General assembly construction digital collaborative management and control platform and multi-system data interaction method

Abstract

The application discloses a general assembly construction digital collaborative management and control platform and a multi-system data interaction method, relates to the technical field of data collaborative management, and comprises the following steps: S1, collecting multi-system general assembly state data in real time and performing data preprocessing; S2, taking a device action completion time as a physical completion reference, depicting generation lag characteristics of different systems, and identifying typical lag modes and fluctuation characteristics generated by the states of the systems; S3, correcting time semantics of multi-system completion states, constructing a credible time window, and determining the alignment degree of the multi-system completion states in the time semantic level; and S4, analyzing the synchronization degree of the multi-system completion states on a time surface and the consistency level between the completion states, identifying a pseudo-synchronization risk state existing in the general assembly task, and performing parallel credible correction and collaborative feedback. The method solves the problem of pseudo-synchronization of multi-system general assembly states in which the states are synchronized on a surface time but actually lag in semantics.

Description

Technical Field

[0001] This invention relates to the field of data collaborative management technology, specifically to a digital collaborative management and control platform for final assembly construction and a multi-system data interaction method. Background Technology

[0002] With the continuous expansion of the scale of large-scale engineering equipment, complex products, and engineering projects' final assembly and construction, the final assembly process typically involves the collaborative operation of multiple business systems, such as equipment control systems, manufacturing execution systems, and building information modeling systems. These different systems record and manage the final assembly status from the perspectives of equipment execution, production management, and structural modeling. In this process, parallel operation of multiple systems, diverse data sources, and differentiated status update mechanisms have become the norm for digital management of final assembly and construction, placing higher demands on progress control, status assessment, and collaborative decision-making in the final assembly process.

[0003] For example, invention patent CN114612004A discloses an enhanced distribution estimation optimization method for the collaborative scheduling of the loader assembly process, belonging to the field of assembly line balancing technology. The method includes: establishing an integer programming model for the collaborative scheduling of the loader assembly process, with assembly line cycle time and smoothing exponent as primary and secondary optimization objectives; and designing an enhanced distribution estimation algorithm to optimize the objectives. This invention uses assembly cycle time as the primary optimization objective and smoothing exponent as the secondary optimization objective, making the workload of the workstations more balanced while ensuring high production efficiency. The enhanced distribution estimation algorithm designed for the process arrangement can quickly generate high-quality solutions that meet the constraints, thus ensuring that the algorithm can quickly respond to changes in the loader assembly process during actual production.

[0004] For example, invention patent CN110363489B discloses a remote collaborative system and method for spacecraft assembly data. This includes assembly process systems and assembly execution systems deployed at the company headquarters and branch offices, respectively. The headquarters' assembly process system and assembly execution system are integrated to achieve assembly business management for headquarters models, and the same applies to branch offices. Both systems have identical business functions to enable the design of process BOMs and the compilation of process procedures. Process data is shared between the headquarters and branch office assembly process systems. This invention avoids the impact of temporary network interruptions on business operations at headquarters and branch offices, improves the user experience of branch office systems, and enables the transfer of business data, meeting the needs of remote collaboration in spacecraft assembly operations.

[0005] However, in the digital collaborative management and control platform for final assembly construction, various business systems typically achieve data timestamp alignment through a unified time synchronization mechanism. However, the timing and triggering conditions for generating status data differ significantly across systems: equipment systems generate status based on action completion, MES based on manual confirmation and updates, and BIM based on model change records. Although the data remains consistent in the time dimension, the actual construction status it reflects is not synchronized, easily leading to a "pseudo-synchronization" phenomenon—superficial synchronization but substantial lag—which in turn affects the platform's accurate assessment of final assembly construction progress and risks.

[0006] Therefore, in order to address the above issues, there is an urgent need for a digital collaborative management and control platform for general assembly and construction, as well as a multi-system data interaction method. Summary of the Invention

[0007] Technical problems to be solved To address the shortcomings of existing technologies, this invention provides a digital collaborative management and control platform for final assembly construction and a multi-system data interaction method, which solves the problem of pseudo-synchronization where the final assembly status of multiple systems appears to be synchronized in time but is actually semantically lagging.

[0008] Technical solution To achieve the above objectives, this invention provides the following technical solution: a digital collaborative management and control platform for final assembly construction and a multi-system data interaction method, comprising the following steps: S1, real-time collection of multi-system final assembly status data, and processing of the multi-system final assembly status data for time benchmark unification, object association, and status type standardization to form an alignable completion-class status dataset; S2, using the equipment action completion time in the multi-system final assembly status data as a physical completion reference, characterizing the generation lag characteristics of different system completion-class states, identifying typical lag patterns and fluctuation characteristics of each system state generation, and forming a system-level state generation feature description; S3, combining system-level... The system generates state features to perform temporal semantic correction on the completion states of multiple systems, constructs a time window representing the credible range of states, and determines the degree of alignment of the completion states of multiple systems at the temporal semantic level based on the overlap of time windows between system pairs, and constructs a time semantic alignment mark for cross-system states; S4, it analyzes the degree of synchronization of the completion states of multiple systems on the time surface through system-level state generation features, analyzes the degree of alignment of the completion states of multiple systems at the temporal semantic level, determines the consistency level between completion states, identifies pseudo-synchronization risk states in the final assembly task, and performs credible correction and collaborative feedback on the final assembly progress and risks based on the identification results.

[0009] Furthermore, the specific process of collecting multi-system assembly status data in real time and performing time benchmark unification, object association, and status type standardization on the multi-system assembly status data to form an alignable completion-type status dataset is as follows: Through the system interfaces of the equipment control system, manufacturing execution system, and modeling system, multi-system assembly status data is collected in real time. This multi-system assembly status data includes: equipment action completion time, process start confirmation time, process completion confirmation time, component installation status change time, and component completion identifier time. Time benchmark unification processing is performed on the collected multi-system assembly status data, converting timestamps from different sources to the same time reference system. Assembly task identifiers, process identifiers, and component identifiers are obtained, and object-level association is performed on the multi-system assembly status data. The associated multi-system assembly status data undergoes validity and completeness screening, identifying and marking missing and abnormally offset data. Status types in different systems are standardized, uniformly mapping equipment action completion status, process completion confirmation status, and component completion identifier status to completion-type statuses, and mapping the corresponding completion time to completion-type status timestamps. A collaborative interaction database is established, and the pre-processed multi-system assembly status data is written into this database.

[0010] Furthermore, using the equipment action completion time in the multi-system assembly status data as a physical completion reference, the specific process of characterizing the generation lag characteristics of different system completion statuses and identifying the typical lag patterns and fluctuation characteristics of each system status generation is as follows: For the same current assembly task, the equipment action completion time is used as the physical completion benchmark, and the difference between the completion status timestamp of each system and the equipment action completion time is calculated to obtain the status confirmation lag difference of each system completion status relative to the physical completion event; Based on the historical assembly task set, the status confirmation lag difference sequence corresponding to each system is obtained; For each system, the median of the status confirmation lag difference is taken as the typical status confirmation lag value, and the product of the median absolute deviation of the status confirmation lag difference and the scale conversion factor is calculated to obtain the status confirmation lag fluctuation scale value; The typical status confirmation lag value is used as the numerator, and the sum of the status confirmation lag fluctuation scale value and the minimum constant value is used as the denominator, and the ratio is calculated. The ratio is then subjected to hyperbolic tangent function operation to obtain the status confirmation lag intensity value.

[0011] Furthermore, the specific process for forming a system-level state generation feature description is as follows: calculate the state confirmation lag strength value corresponding to each system as the system-level state time lag feature parameter; sort the state confirmation lag strength values ​​of all systems in descending order; and classify the completion class states of different systems according to the distribution of state confirmation lag strength values; and write the typical state confirmation lag value, state confirmation lag fluctuation scale value, state confirmation lag strength value, and lag strength classification label corresponding to each system into the collaborative interaction database.

[0012] Furthermore, combining system-level state generation characteristics, the specific process of performing temporal semantic correction on the completion-class states of multiple systems and constructing a time window representing the credible range of states is as follows: For the same current assembly task, obtain the completion-class state timestamp, typical state confirmation lag value, and state confirmation lag fluctuation scale value corresponding to each system, and calculate the difference between the completion-class state timestamp and the typical state confirmation lag value to obtain the corrected state timestamp; with the corrected state timestamp as the center and the state confirmation lag fluctuation scale value as the bandwidth, construct a credible time window.

[0013] Furthermore, based on the overlapping relationship of time windows between system pairs, the specific process for determining the alignment degree of the completion states of multiple systems at the temporal semantic level is as follows: Based on the set of systems participating in the final assembly task, they are combined in pairs to generate a set of system pairs, where each system pair consists of two different systems; for each system pair, the corresponding trusted time window is extracted, and based on the corresponding trusted time window, the start time and end time of the trusted time window for each system pair are determined; the later of the two trusted time window start times is taken as the start time of the overlapping interval, and the earlier of the two trusted time window end times is taken as the end time of the overlapping interval, and the end time of the overlapping interval is subtracted from the start time of the overlapping interval to obtain the trusted overlapping interval length of the system pair; the earlier of the two trusted time window start times is taken as the start time of the merging interval, and the later of the two trusted time window end times is taken as the end time of the merging interval, and the end time of the merging interval is subtracted from the start time of the merging interval to obtain the trusted merging interval length of the system pair; the trusted overlapping interval length is divided by the trusted merging interval length to obtain the trusted overlap consistency value of the cross-system state.

[0014] Furthermore, the specific process of constructing the time semantic alignment mark for cross-system states is as follows: calculate the cross-system state trusted overlap consistency value for all system pairs, sort the cross-system state trusted overlap consistency values ​​in descending order, and perform time semantic alignment hierarchical marking on each system pair according to the distribution of cross-system state trusted overlap consistency values. At the same time, output the cross-system state trusted overlap consistency value and write it together with the corresponding time semantic alignment hierarchical mark into the collaborative interaction database.

[0015] Furthermore, the specific process of analyzing the synchronization degree of multiple system completion class states on the time surface through system-level state generation feature analysis is as follows: obtain the corrected state timestamps corresponding to each system; for the current assembly task, calculate the variance of the corrected state timestamps of each system within the system set to obtain the task-level state time dispersion; simultaneously, obtain the state confirmation lag fluctuation scale value of each system, and calculate the average after summing the squares of the state confirmation lag fluctuation scale values ​​based on the system set to obtain the fluctuation scale mean; divide the task-level state time dispersion by the sum of the fluctuation scale mean and the minimum constant value, and perform an exponential function operation on the comparison value to obtain the surface synchronization risk value.

[0016] Furthermore, the specific process for analyzing the alignment degree of the completed states of multiple systems at the temporal semantic level and determining the consistency level between completed states is as follows: obtain the reliable overlap consistency value of the cross-system states for all system pairs, and add the reliable overlap consistency value of the cross-system states to the minimum constant value and take the natural logarithm. The average value of the natural logarithm results of all system pairs is calculated to obtain the cross-system logarithmic consistency value. The cross-system logarithmic consistency value is then subjected to an exponential function operation, and the semantic deviation value is obtained by subtracting the result of the exponential function operation from the constant.

[0017] Furthermore, the specific process for identifying pseudo-synchronization risk states in the final assembly task and performing reliable correction and collaborative feedback on the final assembly progress and risks based on the identification results is as follows: The surface synchronization risk value is multiplied by the semantic deviation value to obtain the task-level pseudo-synchronization judgment value, and a scale mapping process is performed on the task-level pseudo-synchronization judgment value using a logarithmic compression mapping method. When the mapped task-level pseudo-synchronization judgment value exceeds the reliable threshold, a pseudo-synchronization risk warning message is generated, and the equipment action completion time is used as the completion judgment basis for the progress assessment, causing the progress assessment result to revert to the equipment physical completion benchmark. When the mapped task-level pseudo-synchronization judgment value is less than or equal to the reliable threshold, the original completion progress assessment remains unchanged. The mapped task-level pseudo-synchronization judgment value and the judgment result are used as reliable status assessment results, fed back to each system through the system interface, and synchronously written into the collaborative interaction database.

[0018] Beneficial effects The present invention has the following beneficial effects: (1) This invention, by no longer relying solely on the consistency of timestamps of multiple system states, but instead introducing the physical completion of equipment as a reference benchmark, characterizes the lag patterns and fluctuation characteristics of different system states, fundamentally distinguishing between "time alignment" and "semantic consistency", and effectively avoiding misjudgment of the final assembly completion status due to surface time synchronization.

[0019] (2) This invention extracts typical lag characteristics and fluctuation scales of the state of each system to form a system-level state generation feature description, so that the generation reliability of different business system states has a comparable and graded quantitative basis, and enhances the engineering interpretability of multi-system state fusion analysis.

[0020] (3) This invention constructs a reliable time window and analyzes the time window overlap relationship between system pairs, quantitatively characterizes the alignment degree of the completion state of multiple systems at the time semantic level, realizes the fine identification of cross-system state consistency differences, and provides a reliable basis for subsequent collaborative judgment and risk analysis.

[0021] (4) This invention identifies the pseudo-synchronization risk state in the final assembly task by comprehensively considering the degree of synchronization of the time surface and the degree of alignment of time semantics, and automatically reverts the progress assessment to the physical completion benchmark of the equipment when the risk occurs, thereby realizing the dynamic correction of progress judgment and risk control, and improving the overall credibility and decision-making accuracy of the collaborative management and control of final assembly construction.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] Figure 1 Flowchart of the digital collaborative management and control platform and multi-system data interaction method for final assembly; Figure 2 A bar chart showing the pseudo-synchronization judgment values ​​at the task level; Figure 3 A flowchart for the overall process of identifying and coordinating the status of multiple systems in the final assembly and construction. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. As those skilled in the art will understand, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] Please see Figures 1-3 This invention provides a technical solution: a digital collaborative management and control platform for final assembly construction and a method for data interaction among multiple systems, such as... Figure 1As shown, the process includes the following steps: S1, real-time acquisition of multi-system assembly status data, and processing of the multi-system assembly status data for time benchmark unification, object association, and status type standardization to form an alignable completion class status dataset; S2, using the equipment action completion time in the multi-system assembly status data as a physical completion reference, characterizing the generation lag characteristics of different system completion class states, identifying typical lag patterns and fluctuation characteristics of each system state generation, and forming a system-level state generation feature description; S3, combining the system-level state generation features, performing time semantic correction on the multi-system completion class states, constructing a time window representing the state's credible range, and determining the degree of alignment of the multi-system completion states at the time semantic level based on the overlap relationship of the time windows between system pairs, and constructing a cross-system state time semantic alignment mark; S4, analyzing the degree of synchronization of the multi-system completion class states on the time surface through system-level state generation features, analyzing the degree of alignment of the multi-system completion states at the time semantic level, determining the consistency level between completion class states, identifying pseudo-synchronization risk states in the assembly task, and performing credible correction and collaborative feedback on the assembly progress and risks based on the identification results.

[0026] Specifically, the process of real-time acquisition of multi-system assembly status data, followed by time base unification, object association, and status type standardization processing to form an alignable completion-type status dataset, involves: Real-time acquisition of multi-system assembly status data through system interfaces connecting to the equipment control system, manufacturing execution system, and modeling system. These system interfaces include, but are not limited to, status data subscription interfaces and event feedback interfaces based on industrial Ethernet, fieldbus, or service-oriented interfaces, used to obtain status change time information recorded within the system. The multi-system assembly status data includes: equipment action completion time, process start confirmation time, process completion confirmation time, component installation status change time, and component completion time. The process involves identifying the time frame; performing unified time reference processing on the collected multi-system assembly status data based on a unified time synchronization protocol; and performing time zone correction, format conversion, and offset correction on timestamps reported by different systems to convert timestamps from different sources to the same time reference system. It also involves acquiring assembly task identifiers, process identifiers, and component identifiers, and performing object-level association on the multi-system assembly status data. This object-level association uses the assembly task identifier as the primary index and combines it with process identifiers and component identifiers to establish associations between multi-system status data, ensuring that status information describing the same assembly object in different systems can be aggregated under the same data entity. Finally, it involves processing the associated multi-system assembly status data. The status data undergoes validity and integrity screening, which includes timestamp missing detection, status field null value detection, time reversal detection, and anomaly detection exceeding reasonable time offset thresholds. Specifically: timestamp missing detection is based on field existence judgment rules; when the corresponding timestamp field in the completion-type status data is empty, not reported, or does not meet the preset time format requirements, it is determined as data with a missing timestamp; status field null value detection is based on status field integrity verification rules; when the status type field, status identifier field, and key attribute fields corresponding to the completion-type status are null or have illegal values, it is determined as a status field null value anomaly; time reversal detection is based on the same assembly task... The monotonicity constraint of state time is executed by sequentially comparing the state records of the same system and the same object on the time axis. When the timestamp of a subsequent state is earlier than the timestamp of the previous state, it is judged as a time reversal anomaly. The anomaly offset detection is executed based on the time offset threshold judgment rule. It calculates the time difference between the completion state timestamp and the completion time of the equipment action. When the time difference exceeds the reasonable time offset range obtained based on the statistics of historical assembly tasks, it is judged as abnormal offset data. The reasonable time offset range is preferably determined by the statistical distribution of the historical state confirmation lag difference of the corresponding system, for example, using the median as the allowable range, so as to avoid the failure of fixed experience thresholds under different system or different task conditions.This process identifies and marks missing and abnormally offset data; standardizes state types across different systems based on predefined state mapping rules, unifying semantically different state types that point to completion results; mapping equipment action completion status, process completion confirmation status, and component completion identifier status to completion-class status, and mapping the corresponding completion time to completion-class status timestamps; and establishing a collaborative interaction database to centrally store multi-system assembly state data after time unification, object association, and state standardization, and to support subsequent cross-system state analysis, alignment judgment, and risk assessment processes, writing the preprocessed multi-system assembly state data into the collaborative interaction database.

[0027] In this implementation plan, by interfacing with the equipment control system, manufacturing execution system, and modeling system, assembly status data from multiple systems is collected in a unified manner. The data is then standardized at the levels of time reference, object identification, and status semantics, enabling reliably aligned status information from heterogeneous sources and with significant semantic differences under the same time reference frame and object dimension. Through validity and integrity screening and unified mapping of status types, the availability, consistency, and comparability of multi-system completion status data are significantly improved, providing a stable and implementable data foundation for subsequent cross-system status alignment analysis, pseudo-synchronization identification, and credible assessment of assembly progress and risks.

[0028] Specifically, using the equipment action completion time in the multi-system assembly status data as a physical completion reference, the process of characterizing the generation lag characteristics of different system completion statuses and identifying the typical lag patterns and fluctuation characteristics of each system's status generation is as follows: For the same current assembly task, the equipment action completion time is used as the physical completion benchmark. The equipment action completion time is automatically recorded and reported by the equipment control system after executing key physical operations, objectively reflecting the completion time of the actual construction behavior. The difference between the completion status timestamps of each system and the equipment action completion time is calculated to obtain the status confirmation lag difference of each system's completion status relative to the physical completion event. This difference is used to quantify the degree of response delay of different systems to the same physical event at the status generation and confirmation level. Based on the historical assembly task set, the status confirmation lag difference sequence corresponding to each system is obtained. The historical assembly task set is preferably a task set consistent with the current task's process type to ensure the representativeness and stability of the statistical characteristics. For each system, the median of the status confirmation lag difference is taken as the typical status confirmation lag value. The number of bits is used to suppress the impact of individual abnormal tasks or human delays on the statistical results. Simultaneously, the product of the median absolute deviation of the state confirmation lag difference and the scale conversion factor is calculated to obtain the state confirmation lag fluctuation scale value. This value characterizes the dispersion and acceptable fluctuation range of the state generation lag under normal operating conditions. The scale conversion factor, derived from the scale correction relationship of the median absolute deviation in robust statistics, is used to convert the median absolute deviation from a statistical scale to a fluctuation scale consistent with the time dimension, and is set to 1.4826. The typical state confirmation lag value is used as the numerator, and the sum of the state confirmation lag fluctuation scale value and the minimum constant value is used as the denominator. The ratio is calculated, and a dimensionless relative lag index is constructed using the ratio of the numerator to the denominator, making the lag characteristics of different systems comparable. The comparison value is then subjected to hyperbolic tangent function calculation to obtain the state confirmation lag strength value. The hyperbolic tangent function is used to smooth and compress extreme lag cases, avoiding excessive amplification of the impact of individual system abnormal lags on subsequent analysis, thus forming a stable, continuous, and bounded system-level state generation characteristic quantity. The overall formula models the "typical lag level" and "normal fluctuation capability" of system state generation by ratio, uses the numerator to characterize the average response lag of the system relative to the physical completion event, and uses the denominator to characterize the acceptable lag fluctuation range of the system in historical operation, thus forming a dimensionless relative index that reflects the stability and reliability of system state generation.

[0029] The specific formula for the state confirmation hysteresis strength value is as follows: ; In the formula, This represents the state confirmation lag strength value. It compares the typical lag degree of system completion state with its own historical fluctuation scale after normalization, and then performs nonlinear compression to obtain a stable, bounded, and orderable system-level state confirmation lag strength characterization quantity. This quantity is used to characterize the lag reliability of different system completion states relative to the physical completion of equipment. The superscript indicates... Indicates a system index; This represents the status confirmation lag difference, reflecting the time offset of the completed status relative to the physical completion. This represents the typical lag value for status confirmation, used to characterize the typical lag level of the system; It represents the median absolute deviation of the state confirmation lag difference, and is used to characterize the historical fluctuation scale of the system's lag behavior. This represents the scale conversion factor, which adjusts the median absolute deviation to a scale consistent with the median in terms of size and statistical significance. The preferred value is 1.4826. This represents a very small constant value, used to prevent the denominator from being zero and to improve computational stability. A preferred value is [value to be filled in]. .

[0030] In this implementation plan, by using the equipment action completion time as a unified physical reference, statistical modeling and intensity characterization are performed on the generation lag of multiple system completion states. This technical solution can objectively reflect the response differences of different systems at the state confirmation level, form stable and comparable system-level state generation characteristics, effectively reduce the interference caused by abnormal data and human intervention, and provide a reliable basis for subsequent cross-system time semantic correction, state alignment judgment and pseudo-synchronization risk identification, thereby improving the accuracy and reliability of the final assembly construction progress assessment and collaborative management results.

[0031] Specifically, the process for forming a system-level state generation feature description is as follows: Calculate the state confirmation lag strength value for each system. This value is obtained by calculating the aforementioned typical state confirmation lag value and state confirmation lag fluctuation scale value. It comprehensively reflects the overall lag degree and stability characteristics of the system relative to the physical completion event during state generation, serving as a system-level state time lag feature parameter to characterize the timeliness differences in response to completion status among different business systems during the final assembly and construction process. Sort the state confirmation lag strength values ​​of all systems in descending order. The sorting process is based on a unified comparison rule, used to establish the relative positional relationship between systems in the state generation lag dimension. Then, classify the completion status of different systems according to the distribution of state confirmation lag strength values, grading the lag strength. The lag strength grading is generated based on the statistical distribution of the state confirmation lag strength values ​​of each system. The 30th and 70th quantiles of the state confirmation lag strength values ​​are preferably calculated, and the system is divided into low-... The system is categorized into three lag strength levels: low, medium, and high. When the lag strength value is below the 30th quantile, it is considered to be in the low lag strength range. This indicates that the generation of system completion statuses is close to the physical completion event and fluctuates little, providing high time reliability and serving as a primary reference for progress assessment and collaborative decision-making. When the lag strength value is between the 30th and 70th quantiles, it is considered to be in the medium lag strength range. This indicates that the system completion status lags or fluctuates relative to the physical completion event, providing valuable information for progress analysis, but requiring comprehensive judgment in conjunction with other system statuses. When the lag strength value is above the 70th quantile, it is considered to be in the high lag strength range. This indicates that the generation of system completion statuses lags significantly or is unstable relative to the physical completion event, and its status information may be affected by factors such as manual confirmation, model updates, or process delays. This should be given special attention or used with caution in progress assessment and collaborative decision-making. This allows for the differentiation of system status generation characteristics based on lag strength, including low, medium, and high lag levels. The typical lag value, lag fluctuation scale value, lag strength value, and lag strength classification mark of each system are written into the collaborative interaction database to help staff understand the credibility of the completion status of different business systems. When a system is marked as having a high lag strength level, it indicates that the system's completion status is significantly lagging behind the physical completion event, and its status information should be given special attention or careful reference in progress assessment and collaborative decision-making.

[0032] In this implementation plan, by uniformly calculating, sorting, and classifying the lag intensity of multi-system status confirmation, this technical solution can objectively and statistically characterize the differences in response timeliness of different business systems during the status generation process, forming a stable and comparable description of system-level status generation characteristics. This effectively distinguishes the status generation characteristics of systems with different degrees of lag, providing a clear and implementable classification basis for subsequent cross-system status alignment, weight adjustment, and pseudo-synchronization risk analysis, thereby improving the accuracy and reliability of overall assembly construction collaborative management and progress assessment.

[0033] Specifically, the process of constructing a time window representing the credible range of states by combining system-level state generation characteristics and performing temporal semantic correction on multi-system completion states is as follows: For the same current assembly task, the completion state timestamp, typical state confirmation lag value, and state confirmation lag fluctuation scale value corresponding to each system are obtained. Among them, the completion state timestamp is the system-level completion time obtained after the aforementioned time benchmark unification and state type specification processing, and the typical state confirmation lag value and state confirmation lag fluctuation scale value are system-level state generation characteristic parameters obtained based on historical assembly task statistics; and the difference between the completion state timestamp and the typical state confirmation lag value is calculated to obtain the corrected state timestamp; through the difference calculation, the systemic time offset introduced by the difference in state generation mechanism of different systems is compensated, so that the corrected state timestamp is closer to the physical completion time corresponding to the completion of equipment actions in terms of temporal semantics, thereby realizing the temporal semantic correction of cross-system completion states. A reliable time window is constructed with the corrected state timestamp as the center and the state confirmation lag fluctuation scale value as the bandwidth. The reliable time window is used to describe the reasonable fluctuation range of the system's completed state in the time dimension. The upper and lower boundaries of the window are determined by the state confirmation lag fluctuation scale values ​​extended forward and backward from the corrected state timestamp, respectively, and are used to characterize the reliable time interval of the system's completed state under the condition of considering historical fluctuation characteristics. By constructing reliable time windows for different systems, subsequent cross-system state alignment and consistency analysis can be carried out within a unified temporal semantic framework, avoiding misjudging accidental time offsets or inherent system lags as state inconsistencies.

[0034] In this implementation scheme, semantic correction of the completion state time is performed by constructing system-level state generation features, and a reliable time window is constructed based on historical lag fluctuation characteristics. This technical solution can eliminate the inherent time offset of different business systems, while reasonably characterizing the reliable range of the completion state in the time dimension, providing a stable and reliable time basis for subsequent cross-system state alignment and consistency analysis, thereby effectively reducing the risk of misjudgment and improving the accuracy and reliability of the final assembly construction progress and state assessment results.

[0035] Specifically, the process of determining the alignment of the completion states of multiple systems at the temporal semantic level based on the overlapping relationship of time windows between system pairs is as follows: Based on the set of systems participating in the final assembly task, pairs are combined to generate a set of system pairs, where each system pair consists of two different systems. The system set comprises business systems that actually generate completion-type state data in the current final assembly task. The system pair set is used to characterize the temporal semantic relationship between any two systems in their completion states, avoiding bias caused by judging state consistency solely from the perspective of a single system. For each system pair, a corresponding reliable time window is extracted. Based on the corresponding reliable time window, the start and end times of the reliable time windows for the two systems in the system pair are determined. The reliable time window is a time interval constructed based on the corrected state timestamp and the state confirmation lag fluctuation scale value, used to characterize the reliable existence range of the system completion state in the time dimension. The later of the two trusted time window start times is taken as the start time of the overlapping interval, and the earlier of the two trusted time window end times is taken as the end time of the overlapping interval. Subtracting the start time from the end time of the overlapping interval yields the trusted overlapping interval length for the system pair. Using this method, the trusted overlapping interval length is positive only when the trusted time windows of the two systems intersect on the time axis, reflecting the degree of synchronization of the completed states of the two systems within the trusted time range. Similarly, the earlier of the two trusted time window start times is taken as the start time of the merging interval, and the later of the two trusted time window end times is taken as the end time of the merging interval. Subtracting the start time from the end time of the merging interval yields the trusted merging interval length for the system pair. The trusted merging interval length characterizes the overall coverage of the completed states of the two systems in the time dimension, serving as a reference scale for normalizing the degree of overlap and avoiding unfair influences from different time window widths on the alignment judgment results. Dividing the length of the trusted overlap interval by the length of the trusted merge interval yields the trusted overlap consistency value across system states. The trusted overlap consistency value ranges from 0 to 1. A larger value indicates a higher degree of alignment between the completed states of the two systems at the temporal semantic level, while a smaller value indicates a more significant temporal semantic deviation. The trusted overlap consistency value across system states can be directly used in subsequent system classification, consistency analysis, and pseudo-synchronization risk assessment processes.

[0036] The specific formula for the cross-system state trusted overlap consistency value is as follows: ; In the formula, This represents the cross-system state credibility overlap and consistency value, used to quantify the degree of credibility consistency of the completion class states of two different systems under the same final assembly task at the temporal semantic level, where the superscript... , Indicates a system index; Indicates system to system The reliable time window represents the system The reliable interval representation of its completion-type state in the time dimension reflects the range that may truly correspond to the physical completion event in time; Indicates system to system The reliable time window represents the system A reliable interval representation of its completed state in the time dimension.

[0037] In this implementation scheme, the system completion status is aligned pairwise by using the intersection and union relationship based on a reliable time window. This technical solution can objectively quantify the alignment degree between the completion status of different systems under a unified time semantic framework, effectively distinguish between true synchronization and time semantic deviation, avoid misjudgment caused by comparison of a single timestamp, and provide a reliable basis for subsequent cross-system consistency analysis, hierarchical marking and pseudo-synchronization risk identification, thereby improving the accuracy and reliability of the collaborative management and control of final assembly and construction.

[0038] Specifically, the process of constructing the temporal semantic alignment markers for cross-system states is as follows: Calculate the cross-system state credible overlap consistency value for all system pairs, and sort these values ​​in descending order. The credible overlap consistency value characterizes the degree of temporal semantic alignment between the completion states of the two systems within a credible time range. The descending order is used to establish the relative ranking relationship of different system pairs in the temporal semantic consistency dimension, facilitating subsequent grading and judgment operations. Furthermore, based on the distribution of the cross-system state credible overlap consistency value, each system pair is grading and marking with temporal semantic alignment. This grading distinguishes between system pairs with high temporal semantic consistency, basic temporal semantic consistency, and significant temporal semantic deviation. The grading is based on the statistical distribution of the cross-system state credible overlap consistency value, and the optimal calculation method is used to determine the cross-system state credible overlap consistency value. Different alignment levels are generated based on the 30th and 70th quantiles of the cross-system state trusted overlap consistency value. When the cross-system state trusted overlap consistency value is within the top 30% quantile of the consistency value distribution of all system pairs (i.e., not lower than the 70th quantile), it is marked as "high temporal semantic alignment level," indicating that the completion states of the corresponding system pair highly overlap within the trusted time range, and the temporal semantic consistency is high. When the cross-system state trusted overlap consistency value is between the 30th and 70th quantiles, it is marked as "medium temporal semantic alignment level," indicating that the completion states of the corresponding system pair have some overlap within the trusted time range, but there is still an acceptable temporal semantic deviation. When the cross-system state trusted overlap consistency value is lower than the 30th quantile, it is marked as "low temporal semantic alignment level," indicating that the completion states of the corresponding system pair have little overlap within the trusted time range, and the temporal semantic deviation is significant. This achieves automated labeling of the degree of temporal semantic alignment by the system without introducing human experience judgment. Simultaneously, the cross-system state trusted overlap consistency value is output and written into the collaborative interaction database along with the corresponding temporal semantic alignment level label. Among them, the cross-system state reliability overlap consistency value and the corresponding time semantic alignment grade mark can be used to assist in the selection and collaborative decision-making of multi-system states during the final assembly and construction process. Specifically, when multiple systems report completion status at the same time, the status information corresponding to the system pair with the higher time semantic alignment grade is given priority as the main basis for progress assessment and status summary; for system pairs with lower time semantic alignment grade, their completion status can be used as reference information or the basis for triggering further verification, so as to avoid misjudgment of progress or omission of risks due to time semantic deviation; at the same time, the cross-system state reliability overlap consistency value can also be used to identify system combinations that have been in a low alignment grade for a long time, providing a basis for subsequent system interface optimization, process adjustment or data usage strategy adjustment.

[0039] In this implementation plan, by sorting and classifying the reliable overlap and consistency values ​​of cross-system states, this technical solution can objectively characterize the degree of alignment of the completion states of different systems at the temporal semantic level without relying on human experience thresholds. It can clearly distinguish between system pairs with high temporal semantic consistency and obvious deviations, providing quantifiable and traceable basis for subsequent pseudo-synchronization identification, progress reliability assessment and collaborative management decisions, thereby improving the accuracy and automation level of multi-system collaborative analysis and risk assessment in final assembly and construction.

[0040] Specifically, the process of analyzing the synchronization degree of multiple system completion class states on the time surface through system-level state generation feature analysis is as follows: Obtain the corrected state timestamps corresponding to each system. The corrected state timestamps are the time results obtained after correcting the completion class state timestamps based on the typical lag value of state confirmation during the aforementioned time semantic correction process. This is used to eliminate the influence of the inherent state generation delay of the system on the time alignment analysis. For the current final assembly task, within the system set, calculate the variance of the corrected state timestamps of each system to obtain the task-level state time dispersion. Among them, the task-level state time dispersion is used to characterize the overall dispersion of the completion class states of different systems in the time dimension under the same final assembly task. The larger the value, the more dispersed the system completion states are on the time surface, and the lower the synchronization. Simultaneously, the status confirmation lag fluctuation scale value of each system is obtained. This value is a system-level parameter based on historical assembly task statistics, used to characterize the typical time fluctuation range of the system during the status confirmation process. The average of the squared values ​​of the status confirmation lag fluctuation scale values ​​across the system set is then calculated to obtain the fluctuation scale mean. By squaring and averaging the fluctuation scale values, the time fluctuation capabilities of different systems are comprehensively characterized under the same dimension, serving as a reference scale for assessing whether the current task's time dispersion exceeds the system's normal fluctuation range. The task-level state time dispersion is divided by the sum of the fluctuation scale mean and the minimum constant value, and an exponential function operation is performed on the comparison value to obtain the surface synchronization risk value. The minimum constant value is used to prevent numerical instability caused by a zero or excessively small denominator. The exponential function operation amplifies the risk difference when the task-level state time dispersion is significantly higher than the system's normal fluctuation scale, making the surface synchronization risk value more sensitive to abnormal synchronization states. This reflects the risk level where multiple systems appear synchronized on the time surface but actually deviate.

[0041] In this implementation plan, by combining system-level state generation characteristics to normalize the time dispersion of multiple system completion states, this technical solution can objectively characterize the synchronization risk of multiple system states on the time surface based on the distinction between normal time fluctuations and abnormal dispersion. This avoids misjudging true synchronization based solely on surface time proximity, and provides a reliable, sensitive, and stable quantitative basis for identifying pseudo-synchronous states and subsequent progress and risk assessment, thereby improving the accuracy and reliability of multi-system collaborative analysis in the final assembly and construction.

[0042] Specifically, the process of analyzing the alignment of the completion states of multiple systems at the temporal semantic level and determining the consistency level between completion state pairs is as follows: Obtain the cross-system state credible overlap consistency value of all system pairs. The cross-system state credible overlap consistency value is a system pair-level index calculated based on the overlap relationship of the credible time window. It is used to characterize the alignment of any two system completion state pairs at the temporal semantic level. Its value ranges from 0 to 1. The cross-system state credible overlap consistency value is added to the minimum constant value and the natural logarithm is taken. The minimum constant value is used to avoid the problem of undefined logarithm or numerical instability when the cross-system state credible overlap consistency value is close to zero. At the same time, the logarithmic operation is used to compress the distribution range of consistency values, making the consistency differences between different system pairs more comparable at the numerical level. The cross-system logarithmic consistency value is obtained by summing the natural logarithms of all system pairs and averaging them. By summarizing and averaging the system pair-level consistency results, a task-level indicator reflecting the overall temporal semantic consistency level of the multi-system completion status under the current final assembly task is formed, thereby avoiding the excessive influence of single system pair anomalies on the overall judgment result. An exponential function operation is performed on the cross-system logarithmic consistency value, and the semantic deviation value is obtained by subtracting the result of the exponential function operation from a constant. The exponential function operation is used to map the average consistency result in the logarithmic domain back to the positive range, so that the semantic deviation value maintains a monotonic correspondence with the original consistency concept. The constant subtraction of the mapping result is used to convert the consistency degree into the deviation degree, so that the larger the semantic deviation value, the higher the overall deviation of the multi-system completion status at the temporal semantic level, which is convenient for direct use in subsequent pseudo-synchronization risk assessment and progress credibility evaluation.

[0043] In this implementation scheme, by performing logarithmic compression, task-level aggregation, and deviation mapping on the reliable overlap consistency of cross-system states, this technical solution can stably characterize the overall consistency level of the completion states of multiple systems at the temporal semantic level, reduce the interference of individual systems on abnormal alignment results, and make the degree of semantic deviation have a clear and monotonic interpretation meaning. This provides a reliable and comparable quantitative basis for subsequent pseudo-synchronization risk identification and progress credibility assessment, thereby improving the accuracy and robustness of multi-system collaborative analysis.

[0044] Specifically, the process of identifying pseudo-synchronization risk states in the final assembly task and performing reliable correction and collaborative feedback on the final assembly progress and risks based on the identification results is as follows: The surface synchronization risk value and the semantic deviation value are multiplied to obtain the task-level pseudo-synchronization judgment value. The surface synchronization risk value reflects the degree of abnormal synchronization of the completion states of multiple systems on the temporal surface, while the semantic deviation value reflects the overall deviation of the completion states of multiple systems at the temporal semantic level. Multiplying these two values ​​allows for a joint characterization of situations where "they appear to be synchronized but are semantically inconsistent," ensuring that the task-level pseudo-synchronization judgment value remains sensitive to both temporal dispersion anomalies and insufficient semantic alignment. Furthermore, a logarithmic compression mapping method is used to perform scaling processing on the task-level pseudo-synchronization judgment value. Specifically, the logarithmic compression mapping method is implemented by adding the original task-level pseudo-synchronization judgment value to a constant and then taking the natural logarithm. This compresses the dynamic range of the judgment value, avoiding the unstable impact of extreme values ​​on subsequent threshold judgments, while maintaining the monotonicity of the judgment value as the risk level changes, facilitating unified interpretation under different task scales and system numbers. When the mapped task-level pseudo-synchronization judgment value exceeds the trust threshold, a pseudo-synchronization risk warning message is generated, and the equipment action completion time is used as the completion judgment basis for progress assessment, causing the progress assessment result to revert to the equipment physical completion baseline. By prioritizing the equipment action completion time as the progress judgment basis when pseudo-synchronization risk occurs, interference from factors such as manual confirmation, model updates, or process delays can be effectively avoided, thereby improving the authenticity and reliability of progress statistics. When the mapped task-level pseudo-synchronization judgment value is less than or equal to the trust threshold, the original completion progress assessment remains unchanged to avoid frequent progress corrections when the system status is consistent or the risk is low, ensuring the stability and continuity of system operation. The trust threshold can be statistically obtained based on the mapped task-level pseudo-synchronization judgment values ​​corresponding to historical assembly tasks, used to distinguish between acceptable normal synchronization fluctuations and pseudo-synchronization risk states requiring intervention. The mapped task-level pseudo-synchronization judgment value and judgment result are used as trust status assessment results, fed back to each system through the system interface, and synchronously written to the collaborative interaction database to support unified perception of the current task status consistency level among multiple systems, and to provide a data foundation for subsequent task analysis, risk tracing, and collaborative strategy optimization.

[0045] The specific formula for the task-level pseudo-synchronization determination value is as follows: ; In the formula, This represents the task-level pseudo-synchronization judgment value, which is used to comprehensively determine the degree of pseudo-synchronization risk at the task level. When the completion status of multiple systems appears to be synchronized on the time surface, but lacks consistency at the time semantic level, the task should be judged to have a high risk of pseudo-synchronization. This indicates the corrected status timestamp, which is the completion time after the status confirmation lag, and is used to eliminate the influence of the inherent generation lag of different systems. It represents the time dispersion of task-level states, reflecting the overall dispersion of the corrected completion time of each system; This represents the lag fluctuation scale value for state confirmation, the natural fluctuation scale for system state generation lag, and describes the range of time offset that the system can tolerate under normal conditions. Indicates the number of systems. Represents a system set; It represents the trusted overlap and consistency value of cross-system states, reflecting the degree of trusted consistency of completed state at the temporal semantic level; Indicates the system's relationship to a set. Indicates the system's quantity; To represent extremely small constant values, to prevent the denominator from being zero or the logarithmic operation from being undefined, the preferred value is [value]. .

[0046] In this embodiment, Table 1 is a data table of task-level pseudo-synchronization judgment values. The table details the task-level state time dispersion, mean fluctuation scale, cross-system logarithmic consistency value, and the task-level pseudo-synchronization judgment value after mapping for each of the five assembly tasks. Specifically, the task-level state time dispersion for assembly task 1 is 1.5, the mean fluctuation scale is 4.2, the cross-system logarithmic consistency value is -0.18, and the mapped task-level pseudo-synchronization judgment value is 0.211; the task-level state time dispersion for assembly task 2 is 3.2, the mean fluctuation scale is 4.2, the cross-system logarithmic consistency value is -0.30, and the mapped task-level pseudo-synchronization judgment value is 0.441; the task-level pseudo-synchronization judgment value for assembly task 3... The corresponding task-level state time dispersion is 6.8, the mean fluctuation scale is 4.1, the cross-system logarithmic consistency value is -0.55, and the task-level pseudo-synchronization judgment value after mapping is 1.170; the task-level state time dispersion corresponding to General Assembly Task 4 is 11.5, the mean fluctuation scale is 4.3, the cross-system logarithmic consistency value is -0.80, and the task-level pseudo-synchronization judgment value after mapping is 2.196; the task-level state time dispersion corresponding to General Assembly Task 5 is 28.0, the mean fluctuation scale is 4.0, the cross-system logarithmic consistency value is -1.05, and the task-level pseudo-synchronization judgment value after mapping is 6.569.

[0047] Table 1 Task-level Pseudo-synchronization Judgment Values ​​Data Table like Figure 2The figure shows a bar chart of task-level pseudo-synchronization judgment values. In the chart, the horizontal axis represents the assembly task number, and the vertical axis represents the task-level pseudo-synchronization judgment value; the bars represent the judgment results of each task, and the dashed lines represent the confidence threshold, used to distinguish between normal collaborative states and task states with pseudo-synchronization risk. Combining Tables 1 and 2, it can be seen that the judgment values ​​of tasks 1 and 2 are significantly lower than the confidence threshold, indicating that the completion status of the multi-system maintains good consistency at the temporal surface and temporal semantic levels; task 3 is close to the threshold and is in the risk critical zone; tasks 4 and 5 are significantly higher than the threshold, indicating a significant risk of pseudo-synchronization. As the task number increases, the task-level pseudo-synchronization judgment value generally rises, consistent with the trend of increasing state temporal dispersion and decreasing cross-system consistency in the data table.

[0048] like Figure 3 The diagram shows the overall process flow for pseudo-synchronization identification and collaborative management of multiple system states in the final assembly construction. It illustrates the overall processing flow of multi-system state data from acquisition and analysis to risk feedback within the digital collaborative management platform for final assembly construction. First, final assembly state data from equipment control systems, manufacturing execution systems, and modeling systems are uniformly acquired, and time benchmark unification, state standardization, object-level association, and validity and completeness screening are completed to form an alignable dataset of completed state types. Then, using the equipment action completion time as the physical completion benchmark, the confirmation lag difference of each system's completed state type is extracted, and typical lag and fluctuation characteristics are constructed based on historical tasks to form system-level state generation characteristics. On this basis, time semantic correction is performed on completed state types, and a reliable time window is constructed. By assessing the overlap relationship of reliable time windows between systems, the degree of cross-system time semantic alignment is evaluated, and alignment grading labels are generated, further forming a semantic deviation assessment result. Furthermore, discrete analysis is performed on the corrected state timestamps to obtain the surface synchronization risk assessment result. The surface synchronization risk assessment result and the semantic deviation assessment result are integrated to form a comprehensive task-level pseudo-synchronization judgment. When the result of a task-level pseudo-synchronization judgment exceeds the trust threshold, a pseudo-synchronization risk warning is triggered, and the progress assessment result is reverted to a judgment method based on the physical completion of equipment. When the trust threshold is not exceeded, the original progress assessment result remains unchanged. Finally, the relevant judgment results are fed back to each business system through a collaborative management and control mechanism and the results are stored, providing reliable data support for subsequent progress management, risk control, and decision analysis.

[0049] In this implementation plan, the pseudo-synchronization state is jointly determined by integrating the risk of surface synchronization and the degree of temporal semantic deviation. When the risk is triggered, the progress is dynamically reverted to the physical completion benchmark of the equipment for correction. This technical solution can effectively avoid the problem of surface time consistency masking the deviation of the real state and realize the reliable correction of the final assembly progress assessment and risk judgment. At the same time, through a unified feedback and data accumulation mechanism, a multi-system collaborative perception and closed-loop management are formed, thereby significantly improving the accuracy, stability and reliability of digital collaborative management and control of final assembly construction.

[0050] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0051] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. As those skilled in the art will understand, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A digital collaborative management and control platform for final assembly construction and a multi-system data interaction method, characterized in that, Includes the following steps: S1 collects multi-system assembly status data in real time, performs time base unification, object association and status type standardization processing on the multi-system assembly status data, and forms an alignable completion class status dataset. S2 uses the equipment action completion time in the multi-system assembly status data as a physical completion reference to characterize the generation lag characteristics of different system completion statuses, identify the typical lag patterns and fluctuation characteristics of each system status generation, and form a system-level status generation feature description. S3, combining system-level state generation features, performs temporal semantic correction on multi-system completion state, constructs a time window representing the credible range of the state, and determines the degree of alignment of multi-system completion states at the temporal semantic level based on the overlapping relationship of time windows between system pairs, and constructs a time semantic alignment mark for cross-system states. S4 analyzes the synchronization degree of multiple system completion state on the time surface through system-level state generation feature analysis, analyzes the alignment degree of multiple system completion states at the time semantic level, determines the consistency level between completion state, identifies pseudo-synchronization risk states in the final assembly task, and performs reliable correction and collaborative feedback on the final assembly progress and risks based on the identification results.

2. The digital collaborative management and control platform for final assembly construction and the multi-system data interaction method according to claim 1, characterized in that, The specific process of acquiring multi-system assembly status data in real time, and performing time base unification, object association, and status type standardization on the multi-system assembly status data to form an alignable completion class status dataset is as follows: By connecting to the system interfaces of the equipment control system, manufacturing execution system and modeling system, the system collects multi-system assembly status data in real time. The multi-system assembly status data includes: equipment action completion time, process start confirmation time, process completion confirmation time, component installation status change time and component completion identification time. The collected multi-system assembly status data undergoes unified time reference processing, converting timestamps from different sources to the same time reference system; assembly task identifiers, process identifiers, and component identifiers are obtained, and object-level association is performed on the multi-system assembly status data; the associated multi-system assembly status data undergoes validity and completeness screening, identifying and marking missing and abnormally offset data; and the status types in different systems are standardized, uniformly mapping equipment action completion status, process completion confirmation status, and component completion identifier status to completion-type statuses, and mapping the corresponding completion time to completion-type status timestamps; a collaborative interaction database is established, and the preprocessed multi-system assembly status data is written into the collaborative interaction database.

3. The digital collaborative management and control platform for final assembly construction and the multi-system data interaction method according to claim 1, characterized in that, The specific process of using the equipment action completion time in the multi-system assembly status data as a physical completion reference to characterize the generation lag characteristics of different system completion statuses and identify the typical lag patterns and fluctuation characteristics of each system status generation is as follows: For the same final assembly task, the equipment action completion time is used as the physical completion benchmark. By calculating the difference between the completion status timestamp of each system and the equipment action completion time, the status confirmation lag difference of each system's completion status relative to the physical completion event is obtained. Based on the historical assembly task set, the status confirmation lag difference sequence corresponding to each system is obtained. For each system, the median of the status confirmation lag difference is taken as the typical lag value of status confirmation. At the same time, the product of the median absolute deviation of the status confirmation lag difference and the scale conversion factor is calculated to obtain the status confirmation lag fluctuation scale value. The typical lag value for state confirmation is used as the numerator, and the sum of the lag fluctuation scale value and the minimum constant value for state confirmation is used as the denominator. The ratio is calculated, and the hyperbolic tangent function is applied to the ratio to obtain the lag strength value for state confirmation.

4. The digital collaborative management and control platform for final assembly construction and the multi-system data interaction method according to claim 1, characterized in that, The specific process for forming a system-level state generation feature description is as follows: Calculate the state confirmation lag strength value corresponding to each system, and use it as the system-level state time lag characteristic parameter. Sort the state confirmation lag strength values ​​of all systems in descending order, and classify the lag strength of the completion class state of different systems according to the distribution of state confirmation lag strength values. Write the typical state confirmation lag value, state confirmation lag fluctuation scale value, state confirmation lag strength value and lag strength classification label corresponding to each system into the collaborative interaction database.

5. The digital collaborative management and control platform for final assembly construction and the multi-system data interaction method according to claim 1, characterized in that, The specific process of combining system-level state generation features to perform temporal semantic correction on the completed states of multiple systems and constructing a time window representing the credible range of the state is as follows: For the same final assembly task, obtain the completion status timestamp, typical status confirmation lag value, and status confirmation lag fluctuation scale value corresponding to each system, and calculate the difference between the completion status timestamp and the typical status confirmation lag value to obtain the corrected status timestamp. A reliable time window is constructed with the corrected state timestamp as the center and the state confirmation lag fluctuation scale value as the bandwidth.

6. The digital collaborative management and control platform for final assembly construction and the multi-system data interaction method according to claim 1, characterized in that, The specific process for determining the alignment degree of the completion states of multiple systems at the temporal semantic level based on the overlap relationship of time windows between system pairs is as follows: Based on the set of systems participating in the final assembly task, they are combined in pairs to generate a set of system pairs, where each system pair consists of two different systems. For each system pair, the corresponding trusted time window is extracted. Based on the corresponding trusted time window, the start time and end time of the trusted time window for the two systems in the system pair are determined respectively. The later of the two trusted time window start times is taken as the start time of the overlapping interval, and the earlier of the two trusted time window end times is taken as the end time of the overlapping interval. The length of the trusted overlapping interval of the system pair is obtained by subtracting the start time of the overlapping interval from the end time of the overlapping interval. The earlier of the two trusted time window start times is taken as the start time of the merge interval, and the later of the two trusted time window end times is taken as the end time of the merge interval. The merge interval start time is then subtracted from the merge interval end time to obtain the trusted merge interval length of the system pair. Divide the length of the trusted overlap interval by the length of the trusted merge interval to obtain the trusted overlap consistency value across system states.

7. The digital collaborative management and control platform for final assembly construction and the multi-system data interaction method according to claim 1, characterized in that, The specific process for constructing the time semantic alignment mark across system states is as follows: Calculate the cross-system state credibility overlap consistency value for all system pairs, sort the cross-system state credibility overlap consistency values ​​in descending order, and perform time semantic alignment hierarchical labeling on each system pair according to the distribution of cross-system state credibility overlap consistency values. At the same time, output the cross-system state credibility overlap consistency value and write it together with the corresponding time semantic alignment hierarchical label into the collaborative interaction database.

8. The digital collaborative management and control platform for final assembly construction and the multi-system data interaction method according to claim 1, characterized in that, The specific process of analyzing the degree of synchronization of multiple system-complete state classes on the time surface through system-level state generation feature analysis is as follows: Obtain the corrected state timestamps for each system. For the current assembly task, calculate the variance of the corrected state timestamps for each system within the system set to obtain the task-level state time dispersion. Meanwhile, the state confirmation lag fluctuation scale value of each system is obtained, and the average value of the sum of the squares of the state confirmation lag fluctuation scale values ​​is obtained based on the system set. The surface synchronization risk value is obtained by dividing the task-level state time dispersion by the sum of the mean of the fluctuation scale and the minimum constant value, and then performing an exponential function operation on the comparison value.

9. The digital collaborative management and control platform for final assembly construction and the multi-system data interaction method according to claim 1, characterized in that, The specific process for analyzing the alignment of the completion states of multiple systems at the temporal semantic level and determining the consistency level between completion-class states is as follows: Obtain the cross-system state confidence overlap consistency value for all system pairs, and add the cross-system state confidence overlap consistency value to the minimum constant value and take the natural logarithm. Accumulate the natural logarithm results for all system pairs and calculate the average to obtain the cross-system logarithmic consistency value. Perform an exponential function operation on the cross-system logarithmic consistency value, and subtract the result of the exponential function operation from the constant to obtain the semantic deviation value.

10. The digital collaborative management and control platform for final assembly construction and the multi-system data interaction method according to claim 1, characterized in that, The specific process of identifying pseudo-synchronization risk states in the final assembly task and performing reliable correction and collaborative feedback on the final assembly progress and risks based on the identification results is as follows: The task-level pseudo-synchronization judgment value is obtained by multiplying the surface synchronization risk value and the semantic deviation value. The task-level pseudo-synchronization judgment value is then subjected to scale mapping processing by the logarithmic compression mapping method. When the task-level pseudo-synchronization judgment value after mapping exceeds the confidence threshold, a pseudo-synchronization risk warning message is generated. The equipment action completion time is used as the basis for the completion judgment of the progress assessment, so that the progress assessment result is backed up to the equipment physical completion benchmark. When the task-level pseudo-synchronization judgment value after mapping is less than or equal to the trust threshold, the original completion progress assessment remains unchanged. The task-level pseudo-synchronization judgment value and judgment result after mapping are used as the trusted state evaluation result, fed back to each system through the system interface, and synchronously written into the collaborative interaction database.

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