action process information; Based on the multidimensional behavioral characteristics and the ambient temperature information, as well as the preset attribution rules, it is determined whether the performance deviation is caused by environmental factors or by the performance degradation of the actuator itself, and the deviation judgment result is obtained. Based on the deviation judgment results, the degree of each deviation source is quantified to determine the degree of performance deviation of the actuator.

3. The fully automatic series switching process control method according to claim 1, characterized in that, The step of determining the adjustment value of the switching timing based on the performance deviation and current operating condition information includes: Continuously captures images of the manipulated material after it detaches from the actuator; Based on the continuous images, the position and orientation of the corresponding material in space are tracked to obtain the displacement velocity and amplitude of the corresponding material; Based on the displacement velocity and amplitude of the corresponding material, determine whether the corresponding material has reached a stable state; If it is determined that the corresponding material has reached a stable state, then record the time point when the material reaches a stable state. Obtain the time point at which the action of the executing agency is completed; The material stabilization delay time is obtained by calculating the time difference between the point when the material reaches a stable state and the point when the actuator completes its action. The total effective delay time is obtained by superimposing the performance deviation of the actuator with the material stabilization delay time. Based on the total effective delay time and current operating condition information, calculate the adjustment value for the switching timing.

4. The fully automatic series switching process control method according to claim 3, characterized in that, The step of determining whether the corresponding material has reached a steady state based on the displacement velocity and amplitude of the corresponding material includes: Identify key areas or internal movable parts of the corresponding material from the continuous images; Track the position and orientation of each key area or internal movable component in space; Based on the position and orientation of the movable parts in the key area or inside, calculate the displacement velocity and amplitude of the movable parts in the key area or inside. When the displacement velocity and amplitude of all critical areas or internal movable parts are lower than their respective preset minimum thresholds in multiple consecutive frames of images, the material is judged to have reached a stable state.

5. The fully automatic series switching process control method according to claim 4, characterized in that, The step of identifying the key areas or internal movable parts of the corresponding material from the continuous images includes: A multi-view image sequence is obtained by simultaneously acquiring a continuous sequence of images of the material from at least two different perspectives. Spatial calibration and image registration are performed on the multi-view image sequence to establish the geometric correspondence between images from different viewpoints; In each single-view image of the multi-view image sequence, identify the corresponding key region or internal movable component; When a key area or internal movable part is obscured or the recognition confidence level is lower than a preset threshold, the corresponding key area or internal movable part is identified and confirmed from other viewpoint images in the multi-view image sequence using the geometric correspondence.

6. The fully automatic series switching process control method according to claim 5, characterized in that, The step of identifying and confirming the corresponding key region or internal movable part from other viewpoint images in the multi-view image sequence by utilizing the geometric correspondence when the key region or internal movable part is occluded or the recognition confidence level is lower than a preset threshold includes: Identify key areas that are occluded or the visible parts of internal movable components from the current viewpoint; The visible portion is projected onto other viewpoint images in the multi-view image sequence; In images from other perspectives, identify the corresponding key areas or supplementary visible parts of the internal movable components within the projected area; The visible parts and supplementary visible parts identified from all perspectives are stitched and fused together to construct a complete image of the corresponding key area or internal movable part; When the overall recognition confidence of the complete image reaches a preset threshold, the key area or internal movable part is identified.

7. The fully automatic series switching process control method according to claim 6, characterized in that, The step of stitching and fusing the visible portions and supplementary visible portions identified from all perspectives to construct a complete image of the corresponding key region or internal movable component includes: Before stitching and fusion, local geometric corrections are performed on the visible portions and supplementary visible portions identified from all viewpoints to eliminate the effects of minor deformations and perspective distortions. Identify the overlapping areas between all the visible portions and the supplementary visible portions; Calculate the similarity of image content within the overlapping region; Adjust the fusion weight of the overlapping regions based on the degree of similarity; The visible portion and the supplementary visible portion after local geometric correction are stitched and fused according to the fusion weight to construct a complete image of the key region or internal movable component.

8. The fully automatic series switching process control method according to claim 7, characterized in that, The step of calculating the similarity of image content within the overlapping region includes: The image content within the overlapping area is subjected to local brightness equalization processing to eliminate the effects of uneven local illumination, reflections, or shadows. Edge contour extraction is performed on the image content after local brightness equalization to obtain the structural features of the image content; Based on the structural features of image content, compare the degree of matching of edge contours of overlapping areas under different viewpoints; Based on the matching degree, the similarity of the image content within the overlapping area is determined.

9. The fully automatic series switching process control method according to claim 8, characterized in that, The step of comparing the matching degree of edge contours of overlapping regions under different viewpoints based on the structural features of image content includes: Local adaptive sampling is performed on the edge contour of the overlapping area under each viewpoint to generate a set of uniformly distributed contour points; Calculate the local shape descriptor for each of the contour points; Preliminary matching is performed based on the local shape descriptors of the contour points to identify candidate matching point pairs that meet preset similarity conditions; When the number of candidate matching point pairs is insufficient or the distribution is uneven, the predicted position of the unmatched contour point in another view is predicted based on the spatial relative position relationship of the matched point pairs. Within a local area of ​​the predicted location, supplementary matching is performed by searching and comparing local shape descriptors to obtain an updated contour matching result, which is output as the matching degree.

10. A fully automatic series switching process control system, used to execute fully automatic series switching process control, characterized in that, include: The process information acquisition module is used to acquire the motion process information of the actuators related to the series switching operation in industrial automated production; The deviation degree determination module is used to determine the degree of performance deviation of the actuator based on the action process information; The operating condition information acquisition module is used to acquire the current operating condition information of the actuator; The adjustment value determination module is used to determine the adjustment value of the switching sequence based on the performance deviation and the current operating condition information; The series switching control module is used to adjust and control the series switching operation based on the performance deviation, the adjustment value, and the preset interlocking conditions.

Images