Construction of industrial intelligent commissioning platform based on digital twinning and performance optimization method

Abstract

This invention discloses a method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins, relating to the fields of intelligent manufacturing and digital twin technologies. The method includes the following steps: establishing a unified time-frequency audit baseline; continuously sampling the Ethernet switch routing refresh pulses and the programmable logic controller synchronization beats to extract time drift curves and construct a time consistency reference surface; performing causal replay based on the time consistency reference surface to reconstruct the frame sequence change trajectory before and after the communication link interruption, extracting time sequence misalignment fingerprints and packet loss links, and generating a pseudo-operation risk distribution map. This invention achieves microsecond-level beat synchronization control and pseudo-operation identification by establishing a time consistency reference surface, causal replay, sequence consistency verification, time difference injection, and evidence chain construction. Combined with phase traction and reversible time grids, it realizes abnormal chain circuit breaking and self-healing, comprehensively improving the real-time performance, stability, and intelligence level of the digital twin assembly and adjustment system.

Description

Technical Field

[0001] This invention relates to the fields of intelligent manufacturing and digital twin technology, specifically to a method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins. Background Technology

[0002] The construction and performance optimization of an industrial intelligent assembly and adjustment platform based on digital twins refers to using the HC-JZT-A mechanical assembly and disassembly platform as a prototype, and employing digital twin technology to achieve deep integration of virtual and physical systems. By constructing a high-precision 3D digital model and a real-time mapping relationship with the actual equipment, the intelligent upgrade and dynamic performance optimization of the assembly and adjustment process are achieved. This method uses the electromechanical conceptual design of NX software as its core, establishing a collaborative simulation model of mechanical structure, electrical control, and motion logic; utilizing Siemens PLCs to achieve synchronous interaction between virtual control signals and physical devices; combining an industrial internet architecture to ensure efficient communication between robots, detection systems, and control units; and integrating KUKA collaborative robots and a 3D vision inspection system to intelligently execute gear assembly and disassembly and dimensional inspection and provide data feedback. Through simulation analysis, parameter optimization, and real-time correction, the platform can dynamically optimize assembly accuracy, motion stability, and transmission efficiency, achieving a closed-loop assembly and adjustment system that is synchronized between virtual and real systems, self-learning, and self-correcting. This comprehensively improves the intelligence, precision, and efficiency of industrial training and teaching.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, the dynamic reorganization mechanism of the industrial internet communication chain generally suffers from insufficient synchronization and coordination capabilities during the operation of digital twin-driven industrial assembly and adjustment platforms. When the network switch refreshes its routing table, if this overlaps with the real-time data synchronization cycle of the programmable logic controller, it can easily cause a transient interruption in the communication chain. Although such interruptions are extremely short-lived, they can lead to misalignments in the status information of various collaborative robots, servo systems, vision inspection units, and other devices, causing the mapping relationship between the digital twin model and the physical entity to temporarily fail. More seriously, this failure state is often not immediately recognized by the system's self-checking mechanism, resulting in a pseudo-operational state where the system appears to be running normally but the virtual and physical data are disconnected. This leads to serious consequences such as assembly and adjustment path deviations, incorrect transmission of motion commands, and accumulation of workpiece positioning errors, directly affecting the stability, security, and real-time reliability of the platform and the digital twin system.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide a method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins, so as to solve the problems in the background art mentioned above.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins, comprising the following steps:

[0008] Establish a unified time-frequency audit baseline, continuously sample the Ethernet switch routing refresh pulse and the programmable logic controller synchronization clock, extract the time drift curve, and generate a time consistency reference surface;

[0009] Based on the time consistency reference plane, causal replay is performed to reconstruct the frame sequence trajectory before and after the communication interruption, extract the time sequence misalignment fingerprint and packet loss link, and generate a pseudo operation risk distribution map.

[0010] Based on the pseudo-operational risk distribution map, a sequential consistency verification system is constructed. By comparing the phase linkage of the clock signals of the switch and the programmable logic controller, the overlapping core area is located, the safety boundary area is delineated, and a dynamic suppression window is formed.

[0011] Based on dynamic suppression window execution time difference injection experiments and shadow channel sandbox verification, high-risk link responses are identified, feedforward anchors and confirmation fences are generated, and a dual constraint framework of communication timing and spatial topology is constructed.

[0012] A phase consistency evidence chain is constructed using feedforward anchors and confirmation fences, mapping phase offset, communication delay, and confirmation response to a delay orchestration table and a confirmation window migration sequence, thus putting the evidence chain into a self-constrained state.

[0013] Under the self-constrained state of the evidence chain, the phase conjugate breathing traction mechanism and the reverse diffusion gating strategy are activated, and the reversible time grid is connected in parallel. The synchronous beat is dynamically coupled through the microsecond-level staggered refresh method, which drives the migration of the confirmation window, breaks the pseudo-running chain, and constructs a real-time self-healing closed loop.

[0014] Preferably, the steps for generating the time consistency reference surface are as follows:

[0015] The system collects the physical refresh signal generated by the Ethernet switching device during path reconstruction and the control clock signal output by the programmable logic controller, and performs high-precision time synchronization analysis on the two sets of signals.

[0016] Based on the acquired time series, signal alignment and time drift analysis are performed to extract the offset change trajectory between the two sets of signals;

[0017] Based on the offset change trajectory, a time consistency reference surface covering the entire assembly and adjustment cycle is constructed to form a mapping matrix for calibrating the phase state of the pulse signal and control beat signal of the switching equipment;

[0018] Based on the mapping matrix, a standardized offset coordinate set is established, a time-series label sequence is generated, and potential conflict areas are identified, forming an intervention and early warning mechanism that supports subsequent dynamic control logic.

[0019] The preferred method for generating the pseudo-operational risk distribution map is as follows:

[0020] By selecting regions in the time consistency reference plane where phase offset changes drastically, the high-incidence period of communication anomalies is determined, and four sets of event timestamp information are extracted: the time of issuance of assembly and commissioning instructions, the network forwarding time, the equipment receiving time, and the response feedback time.

[0021] Based on the event timestamp information, the data propagation process before and after the communication link interruption is reconstructed, a complete frame sequence change trajectory is constructed, and path change points, response delay segments, and data frame skipping intervals are identified;

[0022] Based on the reconstructed frame sequence change trajectory, extract the temporal misalignment fingerprint set and packet loss link dataset to generate the link mapping table of the assembly and debugging network;

[0023] By combining the link mapping table and spatial structure information, a pseudo operational risk distribution map is constructed, and the distribution trajectories of misalignment events and packet loss events are drawn to form a hierarchical early warning area.

[0024] Preferably, the dynamic suppression window formation process is as follows:

[0025] First, based on the high-risk links identified in the pseudo-operational risk distribution map, time acquisition devices are deployed to collect the edge change times of the switching equipment clock signal and the programmable logic controller clock signal, and time synchronization is performed under a unified time base.

[0026] Based on the cycle period of the programmable logic controller, the phase trajectory of the switching equipment signal in each cycle is constructed, the time segment with concentrated phase overlap in continuous cycles is identified, and it is determined to be the overlap core region.

[0027] A dynamic security boundary region is constructed based on the overlapping core region, and a conflict level mapping table is generated in conjunction with the task scheduling cycle. On this basis, a dynamic suppression window is constructed and an adaptive update mechanism is set to adjust the boundary position in real time.

[0028] Preferably, the steps for generating feedforward anchor points and confirmation fences are as follows:

[0029] Based on the assembly and adjustment cycle of the dynamic suppression window coverage, a time difference injection experiment was carried out to collect the signal delay response between the switching equipment and the programmable logic controller, record the time offset parameters, the time of response change and the jump path number, and generate abnormal link data.

[0030] Based on the abnormal link data, a communication behavior simulation structure is built in the shadow verification channel. The response behavior of each jump node is collected and measured point by point to form a feedback behavior mapping diagram.

[0031] By combining abnormal link data and feedback mapping, the earliest time node that triggers the unstable state is extracted to generate feedforward anchor points, a confirmation fence is constructed, and a dual temporal and spatial topological constraint framework is formed for stable control of the assembly and adjustment process.

[0032] Preferably, the feedforward anchor point is generated by identifying the inflection point of response change in the feedback mapping diagram and combining it with the control cycle number. It is confirmed that the fence is set as a protection zone that prohibits the passage of control signals and status feedback within half a cycle before and after the feedforward anchor point, so as to realize early intervention and real-time blocking of unstable links.

[0033] Preferably, the steps for the chain of evidence to enter a self-constrained state are as follows:

[0034] Based on the feedforward anchor point and the confirmation fence structure, the control command, link forwarding and feedback response time points within the complete assembly and commissioning cycle are extracted to form the phase offset value and communication delay parameter.

[0035] Establish a delay orchestration table and a confirmation window migration sequence to describe the time offset range of control beat triggering;

[0036] By verifying and filtering the nodes of the evidence chain through periodic consistency closure rules, path feedback matching rules, and fence boundary conflict avoidance rules, the delay orchestration table and the confirmation window migration sequence form an execution chain with self-constraint capabilities.

[0037] Preferably, under the self-constrained state of the evidence chain, the phase conjugate breathing traction mechanism and the reverse diffusion gating strategy are activated, and a reversible time grid is connected in parallel. The synchronization beat is dynamically coupled through a microsecond-level staggered refresh method, driving the confirmation window migration and executing the pseudo-run chain circuit breaking process as follows:

[0038] Under the self-constrained state of the evidence chain, a phase conjugate traction rail is constructed, and a buffer zone is set to guide the assembly cycle beat toward the stable response region;

[0039] An inverse diffusion gating strategy is introduced, which sets phase threshold boundaries by identifying beat offset trends and inserts beat micro-offset intervention points to prevent reverse drift in beat direction;

[0040] Construct a parallel reversible time grid, divide the assembly and adjustment cycle into sub-periods with the smallest time granularity, and initiate a control command rollback or delay mechanism for feedback abnormal cycles.

[0041] Collect periodic residual density maps, identify high-density feedback fluctuation time periods through slope analysis, and relocate the confirmation window position to avoid unstable response areas.

[0042] The execution cycle feedback consistency check is performed. If the feedback path is misaligned or the response is delayed within consecutive cycles, the cycle circuit breaker mechanism is immediately triggered and the scheduling plan is rebuilt based on the safety zone of the previous cycle.

[0043] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0044] This invention establishes a unified time consistency reference surface, enabling fine-grained correlation tracking between switch routing refresh pulses and programmable logic controller (PLC) synchronization beats, accurately capturing microsecond-level time drift behavior. Through causal replay and misalignment fingerprint extraction, it comprehensively grasps the temporal evolution path before and after communication chain breakage, providing a reliable basis for identifying pseudo-operational states. By constructing a sequential consistency verification system and dynamic suppression windows, it achieves precise isolation and defense of key temporal conflict areas. Furthermore, through time difference injection and sandbox verification, it clarifies high-risk feedback paths and establishes feedforward anchors and confirmation barriers as scheduling anchors, forming multi-dimensional communication constraints. Based on the construction of a phase consistency evidence chain, the entire assembly and commissioning process possesses self-driven and self-aware temporal consistency guarantees. Finally, by introducing strategies such as phase traction, inverse diffusion gating, and reversible time grids, the system can achieve microsecond-level dynamic coupling and feedback window migration at the beat level. In the event of anomalies, it can melt down pseudo-operational chains and quickly restore to a stable state without manual intervention, thus endowing the digital twin system with highly real-time, robust, and fully intelligent assembly and commissioning capabilities. Attached Figure Description

[0045] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0046] Figure 1 This is a flowchart illustrating the method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins, as described in this invention.

[0047] Figure 2 This is a schematic diagram of the intelligent assembly system of the present invention. Detailed Implementation

[0048] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0049] This invention provides, for example Figure 1The method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins, as shown, includes the following steps:

[0050] Establish a unified time-frequency audit baseline by continuously sampling the Ethernet switch routing refresh pulse and the programmable logic controller synchronization clock, extracting the time drift curve, and constructing a time consistency reference surface;

[0051] To address the synchronization misalignment issue caused by conflicts between Ethernet communication links and assembly / adjustment control cycles during dynamic network topology reconfiguration, a time-frequency auditing method is proposed. This method establishes a unified time consistency reference surface in the industrial control network to support subsequent misalignment detection and dynamic self-healing processes. The implementation includes the following steps:

[0052] The system collects the physical refresh signals generated by Ethernet switching devices during reconfiguration and the control clock signals output by the programmable logic controller (PLC), and performs high-precision time synchronization analysis on both. In practice, a timing acquisition device with nanosecond-level sampling accuracy is selected and connected to the signal output ports of both the switching devices and the PLC in the target industrial network. Ethernet switching devices typically use Spanning Tree Protocol (STP) or Fast Redundancy Protocol (FRP) for path reconfiguration. When the network structure changes or links switch, port status updates and routing table recalculations are triggered. This behavior is electrically represented as periodic pulse signals with clear rising edges and controllable width. The PLC, as the main control source for device clocking, continuously outputs clock signals during its control logic execution cycle, often sending control trigger commands externally in a fixed-frequency pulse sequence. The acquisition device continuously samples these two independent signal sources, capturing the occurrence times of all pulse edges and recording them as event sequences with unique timestamps, ensuring complete recording of the entire device behavior process at a microsecond or smaller granularity. To ensure the consistency of the acquisition results in the time domain, the acquisition device needs to be connected to the same external stable time base, such as a rubidium atomic clock or a temperature-compensated crystal oscillator, in order to eliminate the time error caused by clock drift between sampling terminals.

[0053] Based on the acquired time series, signal alignment and time drift analysis are performed to construct the offset change trajectory between route refresh and control clock. Specifically, each refresh pulse generated by the Ethernet switching device is used as a starting point to search for the most recent synchronization clock signal output by the programmable logic controller within its adjacent time period and calculate their relative time difference. By traversing all pulse pairs, a set of refresh-clock interval data covering the entire time domain is formed. A moving average filter is performed on this dataset to remove random errors caused by individual pulse anomalies, and then the offset trend curve is plotted in chronological order. This curve typically exhibits a mixed trend of slow jitter and periodic displacement, where periodic jitter mainly originates from the stability error of the controller clock, while sudden displacement is often triggered by link switching, route reconstruction, or data congestion. Furthermore, to more accurately characterize the offset behavior, the offset curve can be processed with its first derivative to extract the offset rate change trajectory, identify points of steep slope change in the curve, and determine whether there are synchronization risk areas or overlapping sensitive windows. Unlike traditional network state analysis methods that only use macroscopic indicators such as data packet loss and communication delay, this step achieves a deeper understanding of time differences based on point-by-point comparison of microscopic signals, providing a foundation for subsequent reference surface construction.

[0054] A time consistency reference surface covering the entire assembly and commissioning cycle is constructed based on the time drift curve to calibrate the relative phase state of the switching equipment pulse signal and the controller clock signal. The specific construction method is as follows: First, the rising edge of the controller synchronization clock signal is used as the period anchor point, and the entire acquisition period is divided into continuous time segments. Then, within each time segment, the relative time offset of the corresponding switching equipment refresh pulse is normalized to the phase position within the control clock cycle, thereby constructing a time alignment matrix in a two-dimensional coordinate space. The horizontal axis of this matrix represents the number of clock cycles executed during assembly and commissioning, and the vertical axis represents the pulse phase offset. Each element in the matrix records the relative time position of the switching equipment refresh pulse within the corresponding cycle. To improve the adaptability of the reference surface in multi-device and multi-link scenarios, device identification and link identification dimensions are further introduced, expanding the matrix into a multi-dimensional structure, thereby supporting independent calibration of the time offset behavior of each type of device and path at different time periods. To address extreme offsets caused by communication mutations within certain special periods, offset entropy is introduced as a rejection threshold to eliminate interference from extreme anomalies on the overall reference surface, ensuring the continuity and generalizability of the time mapping relationship. This approach successfully transforms pulse behavior from a random burst state into a measurable and localizable structured time-domain mapping, significantly enhancing the precision control capability of time synchronization in industrial networks.

[0055] Based on the established time-consistency reference surface, a standardized offset coordinate set is created to support subsequent communication coordination, anomaly identification, and intervention execution. This coordinate set uses the number of clock cycles as the primary index and the phase offset as the reference index to map all refresh pulse behaviors into an ordered sequence of time-series labels. On this basis, each label sequence is regularized to determine whether it falls within a preset sensitive range for controlling the clock cycle. If a potential overlap or synchronization conflict event is identified, it is marked as an intervention warning event, triggering subsequent dynamic control logic. Furthermore, to ensure the continuity of the reference surface under dynamic network structure changes, an adaptive time interpolation mechanism is introduced to extend the boundary of the mapping relationship in critical regions, ensuring that the mapping logic continues to operate even under node changes or route drift. In multi-link parallel and distributed assembly / deployment scenarios on the platform, this reference surface can also be applied to multiple time-series acquisition points via parameter distribution, enabling different nodes to dynamically respond based on a unified time reference, thereby maintaining the consistency of assembly / deployment commands and the synchronization of communication behavior during physical link adjustments.

[0056] Based on the time consistency reference plane, causal replay is performed to reconstruct the frame sequence change trajectory before and after the communication link is interrupted, extract the time sequence misalignment fingerprint and packet loss link, and generate a pseudo operation risk distribution map.

[0057] To further identify and locate synchronization mismatch issues arising during the dynamic reorganization of communication links in the digital twin assembly and adjustment platform, a causal replay operation based on a constructed time consistency reference surface is performed to reconstruct the data transmission evolution trajectory before and after the communication interruption. This process extracts temporal misalignment fingerprints and packet loss links that can be used for subsequent judgment, thereby constructing a pseudo-operational risk distribution map. The specific steps are as follows:

[0058] Regions with drastic phase shift changes in the time consistency reference plane are selected to identify high-incidence periods of communication anomalies and pinpoint critical time windows before and after communication interruptions. In practice, by analyzing the relative offset change rate between the refresh pulses of the switching equipment and the synchronization clock of the programmable logic controller in the reference plane, segments with offset derivatives exceeding a preset threshold are extracted. Combined with the drift direction stability over three or more consecutive assembly and commissioning cycles, these segments are identified as potential communication link anomaly time periods. Within these time periods, timestamp information for four key events—assembly and commissioning command issuance time, network forwarding time, equipment reception time, and response feedback time—is extracted point-by-point and arranged chronologically to construct an event sequence table. To ensure data integrity, the source node, destination node, relay device addresses, and transmission path identifiers are recorded synchronously to ensure that the spatial path and temporal behavior of each frame of data are fully reconstructed, thus providing accurate data support for subsequent frame sequence reconstruction.

[0059] Based on the extracted event sequence list, the propagation process of each frame of control and feedback data is replayed sequentially to construct a complete frame sequence change trajectory and identify the evolution characteristics of the transmission path before and after the interruption. In specific operation, starting with the first frame control command issued by the upper-level control device, and combining the forwarding and receiving time information provided by the event sequence list, the data propagation path is drawn point-by-point on a two-dimensional timeline graph with time as the horizontal axis and node devices as the vertical axis. Each path is represented by an arrow, marking the actual time when the data arrives at each node. Subsequently, after network reconstruction is triggered, the control command and response path trajectories after the link break are drawn in the same way. By overlaying and comparing the two sets of path graphs before and after the interruption, key behavioral characteristics such as path change points, response delay segments, and data frame skipping intervals can be clearly observed. If, in a certain path, the control command fails to reach the target device or the response frame fails to return on time, the path is marked as a communication interruption channel, and the number range of lost frames and the corresponding path structure are recorded on the timeline. This multi-stage point-by-point path drawing method, unlike traditional methods that only count packet loss through frame number changes, has higher spatiotemporal accuracy and behavioral traceability.

[0060] Based on the reconstructed temporal behavior trajectory, a set of temporal misalignment fingerprints for anomaly identification is extracted, and all specific links that caused packet loss are identified. This operation uses the time difference between control commands and response data as the core indicator, combined with the beat reference point, to compare the offset change trend in each link. The specific steps are as follows: statistically analyze the response time difference of all data pairs in each link, construct a response delay sequence, and calculate the delay change during adjacent two-week periods. When a link is found to have a sudden increase in delay in multiple consecutive periods, and the offset distance from the beat point continues to increase, it is identified as a potential misaligned path. Further analysis of the offset pattern can extract various misalignment features, such as delay fluctuation skew, beat jump offset, and feedback delay collapse, all of which can be encoded as misalignment feature labels to form the temporal misalignment fingerprint of the link. By mapping the misalignment fingerprint of each link to its geographical path, a link mapping table with temporal anomaly behavior characteristics in the assembly and commissioning platform network can be formed. Based on this, if a response frame of a certain link is missing, control data skips the receiving stage, or there is a serious misalignment between the device feedback and the control cycle, it is further identified as a packet loss link, and its start and end cycle number, skipped frame range and corresponding network hop count are recorded to construct a refined packet loss link dataset.

[0061] After obtaining the temporal misalignment fingerprint set and packet loss link dataset, a pseudo-operational risk distribution map is constructed by combining the spatial structure information of the communication links. This map describes the temporal distribution and spatial clustering characteristics of abnormal states throughout the entire assembly and commissioning process. The risk map uses the time axis as the horizontal axis and the physical link number as the vertical axis, plotting each detected misalignment and packet loss event in a two-dimensional coordinate system. Each event is marked with a circle or diamond on the map, and the color depth indicates the degree of misalignment or packet loss severity. To enhance the intuitive recognition capability of the risk map, high-density misalignment areas and continuous packet loss paths are connected to form risk connectivity zones. Simultaneously, a time sliding window method is used to statistically analyze the changes in the number and intensity of events within each time period on the risk map, automatically dividing it into high-risk concentration areas, medium-risk buffer zones, and low-risk transition areas, forming a multi-level pseudo-operational early warning area distribution. This map can be directly used to guide the network scheduler in link avoidance, the clock regulator in adjusting the control frequency, and platform administrators in determining whether to trigger path repair operations.

[0062] Based on the pseudo-operational risk distribution map, a sequential consistency verification system is constructed. By comparing the phase linkage between the switch clock signal and the programmable logic controller clock signal, the overlapping core area is located and the safety boundary area is delineated to form a dynamic suppression window.

[0063] To ensure timing consistency between control signals and forwarding behavior in high-risk areas of the communication link, after constructing the pseudo-operational risk distribution map, a sequential consistency verification system must be further established. This involves high-precision phase-coordinated comparison of clock signals between the switching equipment and the programmable logic controller, and delineation of safety boundaries based on overlapping area characteristics, ultimately forming a dynamic suppression window to avoid synchronization conflicts. The specific steps are as follows:

[0064] Based on the high-risk links identified in the pseudo-operational risk distribution map, these were designated as priority sections for clock signal observation. High-resolution time acquisition devices were deployed to capture the edge changes in the clock signals of the switching equipment and the programmable logic controller (PLC). During operation, the acquisition devices were connected to the physical output ports of the switching equipment and the clock output interface of the PLC, respectively, to monitor the rising and falling edges of their output signals in real time. The clock signal of the switching equipment generally originates from an internal crystal oscillator, and its transitions typically occur during path reconstruction, route resetting, or broadcast storm triggering. The clock signal of the PLC is represented by the control beat within the assembly cycle; its frequency is fixed but may fluctuate slightly due to interference. In this process, the acquisition devices uniquely timestamp each signal event and record the behavior of both signal sources within each complete control beat cycle, generating continuous time-series data. To eliminate clock offset errors between devices, the acquisition devices were connected to an external frequency-stabilized time base device to ensure cross-source consistency of all time stamps. The key to this step is ensuring that the two clock signals are synchronously captured under the same time reference, thus providing a rigorous foundation for subsequent phase comparison.

[0065] After acquiring the complete time series of two signal sources, the relative phase trajectory of the switching equipment signal within each cycle is constructed using the programmable logic controller (PLC) cycle as the periodic reference, and time periods where fixed overlap may occur are identified. This process uses the start point of the control cycle as the anchor point, dividing the time within that cycle into several equally spaced phase intervals, and mapping the time points of the switching equipment signal to specific phase positions. Accumulated mapping is performed over multiple consecutive cycles to obtain a phase distribution curve of the switching signal within the control cycle. If, within several consecutive cycles, the phase points in this distribution curve concentrate within a specific cycle interval, and the concentration exceeds a certain set threshold, it indicates a high probability of phase overlap. Furthermore, if this overlap coincides with the occurrence time of a high-misalignment event located in the previously pseudo-operational risk distribution map, the cycle segment can be determined as an overlap core region. To quantify the stability and interference intensity of this overlap, the minimum phase difference of the switching signal within each cycle is statistically analyzed, and a phase difference evolution curve is plotted, marking key indicators such as average offset, peak amplitude, and number of consecutive cycles. All parameters that fall into the preset interference model are saved as boundary clues of the overlapping core region for boundary delineation in the next stage.

[0066] After identifying core region boundaries with stable overlapping characteristics, a dynamic safety boundary region is constructed based on its temporal coverage and phase persistence characteristics, and a runtime dynamic suppression window is generated accordingly. In practice, firstly, the starting and ending phase points of all identified overlapping core regions are merged. Boundary points with high overlap within adjacent cycles are continuously fused to form candidate safety boundary bands with time segment attributes. Then, based on the platform task scheduling cycle, these candidate bands are mapped to the assembly task trigger window, and their degree of overlap with the default sending time points of assembly, detection, and motion control task commands is compared. If a candidate safety band completely covers the control window before and after the command is issued, it is marked as a level-one conflict zone; if it only partially overlaps, it is marked as a level-two interference zone. A conflict level mapping table is established in this way, serving as the basis for configuring the task scheduler's time parameters during operation. The dynamic suppression window originates from this mapping table. Based on the actual assembly and commissioning execution cycle, it sets all primary conflict zones as mandatory command delay zones, prohibiting the issuance of any control commands within these zones; and sets all secondary interference zones as observation buffer zones, allowing commands to be issued only after the task requirement level is low or the link stability is confirmed. Furthermore, considering the temperature drift of the equipment clock during operation, this suppression window has an adaptive update mechanism. Within each fixed cycle, it re-activates the acquisition device, updates the latest phase trajectory and overlap kernel range, and adjusts the suppression boundary position in real time, allowing it to dynamically evolve with changes in the equipment's operating status.

[0067] Based on the dynamic suppression window, time difference injection experiments and shadow channel sandbox verification were conducted to identify high-risk link feedback responses, generate feedforward anchors and confirmation fences, and establish a dual constraint framework of communication timing and spatial topology.

[0068] To improve the communication stability of the assembly and adjustment platform under high-risk link conditions, after constructing the dynamic suppression window, further time difference injection and channel verification experiments are needed to identify unstable link characteristics. Based on the response behavior, feedforward anchors and acknowledgment barriers are then constructed, ultimately forming a dual-constraint structure. The specific steps are as follows:

[0069] Based on the constructed dynamic suppression window structure, a typical assembly / tuning cycle covered by the suppression window was selected as the experimental time period. Targeted time difference injection experiments were conducted within this cycle to verify the robustness of the link response and identify potential timing vulnerabilities. During implementation, three assembly / tuning cycles were first selected from the identified overlapping core regions, with phase interference levels at high, medium, and low levels, respectively. For each cycle, a high-precision signal delay control device was used to delay the synchronization clock signal of the programmable logic controller by three different time steps: 1 microsecond, 3 microseconds, and 5 microseconds. Under each delay condition, 10 rounds of control signals were continuously emitted, and the response time of the switching equipment, the arrival time of the feedback signal, and the number of hops on the return path were recorded in real time by response signal acquisition devices connected downstream of each communication path. To avoid external interference affecting experimental stability, the network topology remained unchanged and the data load was fixed within each experimental cycle. During the experiment, if a non-linear increase in response time, a change in the feedback path, or response loss occurred when the delay exceeded 3 microseconds, the link was determined to be a high-risk channel under the current intervention conditions. The time offset parameters, response mutation time, and jump path number of the link are recorded together as input data for subsequent verification and constraint structure construction.

[0070] Based on the list of abnormal feedback links recorded in the time-difference injection experiment, a simulated communication behavior structure was built in the shadow verification channel using a one-to-one mapping method. Sandbox-level stability playback and response path tracing were then performed on the feedback links. In the specific implementation, a physical simulation line consisting of multiple hop nodes was first built according to the actual switching equipment topology, equipped with a controller signal source and terminal response devices. The injected signal used the same delay combination as in the experimental phase, synchronously issuing control beats and acquiring response paths. Unlike the injection experiment, which only observed the feedback results, this step focused on capturing the intermediate response behavior of each hop node. To this end, a precision voltage capture probe was configured at each relay point to record the level changes of the control signal as it passed through, and a high-frequency oscilloscope was used to measure signal amplitude attenuation and edge jitter. When signal waveform compression, delays exceeding the single-hop average, or edge flipping errors occurred at a node, it indicated that the node might constitute an unstable hop source. The location of such hop sources, the corresponding input offset, and the output delay were all considered structural anomaly data to further confirm the range of unstable segments in the link. At the same time, the multi-round response results of all links are merged to form a complete feedback behavior mapping diagram. This diagram consists of signal injection time, feedback path number, jump point delay, and final response time, and forms a multi-dimensional and comparable channel response baseline.

[0071] By combining link-sensitive data from time-difference injection experiments with feedback response maps collected in sandbox verification, the earliest time node of feedback mutation is extracted as a feedforward anchor point to identify the time segment in the communication path that first triggers link instability. For each link, the inflection point of the response mutation is located in the corresponding response map, and the time offset value of this point is jointly encoded with the control cycle number in which it occurs, forming a set of feedforward anchor points that can be used to predict link-sensitive states in advance. To achieve real-time judgment and early intervention, all anchor points in this set are sorted according to the assembly and commissioning cycle number, forming a continuous time-band structure. For each anchor point, a stability buffer is generated by combining the maximum path hop count and minimum response amplitude within the two preceding and following cycle periods. This buffer is extended outwards by 0.5 cycle periods at both ends, and a confirmation fence is constructed within the extended area. This fence is set as an insurmountable zone, prohibiting all control commands or status feedback frames from passing through during this time period, and a check and matching is performed before the start of each assembly and commissioning cycle. If it is found that a control signal is about to fall into the confirmation fence protection zone, the command is automatically delayed until the next safe cycle. Ultimately, the combination of all anchor points and fences forms a dual timing and topology control map covering the entire assembly and commissioning cycle. By introducing precise time positions, real link structures, and dynamic jumping behaviors, an engineering-deployable constraint framework with predictive, adaptive, and closed-loop feedback characteristics is established. This framework can be widely applied in high-concurrency, multi-path cross-control scenarios of industrial digital twin platforms, improving their ability to suppress communication synchronization failures and tolerate interference.

[0072] By using feedforward anchors and confirmation fences to construct a chain of evidence for phase consistency, the phase offset data, communication delay patterns and confirmation response results in different time segments are mapped into a delay arrangement table and a confirmation window migration sequence, so that the chain of evidence enters a self-constrained state.

[0073] To achieve structured tracking and controllable constraints of communication behavior during multi-cycle control, a phase consistency evidence chain covering the entire assembly and adjustment process needs to be constructed based on the established feedforward anchors and confirmation barriers to support delay control and dynamic migration of the execution window. The specific steps are as follows:

[0074] Based on the feedforward anchor point and confirmation fence structure, and combined with the feedback response data from the preceding shadow channel, representative time segments within the complete assembly and commissioning cycle are selected as the evidence chain construction intervals. During implementation, recorded control command times, switching device forwarding times, and target device feedback times are extracted from multiple consecutive assembly and commissioning cycles, corresponding to three types of time nodes: command initiation point, link relay point, and response receiving point. Each time node is synchronously mapped to its corresponding assembly and commissioning beat within the cycle, converting its time position relative to the start of the control beat into a phase offset value, forming standardized control phase coordinates. To eliminate transient offset errors caused by signal jitter, the phase offset behavior occurring within each assembly and commissioning cycle is processed using a moving average, filtering out sequences where the phase change amplitude steadily increases or converges within consecutive cycles. If a phase offset value tends towards the boundary region within a certain consecutive cycle segment, accompanied by an increase in response time delay, this cycle segment is judged to potentially constitute a high-risk area for communication mismatch. These cycle segments are combined and encoded according to four elements: start and end cycle number, offset change rate, response time abrupt change value, and whether it crosses the confirmation fence boundary, forming a preliminary candidate set of evidence chain nodes. Each candidate node value includes: cycle number, beat phase offset value, total round-trip communication delay, and feedback instruction status label. The set of all initially selected nodes will form the basic building blocks for the subsequent evolution of the evidence chain structure.

[0075] After the candidate set of evidence chains is generated, the behavioral characteristics of each node need to be uniformly mapped to the timeline to establish a delay orchestration table and a confirmation window migration sequence for dynamically orchestrating the timing of command triggering. In practice, all candidate nodes are first arranged in chronological order, and their corresponding phase offset values, communication delay values, and response behaviors are recorded as a multi-dimensional time behavior data structure. A time span label is established for each cycle node, representing the length of time from the start of the current cycle's beat to the complete reception of feedback. Based on this, the deviation of this time span is calculated against the platform's preset safe beat window, serving as the decision-making basis for determining whether the cycle can be safely executed. If the deviation exceeds the platform's maximum allowable response delay tolerance, the cycle is marked as an unschedulable segment and inserted into the delay orchestration table as one of the future controller task scheduling avoidance rules. Furthermore, for all nodes at the edge of the confirmation fence, the relative position of their phase intersection area and the fence boundary is extracted. By calculating their lag range and early response range, a time-slip sequence is generated and appended to the confirmation window attribute field of the corresponding cycle to form the confirmation window migration sequence. This migration sequence records the time range of the confirmation window that can be shifted left or right, serving as a direct reference structure for dynamically adjusting the execution position of the control beat. This step ultimately forms a time graph for cross-cycle instruction control, with the horizontal axis representing the cycle number, the vertical axis representing the offset of the available beat start point, and the intersection points indicating safe and available scheduling positions, thus realizing the periodic sliding and dynamic fine-tuning configuration of the control beat.

[0076] After the delay orchestration table and confirmation window migration sequence are constructed, to ensure that no errors accumulate or path mismatches occur during execution, the evidence chain structure needs to be encapsulated into a self-constraining execution chain through a rule mechanism. The self-constraining mechanism consists of three types of structures: periodic consistency closure rules, path feedback matching rules, and fence boundary conflict avoidance rules. The periodic consistency closure rule requires that any candidate node, to be included in the evidence chain, must be continuous in beat number with the previous node and have a consistent phase offset trend; if there is a beat jump or opposite phase direction between the two, the chain structure extension is terminated. The path feedback matching rule requires that the feedback path number and hop sequence of each node in the evidence chain be consistent with the real path recorded in the previous shadow channel feedback experiment; otherwise, it is judged as path fictitious behavior, and the node is excluded from participating in the evidence chain construction. The fence boundary conflict avoidance rule requires that the delay range of any node must not overlap with the confirmation fence prohibition zone. If a node is found to trigger a signal, execute an instruction, or receive feedback within the confirmation fence window, the system immediately stops writing its delay table entry and removes that time point from the confirmation window migration sequence. The three rules work together to form a filtering, verification, and closure mechanism for the evidence chain, ensuring that it has forward scalability within the control cycle and that the chain does not break or feedback errors due to abnormal behavior. The evidence chain structure that has completed rule verification becomes a stable baseline for scheduling assembly and commissioning tasks during operation. It not only provides a safe time interval for selecting control cycles but also allows for rapid restoration of cycle synchronization by referencing historical evidence chain behavior should future communication chains be disturbed.

[0077] Under the self-constrained state of the evidence chain, the phase conjugate breathing traction mechanism and the reverse diffusion gating strategy are activated, the reversible time grid is connected in parallel, the synchronous beat is dynamically coupled through the microsecond-level staggered refresh method, the residual density is used to drive the migration of the confirmation window, the pseudo running chain is broken, and a real-time self-healing closed loop is constructed.

[0078] To ensure timing synchronization and signal accuracy during the assembly and adjustment process in highly complex link environments, a controllable traction mechanism and dynamic coupling strategy need to be introduced on the basis of the self-constrained evidence chain structure. Through refined peak-shifting scheduling and feedback-driven time migration, rapid circuit breaking and stable closed-loop recovery of pseudo-operation chains can be achieved. The specific steps are as follows:

[0079] After the evidence chain structure enters a self-constrained stable operating state, a phase conjugate traction track is constructed based on the generated phase offset trajectory, confirmation window migration records, and periodic communication delay data. This traction track is a fitted trajectory formed by analyzing the relative phase difference between the command trigger time, response completion time, and the clock reference point within multiple assembly and adjustment cycles, combined with the phase change trend between adjacent cycles. It is used to guide the clock position of the next control cycle towards the stable response region. A fixed-width transition buffer zone, 0.8 microseconds wide, is set on both sides of the traction track as a clock fluctuation tolerance zone. During control task scheduling, all newly triggered assembly and adjustment commands need to be calculated by phase alignment with the fitted region of the traction track to adjust their planned trigger time, so that they are distributed as much as possible within the buffer zone, ensuring that the control clock has a higher probability of successful response in the physical link. This method differs from traditional static clock planning; it is a dynamic scheduling guidance mechanism based on historical physical response evolution data.

[0080] A reverse diffusion gating strategy is introduced to actively limit the beat drift trend in non-target directions, preventing small phase shifts from cascading amplification within consecutive cycles, thus avoiding breaches of the confirmation window safety boundary or entry into a pseudo-synchronization state. Specifically, after each control cycle, the beat shift increment between the current cycle and the previous cycle is calculated and compared with the dominant phase change direction of the traction rail. If a reverse shift is detected in two consecutive cycles, and the shift exceeds 0.6 microseconds, it is determined to be a non-traction shift trend. In this case, a phase threshold boundary is set in the next cycle, locking the allowed beat position outside a reasonable range in the traction rail direction, forcing the scheduler not to deploy new control commands. If this reverse trend continues for a third cycle, the system will actively insert a fine-tuning trigger point, inserting a 0.3 microsecond beat micro-shift intervention within the signal transmission delay. Through physical layer directional adjustment, the phase shift trend is returned to the traction direction, forming a beat retuning point. This beat control strategy based on trend recognition and reverse intervention forms a feedforward intervention capability for control behavior and has strong dynamic constraint characteristics.

[0081] A parallel reversible time grid is constructed to achieve fine-grained allocation and dynamic switching of multi-cycle control cycles. This time grid divides each assembly and commissioning cycle into several consecutive sub-segments with a minimum time granularity (0.2 microseconds), and labels each sub-segment with its scheduling status, including triggerable, warning buffer, and prohibited execution. The labeling is based on the residual density assessment results of the previous cycle, the confirmation window sliding position, and the phase traction rail offset rate. At the start of each assembly and commissioning cycle, the scheduler selects only the earliest available time slice from the triggerable segments to initiate control commands. If an abnormal feedback signal occurs or the response time exceeds the prediction window, a reversible switching mechanism is activated, rolling back the control commands in the current cycle to the reserved time slot of the previous cycle, or postponing them to the earliest triggerable time point of the next cycle. This structure ensures that in environments with high-density command conflicts or multi-link interference, the control task will not completely fail due to time congestion or response failure. Instead, it automatically corrects errors through the time window rollback mechanism, thereby significantly improving the resilience and fault tolerance of the scheduling system.

[0082] A residual density-driven confirmation window migration mechanism is introduced to further identify and avoid unstable signal feedback regions in real time. In actual operation, the difference between the feedback signal response time and the ideal clock cycle completion time in each cycle is defined as the cycle residual. All residual values ​​are accumulated over every ten cycles to form a residual density map, which is used to quantify the stability of feedback behavior in different time periods. Before each control cycle, the system performs slope analysis on the residual density to identify time periods in the current cycle that may trigger feedback instability. If the density map shows a sharp increase in residual density (slope greater than 1.5) in a certain time period, the confirmation window will execute an avoidance strategy during that time period, migrating to a lower density region by about 1 microsecond and resetting the expected feedback reception point position. Simultaneously, this migration behavior is fed back to the scheduler to update the triggerable area settings of the reversible time grid, achieving synchronous sliding of the feedback window and the scheduling clock cycle. This process dynamically adapts to changes in network response behavior, does not rely on fixed delay parameters, and improves the rapid adaptability of the assembly and commissioning system to communication fluctuations.

[0083] The system integrates a traction rail guidance mechanism, anti-diffusion gating rules, a time grid dynamic jump strategy, and a residual density-driven confirmation window sliding mechanism to form a real-time cycle break and self-healing closed loop. This loop is used to detect and terminate pseudo-run chains and drive the assembly and adjustment system back to a stable state. After each cycle, the system performs consistency checks on the feedback consistency, response sequence, and path jump count for the current cycle and the previous two cycles. If there are feedback path misalignments, signal loss, or response lags exceeding the allowable threshold in two consecutive cycles, it is immediately marked as a pseudo-run chain initiation point. The system automatically terminates all unfinished control tasks under this chain path, rolls back the control cycle to the safe triggerable zone in the previous grid, and reconstructs a new cycle execution plan, preserving the inertial continuation of the traction rail direction during this process. All break events are registered and associated with the corresponding confirmation window migration record, serving as input for the next ten-cycle scheduling parameter optimization. In this way, the assembly and adjustment platform not only has the ability to actively disconnect abnormal chain segments, but also can automatically complete fault self-checking, scheduling correction and cycle reconstruction without manual intervention at the signal layer, forming a control closed-loop structure with real-time diagnosis and self-recovery capabilities.

[0084] This invention establishes a unified time consistency reference surface, enabling fine-grained correlation tracking between switch routing refresh pulses and programmable logic controller (PLC) synchronization beats, accurately capturing microsecond-level time drift behavior. Through causal replay and misalignment fingerprint extraction, it comprehensively grasps the temporal evolution path before and after communication chain breakage, providing a reliable basis for identifying pseudo-operational states. By constructing a sequential consistency verification system and dynamic suppression windows, it achieves precise isolation and defense of key temporal conflict areas. Furthermore, through time difference injection and sandbox verification, it clarifies high-risk feedback paths and establishes feedforward anchors and confirmation barriers as scheduling anchors, forming multi-dimensional communication constraints. Based on the construction of a phase consistency evidence chain, the entire assembly and commissioning process possesses self-driven and self-aware temporal consistency guarantees. Finally, by introducing strategies such as phase traction, inverse diffusion gating, and reversible time grids, the system can achieve microsecond-level dynamic coupling and feedback window migration at the beat level. In the event of anomalies, it can melt down pseudo-operational chains and quickly restore to a stable state without manual intervention, thus endowing the digital twin system with highly real-time, robust, and fully intelligent assembly and commissioning capabilities.

[0085] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins, characterized in that, Includes the following steps: Establish a unified time-frequency audit baseline, continuously sample the Ethernet switch routing refresh pulse and the programmable logic controller synchronization clock, extract the time drift curve, and generate a time consistency reference surface; Based on the time consistency reference plane, causal replay is performed to reconstruct the frame sequence trajectory before and after the communication interruption, extract the time sequence misalignment fingerprint and packet loss link, and generate a pseudo operation risk distribution map. Based on the pseudo-operational risk distribution map, a sequential consistency verification system is constructed. By comparing the phase linkage of the clock signals of the switch and the programmable logic controller, the overlapping core area is located, the safety boundary area is delineated, and a dynamic suppression window is formed. Based on dynamic suppression window execution time difference injection experiments and shadow channel sandbox verification, high-risk link responses are identified, feedforward anchors and confirmation fences are generated, and a dual constraint framework of communication timing and spatial topology is constructed. A phase consistency evidence chain is constructed by using feedforward anchors and confirmation fences, and phase offset, communication delay and confirmation response are mapped to delay orchestration table and confirmation window migration sequence, so that the evidence chain enters a self-constrained state. Under the self-constrained state of the evidence chain, the phase conjugate breathing traction mechanism and the reverse diffusion gating strategy are activated, and the reversible time grid is connected in parallel. The synchronous beat is dynamically coupled through the microsecond-level staggered refresh method, driving the confirmation window migration, bridging the pseudo-running chain and constructing a real-time self-healing closed loop. Includes the following steps: Under the self-constrained state of the evidence chain, a phase conjugate traction rail is constructed, and a buffer zone is set to guide the assembly cycle beat toward the stable response region; An inverse diffusion gating strategy is introduced, which sets phase threshold boundaries by identifying beat offset trends and inserts beat micro-offset intervention points to prevent reverse drift in beat direction; Construct a parallel reversible time grid, divide the assembly and adjustment cycle into sub-periods with the smallest time granularity, and initiate a control command rollback or delay mechanism for feedback abnormal cycles. Collect periodic residual density maps, identify high-density feedback fluctuation time periods through slope analysis, and relocate the confirmation window position to avoid unstable response areas. The execution cycle feedback consistency check is performed. If the feedback path is misaligned or the response is delayed within consecutive cycles, the cycle circuit breaker mechanism is immediately triggered and the scheduling plan is rebuilt based on the safety zone of the previous cycle.

2. The method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins according to claim 1, characterized in that, The steps for generating a time-consistent reference surface are as follows: The system collects the physical refresh signal generated by the Ethernet switching device during path reconstruction and the control clock signal output by the programmable logic controller, and performs high-precision time synchronization analysis on the two sets of signals. Based on the acquired time series, signal alignment and time drift analysis are performed to extract the offset change trajectory between the two sets of signals; Based on the offset change trajectory, a time consistency reference surface covering the entire assembly and adjustment cycle is constructed to form a mapping matrix for calibrating the phase state of the pulse signal and control beat signal of the switching equipment; Based on the mapping matrix, a standardized offset coordinate set is established, a time-series label sequence is generated, and potential conflict areas are identified, forming an intervention and early warning mechanism that supports subsequent dynamic control logic.

3. The method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins according to claim 2, characterized in that, The process of generating the pseudo operational risk distribution map is as follows: By selecting regions in the time consistency reference plane where phase offset changes drastically, the high-incidence period of communication anomalies is determined, and four sets of event timestamp information are extracted: the time of issuance of assembly and commissioning instructions, the network forwarding time, the equipment receiving time, and the response feedback time. Based on the event timestamp information, the data propagation process before and after the communication link interruption is reconstructed, a complete frame sequence change trajectory is constructed, and path change points, response delay segments, and data frame skipping intervals are identified; Based on the reconstructed frame sequence change trajectory, extract the temporal misalignment fingerprint set and packet loss link dataset to generate the link mapping table of the assembly and debugging network; By combining the link mapping table and spatial structure information, a pseudo operational risk distribution map is constructed, and the distribution trajectories of misalignment events and packet loss events are drawn to form a hierarchical early warning area.

4. The method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins according to claim 3, characterized in that, The dynamic suppression window formation process is as follows: First, based on the high-risk links identified in the pseudo-operational risk distribution map, time acquisition devices are deployed to collect the edge change times of the switching equipment clock signal and the programmable logic controller clock signal, and time synchronization is performed under a unified time base. Based on the cycle period of the programmable logic controller, the phase trajectory of the switching equipment signal in each cycle is constructed, the time segment with concentrated phase overlap in continuous cycles is identified, and it is determined to be the overlap core region. A dynamic security boundary region is constructed based on the overlapping core region, and a conflict level mapping table is generated in conjunction with the task scheduling cycle. On this basis, a dynamic suppression window is constructed and an adaptive update mechanism is set to adjust the boundary position in real time.

5. The method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins according to claim 4, characterized in that, The steps for generating feedforward anchor points and confirmation fences are as follows: Based on the assembly and adjustment cycle of the dynamic suppression window coverage, a time difference injection experiment was carried out to collect the signal delay response between the switching equipment and the programmable logic controller, record the time offset parameters, the time of response change and the jump path number, and generate abnormal link data. Based on the abnormal link data, a communication behavior simulation structure is built in the shadow verification channel. The response behavior of each jump node is collected and measured point by point to form a feedback behavior mapping diagram. By combining abnormal link data and feedback mapping, the earliest time node that triggers the unstable state is extracted to generate feedforward anchor points, a confirmation fence is constructed, and a dual temporal and spatial topological constraint framework is formed for stable control of the assembly and adjustment process.

6. The method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins according to claim 5, characterized in that, The feedforward anchor point is generated by identifying the inflection point of response mutation in the feedback mapping diagram and combining it with the control cycle number. It is confirmed that the fence is set as a protection zone that prohibits the passage of control signals and status feedback within half a cycle before and after the feedforward anchor point, so as to realize early intervention and real-time blocking of unstable links.

7. The method for constructing and optimizing the performance of an industrial intelligent assembly and adjustment platform based on digital twins according to claim 5, characterized in that, The steps for the chain of evidence to enter a self-constrained state are as follows: Based on the feedforward anchor point and the confirmation fence structure, the control command, link forwarding and feedback response time points within the complete assembly and commissioning cycle are extracted to form the phase offset value and communication delay parameter. Establish a delay orchestration table and a confirmation window migration sequence to describe the time offset range of control beat triggering; By verifying and filtering the nodes of the evidence chain through periodic consistency closure rules, path feedback matching rules, and fence boundary conflict avoidance rules, the delay orchestration table and the confirmation window migration sequence form an execution chain with self-constraint capabilities.

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