Auxiliary control system and method for abnormal intervention of manufacturing and assembling production line

Abstract

The invention discloses an auxiliary control system and method for abnormal intervention of a manufacturing and assembling production line, and relates to the technical field of intelligent manufacturing workshop auxiliary equipment and physical execution control of the intelligent manufacturing workshop auxiliary equipment, and the system comprises a multi-dimensional physical state collection unit which is used for generating a multi-dimensional physical state data set; the dynamic mapping and anomaly detection unit is used for detecting an abnormal state in real time; the multi-process linkage intervention unit is used for generating a flexible intervention control instruction; the auxiliary control unit is used for executing an intervention action; and the closed-loop optimization unit is used for constructing feedback comparison and residual attitude error judgment of an intervention action effect and forming a self-adaptive correction closed loop of the auxiliary device. The flexible adaptive capacity and the plug-in mounting stability of the workshop auxiliary mechanical device are enhanced, and the workshop auxiliary mechanical device has the characteristics of non-intrusive mounting, modular expansion, multi-process rapid adaptation and the like, and is particularly suitable for auxiliary control and accurate intervention of various and batch workpieces in a complex assembly scene.

Description

Technical Field

[0001] The present invention relates to the field of intelligent manufacturing workshop auxiliary equipment and physical execution control technology thereof, and in particular to an auxiliary control system and method for abnormal intervention in manufacturing and assembly production lines. Background Art

[0002] With the continuous increase in product complexity, batch differences, and flexible production requirements in the manufacturing industry, assembly lines in workshops are facing challenges in terms of higher workpiece docking accuracy, process rhythm stability, and dynamic adaptability of multiple tasks. Especially in the assembly scenario of multiple varieties and small batches, the dimensional tolerances, assembly matching methods, and process connection rhythms of different workpieces are different, which often leads to slight deviations in the workpiece posture or insufficient support alignment rigidity in the process execution link, affecting the final assembly success rate. Traditional production lines usually rely on static clamping equipment, fixed-beat logistics buffers, and single support tables in the parallel operation of multiple processes. They lack flexible buffering and real-time compensation mechanisms for new workpieces or new tasks, making it difficult to meet the needs of rapid adaptation to instantaneous matching offsets in the assembly link. This limitation is particularly evident in complex assembly, variable batch switching, or multi-station linkage operations, and often directly affects the overall rhythm continuity, physical movement matching, and quality consistency of the final product of the production line.

[0003] At present, the auxiliary device structures commonly used in workshops are mostly based on single-process rigid fixtures or single-axis supports, which lack the multi-dimensional flexible adjustment capabilities for complex coordination of multiple processes. Although sensors have been introduced in some scenarios to perform basic monitoring of clamping force or support angle, these monitoring are often only used for safety limit alarms and cannot support the dynamic linkage of physical actions in multiple processes. Specifically, when there is a deviation between the rigidity curve of the clamping force and the buffer zone of the support arm, the system lacks an effective physical adjustment or fine-tuning channel, resulting in interference or insufficient support when the workpiece geometry fluctuates or the assembly posture changes. In addition, logistics buffer rollers are usually based on fixed beats or simple beats control, and cannot achieve dynamic buffering according to the actual assembly synchronization beat, resulting in beat conflicts or cache overflows when multiple processes are executed concurrently. These problems are particularly prominent in the case of multi-tasking and rapid switching of workpieces on the production line, and have become an important obstacle to the flexible upgrade of the production line and the matching of process safety.

[0004] Especially in the assembly production process of mixed-product lines, traditional rigid fixtures and logistics devices lack the ability to quickly respond to the diversity of workpieces, the variability of processes, and dynamic beat fluctuations, making it difficult to support the efficient assembly adaptation of intelligent flexible production lines to changes in workpiece size and shape differences. Faced with complex assembly steps and rapid switching between multiple tasks, existing workshop auxiliary devices are usually unable to provide dynamic buffering, beat flexibility absorption, and intelligent compensation for physical movements at the mechanized action level, resulting in problems such as assembly mismatch, process delays, and beat disorders in actual production line operation. Currently, there is a lack of an overall control system that integrates multi-dimensional mechanized auxiliary modules such as multi-degree-of-freedom fixtures, flexible support arms, and adjustable logistics buffer rollers, and has the ability to sense the physical state in real time, adjust flexible movements, and adaptively compensate for multi-process movements. This makes it difficult to meet the actual assembly needs of intelligent manufacturing workshops facing high-frequency batch changes and differentiated process beats, and also restricts the safety assurance capabilities and flexible production adaptability of workshop-level production lines. Summary of the Invention

[0005] In order to solve the problems of physical coordination fragmentation and insufficient flexible adaptability of multiple processes in multi-process production lines of assembly workshops caused by problems such as assembly rhythm fluctuations, unstable clamping force of fixtures, support table posture mismatch and logistics congestion, the present invention proposes an auxiliary control system and method for abnormal intervention in manufacturing and assembly production lines, to ensure the adaptive productivity and continuous optimization capabilities of intelligent manufacturing production lines under dynamic switching of multiple tasks.

[0006] On the one hand, to achieve the above-mentioned objectives, the present invention provides an auxiliary control system for abnormal intervention of manufacturing and assembly production lines, comprising:

[0007] Multi-dimensional physical state acquisition unit: used to collect the operating status of equipment in the production line, the clamping force of fixtures, the status of workpieces in the logistics path, and the workpiece assembly angle error, and generate a multi-dimensional physical state data set covering process-level actions;

[0008] Dynamic mapping and anomaly detection unit: used to construct a multi-dimensional device-based mapping model containing multi-source physical state mapping based on the multi-dimensional physical state data set, and detect workpiece residual alignment errors in real time, wherein the workpiece residual alignment errors include fixture clamping force overload, support arm posture angle drift, or logistics beat deviation;

[0009] Multi-process linkage intervention unit: used to automatically generate flexible intervention control instructions when abnormal conditions are detected, triggering dynamic correction of clamping force, flexible fine-tuning of multiple degrees of freedom of support arms, and adaptive adjustment of the transmission rate of logistics buffer rollers;

[0010] Auxiliary control unit: used for instruction decomposition and modular action issuance, refining system control instructions into mechanized low-level execution parameters and executing intervention actions;

[0011] Closed-loop optimization unit: It is used to build feedback comparison of intervention action effects and residual posture error judgment based on the clamping force flexibility loading curve, support arm posture compensation amplitude and logistics buffer status changes collected in real time by the sensor array, thus forming an adaptive correction closed loop for the auxiliary device.

[0012] Preferably, the multidimensional physical state data set includes:

[0013] The clamping force signal of the fixture, the multi-dimensional posture angle vector of the support arm, the transmission speed of the logistics roller, and the temperature and humidity of the workstation.

[0014] Preferably, the multi-dimensional physical state acquisition unit generates the multi-dimensional physical state data set covering the process-level actions through an adjustable weighting coefficient matrix and a nonlinear mapping activation function, specifically:

[0015] ,

[0016] Where, is the nonlinear mapping activation function, A multi-dimensional physical state dataset covering process-level actions, are all weight matrices, is the clamping force of the fixture, is the multi-dimensional posture angle vector of the supporting arm, is the conveying speed of the logistics roller, It is the temperature and humidity of the work station.

[0017] Preferably, the multi-source physical state mapping is a multi-source physical state mapping including a clamp module, a flexible support arm buffer structure and a logistics buffer device;

[0018] The fixture module has a built-in servo electric cylinder and a force sensor, which are used to adjust the clamping force of the fixture according to the real-time physical state of the workpiece;

[0019] The flexible support arm buffer structure includes a multi-degree-of-freedom rotation pair, a guide rail slider and an electric actuator, which is used to adaptively adjust the spatial position of the support arm in real time according to the assembly posture of the workpiece, compensating for the posture deviation of the workpiece during the insertion process;

[0020] The logistics cache device consists of an electric roller drive module, a buffer support stand, a built-in encoder and a cache area sensor array, and is used to adjust the cache roller transmission speed in real time according to the rhythm changes of the assembly task and the dynamic logistics accumulation.

[0021] Preferably, the clamping force of the clamp in the clamp module is adjusted in real time by a clamping control model, wherein the clamping control model is:

[0022] ;

[0023] Where, is the clamping control model, is the stiffness coefficient, is the damping coefficient, is the instantaneous rate of change of the workpiece docking error.

[0024] Preferably, the transmission speed of the buffer drum is calculated based on a dynamic adjustment model, specifically:

[0025] ;

[0026] Where, To cache the roller transmission speed, is the base speed of the drum, It is the cache flexibility adjustment coefficient.

[0027] Preferably, the dynamic mapping and anomaly detection unit detects the workpiece residual alignment error in real time by integrating a posture sensor, wherein the workpiece residual alignment error is:

[0028] ;

[0029] Where, is the residual alignment error of the workpiece, Assemble the pose for the desired target, This is the detection pose after the actual assembly is completed. is the L2 norm.

[0030] Preferably, when the multi-process linkage intervention unit detects that the residual alignment error exceeds the set safety tolerance, it triggers multi-dimensional flexible adaptive compensation through the auxiliary control unit, automatically lowers the upper limit of the clamping force of the fixture, extends the logistics cache cycle, widens the production line buffer zone, and adjusts the support arm fine-tuning angle buffer zone in real time.

[0031] Preferably, the system further comprises:

[0032] Modular quick-release structure, used to enable rapid replacement of fixture modules, flexible support arm buffer structures, and logistics buffer devices through standardized interfaces;

[0033] Human-computer interaction panel and visual operation interface: used to display the clamping force curve, support arm posture buffer zone and logistics rhythm fluctuation in real time, and supports manual fine-tuning and safety warning.

[0034] On the other hand, to achieve the above-mentioned purpose, the present invention also provides an auxiliary control method for abnormal intervention of manufacturing and assembly production lines, comprising:

[0035] The multi-dimensional physical state acquisition unit collects the operating status of equipment in the production line, the clamping force of fixtures, the status of workpieces in the logistics path, and the workpiece assembly angle error, generating a multi-dimensional physical state data set covering process-level actions;

[0036] Based on the multidimensional physical state data set, a multidimensional device-based mapping model including multi-source physical state mapping is constructed, and the clamping force overload, support arm posture angle drift or logistics beat deviation are detected in real time;

[0037] Abnormal detection is performed through the multi-process linkage intervention unit. When an abnormal state is detected, flexible intervention control instructions are automatically generated to trigger dynamic correction of the clamping force, flexible fine-tuning of the support arm with multiple degrees of freedom, and adaptive adjustment of the transmission rate of the logistics buffer roller;

[0038] Through the auxiliary control unit, the system control instructions are broken down into mechanized low-level execution parameters and intervention actions are executed;

[0039] Based on the clamping force flexibility loading curve, support arm posture compensation amplitude and logistics buffer status changes collected in real time by the sensor array, feedback comparison of the intervention action effect and residual posture error judgment are constructed to form an adaptive correction closed loop of the auxiliary device.

[0040] Compared with the prior art, the present invention has the following advantages and technical effects:

[0041] (1) This invention uses real-time fusion of multi-dimensional physical sensor data to cover the core dimensions of mechanical actions, such as fixture loading force, support arm fine-tuning posture, and logistics roller beat, to form a unified physical state representation model for auxiliary devices. This representation breaks through the limitations of the traditional "fixture-support arm-logistics roller" separation and single-point configuration in workshops, and can achieve high-dimensional unification of multi-modal physical characteristics in complex assembly task switching, laying a data foundation for the flexible matching and dynamic adaptation of subsequent mechanical auxiliary actions, significantly improving the physical adaptability and data-driven capabilities of workshop-level assembly multi-process workstations.

[0042] (2) The present invention innovatively introduces a dynamic physical compensation control mode, which combines the flexible loading curve of the clamping force, the buffer posture range of the support arm and the flexible absorption zone of the logistics buffer rhythm to form a "real-time perception-buffer adjustment-safety correction" mechanism of multi-dimensional physical actions; this control mode can dynamically correct the stiffness range of the clamp loading force, the buffer angle of the support arm and the floating range of the logistics roller rhythm when assembling new workpieces and multi-task conditions in parallel, avoiding assembly mismatches caused by workpiece size differences, rigidity differences or rhythm fluctuations, ensuring the stability of the assembly process and the physical matching performance under high rhythm, and further improving the safety margin and dynamic balance capability of the flexible equipment in the workshop.

[0043] (3) The present invention is based on modular device configuration and multi-working condition maps, and establishes a similar working condition matching mechanism for physical states and a dynamic evolution mechanism for working condition nodes enhanced by graph neural networks, thereby realizing the rapid configuration of the fixture-support arm-logistics cache module and efficient self-adaptation between multiple tasks. Through the linkage matching of the physical states of each device in the multi-working condition map and the multi-strategy fusion driven by similarity, the system can quickly generate a reasonable initial configuration of mechanical actions for the complex physical adaptation requirements of new tasks, and make real-time fine-tuning and closed-loop corrections during the execution of the task. This mechanism not only improves the safety and process stability of the workshop assembly process, but also significantly enhances the flexibility of the device level in the face of diversified processes, beat fluctuations and workpiece changes, ensuring the adaptive productivity and continuous optimization capabilities of the intelligent manufacturing production line under the dynamic switching of multiple tasks. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0045] Figure 1 This is a schematic diagram of the structure of an auxiliary control system for abnormal intervention in manufacturing and assembly production lines according to an embodiment of the present invention;

[0046] Figure 2 This is a flow chart of an auxiliary control method for abnormal intervention in manufacturing and assembly production lines according to an embodiment of the present invention. DETAILED DESCRIPTION

[0047] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0048] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0049] This embodiment proposes an auxiliary control system for abnormal intervention in manufacturing and assembly production lines, such as Figure 1 ,include:

[0050] Multi-dimensional physical state acquisition unit: used to collect the operating status of equipment in the production line, the clamping force of fixtures, the status of workpieces in the logistics path, and the workpiece assembly angle error, and generate a multi-dimensional physical state data set covering process-level actions;

[0051] Dynamic mapping and anomaly detection unit: used to construct a multi-dimensional device-based mapping model containing multi-source physical state mapping based on the multi-dimensional physical state data set, and detect workpiece residual alignment errors in real time, wherein the workpiece residual alignment errors include fixture clamping force overload, support arm posture angle drift, or logistics beat deviation;

[0052] Multi-process linkage intervention unit: used to automatically generate flexible intervention control instructions when abnormal conditions are detected, triggering dynamic correction of clamping force, flexible fine-tuning of multiple degrees of freedom of support arms, and adaptive adjustment of the transmission rate of logistics buffer rollers;

[0053] Auxiliary control unit: used for instruction decomposition and modular action issuance, refining system control instructions into mechanized low-level execution parameters and executing intervention actions;

[0054] Closed-loop optimization unit: It is used to build feedback comparison of intervention action effects and residual posture error judgment based on the clamping force flexibility loading curve, support arm posture compensation amplitude and logistics buffer status changes collected in real time by the sensor array, thus forming an adaptive correction closed loop for the auxiliary device.

[0055] Specifically, this embodiment enhances the physical adaptability of workshop-level assembly intervention and the safety robustness of process rhythm through the integrated response and action adaptive evolution mechanism of multi-functional mechanized auxiliary devices. It is particularly suitable for physical flexible auxiliary control and real-time assurance of assembly quality in multi-batch and mixed-line assembly processes, and meets the rapid adaptation, rhythm balance and dynamic safety requirements of multi-task assembly in intelligent manufacturing workshops.

[0056] Furthermore, the multidimensional physical state data set includes:

[0057] The clamping force signal of the fixture, the multi-dimensional posture angle vector of the support arm, the transmission speed of the logistics roller, and the temperature and humidity of the workstation.

[0058] Specifically, by integrating multi-type sensor arrays (such as force sensors, rotary encoders, photoelectric speed sensors, temperature and humidity probes) to collect the physical working status of each auxiliary device in real time, the system fuses multimodal signals into a comprehensive physical state vector, that is, a multidimensional physical state data set, which is used to accurately characterize the instantaneous working conditions of the mechanized auxiliary actions of the workstation.

[0059] Furthermore, an adjustable weighting coefficient matrix and a nonlinear mapping activation function are used to generate the multi-dimensional physical state data set covering the process-level actions.

[0060] Specifically, the fusion process uses an adjustable weighting coefficient matrix and a nonlinear mapping activation function, and the fusion expression is:

[0061] ,

[0062] Where, is the nonlinear mapping activation function, A multi-dimensional physical state dataset covering process-level actions, are all weight matrices, is the clamping force of the fixture, is the multi-dimensional posture angle vector of the supporting arm, is the conveying speed of the logistics roller, It is the temperature and humidity of the work station.

[0063] Specifically, in this embodiment, Optional Swish, Tanh, or ReLU are used to improve the fusion representation capability under nonlinear mapping, ensure the sensitivity and robustness of the multi-dimensional physical state dataset X covering process-level actions to physical state changes under multi-process assembly rhythms, and support subsequent dynamic adaptive adjustment and flexible compensation action generation.

[0064] Furthermore, the multi-source physical state mapping is a multi-source physical state mapping including a fixture module, a flexible support arm buffer structure and a logistics buffer device;

[0065] The fixture module has a built-in servo electric cylinder and force sensor, which are used to adjust the clamping force of the fixture according to the real-time physical state of the workpiece;

[0066] The flexible support arm buffer structure includes a multi-degree-of-freedom rotation pair, a guide rail slider and an electric actuator, which is used to adaptively adjust the spatial position of the support arm in real time according to the assembly posture of the workpiece, compensating for the posture deviation of the workpiece during the insertion process;

[0067] The logistics buffer device consists of an electric roller drive module, a buffer support stand, a built-in encoder and a buffer area sensor array, which is used to adjust the buffer roller transmission speed in real time according to the rhythm changes of the assembly task and the dynamics of logistics accumulation.

[0068] Specifically, the clamping force of the clamp in the clamp module is adjusted in real time through the clamping control model, and the clamping control model is:

[0069] ,

[0070] Where, is the clamping control model, is the stiffness coefficient, is the damping coefficient, is the instantaneous rate of change of the workpiece docking error.

[0071] The above control mode enables the fixture to quickly adjust the clamping force according to the real-time physical state of the workpiece during the switching of multi-process assembly rhythms and the rotation of complex workpieces, improving the flexible adaptability to different workpiece stiffness, size and fitting conditions, and ensuring physical safety and stability during the assembly process.

[0072] Specifically, the flexible support arm buffer structure can adaptively adjust the spatial position of the support arm in real time according to the assembly posture of the workpiece, compensate for the posture deviation of the workpiece during the insertion process, and avoid assembly failure caused by posture mismatch.

[0073] Especially in complex assembly processes or multi-task parallel working conditions, the flexible buffering action of the support arm can connect with the rhythm fluctuation of the logistics cache roller, realize the coupling adaptation of mechanized actions, and enhance the collaborative flexibility and process stability of the workstation-level mechanical auxiliary device.

[0074] Specifically, the buffer drum transmission speed is calculated based on the dynamic adjustment model as follows:

[0075] ,

[0076] Where, To cache the roller transmission speed, is the base speed of the drum, In order to cache the flexibility adjustment coefficient, it is adaptively adjusted in real time according to the workpiece stacking height in the cache area, process rhythm fluctuations and target buffer capacity safety margin to prevent rhythm mismatch caused by logistics interruption or excessive accumulation.

[0077] The logistics buffer device has the ability to respond quickly and manage buffers flexibly. It is a key mechanical auxiliary device for workshop-level beat balance and process beat safety.

[0078] Furthermore, after each round of intervention, the system integrates posture sensors (such as laser ranging displacement sensors, industrial camera vision sensors) to detect the residual alignment error of the workpiece in real time. , which is used to quantify the assembly accuracy and the stability of physical action matching. Among them, the residual alignment error of the workpiece is:

[0079] ,

[0080] Where, is the residual alignment error of the workpiece, Assemble the pose for the desired target, This is the detection pose after the actual assembly is completed. is the L2 norm, which is used to characterize the comprehensive error in the multi-dimensional posture space.

[0081] Specifically, this indicator serves as the core basis for closed-loop verification of mechanized assisted actions and subsequent adaptive correction, supporting multi-dimensional quality assurance of assembly safety and rhythm robustness.

[0082] Furthermore, when the multi-process linkage intervention unit detects that the residual alignment error exceeds the set safety tolerance, it triggers multi-dimensional flexible adaptive compensation, automatically lowers the upper limit of the clamping force of the fixture, extends the logistics cache cycle, widens the production line buffer zone, and adjusts the support arm fine-tuning angle buffer zone in real time.

[0083] Specifically, when the workpiece residual alignment error is detected p exceeds the set safety tolerance When the auxiliary control unit triggers the multi-dimensional flexible adaptive compensation:

[0084] Automatically lower the upper limit of the fixture's clamping force to reduce the risk of rigid interference, extend the logistics cache cycle to widen the production line buffer zone, and adjust the support arm in real time to fine-tune the angle buffer zone to ensure that the workpiece is accurately aligned in the next assembly cycle, reducing cycle impact and assembly failure rate.

[0085] The above-mentioned comprehensive flexible adjustment mechanism forms a dynamic self-balancing and safety correction path for mechanical movements. It is particularly suitable for highly flexible assembly conditions such as rapid rhythm fluctuations, diversified workpieces and complex assembly, and supports the safety margin and execution consistency of the workshop assembly process.

[0086] Furthermore, the system further comprises:

[0087] Modular quick-release structure, used to enable rapid replacement of fixture modules, flexible support arm buffer structures, and logistics buffer devices through standardized interfaces;

[0088] Human-computer interaction panel and visual operation interface: used to display the clamping force curve, support arm posture buffer zone and logistics rhythm fluctuation in real time, and supports manual fine-tuning and safety warning.

[0089] Specifically, the system is particularly suitable for rapid switching in multi-variety and variable-batch production scenarios. The auxiliary devices all adopt a modular quick-release structure, including replaceable clamp modules, flexible support arm assemblies and logistics buffer roller modules. Each module is connected through a standardized mechanical interface, quick-insert pneumatic or hydraulic locking pair, and has the function of rapid disassembly and reassembly.

[0090] The modular quick-release structure not only supports the modular combination of auxiliary devices and flexible mechanical reconstruction when different batches or diversified workpieces are put on the line, but also effectively reduces the workstation adjustment time and equipment debugging cycle, avoids the bottleneck of auxiliary devices caused by model changeover, and ensures the continuity and consistency of assembly actions during process switching.

[0091] Specifically, the fixture module can be quickly replaced or fine-tuned under different workpiece geometries, the support arm assembly can replace the multi-degree-of-freedom posture buffer structure to adapt to different fitting states, and the logistics roller module can quickly increase or decrease the cache capacity according to the fluctuations in the logistics rhythm, realizing the mechanized assisted control of "plug and play-fast switching-highly flexible adaptation", and comprehensively improving the flexibility level and efficient adaptability of the multi-process collaborative assembly production line.

[0092] Specifically, the human-machine interaction panel and visual operation interface intuitively display key physical states such as the real-time curve of the clamping force, the support arm posture buffer zone, and the logistics roller cache rhythm fluctuation. Combined with the threshold safety warning and action limit alarm, it ensures that operators can grasp the operating status of the production line machinery in real time.

[0093] The interface supports operators to quickly fine-tune auxiliary actions, correct the upper limit of clamping force or switch to manual operation mode according to prompts, enhancing the workshop's flexible scheduling capabilities and adapting to sudden flexible compensation needs in multi-process assembly.

[0094] During intervention execution, the system synchronously records multi-dimensional sensor data and auxiliary device response logs, forming a complete execution history and safe operation record to facilitate subsequent quality traceability and production line maintenance.

[0095] In addition, this modular visual interactive design supports rapid information sharing and collaborative adjustments between workstations, achieving dynamic safety assurance and flexible upgrades of production lines for multi-process linkage. It is particularly suitable for comprehensive assurance and continuous optimization of multi-process production line assembly safety, intelligent mechanical movements, and flexibility of human-machine collaboration.

[0096] This embodiment also provides an auxiliary control method for abnormal intervention of manufacturing and assembly production lines, including:

[0097] The multi-dimensional physical state acquisition unit collects the operating status of equipment in the production line, the clamping force of fixtures, the status of workpieces in the logistics path, and the workpiece assembly angle error, generating a multi-dimensional physical state data set covering process-level actions;

[0098] Based on the multi-dimensional physical state data set, a multi-dimensional device mapping model including multi-source physical state mapping is constructed to detect fixture clamping force overload, support arm posture angle drift or logistics beat deviation in real time;

[0099] Abnormal detection is performed through the multi-process linkage intervention unit. When an abnormal state is detected, flexible intervention control instructions are automatically generated to trigger dynamic correction of the clamping force, flexible fine-tuning of the support arm with multiple degrees of freedom, and adaptive adjustment of the transmission rate of the logistics buffer roller;

[0100] Through the auxiliary control unit, the system control instructions are broken down into mechanized low-level execution parameters and intervention actions are executed;

[0101] Based on the clamping force flexibility loading curve, support arm posture compensation amplitude and logistics buffer status changes collected in real time by the sensor array, feedback comparison of the intervention action effect and residual posture error judgment are constructed to form an adaptive correction closed loop of the auxiliary device.

[0102] In order to more clearly express the technical solution of the present invention, the following specific embodiments are provided to introduce the solution:

[0103] An auxiliary control method for abnormal intervention of manufacturing and assembly production lines, such as Figure 2 ,include:

[0104] S1. In the multi-process production line of the assembly workshop, the system takes mechanical auxiliary devices as the core, fully collects and analyzes multi-source physical state data involving fixtures, support arms and logistics buffers, and constructs auxiliary action perception input with multi-modal and hierarchical characteristics.

[0105] First, the clamping force It is a key physical parameter of the auxiliary action. The system integrates a multi-point force sensor array at the gripping end of the fixture to monitor the evolution curve of the clamping force over time in real time, ensuring that the assembled workpiece has stable clamping matching performance under different stiffness and shape conditions, avoiding assembly failure due to workpiece deformation or material brittleness.

[0106] Secondly, the multi-dimensional posture angle vector of the supporting arm Through high-precision angle sensors, IMU and limit detection devices, the fine-tuning angle, buffer stroke and force-energy coupling characteristics are collected in real time to ensure that slight posture differences in the assembly process can be dynamically compensated during the assembly process, achieving flexible docking and precise alignment of the workpiece.

[0107] In addition, the logistics roller transmission speed Relying on multi-point rotary encoders, speed measurement laser modules and cache roller self-balancing monitoring units, the linear speed and beat response rate of the logistics roller are collected in real time, and states such as beat fluctuations, cache capacity saturation and beat flexible matching range are identified.

[0108] Based on environmental parameters, temperature and humidity sensors, vibration and acoustic monitoring modules are placed around the workstations to analyze the potential impact of workshop environmental disturbances on the action response of auxiliary devices, and to assist in achieving refined dynamic compensation.

[0109] The system normalizes the above multi-dimensional mechanical action and working condition data into equipment state vectors (fixture mechanical data), process execution vector (support arm and auxiliary structure execution status), logistics state vector (cache beat and logistics posture), environmental parameter vector (Workstation environment fluctuations).

[0110] In order to adapt to the variable working conditions in highly flexible assembly scenarios, the system introduces the following nonlinear fusion mapping to form an input state vector that comprehensively reflects the multi-dimensional dynamic characteristics of the auxiliary action: :

[0111] ,

[0112] in, are all weight matrices, It is a nonlinear mapping activation function that improves the discrimination and robust adaptability of the fusion state under complex working conditions.

[0113] After state fusion, the system can optionally introduce sparse principal component analysis or multimodal variational autoencoder to reduce the dimension and compress the fusion vector, reduce redundant channels and highlight the mechanical action characteristics, ensuring that the input state vector is consistent in complex multi-process scenarios. Possesses high-resolution and highly generalized mechanically assisted motion expression capabilities.

[0114] Through this mechanized perception and fusion mapping step, the system not only constructs a multi-dimensional dynamic state input that is closely coupled with the production line conditions, but also provides a solid physical perception foundation for subsequent multi-objective motion strategy learning and adaptive control of assembly motions.

[0115] S2, based on the input state vector obtained in S1 The system enters the learning stage of multi-objective physical assisted action strategies. The core is to train mechanical action strategies that can adapt to changing working conditions and improve the collaborative efficiency and adaptability of mechanical assisted actions on the production line.

[0116] At this stage, the system builds a state-action-result triple decision model with mechanical assisted action as the core. The core goal is to optimize the rigidity / flexibility matching of the clamping fixture, the stability of the support arm posture compensation, and the smooth absorption capacity of the logistics buffer beat. The "state-action-result" triple decision model is based on the input state vector The state is input and combined with the current assembly task goal, the optimal physical action strategy combination (clamping force adjustment, posture compensation, and beat adjustment) is output.

[0117] Specifically, the following immediate reward function is designed , comprehensively considering the performance of multi-target physical actions:

[0118] ,

[0119] Where, Indicates the rhythm coordination efficiency of logistics buffer and support arm movement; The energy consumption of the support arm and fixture movement per unit time is used to measure execution efficiency and energy saving level; Represents the workpiece assembly yield after mechanical assisted action; is the instantaneous posture deviation modulus of the support arm during physical compensation, reflecting the stability of flexible compensation; The failure probability of logistics buffering or clamping action (such as jamming, crushing of workpiece, etc.); They are all weight coefficients, which can be flexibly adjusted according to the needs of workshop flexibility, energy saving and high-reliability assembly.

[0120] This reward function achieves a comprehensive balance of multi-dimensional auxiliary action performance by introducing mechanized physical action constraint indicators, ensuring that the clamp and support arm actions meet both flexible adaptation requirements and efficient execution.

[0121] Subsequently, a policy gradient-based reinforcement learning framework, such as proximal policy optimization or deterministic policy gradient, is used to iteratively update the policy network in the robot action decision space. .

[0122] The updated gradient formula is as follows:

[0123] ,

[0124] Where, Indicates that the status The probability of mechanical action distribution under For comprehensive long-term returns, is the gradient direction of the policy parameters, is the strategy parameter The optimization target gradient direction is For the current strategy The expected value operator for state-action pairs is: For a given state Next, action Gradient information relative to the current policy parameters.

[0125] During the training process, a dynamic weight adjustment mechanism for mechanical actions is introduced to dynamically update the reward function weight coefficient based on physical layer feedback such as the clamping force / support arm buffer response rate and the clamping overload trigger rate. , realizing flexible adaptive compensation for the policy network.

[0126] To adapt to high-speed, multi-task switching, the training strategy also considers the multi-channel physical action coupling of the fixture, support, and logistics buffer. This means that the interaction between these three factors is incorporated into the reward function and policy updates to ensure the multi-step consistency of the auxiliary actions and the robustness of the mechanical coupling.

[0127] Ultimately, the S2 stage not only forms a multi-objective physical motion optimization strategy network for mechanical auxiliary devices, but also realizes adaptive intelligent control of complex assembly scenarios in the workshop through dynamic adjustment and multi-objective balance, providing a reliable physical motion strategy foundation for subsequent strategy matching, fusion and issuing instructions.

[0128] S3. When the production line is facing a new task or switching to multi-task working conditions, the system enters the similar working condition matching and multi-strategy fusion stage of the mechanical auxiliary action strategy to ensure the flexible adaptation and rapid execution of the mechanical action configuration under the new task.

[0129] In this stage, the physical auxiliary action status of the current working condition, including the clamping force distribution of the fixture, the flexible posture of the support arm, the dynamic rhythm of the logistics cache and the adaptability of the workstation environment, is first mapped to the historical working condition library for retrieval and matching.

[0130] During the matching process, the distance function Combining numerical similarity with physical action pattern similarity, not only the vector space distance is considered, but also physical layer matching tags such as "clamp flexibility absorption curve similarity" and "matching rate between support arm dynamic buffers" are introduced to ensure that the retrieval results can truly reflect the safety and adaptability of the mechanical action level.

[0131] The matched optimal working conditions and the corresponding mechanical auxiliary action configuration include the fixture loading force range, support arm compensation bandwidth, logistics cache dynamic safety margin, etc., which are the preliminary mechanical action configurations for the new task.

[0132] However, when a new task involves a completely new process segment or assembly path (such as first-installation splicing, new configuration modules, etc.), the historical working conditions may not be fully matched. For this reason, the system introduces a multi-strategy fusion mechanism.

[0133] In the historical working condition library, select the first n most similar working condition configurations , , , and based on the dynamic weight of similarity Perform physical action fusion:

[0134] ,

[0135] Where, is the fused parameter, is the set of physical action parameters executed under the i-th historical working condition, including the optimal configuration values ​​of key control quantities such as the clamping force of the fixture, the posture angle of the support arm, and the speed of the logistics roller in this working condition. is the dynamic weight of similarity, and n is the number of historical similar working conditions used for fusion.

[0136] During fusion, the clamping force of the fixture not only takes into account the loading stiffness under different working conditions, but also integrates the flexible section of the fixture opening and closing curve to avoid mechanical fatigue and brittle damage to the workpiece; the support arm movement integrates the fine-tuning amplitude and dynamic response speed of each working condition to ensure flexible adaptability under the new assembly positioning deviation; the logistics cache rhythm area integrates the cache step length and roller speed distribution under multiple working conditions to ensure the impact resistance and absorption stability of the production line logistics under the new rhythm.

[0137] After the integration is completed, the system is specially designed with a mechanical action safety self-check and buffer strategy correction mechanism. Specifically including:

[0138] The system detects the maximum / minimum force limits of the fixture loading force and automatically adds a flexible buffer section based on the workpiece brittle tolerance.

[0139] For the support arm fine-tuning range, the dynamic compensation limit is calculated. If there is a risk of overshoot or undercompensation, the buffer arm torque stability range is automatically increased.

[0140] For logistics cache configuration, analyze the relationship between beat fluctuation range and process beat coupling. If there is a risk of cache overflow, the system increases the "cache flexibility adjustment coefficient" ”, dynamic smooth roller acceleration and deceleration curve.

[0141] This process not only ensures that the initial configuration of the mechanically assisted actions for new tasks is safe and reliable, and that the mechanical matching is reasonable, but also significantly shortens the time and error probability of manual configuration.

[0142] After generating the fusion results, the operating terminal provides an intuitive multi-dimensional motion curve visualization interface. The operator can view the fitting degree and safety margin of the clamping force curve, support arm buffer curve and logistics rhythm absorption curve in real time, and can perform manual fine-tuning when necessary, forming an efficient mechanical motion configuration adaptation process of human-machine integration + system adaptation.

[0143] Through this step, the entire process closed loop from "historical experience-multi-strategy fusion-physical layer security correction-manual visualization fine-tuning" to "initial configuration of mechanically assisted actions" is achieved, ensuring that under new working conditions and new assembly modes, the flexible execution of fixtures, support arms and logistics-assisted actions has high robustness and high intelligence.

[0144] S4. After completing the initial configuration of auxiliary actions under the new task and the integration of multiple strategies, it enters the stage of physical action instruction generation, conflict detection and issuance execution to ensure the safety, flexible adaptability and dynamic collaboration capabilities of mechanical auxiliary actions in the multi-process environment of the workshop.

[0145] First, for the action configurations such as fixture clamping, support arm buffering and logistics buffering, the fused parameters are Converted into a standardized multi-dimensional instruction structure, the format is as follows:

[0146] ,

[0147] in, Indicates the station or equipment number where the action is performed; It is refined into specific physical actions such as "dynamic range adjustment of fixture loading force", "flexible buffer adjustment of support arm posture", and "beat absorption of logistics roller". Core parameters of physical actions, such as "upper limit of clamping force ","Fine-adjust the angle of the support arm ", "Cache beat step "wait; The acceptable deviation range of the action is to ensure the safety margin of the action; Setting a value for the target of the physical action to be performed; Executes instruction structures for standardized physical actions.

[0148] After the instruction is generated, the first step is to coordinate the mechanized actions of multiple processes and detect conflicts. In this detection, the system dynamically analyzes the physical coupling relationship between the fixture, support arm, and logistics buffer in the instruction based on the working condition association model, including:

[0149] Conflict detection between fixture clamping force and support arm buffer: Real-time analysis of the maximum support load range of the support arm. If the clamping force in the command exceeds the maximum support load range, the system will trigger the "support buffer overload warning" and automatically adjust the fixture loading curve downward to prevent overload jamming or workpiece damage.

[0150] Matching the fixture / support with the logistics buffer beat sequence: If the logistics buffer roller acceleration If the assembly synchronization beat is exceeded, the "flexible buffer factor" will be automatically increased to dynamically slow down the roller beat to prevent the fixture / support arm from being pushed into place and overflowing due to logistics.

[0151] Concurrent process execution safety check: For scenarios where multiple processes are executed simultaneously, based on the dependency links in the process map, ensure that the physical execution sequence and beat intervals of actions such as fixtures, support arms, and logistics buffers are reasonable to avoid action conflicts or hard collisions.

[0152] To further improve the adaptability of mechanized action delivery, a dynamic safety buffer and fine-tuning compensation mechanism has been introduced. Specifically, the system automatically generates a buffer segment for the instruction set based on the workshop's flexibility requirements and the equipment's rigidity limits, and dynamically adjusts the following:

[0153] Clamping force buffer section : When the fixture is loading, an elastic loading curve is introduced to ensure that the clamping force climbs flexibly during the unstable period of workpiece assembly to avoid excessive rigidity in one-time loading.

[0154] Support arm fine-tuning attitude buffer zone : If the support arm needs to absorb the difference in workpiece posture during the assembly process, the system allows dynamic expansion of the support arm fine-tuning range to improve the flexible matching of assembly docking.

[0155] Logistics cache rhythm flexible area : In the peak area of ​​assembly rhythm, the system allows the roller line speed to float dynamically, forming a flexible rhythm absorption belt to ensure smooth coupling of logistics buffer and upstream and downstream processes.

[0156] During the instruction issuance process, the system leverages the shop floor's edge computing nodes to convert instruction sets into standard industrial protocol data packets (OPC UA, Modbus TCP, CANopen, etc.) and pushes them in real time to the production line's underlying controllers (such as PLCs, embedded motion controllers, and intelligent support arm modules). This instruction issuance process supports the dynamic allocation of high- and low-priority actions, forming a dynamic priority execution logic for multi-step mechanized actions, preventing process delays or physical interference caused by incorrect action coupling sequences.

[0157] To ensure the accurate execution of mechanical motion instructions, real-time physical motion curve monitoring is started at the same time as the instructions are issued, and the following physical data are monitored:

[0158] Real-time change curve of fixture clamping force : Analyze the rising / falling slope of the clamping force to detect whether there is mechanical hysteresis or oscillation;

[0159] Support arm posture angle response curve :Detect the dynamic response of the support arm's fine-tuning compensation to prevent coordination deviation caused by movement jamming;

[0160] Logistics cache drum beat response : Determine whether the roller line speed meets the flexible beat absorption requirements to avoid assembly beat instability.

[0161] If a deviation from the physical execution curve is detected (such as instantaneous fluctuations in the clamping force exceeds the tolerance bandwidth), the system immediately triggers the "fine-tuning secondary compensation" action and makes small flexible corrections within the physical action space, such as fine-tuning the clamping force flexible loading curve, relaxing the support arm buffer posture bandwidth, etc., to ensure the safety, consistency and precision of the mechanical assisted action during execution.

[0162] The operating end is equipped with a motion curve and safety margin visualization panel, which displays in real time the changes in key physical indicators such as the fixture clamping force-time curve, the support arm compensation angle-time curve, and the logistics buffer roller linear speed-time curve. It supports operators to dynamically view, manually fine-tune, and conduct process safety assessments during production line operation, forming a dual safety guarantee of "human-machine integration" and "physical motion adaptation".

[0163] Through this stage, a full-link mechanized action closed loop from "physical action configuration-flexible conflict detection-safety instruction issuance-physical execution monitoring-fine-tuning correction" was achieved, enhancing the physical adaptability, execution safety and production line collaborative efficiency of mechanical assisted actions in multi-process and complex assembly scenarios.

[0164] S5. As the production line continuously switches tasks and introduces new assembly processes, the system faces the challenge of how to quickly adapt the mechanical auxiliary action configuration to the new working conditions to ensure the flexibility of the mechanical action and the continuity of the process.

[0165] To this end, a new task launch strategy initialization and self-evolution dynamic adjustment mechanism were introduced in the S5 stage to ensure that mechanical assisted actions have instant adaptation capabilities and continuous optimization potential when new tasks are launched.

[0166] First, when a new task is launched, based on the fused state vector , in the historical operating conditions map Node matching is performed in Indicates the historical working status. Represents the path of transferable mechanical assisted actions. Dynamic update of node features is achieved through graph neural network:

[0167] ,

[0168] Where, is the node embedding of layer l, is the set of neighbor nodes, is the weight matrix, is the bias, is the nonlinear mapping activation function, is the node embedding of the l+1th layer, For the node in the historical operating diagram The adjacent The embedding representation vector of the layer neighbor node, u is the adjacent historical working condition node that has a migration edge with the current target node.

[0169] This process not only considers the historical mechanical experience of the fixture loading stiffness curve and the dynamic compensation characteristics of the support arm, but also takes into account the dynamic adaptability of the flexible section of the logistics cache rhythm under the coupling of multiple process rhythms, and generates recommendations for the optimal initial mechanical motion configuration for new tasks.

[0170] The matching working condition node Mechanical action configuration in , and conduct multi-layer safety self-checks based on the physical working conditions of the new task, including:

[0171] Fixture clamping force safety assessment: Combine the material brittleness of the new workpiece and the upper and lower limits of the fixture clamping force to prevent overloading or insufficient clamping of the initial configuration;

[0172] Support arm dynamic compensation limit test: taking into account the geometric deviation of the new assembly to ensure the maximum fine-tuning angle of the support arm Sufficient to cover the actual assembly fit error;

[0173] Dynamic Absorption Evaluation of Logistics Cache Beat: Detecting Cache Beat Step Whether the range meets the safety margin of the process buffer capacity to avoid logistics interruption or overflow under sudden fluctuations in the beat.

[0174] For new tasks with completely new working condition parameter combinations (such as parallel installation of new modules for the first time, complex assembly sequences, etc.), historical working conditions may not be able to directly provide a highly matching physical configuration.

[0175] To this end, a multi-strategy fusion compensation mechanism is introduced, starting from the most similar Extract mechanical action configuration from each working condition , and fused according to the dynamic weight of similarity:

[0176] ,

[0177] Where, is the fused parameter, is the set of physical action parameters executed under the i-th historical working condition, including the optimal configuration values ​​of key control quantities such as the clamping force of the fixture, the posture angle of the support arm, and the speed of the logistics roller in this working condition. is the dynamic weight of similarity, and n is the number of historical similar working conditions used for fusion.

[0178] During the fusion process, physical parameters such as the flexible absorption range of the fixture, the buffer width of the support arm, and the flexible beat curve of the logistics cache participate together to form a comprehensive configuration that is more in line with the physical adaptability of the new task.

[0179] At the same time, adaptive buffering for mechanical action safety is introduced, such as automatically inserting a flexible climbing curve in the fixture loading force section to avoid excessive instantaneous impact when clamping a new workpiece; and introducing a dynamic buffer correction belt in the support arm movement to ensure the fault tolerance and impact resistance of the flexible range of the support movement in new tasks.

[0180] After the new task is launched, real-time monitoring is performed through multi-dimensional physical actions, including the real-time change curve of the clamping force , support arm posture angle response curve , Logistics cache drum beat response etc., to form real-time execution feedback.

[0181] If detected:

[0182] There are super-threshold fluctuations in the clamp loading force (such as instantaneous fluctuations in the clamping force). Safety threshold of clamping force change );

[0183] The support arm posture response is delayed or unstable;

[0184] The logistics cache beat overshoot causes overflow;

[0185] The system immediately triggers the physical action secondary buffer compensation, dynamically adjusts the fixture stiffness, support arm buffer section and logistics cache flexibility step, forming a closed-loop correction mechanism of "secondary buffering-real-time compensation-dynamic rebalancing".

[0186] In addition, real-time physical feedback data is used as self-evolutionary update samples for the reinforcement learning module, continuously enriching the policy network's multi-dimensional physical working condition perception and multi-task assembly adaptability. Specifically, after each round of auxiliary assembly action, the system summarizes multi-dimensional physical feedback information, such as the fixture loading force curve, the support arm angle buffer amplitude, the logistics cache roller beat adjustment results, and the workpiece residual alignment error, into dynamic sample pairs (state-action-result), forming an incremental self-learning data pool for reinforcement learning. By comparing the difference between actual feedback and expected action effects, the system automatically identifies the flexible matching characteristics of the current assembly working condition, extracts the assembly action correction rules under the new physical scenario, and further enriches the dynamic adaptation space of the policy parameters.

[0187] During the update process, a "freeze-and-fine-tune" structured update strategy is employed: the underlying physical motion feature extraction module is frozen to maintain a fundamental understanding of physical signals and long-term experience, while only the high-level policy generation components are refined. This ensures that knowledge of flexible matching and physical motion adaptation under historical working conditions is not lost, while also enhancing the robustness of motion generation and assembly safety and stability under new working conditions, new beats, and multi-batch switching scenarios. This mechanism forms a self-evolving closed loop of mechanical action, physical feedback, incremental policy updates, and re-execution verification, ensuring that mechanized auxiliary devices maintain multi-dimensional flexible matching capabilities, stability, and safety margins in complex and changing assembly environments, meeting the efficient adaptive control requirements of workshop-level intelligent manufacturing production lines.

[0188] On the operating side, the system provides a visual interface for the self-evolution of mechanical motion, dynamically displaying key physical indicators such as the evolution of the clamping force segment, changes in the flexible bandwidth of the support arm posture, and flexible adjustment of the logistics cache rhythm. The operator can perform manual fine-tuning, combined with the system's self-evolution update, to ensure the safety, adaptability and process stability of the mechanized motion configuration when new tasks are put on the production line.

[0189] This process completes the path from historical experience matching, multi-strategy integration, safety self-checking and buffering, real-time feedback compensation, self-evolutionary updates, and manual fine-tuning to an "adaptive flexible closed loop" in the execution of mechanized actions. Especially under variable operating conditions and highly flexible parallel processes, the mechanical action configuration boasts enhanced safety margins, flexible adaptability, and robust execution efficiency, enabling the production line to achieve intelligent adaptation capabilities for new tasks with the goal of "safety, flexibility, and efficiency."

[0190] S6. After the production line task is completed, the system will conduct systematic multi-dimensional feedback collection and performance evaluation of the auxiliary device's action history and assembly results to form a continuous optimization mechanism for multi-process flexible auxiliary actions.

[0191] First, the system collects the full-process time series data including the clamping force of the fixture, the multi-dimensional posture angle vector of the support arm, the transmission speed of the logistics roller, and the temperature and humidity of the workstation. Combining the workstation alignment results with the distribution of the workpiece clearance, the system constructs a production line feedback quality evaluation data set. :

[0192] ,

[0193] Where, represents the residual posture error after assembly is completed, is the failure rate of assembly action (such as insertion failure, support instability, etc.), It is the time series data of the workstation environment temperature and humidity.

[0194] For these data, the system will conduct the following multi-dimensional analysis in stages:

[0195] Dynamic matching accuracy analysis: based on residual posture error after assembly The time distribution trend of the error can be used to determine the evolution of the docking error during the assembly process and whether there are problems such as assembly eccentricity, fixture slippage or support arm vibration.

[0196] Force-posture coupling characteristic analysis: Analyze the cross-correlation between the clamping force and the support arm posture fine-tuning curve to determine the collaborative adaptability of physical movements under complex assembly conditions.

[0197] Logistics rhythm buffer performance analysis: Analyze the rhythm matching of the logistics roller transmission speed during the peak and flat periods of the task, and confirm the rhythm adjustment response of the flexible buffer device.

[0198] Environmental adaptability analysis: Analyze the potential correlation between fluctuations in workstation temperature and humidity and assembly quality, and further optimize the support arm material's resistance to thermal deformation or the fixture's micro-compensation mechanism.

[0199] On this basis, the system defines the comprehensive performance improvement rate , whose expression is:

[0200] ,

[0201] Where, In order to comprehensively consider multiple process indicators such as fixture loading efficiency, workpiece assembly accuracy, logistics beat matching and energy consumption utilization, it is used as the global evaluation function of the auxiliary control action. If it keeps rising during the continuous task cycle, it proves that the physical intervention of the auxiliary control strategy is effective; otherwise, it is necessary to trigger the parameter rollback or strategy re-optimization process of the local auxiliary action. It is the comprehensive performance index value of auxiliary control obtained by system evaluation after the current task cycle is completed. This is the baseline auxiliary control performance indicator value before the task is initially launched.

[0202] In order to quantitatively characterize the robustness and safety of the auxiliary control device, the control abnormality rate is introduced , calculated as:

[0203] ,

[0204] in, The number of abnormal physical intervention actions detected (including excessive clamping force, support arm shaking, logistics buffer instability, etc.) The total number of auxiliary actions in this round of tasks.

[0205] When controlling the abnormal rate Continuously above the set threshold , automatically marking the assisted action strategy as "low stability" and triggering parameter fine-tuning or replacement mode to ensure the safety boundary and long-term stability of physical assisted actions.

[0206] At the same time, the system will use the self-evolution learning module to record the auxiliary action history and control abnormality rate. The results are updated by graph neural network, and the node embedding representation of the working condition graph is Adaptive optimization will be performed according to the following formula:

[0207] ,

[0208] In this way, after each assembly process is completed, the node embedding representation not only contains the historical experience of the assembly action, but also realizes self-evolution and task adaptation according to the new round of physical experience, forming a flexible and transferable auxiliary control capability.

[0209] To ensure operational safety, the residual error of each auxiliary action is displayed in real time and visualized , fixture loading force The system also supports the "manual safety window" function. When the auxiliary device detects that the safety risk is too high (such as the control abnormality rate), the system will automatically detect the deviation of the operation and the safety boundary of the device. sudden increase), automatically requesting the operator to confirm or intervene to correct, to avoid damage to the production line caused by loss of control of mechanized auxiliary actions.

[0210] Finally, the system writes the updated auxiliary action configuration into the working condition map G, forming a complete closed loop of "task execution - physical feedback - action correction - state update", achieving the adaptive and long-term evolution of mechanized auxiliary actions. This is particularly suitable for dynamic production lines with multiple varieties and multiple cycles, significantly improving the intelligence and safety of flexible assembly assistance in the workshop.

[0211] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. An auxiliary control system for abnormal intervention in manufacturing and assembly production lines, characterized in that: include: Multi-dimensional physical state acquisition unit: used to collect the operating status of equipment in the production line, the clamping force of fixtures, the status of workpieces in the logistics path, and the workpiece assembly angle error, and generate a multi-dimensional physical state data set covering process-level actions; Dynamic mapping and anomaly detection unit: used to construct a multi-dimensional device-based mapping model containing multi-source physical state mapping based on the multi-dimensional physical state data set, and detect workpiece residual alignment errors in real time, wherein the workpiece residual alignment errors include fixture clamping force overload, support arm posture angle drift, or logistics beat deviation; Multi-process linkage intervention unit: used to automatically generate flexible intervention control instructions when abnormal conditions are detected, triggering dynamic correction of clamping force, flexible fine-tuning of multiple degrees of freedom of support arms, and adaptive adjustment of the transmission rate of logistics buffer rollers; Auxiliary control unit: used for instruction decomposition and modular action issuance, refining system control instructions into mechanized low-level execution parameters and executing intervention actions; Closed-loop optimization unit: This unit uses the gripping force flexibility loading curve, support arm posture compensation amplitude, and logistics buffer status changes collected in real time by the sensor array to build feedback comparison of intervention action effects and determine residual posture errors, forming an adaptive correction closed loop for the auxiliary device. After the production line tasks are completed, the system will systematically collect multi-dimensional feedback and conduct performance evaluation on the auxiliary device's action history and assembly results, forming a continuous optimization mechanism for flexible auxiliary actions in multiple processes. The system first collects the full-process time series data including the clamping force of the fixture, the multi-dimensional posture angle vector of the support arm, the transmission speed of the logistics roller, and the temperature and humidity of the workstation. It then combines the workstation alignment results with the distribution of the workpiece clearance to build a production line feedback quality assessment data set. ; Production line feedback quality assessment dataset , conduct dynamic matching accuracy analysis, force-posture coupling characteristics analysis, logistics beat buffer performance analysis, and environmental working condition adaptability analysis; Finally, calculate the comprehensive performance improvement rate , specifically: ， Where, It is the comprehensive performance index value of auxiliary control obtained by system evaluation after the current task cycle is completed. This is the baseline auxiliary control performance indicator value before the task is initially launched.

2. The auxiliary control system for abnormal intervention in manufacturing and assembly production lines according to claim 1 is characterized in that: The multidimensional physical state data set includes: The clamping force signal of the fixture, the multi-dimensional posture angle vector of the support arm, the transmission speed of the logistics roller, and the temperature and humidity of the workstation.

3. The auxiliary control system for abnormal intervention in manufacturing and assembly production lines according to claim 2 is characterized in that: The multi-dimensional physical state acquisition unit generates the multi-dimensional physical state data set covering the process-level actions through an adjustable weighting coefficient matrix and a nonlinear mapping activation function, specifically: ， Where, is the nonlinear mapping activation function, A multi-dimensional physical state dataset covering process-level actions, are all weight matrices, is the clamping force of the fixture, is the multi-dimensional posture angle vector of the supporting arm, is the conveying speed of the logistics roller, It is the temperature and humidity of the work station.

4. The auxiliary control system for abnormal intervention in manufacturing and assembly production lines according to claim 1, characterized in that: The multi-source physical state mapping is a multi-source physical state mapping including a fixture module, a flexible support arm buffer structure and a logistics buffer device; The fixture module has a built-in servo electric cylinder and a force sensor, which are used to adjust the clamping force of the fixture according to the real-time physical state of the workpiece; The flexible support arm buffer structure includes a multi-degree-of-freedom rotation pair, a guide rail slider and an electric actuator, which is used to adaptively adjust the spatial position of the support arm in real time according to the assembly posture of the workpiece, compensating for the posture deviation of the workpiece during the insertion process; The logistics cache device consists of an electric roller drive module, a buffer support stand, a built-in encoder and a cache area sensor array, and is used to adjust the cache roller transmission speed in real time according to the rhythm changes of the assembly task and the dynamic logistics accumulation.

5. The auxiliary control system for abnormal intervention in manufacturing and assembly production lines according to claim 4 is characterized in that: The clamping force of the clamp in the clamp module is adjusted in real time by a clamping control model, wherein the clamping control model is: ； Where, is the clamping control model, is the stiffness coefficient, is the damping coefficient, is the residual alignment error of the workpiece, is the instantaneous rate of change of the workpiece docking error.

6. The auxiliary control system for abnormal intervention in manufacturing and assembly production lines according to claim 4, characterized in that: The transmission speed of the buffer drum is calculated based on the dynamic adjustment model, specifically: ； Where, To cache the roller transmission speed, is the base speed of the drum, It is the cache flexibility adjustment coefficient.

7. The auxiliary control system for abnormal intervention in manufacturing and assembly production lines according to claim 1 is characterized in that: The dynamic mapping and anomaly detection unit detects the workpiece residual alignment error in real time through an integrated posture sensor, wherein the workpiece residual alignment error is: ； Where, is the residual alignment error of the workpiece, Assemble the pose for the desired target, This is the detection pose after the actual assembly is completed. is the L2 norm.

8. The auxiliary control system for abnormal intervention in manufacturing and assembly production lines according to claim 7, characterized in that: When the multi-process linkage intervention unit detects that the residual alignment error exceeds the set safety tolerance, it triggers multi-dimensional flexible adaptive compensation through the auxiliary control unit, automatically reduces the upper limit of the clamping force of the fixture, extends the logistics cache cycle, widens the production line buffer zone, and adjusts the support arm fine-tuning angle buffer zone in real time.

9. The auxiliary control system for abnormal intervention in manufacturing and assembly production lines according to claim 1, characterized in that: The system further comprises: Modular quick-release structure, used to enable rapid replacement of fixture modules, flexible support arm buffer structures, and logistics buffer devices through standardized interfaces; Human-computer interaction panel and visual operation interface: used to display the clamping force curve, support arm posture buffer zone and logistics rhythm fluctuation in real time, and supports manual fine-tuning and safety warning.

10. An auxiliary control method for abnormal intervention of manufacturing and assembly production lines, characterized in that: include: The multi-dimensional physical state acquisition unit collects the operating status of equipment in the production line, the clamping force of fixtures, the status of workpieces in the logistics path, and the workpiece assembly angle error, generating a multi-dimensional physical state data set covering process-level actions; Based on the multidimensional physical state data set, a multidimensional device-based mapping model including multi-source physical state mapping is constructed, and the clamping force overload, support arm posture angle drift or logistics beat deviation are detected in real time; Abnormal detection is performed through the multi-process linkage intervention unit. When an abnormal state is detected, flexible intervention control instructions are automatically generated to trigger dynamic correction of the clamping force, flexible fine-tuning of the support arm with multiple degrees of freedom, and adaptive adjustment of the transmission rate of the logistics buffer roller; Through the auxiliary control unit, the system control instructions are broken down into mechanized low-level execution parameters and intervention actions are executed; Based on the gripping force flexibility loading curve, support arm posture compensation amplitude, and logistics buffer status changes collected in real time by the sensor array, feedback comparison of intervention action effects and residual posture error determination are constructed to form an adaptive correction closed loop for the auxiliary device. After the production line tasks are completed, the auxiliary device's action history and assembly results will be systematically collected for multi-dimensional feedback and performance evaluation, forming a continuous optimization mechanism for flexible auxiliary actions in multiple processes. First, the full-process time series data including the clamping force of the fixture, the multi-dimensional posture angle vector of the support arm, the transmission speed of the logistics roller, and the temperature and humidity of the workstation are collected. Combined with the workstation alignment results and the distribution of the workpiece clearance, a production line feedback quality evaluation data set is constructed. ; Production line feedback quality assessment dataset , conduct dynamic matching accuracy analysis, force-posture coupling characteristics analysis, logistics beat buffer performance analysis, and environmental working condition adaptability analysis; Finally, calculate the comprehensive performance improvement rate , specifically: ， Where, It is the comprehensive performance index value of auxiliary control obtained by system evaluation after the current task cycle is completed. This is the baseline auxiliary control performance indicator value before the task is initially launched.

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