A full-process tracing system for a radio frequency coaxial connector production line

Abstract

The present application relates to the technical field of production process data processing and traceability, and discloses a full-process traceability system for a radio frequency coaxial connector production line, which pre-sets a golden rhythm template containing a physically feasible time constraint, uses a verification gateway to perform prepositive access examination on a collected time stamp sequence, and then generates an individual rhythm fingerprint for the sequence that passes the examination and calculates a difference degree RDS, and a set of unknown abnormality identification rules for rejecting and isolating low-confidence matching results are also established, the present application improves traceability information from discrete static event logs to a quantitative representation of dynamic stability of the production process, so that managers can perceive and distinguish the internal process quality differences between different qualified products, and provide decision basis for diagnosing known deviation patterns and discovering unknown abnormality risks.

Description

TECHNICAL FIELD

[0001] The present application relates to a full-process tracing system of a radio frequency coaxial connector production line, belonging to the technical field of production process data processing and tracing. BACKGROUND

[0002] In the management of a radio frequency coaxial connector production line, a manufacturing execution system (MES) is generally used to trace the whole process of production. Such a system provides a basic data log for production control by recording discrete events of products at various preset checkpoints. This constitutes the current common way of data processing in this field.

[0003] However, for application scenarios with higher requirements for long-term reliability of products, the limitations of the above-mentioned data management method centered on discrete event recording also become apparent. Specifically, the system only records whether the parameters of products at the checkpoint time are qualified, but cannot reflect the dynamic stability of products during the process between checkpoints. For example, two product event logs may be exactly the same, but one may have experienced a smooth production process, while the other may have encountered a small mechanical disturbance or a short material waiting time without triggering an alarm. The existing system will identify the production history of these two process differences as identical, resulting in the technical problem of process false equivalence.

[0004] To obtain more detailed process information, one approach is to increase the deployment density of physical sensors, which will increase the hardware overhead and data processing load of the system. Another approach is to apply complex statistical algorithm models for defect prediction, but the conclusions often lack intuitive process correspondence, making it difficult to serve as direct process supervision basis. Analysis shows that the existing data processing method has the following shortcomings: in the conversion of tracing information from continuous production process to discrete event recording, the dynamic rhythm information representing process stability is lost; at the management level, there is a lack of an effective method that can use existing data to directly and quantitatively represent the process quality differences between different qualified products; therefore, how to establish a data processing method based on process timestamps, by establishing a standardized process rhythm benchmark and calculating the difference between the actual process rhythm of any production object and the benchmark, to realize the quantitative representation of the dynamic stability of the production process and provide decision-making basis for managers to distinguish the deep process quality differences between different qualified products, has become a technical problem to be solved by the present application. SUMMARY

[0005] The present application provides a full-process tracing system of a radio frequency coaxial connector production line, which mainly aims to solve the problem that the existing tracing system cannot quantitatively and trace the dynamic rhythm characteristics of the production process due to information model defects, thereby failing to effectively identify the deep process quality differences between qualified products.

[0006] In order to achieve the above object, the application provides a full-process tracing system of a radio frequency coaxial connector production line, which comprises:

[0007] A template establishing module is configured to generate a golden rhythm template containing a plurality of process time increments based on timestamp data of a reference batch, and preset a rationality boundary composed of a minimum physically feasible time MPT and a maximum physically feasible time XPT for each process time increment in the golden rhythm template;

[0008] A verification gateway module is configured to collect a timestamp sequence of a traced object, calculate a measured value of each process time increment based on the timestamp sequence, and compare the measured value of each process time increment with its corresponding rationality boundary, wherein only when the measured values of all process time increments in the timestamp sequence are within their respective corresponding rationality boundaries, the timestamp sequence is determined as a verified timestamp sequence.

[0009] A fingerprint and difference degree calculation module is configured to generate an individual rhythm fingerprint from the verified timestamp sequence and calculate a difference degree RDS between the individual rhythm fingerprint and the golden rhythm template only when the timestamp sequence is determined as the verified timestamp sequence by the verification gateway module.

[0010] A data association module is configured to store the difference degree RDS as tracing metadata in association with a unique identity of the traced object.

[0011] An abnormality determination module is configured to perform similarity matching between the individual rhythm fingerprint and any prototype in a prototype library to obtain a highest similarity score, and when the highest similarity score is lower than a preset confidence threshold, store an exclusive label representing an unclassified abnormal pattern in association with the unique identity, and store the verified timestamp sequence used to generate the individual rhythm fingerprint.

[0012] Preferably, the template establishing module is configured to generate the golden rhythm template and the individual rhythm fingerprint as multi-dimensional vectors, each dimension of the multi-dimensional vector corresponding to a process time increment determined by the difference between the timestamps of two adjacent predefined process nodes in the production process.

[0013] Preferably, the fingerprint and difference degree calculation module is configured to use a weighted Euclidean distance calculation method or a dynamic time warping DTW algorithm to calculate the difference degree RDS between the individual rhythm fingerprint and the golden rhythm template.

[0014] Preferably, the check gateway module is further configured to abort the generation of the individual rhythm fingerprint for the time stamp sequence and store a management label representing an abnormality of the process time series in association with the unique identity when any measured value of the time increment of any process in the time stamp sequence is not within its corresponding rationality boundary.

[0015] Preferably, the abnormality determination module is further configured to determine a drift vector based on the difference between the individual rhythm fingerprint and the golden rhythm template before performing the similarity matching; and the prototype library comprises a plurality of drift prototypes, each drift prototype corresponding to a predefined management label.

[0016] Preferably, the management labels included in the prototype library comprise a global retardation label representing a global time deviation of the production process and a single-point blockage label representing a time deviation of a time increment of a single process in the production process.

[0017] Preferably, the abnormality determination module is configured to determine the highest similarity score as the classification confidence of the current matching. , and the determination rule for generating the exclusive label is limited to satisfy the condition , wherein is a confidence threshold, which is a preset value for determining whether the individual rhythm fingerprint belongs to an unknown abnormal pattern.

[0018] Preferably, the system further comprises a benchmark evolution analysis module configured to periodically perform clustering analysis on a plurality of individual rhythm fingerprints generated within a specified time range, and based on the result of the clustering analysis, identify whether there is a dominant new rhythm paradigm that is different from the current golden rhythm template and meets a preset management condition; and a decision proposal module configured to generate a management decision proposal for updating the golden rhythm template when the benchmark evolution analysis module identifies the dominant new rhythm paradigm.

[0019] Preferably, the benchmark evolution analysis module is configured to define the preset management condition as: the vector distance between the center of a new cluster and the current golden rhythm template exceeds a preset drift threshold, and the proportion of the number of individual rhythm fingerprints included in the new cluster to the total number within the specified time range exceeds a preset coverage threshold.

[0020] Preferably, the system further comprises a downstream process triggering module configured to trigger a differentiated quality inspection process or an automated work order assignment process based on the value of the difference degree RDS, or based on the management label obtained after matching with the drift prototype, or based on the exclusive label.

[0021] Compared with the prior art, the present application has the following beneficial effects:

[0022] 1. By establishing a standardized golden rhythm template, and obtaining the individual rhythm fingerprint of any traced object in real time, and then determining the rhythm drift degree by calculating the difference between the two, finally associating this drift degree with the unique identity of the object as a process quality metadata; This information processing method changes the data record of the traceability system from discrete and static workstation event logs to quantitative representation of the continuous dynamic characteristics of the production process in the time dimension, providing a management dimension for managers to examine and compare the process stability differences between different qualified products beyond the binary judgment of qualified and unqualified.

[0023] 2. By determining the drift vector and matching it with the preset drift prototype library, a management tag representing the drift pattern of the traced object is determined; This mechanism structurally couples the quantitative drift degree value with the qualitative tag representing the nature of the abnormal pattern, so that the traceability information not only reflects the degree of process deviation, but also reveals the specific form of deviation, providing a decision basis for subsequent process diagnosis or automatic assignment of management processes, avoiding the inherent pattern ambiguity when troubleshooting based on a single drift value.

[0024] 3. When performing similarity matching, the highest similarity score obtained is determined as the classification confidence, and it is compared with the preset confidence threshold; When the classification confidence is lower than the threshold, the system replaces the management tag with a special tag representing an unclassified abnormal pattern, and stores the drift vector that leads to this matching; This takes advantage of the accompanying information in the similarity calculation process to establish a recognition and representation mechanism for unknown abnormal patterns, enabling the system to actively reject incorrect classification when faced with novel drift patterns outside its knowledge base, and to isolate and store the original data of the unknown pattern, providing a reliable data input source for the continuous evolution and organizational learning of the process knowledge base. BRIEF DESCRIPTION OF DRAWINGS

[0025] Figure 1 The architecture and core data flow chart of the rhythm traceability system of the present application;

[0026] Figure 2 RDS quantitative comparison chart of different production process stabilities of the present application;

[0027] Figure 3 Hierarchical information processing and interaction architecture diagram of the system of the present application. DETAILED DESCRIPTION

[0028] In order to make the technical solutions and advantages of the present application clearer, the technical solutions of the present application will be described in detail below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0029] The embodiment of the present application provides a full-process tracing system of a radio frequency coaxial connector production line, which is in the information processing level, mainly composed of a template establishing module, a verification gateway module, a fingerprint and difference degree calculation module, a data association module, and an abnormality judgment module, and further comprising a benchmark evolution analysis module and a decision proposal module in the optimization level of management process. These modules work together to convert the discrete event-based timestamp data in the manufacturing execution system (MES) into management information capable of quantitatively representing the dynamic stability of the production process. The top-level data flow is that the verification gateway module performs a preliminary data quality access review on the collected original timestamp sequence, the data sequence passing the review is processed by the fingerprint and difference degree calculation module to generate process quality metadata, then the data association module binds the metadata with the unique identity of the object to be traced, finally, the abnormality judgment module and the benchmark evolution analysis module use the continuously generated metadata to perform process pattern diagnosis and long-term benchmark validity maintenance respectively, thereby providing new decision basis for production management and supervision.

[0030] In engineering practice, it is a basic requirement to set an objective evaluation benchmark for the rhythm of process stability. To meet this requirement, the template establishing module in the present application is configured to perform a standardized off-line establishing procedure of a golden rhythm template; the input of the procedure is the production data of one or more golden batches confirmed by a process engineer, the golden batch refers to a reference production batch produced under the optimal state of equipment status, material supply and personnel operation, and having benchmark significance, the system retrieves all process timestamp data of products in the batch at preset process nodes from the MES database, and calculates a multi-dimensional vector of process time increment for each product based on the data, each dimension of the multi-dimensional vector corresponds to the difference between the timestamps of two adjacent predefined process nodes, for example, a part of the vector of an object to be traced can be represented as [station 1 residence time, station 1 to 2 transfer time, station 2 processing time,...]; then, the template establishing module statistically averages the process time increment vectors of all selected golden batch samples by dimension, and finally generates a standardized golden rhythm template representing the ideal working rhythm of the production line, which is , the data structure of which is a multi-dimensional floating-point number vector, for example Once established, the template is stored in the system's core database as a unified benchmark for all subsequent online real-time comparisons. This approach provides the entire traceability system with a data-driven, interpretable, and management-consensus-based ideal benchmark, giving a logical starting point for the quantitative assessment of process stability. The template creation module includes a specific offline creation procedure, referencing the objective screening and template generation of batches or "golden batches," comprising the following procedural steps: First, based on historical production data, several candidate batches with a process capability index (Cpk) higher than 1.67 and a first-pass yield rate higher than 99.5% are initially selected. Second, for each candidate batch, the timestamp sequence of all products at each process node is extracted, and their respective process time increment multidimensional vectors are calculated. Then, for all vectors within each candidate batch, the coefficient of variation (CV), i.e., the ratio of the standard deviation to the mean of that dimension, is calculated for each dimension. Finally, the candidate batch with the smallest average CV across all dimensions is selected as the "golden batch," and the initial Golden Rhythm template (GRT) is generated by taking the arithmetic mean of all process time increment vectors within that batch dimension.

[0031] In production information systems, the reliability of any timestamp-based analysis depends entirely on the quality of the data source. Network latency or instantaneous load fluctuations in data acquisition software can generate distorted timestamp records that are irrelevant to the actual process, leading to incorrect judgments by the management system. To prevent such data contamination from interfering with business logic at the source, this invention defines a golden rhythm template in the template creation module. At the same time, it was also configured to require process engineers to For each process time increment dimension, an additional pre-defined minimum physical feasible time is included. With maximum physical feasible time The reasonable boundaries of the structure; the setting of these two parameters is based on the physical and engineering constraints of the production line, rather than statistical data. For example, for the time increment of the robotic arm transfer process from workstation 1 to 2, its This can be set to the time required for the robotic arm to operate at its maximum speed, such as 1.5 seconds. Any data below this value can be considered erroneous due to clock asynchrony or other reasons. This can be set to a management tolerance limit much larger than the normal processing time, such as 30 seconds. Any data exceeding this value can be judged as equipment lag or abnormal process waiting. Parameter pairs, and Together constitute a complete definition of the template, they work together, by the subsequent verification gateway module to perform a pre-data access review; Specifically, when the verification gateway module from the MES system real-time acquisition to a traceability object original timestamp sequence, and will not immediately send it into the analysis engine, but first based on the timestamp sequence calculated each process time increment measured value, and each measured value with its in the template library pre-stored corresponding rationality boundary Comparison, only when the sequence of all process time increment measured value, are within their respective corresponding rationality boundary, the system determines that the timestamp sequence is a verified timestamp sequence, and will be released to the downstream module processing; otherwise, if there is any process time increment measured value exceeds its rationality boundary, the verification gateway module will stop for the timestamp sequence of individual rhythm fingerprint of all subsequent calculation steps, and for the traceability object of the unique identity label in the traceability database associated with a process time sequence data abnormal management label, at the same time trigger a warning information to the system administrator, which contains the specific which link timestamp data violates the physical constraints of the detailed record; by setting this way verification gateway, the system claimed in the present invention will be derived from the information system itself uncertainty data noise, and the process signal reflecting the production process fluctuations effectively separated, improve all upper management analysis and decision-making based on the effectiveness of the data, and a potential system failure point, into a management dimension that can be used to monitor the production information system health status.

[0032] For the timestamp sequence has passed the verification gateway module review, fingerprint and difference degree calculation module will be online, real-time for its generation of an individual rhythm fingerprint, this is , its generation logic and the golden rhythm template The calculation process is the same, it is also the timestamp sequence into a multi-dimensional vector containing multiple process time increments, its data structure and Maintain the consistency of the dimension; followed by the module, is to calculate the individual rhythm fingerprint Just captured between the golden rhythm template Pre-exist in the database difference degree, and the difference degree defined as a single numerical value, namely rhythm drift degree, this is ; in a preferred embodiment, the difference The calculation of the Euclidean distance calculation method can be defined as , wherein, is the dimension of the vector, and are the and In the first The component on the dimension, while is a preset weight coefficient for characterizing the importance of the first process time increment in the overall process flow, which is calibrated by a process engineer according to field knowledge, for example, a key process that has a decisive influence on the final performance of the product can be given a higher weight value; as an example of numerical illustration, assuming a simplified three-dimensional rhythm vector, the golden rhythm template , the weight vector , there is a verified individual rhythm fingerprint , the calculation process of the rhythm drift degree is , and the calculation result is ; in the case of no conflict, to deal with the scenario that the length of each process may exist nonlinear stretching in some production process, the difference degree can also be calculated using dynamic time warping (DTW) algorithm or its simplified form; the calculated value is a trace metadata, which is associated with the unique identity of the traced object through the data association module, and is stored in the trace database; thus, the present scheme has completed the expansion of the traditional trace information, so that when querying any product, the manager can not only see its static event log, but also get a dynamic quality score quantifying the process smoothness.

[0033] A single scalar value can reflect the degree of process deviation, but it cannot reveal the pattern information of the deviation, which is valuable for accurate fault diagnosis, therefore, the present scheme further provides an abnormality determination module, which determines the deviation pattern at the same time of calculating ; first, based on the difference between the individual rhythm fingerprint and the golden rhythm template , a drift vector is determined, which is , this vector describes the specific time deviation of each link in this production; at the same time, the system predefines a drift prototype library defined by a process engineer according to typical fault patterns, the prototype library contains multiple drift prototypes, each prototype is a standardized vector, and corresponds to a label with clear management meaning, for example, the prototype library can contain a global delay label, which corresponds to the prototype vector , to represent the general slight delay of the production process, which may indicate problems such as unstable air pressure or voltage fluctuation in the whole workshop, and the library can also contain one or more single-point blocking workstation labels, which correspond to a prototype vector that is only in the first a sparse vector with large positive values in one dimension, such as to represent a localized equipment jam or material waiting at a specific station; the workflow of the anomaly determination module is to associate the drift vector with any prototype in the prototype library, for example, using a cosine similarity algorithm to obtain a highest similarity score, and the management label corresponding to the highest score is associated with the unique identity of the object to be traced and stored; this coupling of quantitative drift value and qualitative label representing the nature of the abnormal pattern provides higher quality decision information for managers, enabling subsequent management processes such as automated work order assignment to be distributed to the correct responsible unit based on the specific nature of the problem.

[0034] In real industrial environments, in addition to known, pre-defined abnormal patterns, there is a potential risk of unknown unknown abnormal patterns. Misclassification of such patterns can mislead the diagnostic direction of managers. To establish a mechanism for identifying and characterizing unknown abnormalities, the anomaly determination module of the present solution also embeds a set of decision bifurcation rules based on confidence evaluation in the similarity matching logic; specifically, the system determines the highest similarity score obtained in the matching process as the classification confidence of this matching, which is At the same time, the system adds a management parameter defined by the manager, i.e., the confidence threshold, which is a value between 0 and 1, used to define the boundary between known and unknown; the determination rule of the anomaly determination module is defined as When the condition is met, the system will determine that the drift occurred this time is an unknown abnormal pattern; under this condition, the module will refuse to associate any known management label with the object, but generate a dedicated label representing the unclassified abnormal pattern and store it with the unique identity of the object, and at the same time, the system will also complete data isolation evidence of the original drift vector or the validated timestamp sequence used to generate it, such as storing it in a new pattern candidate pool database for subsequent in-depth analysis by process experts; the introduction of this mechanism enables the entire tracing system to distinguish between known and newly discovered problems, avoiding the risk of misleading diagnosis.

[0035] Further, the system claimed in the present application also comprises a downstream process triggering module configured to convert the traceability metadata generated by the upstream modules into automated management instructions; in a specific embodiment, a set of rule-based decision logic matrices are preset in the module, for example, rule one is set to automatically associate an enhanced inspection label with any object being traced when the rhythm drift degree RDS value of the object exceeds the preset first threshold T1 (such as 0.8) but is lower than the second threshold T2 (such as 1.5), the label is read by the quality management system through the data interface, and the object is automatically guided to a supplemental high-precision size re-inspection station; rule two is set to automatically trigger a single-point blocking alert when the management label output by the anomaly judgment module is the single-point blocking label When the station 3, the system not only performs the enhanced inspection process of rule one, but also automatically generates a device maintenance work order containing the time stamp, product batch number, and diagnostic information of suspected local device jamming or material waiting at the station 3, and according to the preset responsibility matrix, the work order is pushed to the work order pool of the device maintenance department; rule three is set to trigger the highest priority management alert when the output of the anomaly judgment module is the exclusive label for unclassified abnormal mode, in addition to performing all the above processes, the original drift vector DV and related data used to generate this judgment are sent to the designated mailbox of the process engineer in the form of an email to prompt that a novel event requiring manual in-depth analysis has occurred.

[0036] Any static management benchmark will face the law of applicability decay in a dynamically evolving production environment, the originally defined golden rhythm template may become inapplicable due to continuous process improvement or benign evolution of equipment state, in order to ensure the long-term effectiveness of the scale used by the traceability system, the present application scheme can also include a benchmark evolution analysis module and a decision proposal module; the benchmark evolution analysis module is configured to automatically perform a retrospective clustering analysis task in the background at a preset management period, it collects all the individual rhythm fingerprints generated in this period and stored in the database , and uses an unsupervised clustering algorithm to divide these vectors, the purpose is to automatically find out whether there is a new high-density rhythm cluster in the data; after clustering, the module analyzes the clustering results to identify whether there is a superior new rhythm paradigm that is different from the current and meets the preset management conditions, the preset management conditions here are a set of explicit and quantifiable business rules, for example, the center of a new cluster is more than a preset drift threshold away from the current The proportion of the number of occurrences within the period exceeds the preset coverage threshold; once such a dominant new rhythmic paradigm that has become the mainstream is identified, the decision proposal module will not be automatically updated. Instead of generating a structured rhythm baseline evolution suggestion report, this invention pushes the report as a management decision proposal to the workflow approval interface of the designated process engineer or manager. The manager then makes the final decision on whether to approve this new model as the golden rhythm template for the next stage. In this way, the present invention constructs a closed loop of self-examination and dynamic evolution for the main system, which helps the long-term applicability of its core management tools and transforms implicit process improvement results into solidifiable management assets.

[0037] Example 1: In a specific industrial application, two automated production lines, A and B, for producing coaxial connectors operate in parallel. Their equipment configurations, material supplies, and process flow settings are consistent. The existing Manufacturing Execution System (MES) provides management data, including output, first-pass yield, and single-point process capability index for each workstation. For several consecutive production cycles, no statistically significant differences were observed between the two production lines. However, during accelerated aging tests for long-term reliability of the finished products, the quality management department discovered that products from production line B had a higher probability of early failure within their batches compared to those from production line A, even though all products from production line B passed all electrical performance and dimensional tests upon leaving the factory. This phenomenon reveals a blind spot in traditional data traceability methods regarding management information: the inability to distinguish the stability differences experienced during the production process among qualified products that passed all tests. To address this situation, the full-process traceability system of this invention was deployed in this scenario. First, the template establishment module uses all product batches produced by production line A within a complete production cycle as reference batches, extracts the full-process timestamp data recorded in the MES, and calculates the process time increment vector for each product. By statistically averaging these vectors, a standardized golden rhythm template is established. Subsequently, the system uses the verification gateway module to conduct data access review on the timestamp sequences collected in real time from production lines A and B. After confirming that the data has not been contaminated due to factors such as network latency, the fingerprint and difference calculation modules generate individual rhythm fingerprints online for each traceable object passing through the two production lines. And calculate its relationship with Difference between Data shows that all products on production line A... The values ​​are all stably distributed within a low-level range close to zero, while the products of production line B... The values ​​show a consistently high level, and the data dispersion is relatively large.

[0038] In response to the persistent high [risk] on production line B The numerical value, the abnormality determination module automatically starts the abnormality attribution analysis, the module first determines a drift vector based on the difference of each high individual and ; accordingly, the system will match the and the plurality of prototypes pre-stored in the drift prototype library, the matching result shows that the of the majority of high individuals in the B production line , all have a cosine similarity higher than the preset threshold with a drift prototype in the prototype library defined as a single-point blocking station 3 by a management label; the single-point blocking station 3 prototype, whose vector characteristics are that there is only a positive deviation in the dimension corresponding to the processing time of station 3, and the remaining dimensions are basically consistent with ; this result points to an abnormal event located at station 3 and having locality and repeatability, which is the process instability of the B production line as a whole; based on this data output, the equipment maintenance department checks the material clamp of station 3 of the B production line and finds that a pneumatic component inside the clamp has a slight and intermittent pressure deficiency, which causes the clamp to occasionally produce a slight jitter of about hundreds of milliseconds in positioning. The jitter does not cause the product to be scrapped due to excessive difference, so it has never been captured by the traditional SPC system. However, it is this repetitive slight disturbance that causes the process time increment of this process segment to deviate continuously, so it is recognized and located by the system of the present application; after the pneumatic component is replaced, the numerical value of the products of the subsequent production batches of the B production line quickly falls back to the same low level interval as the A production line, and the early failure probability of the long-term reliability accelerated aging test of its products also returns to the same level as the A production line.

[0039] Example 2: To objectively verify the effectiveness of the whole-process tracing system claimed in the present application in identifying different types of process abnormalities, this example constructs a test environment based on discrete event simulation; the simulation platform can generate time-stamped logs consistent with the data structure of the real production line MES system, and is configured to accurately inject a preset time disturbance, and its core function specification is that the time stamp generation accuracy is better than 1ms; the purpose of this test is to compare the recognition and classification ability of the traditional process control method based on the upper and lower limits of the parameters and the system of the present application when dealing with data containing different disturbance modes.

[0040] The experiment included a control group and an experimental group using the method of this invention. The control group was treated by simply determining whether the time consumed in each process segment exceeded the traditional SPC control limits, which were set according to the standard Six Sigma principle based on baseline data. The experimental group, on the other hand, applied the complete method of this invention, including establishing a golden rhythm template. Calculating individual rhythm fingerprints With rhythmic drift And perform anomaly detection; in the experiment, the confidence threshold of the key parameter in the anomaly detection module. The value was set to 0.7. This value was determined through receiver operating characteristic curve analysis of historical data, balancing the sensitivity to novelty anomaly detection with the need to avoid overreacting to normal process fluctuations. The experiment included five operating conditions, each generating 100 independent product traceability data samples: Condition 1 (baseline state): The simulation platform operated according to a standard rhythm without any human disturbance; the generated data served as sample group A of this invention. Condition 2 (global delay): The time consumption of all process segments was increased by a fixed 5% delay based on the standard value, which remained within the single-point SPC control limits; the data served as... Sample group B of this invention; Case 3 (single-point blockage): An additional delay of 100ms to 300ms is randomly injected only in the third process segment, and its data is used as sample group C of this invention; Case 4 (composite disturbance): The time consumption of the first and second process segments is reduced by 5%, while the time consumption of the fourth process segment is increased by 15%, simulating an atypical abnormal mode, and its data is used as sample group D of this invention; After the experiment was carried out, the analysis results of the control group showed that for all 400 samples under all four cases, the time consumption of each process segment did not exceed the preset SPC control limit, and the detection rate was 0%; while the experimental group using the method of this invention obtained quantitative results with discrimination. Table 1 shows the key experimental data records.

[0041] Table 1: Recording table of key experimental data.

[0042] Sample name Injected anomaly type Average RDS RDS standard deviation Abnormality determination module attribution result Invention sample group A Baseline state 0.012 0.005 None (RDS below trigger threshold) Invention sample group B Global stalling 0.855 0.021 Global stalling Invention sample group C Single point blockage 1.231 0.453 Single point blockage @ station 3 Invention sample group D Composite disturbance 1.057 0.158 Unclassified anomaly pattern

[0043] Referring to Table 1, the present invention sample group A... A value close to zero indicates the stability of the system under baseline conditions; the value of sample group B in this invention... When the numerical value increases and the standard deviation is small, its drift vector Matched with the global hysteresis prototype in the prototype library, and accurately attributed; the present invention sample C The highest value and the largest fluctuation reflect the characteristics of random blocking events and are accurately located to specific workstations; for sample group D of this invention, its The numerical values ​​are also relatively high, but the anomaly detection module calculates the highest similarity score when performing similarity matching. The value is 0.61, which is lower than the preset confidence threshold. That is, 0.7. Therefore, the system refuses to classify it into any known prototype, but instead associates it with a specific label for an unclassified abnormal pattern and its drift vector. The data is stored in a new pattern candidate pool; experimental results show that the system claimed in this invention, by calculating the rhythm drift degree... It can quantitatively detect process anomalies that traditional SPC methods cannot identify; moreover, its anomaly determination module can qualitatively attribute known anomaly patterns and has the ability to identify unknown anomaly patterns, providing objective data basis for in-depth monitoring and management of production processes.

[0044] Example 3: This example combines Figures 1 to 3 This paper describes a full-process traceability system for an RF coaxial connector production line, such as... Figure 1 As shown, the first step is to enter the data verification module, using the preset templates in the Golden Rhythm Template Library. The boundaries are reviewed, and sequences that fail are marked as abnormal time-series data, while timestamp sequences that pass the verification are sent to the fingerprint generation and difference calculation module, which uses the golden rhythm template. Generate individual rhythmic fingerprints for sequences And calculate the degree of difference Subsequently, the abnormal mode determination module receives... and The value is calculated by comparing the drift vector (DV) with drift prototypes in the drift prototype library. If a match is found, a management label is output; if the match confidence is too low, an unclassified anomaly label is output and the data is stored in the new pattern candidate pool. These analysis results are finally integrated by the associated traceability metadata module to form associated traceability metadata, which is then stored in the traceability database. This database not only supports traceability metadata queries but also allows for the storage of historical data. The data is provided to the analysis benchmark evolution module. The analysis results of this module are submitted to process engineers / managers in the form of management decision proposals. This is used to update the golden rhythm template library and drift prototype library by establishing standard template processes, forming a closed-loop optimization process. At the same time, the system can also trigger downstream processes based on traceability data and issue automated instructions to downstream management systems such as quality and equipment.

[0045] like Figure 2 As shown, the graph uses the production date as the horizontal axis and the... The value is the ordinate, clearly shows the quantitative difference between line A and line B in process stability, in which the solid line representing line A is always at a low level close to 0, indicating that its production process is highly consistent with the golden rhythm, while the dashed line representing line B fluctuates in a higher value range, deviating from the ideal state, which intuitively reveals that there is a deep dynamic instability in the production process that cannot be found by traditional detection methods. At the same time, the graph also contains a control limit to indicate the management warning threshold for process deviation.

[0046] As shown in Figure 3 , the schematic diagram logically divides the system into four levels. The bottom layer is the production physical layer, which provides the system with massive real-time timestamp data. The middle layer is the real-time computing core and data storage core of the system, where the real-time computing core includes dynamic processing units such as verification gateway module, fingerprint calculation module, anomaly determination module, data correlation module, and process triggering module. The data storage core includes static data assets such as traceability database, golden rhythm template library, drift prototype library, and new mode candidate pool, and there is a self-learning and benchmark evolution closed-loop mechanism. The outermost layer is the interaction layer, with the left side being the human-computer interaction layer for interaction with managers, responsible for model calibration and strategy configuration, and output decision analysis and report. The right side is the external collaboration layer for interaction with external systems, responsible for issuing precise automated instructions.

[0047] In a specific system deployment scenario, a set of radio frequency coaxial connector production lines is about to be put into production. Due to the use of new models of processing and conveying equipment, there is a lack of historical production data and process management experience that can be directly used. At this early stage of the project, to ensure that the full-process traceability system claimed in the invention can be objectively and effectively initialized and configured, a standardized offline calibration procedure needs to be performed to determine two core system parameters, namely the weight vector for calculating rhythm drift degree , and the initial entries of the drift prototype library for anomaly attribution. To calibrate the weight vector , first, a golden batch is run to establish an initial golden rhythm template ; then, a process sensitivity analysis method is used, which quantifies the importance of a specific link in the overall process by injecting a small, standardized time disturbance into that link and measuring the impact of the disturbance on the final product's key performance indicator. Specifically, taking the voltage standing wave ratio of the final product as the key performance indicator, first measure the baseline value of the golden batch product; then, while strictly following the other process segments, a small disturbance is injected into the target link, and the voltage standing wave ratio of the final product is measured again. The ratio of the two values is the process sensitivity of the target link.Under the premise that a fixed delay of 100ms is artificially added only to the first process time increment (station 1 dwell time), and a batch of products is produced, and the batch of products is measured. Average degradation ; in turn All of them Repeat this operation for each process time increment to obtain a set of... Deterioration Weight vector Each weight component in The value is determined to be proportional to the corresponding performance degradation, obtained through normalization calculation, i.e. ,in, Indicates the relationship with the first The time increment of each process corresponds to Deterioration amount This indicates that the value used to iterate through the degradation values ​​in this group is the 1st. This procedure transforms the setting of weighting coefficients from relying on subjective experience to a data-driven calibration process that can be reproduced through controlled experiments.

[0048] In the weight vector Once determined, the initial construction of the drift prototype library is carried out. This construction process utilizes a fault injection experiment method, which maps physically meaningful abnormal operating conditions to a standardized drift vector. To establish a global hysteresis prototype, engineers reduced the overall drive air pressure of the production line by 5%, ran a batch under these conditions, and collected individual rhythmic fingerprints for all products. Calculate the relative value of each product to drift vector Subsequently, through the analysis of all samples within this batch... Vector averaging is performed to obtain a stable average drift vector that characterizes the global hysteresis condition, eliminating random noise. This vector is then stored in the prototype library and associated with the management tag for global hysteresis. Similarly, by setting a tiny physical limit on the material handling robot arm at station 3 to simulate a single-point blockage condition, the same process can be used to generate and calibrate the single-point blockage. The prototype of workstation 3; through this series of calibration experiments based on fault injection, the initial construction of a drift prototype library containing several core abnormal modes with clear physical root causes was completed; after completing the above two calibration procedures, the full-process traceability system claimed by this invention has all the initial configuration parameters for effective deployment on a new production line.

[0049] Example 5: In a specific system commissioning scenario, to ensure that the physical feasible time boundary parameters of the verification gateway module are objectively set, a set of targeted on-site calibration procedures needs to be executed; to determine the minimum physical feasible time corresponding to any process time increment. The production equipment involved in the increment is then independently operated at its rated speed under no material load conditions, and this operation is repeated 10 times. The time taken for each operation is recorded, and the shortest recorded time is selected as the time increment for this process. To determine the maximum physically feasible time Then it is set as a template related to management strategy and based on the golden rhythm. Multiples of, for the first The time increment of the process, The calculation formula is: ,in, For the first The maximum physical feasible time for each process time increment This is the standard value corresponding to the time increment of this process in the golden rhythm template. It is a global management tolerance coefficient, which is set to 5 in this embodiment. This provides a unified benchmark for all process segments to identify extreme abnormal waiting or equipment stall events.

[0050] Accordingly, in order to calibrate the key trigger thresholds in the baseline evolution analysis module, after the production line has been running stably for one full month, the system first collects all individual rhythm fingerprints generated during this period. Data was collected, and the rhythmic drift of all corresponding products was calculated. Numerical value; based on this initial value The dataset is used to calculate the overall standard deviation. Therefore, the drift threshold used to determine whether new cluster centers have drifted is set to 6 times the initial standard deviation, i.e. This setting links the threshold to the inherent process fluctuation level of the production line at this stage; at the same time, based on the management's requirements for the process change verification cycle, the coverage threshold for judging whether a new cluster has become mainstream is set at 30%, that is, a potential advantageous new rhythmic paradigm must account for at least 30% of the total output in the entire analysis cycle before it can be identified by the system and generate corresponding management decision proposals.

[0051] Example 6: In a specific production management scenario, the production line needs to temporarily switch to a batch of certified but slightly different process characteristics of backup supplier raw materials. To avoid this known planned process adjustment being incorrectly identified as a process anomaly by the system, a pre-set management procedure needs to be implemented. Before this batch of materials is put into use, the system administrator pre-registers a temporary process change event in the traceability system and sets the product batch number range associated with the event. When the first product belonging to this batch number range on the production line passes through all processes, the system automatically captures its individual rhythm fingerprint. This is then set as a temporary golden rhythm template for that specific batch; during the validity period of this temporary template, the rhythm drift of all subsequent products belonging to that batch number range will be... The calculations all involve taking their respective... Compare with this temporary golden rhythm template, rather than with the production line standard. In comparison, under these temporary process conditions, the system continues to effectively monitor the dynamic stability of the production process until all products in that batch are produced, at which point the system automatically reverts to the standard operating conditions. The default state for comparison.

[0052] To ensure that the unsupervised clustering algorithm used in the baseline evolution analysis module can be deterministically configured, this embodiment discloses a method for addressing the key hyperparameter of the K-Means algorithm: the number of clusters. The system employs a systematic optimization procedure; it first extracts all individual rhythm fingerprints generated within the past quarter from the traceability database. The data forms a historical dataset for calibration; subsequently, the system iteratively executes commands on this dataset. K-Means clustering operations with values ​​from 1 to 10 are performed, and the sum of squared intra-cluster errors is calculated for each operation. ; by drawing Follow The system analyzes the curve of value changes and automatically finds the inflection point of the curve's slope using the elbow rule. This allows the system to identify the optimal number of clusters within the historical dataset. Finally, the optimization procedure outputs... The value is set as the clustering quantity parameter used by the baseline evolution analysis module when performing periodic retrospective analysis. This transforms the selection of key algorithm hyperparameters from relying on trial and error by the operator into an automatically executed optimization process based on the distribution characteristics of the data itself.

[0053] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0054] Finally, it should be noted that the above examples are merely intended to illustrate the technical solutions of the present application and not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. A full-process traceability system for an RF coaxial connector production line, characterized in that, The system includes: A template building module is configured to generate a golden rhythm template containing multiple process time increments based on timestamp data of a reference batch, and to pre-define a rationality boundary consisting of the minimum physical feasible time (MPT) and the maximum physical feasible time (XPT) for each process time increment in the golden rhythm template. A verification gateway module is configured to collect the timestamp sequence of the traceable object, calculate the measured value of each process time increment based on the timestamp sequence, and then compare the measured value of each process time increment with its corresponding reasonableness boundary. The timestamp sequence is determined to be a verified timestamp sequence only when the measured values ​​of all process time increments in the timestamp sequence are within their respective reasonableness boundaries. A fingerprint and difference calculation module is configured to generate an individual rhythm fingerprint from the verified timestamp sequence and calculate the difference RDS between the individual rhythm fingerprint and the golden rhythm template only when the verification gateway module determines that the timestamp sequence is a verified timestamp sequence. A data association module is configured to associate and store the difference RDS as traceability metadata with the unique identifier of the traceable object; An anomaly detection module is configured to perform similarity matching between an individual's rhythm fingerprint and any prototype in the prototype library to obtain the highest similarity score. When the highest similarity score is lower than a preset confidence threshold, an exclusive label representing the unclassified anomaly pattern is generated and associated with a unique identifier, and a verified timestamp sequence used to generate the individual's rhythm fingerprint is stored. Furthermore, the template creation module is configured to generate both the golden rhythm template and the individual rhythm fingerprint as multi-dimensional vectors, with each dimension of the multi-dimensional vector corresponding to a process time increment determined by the difference in timestamps between two adjacent predefined process nodes in the production process.

2. The full-process traceability system for an RF coaxial connector production line according to claim 1, characterized in that, The fingerprint and difference calculation module is configured to use either a weighted Euclidean distance calculation method or a dynamic time warping (DTW) algorithm to calculate the difference RDS between an individual rhythm fingerprint and a golden rhythm template.

3. The full-process traceability system for an RF coaxial connector production line according to claim 1, characterized in that, The verification gateway module is also configured to: when the measured value of any process time increment in the timestamp sequence is not within its corresponding reasonable boundary, stop generating individual rhythm fingerprints for the timestamp sequence, and generate a management label representing the abnormality of the process time series data and associate it with a unique identity identifier for storage.

4. The full-process traceability system for an RF coaxial connector production line according to claim 1, characterized in that, The anomaly detection module is also configured to: determine a drift vector based on the difference between the individual rhythm fingerprint and the golden rhythm template before performing similarity matching; the prototype library contains multiple drift prototypes, each of which corresponds to a predefined management label.

5. The full-process traceability system for an RF coaxial connector production line according to claim 4, characterized in that, The prototype library contains management tags, including global sluggish tags that characterize global time deviations in the production process, and single-point blockage tags that characterize time deviations in the time increments of individual processes in the production process.

6. The full-process traceability system for an RF coaxial connector production line according to claim 1, characterized in that, The anomaly detection module is configured to determine the highest similarity score as the classification confidence level for this match. Furthermore, the rules for determining the generation of unique tags are limited to meeting certain conditions. ,in, This is the confidence threshold.

7. The full-process traceability system for an RF coaxial connector production line according to claim 1, characterized in that, The system also includes: a baseline evolution analysis module, configured to periodically perform cluster analysis on multiple individual rhythm fingerprints generated within a specified time range, and based on the results of the cluster analysis, identify whether there is a superior new rhythm paradigm that is different from the current golden rhythm template and meets preset management conditions; and a decision proposal module, configured to generate a management decision proposal to update the golden rhythm template when the baseline evolution analysis module identifies a superior new rhythm paradigm.

8. The full-process traceability system for an RF coaxial connector production line according to claim 7, characterized in that, The baseline evolution analysis module is configured to define the preset management conditions as follows: the vector distance between the center of a new cluster and the current golden rhythm template exceeds a preset drift threshold, and the proportion of the number of individual rhythm fingerprints contained in the new cluster to the total number within a specified time range exceeds a preset coverage threshold.

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