Precision pulley mold life cycle management method and system

Abstract

The application discloses a precision belt wheel mold life cycle management method and system, and relates to the technical field of mechanical manufacturing, comprising the following steps: in the whole life management process of the precision belt wheel mold, continuously collecting design version change data, processing process record data, running condition waveform data and maintenance disposal track data, and uniformly writing the collected data into a data chain of the same time reference for time sequence management, and generating a time alignment index at the end of the data chain. The application realizes continuous connection and anti-overlapping collection of multi-source data by constructing a data chain with time as the main line and introducing a precursor anchor point, a previous priority constraint and a time sequence traction control, ensures that the deterioration signal is continuously visible in the time dimension, accurately reflects the mold performance attenuation process, and improves the reliability of life prediction and maintenance decision.

Description

Technical Field

[0001] This invention relates to the field of mechanical manufacturing technology, specifically to a method and system for lifecycle management of precision pulley molds. Background Technology

[0002] Precision pulley mold lifecycle management refers to a continuous management approach centered on big data processing, encompassing the entire process of a precision pulley mold from design and initiation, processing and manufacturing, assembly and debugging, production and use, condition monitoring, maintenance and repair, to decommissioning and scrapping. This process involves the continuous collection, unified storage, and correlation analysis of multi-source data, including mold design parameters, processing accuracy data, production batch records, operating condition information, wear and failure characteristics, maintenance history, and downtime, to construct a complete mold lifecycle data chain and systematically depict the evolution of mold conditions. Based on this, the combined processing results of historical and real-time data are used to dynamically manage mold performance degradation trends, maintenance timing, load allocation, and replacement decisions. This transforms mold management from experience-based judgment to a data-driven, refined, traceable, and predictable approach, thereby supporting the stability and consistency of the precision pulley production process.

[0003] The existing technology has the following shortcomings:

[0004] In the existing lifecycle management of precision pulley molds, lifecycle management analysis of mold operating status typically relies on the correlation processing of information collected at different time periods to reflect the trend of mold performance changes. However, in the data processing stage of lifecycle management, a summary method based on overall statistical characteristics is commonly used to centrally integrate data from time series, without establishing an effective constraint mechanism for the temporal sequence of degradation signals in the lifecycle management logic. This can easily lead to reverse superposition of correlation sequences in the lifecycle data chain. Specifically, key degradation signals such as microcrack initiation, local stiffness reduction, and wear path deviation that appear in the early or middle stages of mold lifecycle management are often covered or diluted when a large amount of normal operating data generated during subsequent continuous production participates in the lifecycle management summary. This makes the lifecycle management results appear stable at the overall level, thus masking the true structural degradation process. Under this lifecycle management model, existing technologies struggle to identify the continuous cumulative effect of degradation signals in lifecycle data in a timely manner and cannot effectively manage and track their evolution direction in the lifecycle data chain. Ultimately, this leads to sudden and irreversible structural damage to the mold during long-term continuous production, posing a serious threat to production stability.

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

[0006] The purpose of this invention is to provide a method and system for lifecycle management of precision pulley molds to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a lifecycle management method for precision pulley molds, comprising the following steps:

[0008] In the whole life management of precision pulley molds, design version change data, machining process record data, operating condition waveform data, and maintenance and handling trajectory data are continuously collected, and the collected data are uniformly written into a data chain with the same time base for time sequence management, and a time alignment index is generated at the end of the data chain.

[0009] Abnormal segments with continuous changing trends are solidified and managed in chronological order, generating a chain of precursor anchor points to characterize the starting point of degradation.

[0010] Based on the precursor anchor chain, a priori priority constraint is introduced in the subsequent data association process of the data chain, so that the running data is only received segment by segment along the time order of the precursor anchor chain, and a sequential reception rule table is generated based on the priori priority constraint.

[0011] The data aggregation method of the data chain is rearranged and managed based on the sequential acceptance rule table. The acceptance time window and dilution time window are divided before and after the precursor anchor chain. Restricted write management control is applied to the data entering the dilution time window to generate anti-overwrite aggregation results.

[0012] Based on the summary results of anti-coverage, the timing-driven management and control are implemented. When the precursor anchor chain continues to appear, the data release rhythm is adjusted to suppress the data input within the dilution time window, and the running data is released in the order of pulse silence interleaving, so that the precursor anchor chain can be continuously received in the data chain.

[0013] Preferably, the time-aligned index generation steps are as follows:

[0014] In the whole life cycle management of precision pulley molds, design version change data, machining process record data, operating condition waveform data and maintenance trajectory data are collected for each stage of the mold from design to operation and maintenance, while maintaining the continuity of the data generation sequence.

[0015] After completing the collection of various data, the collected data are organized under the same time benchmark. By attaching a time stamp of the time of generation to each data, data from different sources are mapped to the same time axis to form a continuous relationship.

[0016] Based on a unified time reference, data is written into the data chain structure one by one in chronological order, so that the data chain truly reflects the continuous evolution process of the mold from design to maintenance;

[0017] After the data chain is built, a time-aligned index is generated at the end of the data chain to record the time identifier and arrangement position of each data unit, so as to realize time-order-based access and association during the lifecycle management process.

[0018] Preferably, the steps for generating the precursor anchor chain are as follows:

[0019] After establishing the time-aligned index, the design version change data, processing record data, operating condition waveform data and maintenance and handling trajectory data arranged in chronological order in the data chain are analyzed segment by segment to determine the time position and status information of each type of data.

[0020] After determining the time location, adjacent time periods are compared and connected based on time continuity to identify data segments that deviate from the normal operating mode, and the start time of the first occurrence of abnormal changes is recorded.

[0021] After identifying the first occurrence of the abnormal change, continue to track the continuation of the abnormality in subsequent time periods, and link the abnormal segments with a continuous changing trend sequentially to form an abnormal segment sequence.

[0022] After obtaining the abnormal fragment sequence, it is solidified according to the order in the time alignment index to generate a precursor anchor chain to characterize the starting point of degradation, so that the abnormal changes are continuously inherited along the time direction.

[0023] Preferably, when generating the precursor anchor chain, the start time of each abnormal segment sequence is used as the time identifier of the precursor anchor, and the subsequent continuous abnormal data segments are used as the extension of the anchor chain, so that the time order between the precursor anchors is consistent with the original time order in the data chain, and the precursor anchor chain has the characteristics of continuous continuity and traceability in the time dimension.

[0024] Preferably, the steps for generating the sequential succession rule table are as follows:

[0025] After the precursor anchor chain is formed, all running data in the data chain is retrieved according to the time alignment index to determine the position of each running data in the time chain and the time distance between it and the corresponding precursor anchor. Then, continuous time continuation segments are divided according to the time sequence of the precursor anchor.

[0026] After completing the division of time-bound sections, the operational data within each section is connected segment by segment, using the time sequence of the precursor anchor chain as the main line of the sequence, so that the operational data can only be extended along the time sequence.

[0027] After the data connection is completed, a priori constraint is introduced so that each time segment takes over the data association will preferentially use the termination state of the previous segment as the starting condition.

[0028] After establishing the priority constraints, a sequential succession rule table is generated based on the succession relationship to record the sequential dependencies and connection conditions between time succession segments.

[0029] Preferably, when generating the sequential succession rule table, the start and end times corresponding to each precursor anchor point, the time span between adjacent time succession segments, the time distribution range of the running data, and the succession boundary between the preceding and succeeding data are recorded. During the running data writing process, the writing order and time connection conditions of the data are limited according to the sequential succession rule table, so that the newly added running data automatically matches the corresponding time succession segment.

[0030] Preferably, the steps for generating the anti-coverage summary results are as follows:

[0031] Based on the time position of the precursor anchor point, the time span of the preceding and following sections, and the distribution range of the running data recorded in the sequential succession rule table, all data in the data chain is re-scanned and located to form a continuous time succession interval.

[0032] After completing the division of the time transition intervals, each interval is further subdivided based on the time density, data type, and time distance from the precursor anchor chain of the running data, and the transition time window and dilution time window are defined respectively.

[0033] After the time window is divided, restricted write control is applied to the data entering the dilution time window, limiting its time interval, write frequency and write priority;

[0034] After completing the restricted write control, the data chain is rearranged and summarized according to the constraints of the rule table in sequence to generate the anti-overwrite summary result.

[0035] Preferably, when applying restricted write control to data entering the dilution time window, the write order of running data is constrained by the time direction preset in the sequential succession rule table, and data is only allowed to be updated in a time interval manner. When the succession time window and the dilution time window are adjacent, the write priority of data in the dilution time window is automatically reduced to prevent normal running data from covering the deterioration information in the succession time window.

[0036] Preferably, based on the summary results of anti-coverage, the timing-driven management control is implemented. When the precursor anchor chain continues to appear, the data release rhythm is adjusted to suppress data input within the dilution time window, and the running data is released in a pulse-silent interleaved sequence. The steps are as follows:

[0037] After the summary results of the anti-coverage measures are generated, the time correlation between the recorded acceptance time window and dilution time window is identified, the release management logic is established according to the time sequence, and a release identifier is assigned to each time period.

[0038] After completing the release marking, the time period during which the precursor anchor chain continuously appears in the anti-coverage summary results is detected. The continuously appearing precursor anchors are marked, high-sensitivity sections are defined, and controlled release is implemented.

[0039] After identifying highly sensitive sections, the data release rhythm is dynamically adjusted based on the summary results of anti-coverage and release indicators. The operation data is released in a pulse-silent staggered sequence, and data input within the dilution time window is suppressed.

[0040] After completing the pulse silence interleaved release, the data link controlled by the timing traction is continuously integrated to ensure that the precursor anchor link maintains a temporal sequence in the data link.

[0041] The precision pulley mold lifecycle management system includes a time reference data construction module, a precursor anchor point generation module, a sequential acceptance constraint module, an anti-overwrite summary control module, and a time-series traction scheduling module.

[0042] The time-base data construction module continuously collects design version change data, machining process record data, operating condition waveform data, and maintenance and handling trajectory data during the entire life cycle management of precision pulley molds. The collected data is uniformly written into a data chain with the same time base for time-series management, and a time alignment index is generated at the end of the data chain.

[0043] The precursor anchor point generation module solidifies and manages abnormal segments with continuous changing trends in chronological order, and generates a precursor anchor point chain to characterize the starting point of degradation.

[0044] The sequential acceptance constraint module introduces prior priority constraints in the subsequent data association process of the data chain based on the precursor anchor chain, so that the running data is accepted only segment by segment along the time order of the precursor anchor chain, and a sequential acceptance rule table is generated based on the prior priority constraints.

[0045] The anti-overlay aggregation control module rearranges the data aggregation method of the data chain based on the sequential acceptance rule table, divides the acceptance time window and dilution time window before and after the precursor anchor point chain, and applies restricted write management control to the data entering the dilution time window to generate anti-overlay aggregation results.

[0046] The timing-driven scheduling module performs timing-driven management and control based on the anti-coverage summary results. When the precursor anchor chain continues to appear, it adjusts the data release rhythm, suppresses and dilutes the data input within the time window, and releases the running data in the pulse silence interleaved sequence, so that the precursor anchor chain remains continuously connected in the data chain.

[0047] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0048] This invention establishes a time-based data chain management structure throughout the entire lifecycle of a precision pulley mold, enabling continuous and unified management of multi-source data generated during the design, processing, operation, and maintenance phases, all within the same timeframe. By implementing time alignment management and early warning anchor point solidification management of the data chain, subtle early degradation signals maintain their independent temporal position and continuous visibility in subsequent data processing. This fundamentally avoids the problem of degradation information being overwritten or diluted by subsequent normal operation data, thus ensuring continuous and traceable management of the mold's state evolution process in the time dimension. This provides accurate and time-consistent basic data support for the early identification of degradation trends.

[0049] This invention introduces a priori constraint management mechanism and a time-driven control management mechanism, ensuring that data strictly follows time-sequence logic during aggregation and release management. Operational data can only extend segment by segment along the sequence of precursor anchor points. Through anti-overwrite aggregation management and restricted write management mechanisms, the dominant position of degradation signals in the time dimension is maintained, enabling the data chain management process to accurately reflect the dynamic changes in mold performance degradation. This allows for proactive control of mold status in lifespan prediction management, maintenance decision management, and load allocation management, improving the stability and consistency of production operations. Attached Figure Description

[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0051] Figure 1 This is a flowchart of the lifecycle management method for precision pulley molds according to the present invention.

[0052] Figure 2 This is a schematic diagram of the modules of the precision pulley mold lifecycle management system of the present invention. Detailed Implementation

[0053] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0054] This invention provides, for example Figure 1 The lifecycle management method for precision pulley molds shown includes the following steps:

[0055] In the whole life management of precision pulley molds, design version change data, machining process record data, operating condition waveform data, and maintenance and handling trajectory data are continuously collected, and the collected data are uniformly written into a data chain with the same time base for time sequence management, and a time alignment index is generated at the end of the data chain.

[0056] To ensure consistent data flow across all stages of the precision pulley mold's lifecycle management, a time-based data chain structure is constructed by uniformly organizing and continuously associating data from different sources and production stages. The specific implementation steps are as follows:

[0057] In the full lifecycle management of precision pulley molds, continuous data is collected at each stage from design to actual operation and maintenance. This includes version changes during the design phase, process records during the machining phase, operating conditions during the usage phase, and maintenance tracking data. Specifically, design version change data reflects the version sequence relationships formed during design adjustments; machining process records describe the machining paths, sequences, and phased states during mold manufacturing; operating condition waveform data characterizes the load changes, operating rhythm, and state fluctuations experienced by the mold during production; and maintenance tracking data records maintenance actions, timing, and corresponding state changes during use. All data types are collected in their original order of generation without compression, cropping, or merging, ensuring a complete reflection of the mold's state evolution across different lifecycle stages.

[0058] After continuous collection of various data, design version change data, processing record data, operating condition waveform data, and maintenance and handling trajectory data are uniformly organized under the same time benchmark, ensuring that data from different sources are calibrated on a unified time scale. Specifically, by attaching a time stamp corresponding to the time of generation to each piece of collected data, version changes formed during the design phase, process records formed during the processing phase, operating condition waveforms formed during the operation phase, and handling trajectories formed during the maintenance phase can all be mapped to the same continuous time axis. In this way, data that was originally scattered across different management stages and different recording media forms a continuous relationship in the time dimension, providing a unified time reference basis for subsequent overall description of the mold state evolution process.

[0059] Based on a unified time benchmark, the aforementioned data are written sequentially into the same data chain structure, ensuring that the data arrangement strictly follows the actual time sequence of data generation. During the construction of the data chain, different types of data are not stored in segments or arranged independently. Instead, design version change data, processing record data, operating condition waveform data, and maintenance and handling trajectory data are interleaved according to chronological order, ensuring that data at any given time point has a unique position within the data chain. This approach allows the data chain to accurately reflect the continuous evolution of the mold throughout its entire lifecycle management process, from design, processing, operation to maintenance, avoiding time breaks or sequential confusion caused by differences in data sources.

[0060] After the data chain is constructed, a time alignment index corresponding to the overall structure of the data chain is generated at the end of the data chain. This index identifies the positional relationship of each data unit in the data chain under a unified time base. By recording the time identifier corresponding to each piece of data in the data chain and its position within the chain, the time alignment index allows for direct location of the data range within the corresponding time period when accessing, associating, or processing the data chain. By centrally generating time alignment indexes at the end of the data chain, the data chain maintains continuous write characteristics while possessing clear time positioning capabilities, thus providing a stable data infrastructure for subsequent data transfer, state association, and lifecycle management based on time sequence.

[0061] Abnormal segments with continuous changing trends are solidified and managed in chronological order, generating a chain of precursor anchor points to characterize the starting point of degradation.

[0062] After constructing the unified time-base data chain, in order to achieve temporal locking and persistent characterization of early abnormal changes in the lifecycle data of precision pulley molds, a time-aligned index-based temporal localization method is used to solidify abnormal segments with continuous changing trends in the data chain, generating a precursor anchor chain to characterize the deterioration starting point. This process includes the following steps:

[0063] Based on the established time-aligned index, segmented time-series analysis is performed on the design version change data, processing record data, operating condition waveform data, and maintenance and handling trajectory data arranged chronologically in the data chain to determine the temporal position and corresponding status information of each type of data within each time period. The time-aligned index ensures that data from different sources at any given time point can be compared on the same time scale, allowing structural parameters in the design phase, accuracy changes in the processing phase, vibration waveforms in the operating phase, and handling records in the maintenance phase to be uniformly mapped to the same time node. This time-unified approach forms a continuously traceable timeline in the data chain, providing a complete temporal foundation for subsequently identifying the initial occurrence time of abnormal changes.

[0064] After clarifying the temporal position of various data types based on the time alignment index, the information reflecting the mold state evolution in the data chain is compared and connected according to temporal continuity. The focus is on analyzing the state differences between adjacent time periods to identify data segments that deviate from the normal operating mode. These abnormal changes may manifest as sudden changes in design parameters, deviations in machining accuracy, abnormal fluctuations in operating waveforms, or frequent occurrences of maintenance records. When these abnormal changes first appear in the time series, the time of occurrence is recorded as the start time of the abnormal change, based on the time node marked by the time alignment index. This method of locking in the first occurrence time ensures that the abnormal state has a clear starting point in the time dimension, thus maintaining a temporally continuous identification basis in subsequent data processing. Simultaneously, the start time of all abnormal changes corresponds one-to-one with the corresponding time identifier in the time alignment index, ensuring that each abnormal event can be uniquely located.

[0065] After identifying the initial occurrence of the anomalous change, the continuation of this change in subsequent time periods is tracked. When an anomalous state persists across multiple time periods and exhibits a consistent trend, these consecutive anomalous data segments are sequentially linked to form a temporally extended sequence of anomalous segments. Continuous tracking of this sequence allows for a continuous record of the mold's degradation process over time, revealing the complete evolutionary path of the anomalous change from its emergence to its expansion. To avoid interference from occasional fluctuations within a single time segment, all linked anomalous segments must have a temporal sequence with the initial anomalous event and be consecutively arranged in a time alignment index. This sequential temporal linking method creates a stable time series structure for the anomalous segments, providing continuous support for fixing the starting point of subsequent degradation.

[0066] After obtaining multiple sequences of anomalous fragments with temporal continuity, these sequences are solidified according to their chronological order in the time alignment index, forming a chain of precursor anchors to characterize the starting point of degradation. During the solidification process, the start time of each anomalous fragment sequence is used as the time identifier of the precursor anchor, and the continuous anomalies within its subsequent time period are considered as extensions of the anchor chain, maintaining complete consistency between the temporal order of each precursor anchor and the original temporal order in the data chain. Through this solidification method, anomalous changes that were originally scattered across different time periods and data types are organized into a continuously connected chain structure along the time direction, enabling each precursor anchor to represent a starting point in the degradation process of the mold state. The formation of the precursor anchor chain gives the data chain the ability to locate and continuously track early anomalous changes in time, providing a solid data structure foundation for subsequent time-series-based state evolution analysis, constraint setting, and anti-overlay aggregation in lifecycle management.

[0067] Based on the precursor anchor chain, a priori priority constraint is introduced in the subsequent data association process of the data chain, so that the running data is only received segment by segment along the time order of the precursor anchor chain, and a sequential reception rule table is generated based on the priori priority constraint.

[0068] After generating the precursor anchor chain to characterize the starting point of degradation, to ensure the uniqueness and directionality of the temporal sequence of the precision pulley mold lifecycle data in subsequent correlation processing, a priori constraint is introduced to ensure that the operational data is sequentially inherited only along the temporal order of the precursor anchor chain, and a sequential inheritance rule table is further generated. This process includes the following steps in its implementation:

[0069] After the precursor anchor chain is formed, all running data in the data chain is retrieved again based on the time alignment index to clarify the position of each piece of running data in the time chain and the time distance between it and the corresponding precursor anchor. In this way, a mapping relationship can be established between running data and corresponding precursor anchors on the time axis, giving all data units in the data chain a clear temporal assignment. Subsequently, based on the chronological order of each precursor anchor in the time series, the data chain is divided into several consecutive time-series segments. Each time-series segment starts with a precursor anchor and ends with the time position of the next precursor anchor, thus naturally dividing the data chain into interval structures with sequential dependencies in time. This provides the basis for establishing subsequent preorder priority constraints in terms of time partitioning.

[0070] After dividing the time-series segments, the temporal sequence of the precursor anchor chain is used as the sole main thread to sequentially connect the operational data within each segment, ensuring that the order of the operational data perfectly matches the temporal sequence of the precursor anchors. To guarantee the continuity of data continuity, the time identifier of each piece of operational data is based on the starting anchor point of its segment. When operational data crosses a segment boundary, it is not allowed to connect backwards with data in the previous segment; instead, the operational data can only begin time continuity from the starting anchor point of the current segment. This method ensures that the flow of operational data in the data chain remains unidirectional, extending from earlier precursor anchor points to subsequent precursor anchor points, preventing reverse superposition or jumps in the data association sequence. At this point, the data chain forms a unidirectional time-series continuity channel guided by precursor anchors, ensuring that the attribution of operational data at each stage corresponds perfectly to the direction of temporal evolution, providing a continuous data foundation for the establishment of subsequent constraint rules.

[0071] After completing the temporal arrangement of the operational data, a priori constraint is introduced. This ensures that each temporal segment, when performing data association, must preferentially reference the termination state of its preceding segment as the starting condition. This priori constraint is implemented through a temporal correspondence; that is, each piece of operational data, when written into the data chain, must reference the state characteristic value of the termination time of its preceding segment as a continuation parameter, thus guaranteeing the temporal continuity of the data chain. Through this priori constraint, each part of the data chain is driven by the output of the preceding segment, resulting in a strict unidirectional dependency in the temporal order of all data in the chain. When new operational data appears, it can only connect to the segment endpoint corresponding to the nearest preceding precursor anchor point, and cannot directly associate with earlier or later time periods across intermediate segments. This approach ensures that the entire data chain forms an irreversible, time-to-time continuity logic, preventing subsequent operational data from overwriting or overlapping earlier data states.

[0072] After establishing the priority constraints, a sequential inheritance rule table is generated based on the established inheritance relationships. This table records the sequential dependencies and connection conditions between each time inheritance segment. The sequential inheritance rule table includes the start and end times corresponding to each precursor anchor point, the time span between adjacent segments, the time distribution range of the running data, and the inheritance boundaries between preceding and subsequent data within each segment. The generation of the sequential inheritance rule table gives the data chain a traceable sequential logical framework, enabling subsequent newly added data to automatically match the corresponding inheritance segment and follow the established priority constraints when written. The sequential inheritance rule table not only limits the writing order of the running data but also defines the time connection conditions and data inheritance boundaries between different segments, ensuring that the lifecycle data maintains a unified temporal direction and inheritance consistency during subsequent processing. This method ensures that the lifecycle data of the precision pulley mold has a continuous and controllable evolutionary relationship in the time dimension, providing complete temporal logical support for subsequent anti-overwrite aggregation and time-series traction processing.

[0073] The data aggregation method of the data chain is rearranged and managed based on the sequential acceptance rule table. The acceptance time window and dilution time window are divided before and after the precursor anchor chain. Restricted write management control is applied to the data entering the dilution time window to generate anti-overwrite aggregation results.

[0074] After generating the sequential inheritance rule table, to further ensure the continuity and authenticity of the precision pulley mold lifecycle data in the time dimension, the data aggregation method in the data chain is structurally rearranged based on the sequential inheritance rule table. This allows the data to be effectively partitioned in the time direction according to the preceding and following order of the precursor anchor chain. Within each partition, inheritance time windows and dilution time windows are divided. Restricted write control is applied to data entering the dilution time window to form an anti-overwrite aggregation result that prevents early degradation information from being overwritten by subsequent data. The entire process includes the following steps:

[0075] Based on the time position of each precursor anchor point, the time span of the preceding and following segments, and the distribution range of the running data recorded in the sequential succession rule table, all data in the data chain is re-scanned and located. Through the time index relationship provided by the sequential succession rule table, the start and end positions of each precursor anchor point in the data chain can be clearly defined, thus forming a series of succession intervals with time boundaries on the time axis. To maintain temporal continuity, the time boundaries between each succession interval are connected using the time sequence of the precursor anchor chain, ensuring that the end time of each interval is adjacent to the start time of the next interval, avoiding time overlap or discontinuities. On this basis, according to the time interval before and after the precursor anchor point corresponding to each succession interval, the time structure of the data chain is divided into multiple independently manageable time segments, laying a continuous time framework for subsequent division of succession time windows and dilution time windows.

[0076] After dividing the time intervals into accepting periods, each interval is further subdivided based on the temporal density, data type, and temporal distance from the precursor anchor chain. Accepting period windows and dilution period windows are defined for each interval. The accepting period window is used to receive operational data that is adjacent to the precursor anchor's time position and directly reflects the trend of deterioration. The dilution period window is used to include data that is far from the precursor anchor, has a smaller change amplitude, and contributes limitedly to the state evolution. In practice, the accepting period window is extended forward and backward from the precursor anchor and is limited according to the proportion of the segment time span in the sequential accepting rule table. This ensures that the data covered by the accepting period window can fully describe the state changes before and after the precursor anchor. The dilution period window is located outside the accepting period window and is mainly used to record normal data generated during normal operation. This two-layer time window division creates a hierarchical structure in the data chain, consisting of a "deterioration state main segment" and a "normal state buffer segment," providing clear spatial boundaries for subsequent restricted write control.

[0077] After dividing the time windows, restricted write control is applied to data entering the dilution time window to prevent normal operating data from overwriting deteriorated information within the subsequent time window. The core of restricted write control lies in specifying the time interval, frequency, and priority of data written to the data chain within the dilution time window. Specifically, when new operating data enters the dilution time window, the system, according to the preset time directionality requirements in the sequential succession rule table, only allows it to be updated intermittently. This means that representative data points are retained on the time chain instead of continuous records, thereby reducing the proportion of normal data. When the dilution time window is adjacent to the subsequent time window, the data entering the dilution time window is automatically given a lower write priority, preventing it from overwriting deteriorated information previously formed within the subsequent time window. Through this restricted write method, it is ensured that early abnormal changes are continuously preserved over time within the entire data chain, without being diluted or obscured by a large amount of normal operating data.

[0078] After completing restricted write control, the data chain, after time window partitioning and write control processing, is rearranged and summarized to generate an anti-overwrite summary result. The rearrangement and summarization process uses a sequential succession rule table as a constraint, aggregating data within each time window according to the chronological order of the precursor anchor chain. This ensures that the data from the succession time windows remains complete and continuous in the summary result, while the data from the dilution time windows is presented in a compressed or intermittent manner. The anti-overwrite summary result not only preserves the integrity of degradation information in the time series but also achieves structured balance in data volume control, enabling the lifecycle data to prominently reflect the time trend of mold performance degradation after overall summarization. This anti-overwrite summary result ultimately forms a data structure organized chronologically with degradation state as the main thread, providing an input foundation with time-discriminative and state-continuous characteristics for subsequent time-driven control.

[0079] Based on the summary results of the anti-coverage, the timing-driven management and control are implemented. When the precursor anchor chain continues to appear, the data release rhythm is adjusted to suppress the data input within the dilution time window and release the running data in the pulse silence interleaved order so that the precursor anchor chain can be continuously received in the data chain.

[0080] After generating the anti-coverage summary results, to ensure the continuous continuity of the precision pulley mold lifecycle data across time, a time-series traction control method is introduced into the data chain. This dynamically adjusts the data release rhythm, enabling proactive suppression of data input within the diluted time window when precursor anchor chains continuously appear in the time series. Operational data is released in a pulse-silent interleaved manner, thus ensuring the continuity and integrity of the precursor anchor chains within the data chain. This process includes the following steps:

[0081] After the anti-coverage summary results are generated, the recorded acceptance time windows and dilution time windows are identified by time correlation, and a release management logic based on time sequence is established. The anti-coverage summary results already include the time distribution of each precursor anchor chain, the boundaries of the acceptance interval, and the distribution of the dilution interval. Therefore, before the start of time-series traction control, it is necessary to extract the arrangement order of each time window on the time axis based on the summary results. By reorganizing the time distribution, the time interval between each acceptance time window and the adjacent dilution time window and its relative position in the data chain can be clarified, so that the entire data chain forms a continuous release sequence in the time dimension. On this basis, an independent release identifier is assigned to each time period, so that different time windows can be executed sequentially during the release process, thereby providing an accurate time reference for subsequent release rhythm adjustments. Through this step, each time window in the data chain is mapped to a tractionable release unit, so that subsequent timing adjustments have a locatable time control object.

[0082] After marking the time window for release, the time periods in the anti-coverage summary results that are in a state of continuous occurrence of the precursor anchor chain are detected and calibrated. When multiple consecutive precursor anchors appear adjacently in the data chain, it indicates that the mold degradation state is continuously accumulating. At this time, it is necessary to adjust the input rhythm of normal data to prevent the degradation state from being diluted. In this stage, by traversing the time sequence of the anti-coverage summary results, the interval time between consecutive anchors in the precursor anchor chain is identified, and these time periods are defined as high-sensitivity segments. When releasing the time window in the high-sensitivity segment, the running data is no longer input according to the normal time rhythm, but is released in a controlled manner according to the continuous state of the precursor anchors. That is, data associated with the degradation state is given priority to enter the data chain first, while the input of normal running data that is far away from the degradation state is temporarily suspended. In this way, the data chain dynamically guides the degradation stage in the time dimension, and the information flow of abnormal state is prioritized to extend backward, laying the foundation for the adjustment of the subsequent release rhythm.

[0083] After identifying highly sensitive sections, time-driven control is initiated, dynamically adjusting the data release rhythm based on the anti-coverage summary results and release indicators. During this process, the receiving time window and the dilution time window are released alternately in chronological order. When a precursory anchor chain is detected to persist within the current time period, rhythm adjustment is immediately triggered, suppressing data input within the dilution time window. This suppression is achieved by controlling the time interval and release ratio; specifically, extending the input interval of normal data within highly sensitive sections and shortening the data transmission interval of the receiving time window, ensuring that the transmission frequency of degraded information in the data chain is higher than that of normal data. Simultaneously, to prevent time gaps in the data chain due to prolonged suppression, the release process employs a pulse-silent interleaved sequence. This involves releasing a portion of degraded data in a pulsed manner for a short period, followed by a silent phase pausing dilution data input, and then interleaving to release the next batch of normal data. This alternating pulse and silent time control method creates a regular, intermittent data flow on the timeline, resulting in a rhythmic progression that ensures continuous reception of degraded data while maintaining the temporal integrity of the data chain.

[0084] After completing the pulse silence interleaved release, the data chain controlled by time sequence is continuously integrated to ensure that the precursor anchor chain remains structurally coherent and traceable within the data chain. During this stage, the released data is sequentially checked using the timestamps in the anti-coverage summary results. This ensures that the degradation-related data from the pulse release is strictly arranged along the sequence of the precursor anchors in the time chain. Data temporarily deferred during the silence phase is supplemented in the next release cycle with a time delay, thus forming a continuous, time-progressive structure. After this integration process, the data chain exhibits a time-based structure centered on the precursor anchor chain. The time intervals between each precursor anchor are preserved, and the boundary between the transition time window and the dilution time window remains clear. The data chain controlled by time sequence not only retains the continuous state information of the mold during the degradation phase but also forms a dynamic data structure with controllable temporal rhythm at the time level. This allows subsequent lifecycle management processes to predict states and allocate loads based on a complete, continuous, and directional data foundation.

[0085] This invention establishes a time-based data chain management structure throughout the entire lifecycle of a precision pulley mold, enabling continuous and unified management of multi-source data generated during the design, processing, operation, and maintenance phases, all within the same timeframe. By implementing time alignment management and early warning anchor point solidification management of the data chain, subtle early degradation signals maintain their independent temporal position and continuous visibility in subsequent data processing. This fundamentally avoids the problem of degradation information being overwritten or diluted by subsequent normal operation data, thus ensuring continuous and traceable management of the mold's state evolution process in the time dimension. This provides accurate and time-consistent basic data support for the early identification of degradation trends.

[0086] This invention introduces a priori constraint management mechanism and a time-driven control management mechanism, ensuring that data strictly follows time-sequence logic during aggregation and release management. Operational data can only extend segment by segment along the sequence of precursor anchor points. Through anti-overwrite aggregation management and restricted write management mechanisms, the dominant position of degradation signals in the time dimension is maintained, enabling the data chain management process to accurately reflect the dynamic changes in mold performance degradation. This allows for proactive control of mold status in lifespan prediction management, maintenance decision management, and load allocation management, improving the stability and consistency of production operations.

[0087] This invention provides, for example Figure 2 The precision pulley mold lifecycle management system shown includes a time reference data construction module, a precursor anchor point generation module, a sequential acceptance constraint module, an anti-overlay summary control module, and a time-series traction scheduling module.

[0088] The time-base data construction module continuously collects design version change data, machining process record data, operating condition waveform data, and maintenance and handling trajectory data during the entire life cycle management of precision pulley molds. The collected data is uniformly written into a data chain with the same time base for time-series management, and a time alignment index is generated at the end of the data chain.

[0089] The precursor anchor point generation module solidifies and manages abnormal segments with continuous changing trends in chronological order, and generates a precursor anchor point chain to characterize the starting point of degradation.

[0090] The sequential acceptance constraint module introduces prior priority constraints in the subsequent data association process of the data chain based on the precursor anchor chain, so that the running data is accepted only segment by segment along the time order of the precursor anchor chain, and a sequential acceptance rule table is generated based on the prior priority constraints.

[0091] The anti-overlay aggregation control module rearranges the data aggregation method of the data chain based on the sequential acceptance rule table, divides the acceptance time window and dilution time window before and after the precursor anchor point chain, and applies restricted write management control to the data entering the dilution time window to generate anti-overlay aggregation results.

[0092] The timing-driven scheduling module performs timing-driven management and control based on the anti-coverage summary results. When the precursor anchor chain continues to appear, it adjusts the data release rhythm, suppresses and dilutes the data input within the time window, and releases the running data in the pulse silence interleaved sequence, so that the precursor anchor chain remains continuously connected in the data chain.

[0093] The precision pulley mold lifecycle management method provided in this embodiment of the invention is implemented through the aforementioned precision pulley mold lifecycle management system. For details of the specific methods and processes of the precision pulley mold lifecycle management system, please refer to the embodiments of the aforementioned precision pulley mold lifecycle management method, which will not be repeated here.

[0094] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A lifecycle management method for precision pulley molds, characterized in that, Includes the following steps: In the whole life management of precision pulley molds, design version change data, machining process record data, operating condition waveform data, and maintenance and handling trajectory data are continuously collected, and the collected data are uniformly written into a data chain with the same time base for time sequence management, and a time alignment index is generated at the end of the data chain. Anomaly fragments with a continuous changing trend are solidified in chronological order to generate a precursor anchor chain; The steps for generating the precursor anchor chain are as follows: After establishing the time-aligned index, the design version change data, processing record data, operating condition waveform data and maintenance and handling trajectory data arranged in chronological order in the data chain are analyzed segment by segment to determine the time position and status information of each type of data. After determining the time location, adjacent time periods are compared and connected based on time continuity to identify data segments that deviate from the normal operating mode, and the start time of the first occurrence of abnormal changes is recorded. After identifying the first occurrence of the abnormal change, continue to track the continuation of the abnormality in subsequent time periods, and link the abnormal segments with a continuous changing trend sequentially to form an abnormal segment sequence. After obtaining the abnormal fragment sequence, it is solidified according to the order in the time alignment index to generate a precursor anchor chain to characterize the deterioration starting point; Based on the precursor anchor chain, a priori priority constraint is introduced in the subsequent data association process of the data chain, so that the running data is only received segment by segment along the time order of the precursor anchor chain, and a sequential reception rule table is generated based on the priori priority constraint. The data aggregation method of the data chain is rearranged and managed based on the sequential acceptance rule table. The acceptance time window and dilution time window are divided before and after the precursor anchor chain. Restricted write management control is applied to the data entering the dilution time window to generate anti-overwrite aggregation results. Based on the summary results of anti-coverage, the timing-driven management and control are implemented. When the precursor anchor chain continues to appear, the data release rhythm is adjusted to suppress data input within the dilution time window and release the running data in the pulse silence interleaved sequence.

2. The lifecycle management method for precision pulley molds according to claim 1, characterized in that, The steps for generating a time-aligned index are as follows: In the whole life cycle management of precision pulley molds, design version change data, machining process record data, operating condition waveform data and maintenance trajectory data are collected for each stage of the mold from design to operation and maintenance, while maintaining the continuity of the data generation sequence. After completing the collection of various data, the collected data are organized under the same time benchmark. By attaching a time stamp of the time of generation to each data, data from different sources are mapped to the same time axis to form a continuous relationship. Based on a unified time reference, data is written into the data chain structure one by one in chronological order, so that the data chain truly reflects the continuous evolution process of the mold from design to maintenance; After the data chain is built, a time-aligned index is generated at the end of the data chain to record the time identifier and arrangement position of each data unit, so as to realize time-order-based access and association during the lifecycle management process.

3. The lifecycle management method for precision pulley molds according to claim 1, characterized in that, When generating the precursor anchor chain, the start time of each abnormal segment sequence is used as the time identifier of the precursor anchor, and subsequent data segments with continuous abnormalities are used as extensions of the anchor chain, so as to keep the time order between precursor anchors consistent with the original time order in the data chain.

4. The lifecycle management method for precision pulley molds according to claim 1, characterized in that, The steps for generating the sequential succession rule table are as follows: After the precursor anchor chain is formed, all running data in the data chain is retrieved according to the time alignment index to determine the position of each running data in the time chain and the time distance between it and the corresponding precursor anchor. Then, continuous time continuation segments are divided according to the time sequence of the precursor anchor. After completing the division of time-bound sections, the operational data within each section is sequentially connected using the time sequence of the precursor anchor chain as the main thread. After the data connection is completed, a priori constraint is introduced so that each time segment takes over the data association will preferentially use the termination state of the previous segment as the starting condition. After establishing the precedence priority constraint, a sequential succession rule table is generated based on the succession relationship.

5. The lifecycle management method for precision pulley molds according to claim 4, characterized in that, When generating the sequential succession rule table, the start and end times corresponding to each precursor anchor point, the time span between adjacent time succession segments, the time distribution range of the running data, and the succession boundaries of the preceding and following data are recorded. During the writing of running data, the writing order and time connection conditions of the data are limited according to the sequential succession rule table.

6. The lifecycle management method for precision pulley molds according to claim 4, characterized in that, The steps for generating the anti-coverage summary results are as follows: Based on the time position of the precursor anchor point, the time span of the preceding and following sections, and the distribution range of the running data recorded in the sequential succession rule table, all data in the data chain is re-scanned and located to form a continuous time succession interval. After completing the division of the time transition intervals, each interval is further subdivided based on the time density, data type, and time distance from the precursor anchor chain of the running data, and the transition time window and dilution time window are defined respectively. After the time window is divided, restricted write control is applied to the data entering the dilution time window, limiting its time interval, write frequency and write priority; After completing the restricted write control, the data chain is rearranged and summarized according to the constraints of the rule table in sequence to generate the anti-overwrite summary result.

7. The lifecycle management method for precision pulley molds according to claim 6, characterized in that, When restrictive write control is applied to data entering the dilution time window, the write order of running data is constrained by the time direction preset in the sequential succession rule table. Data is only allowed to be updated in a time interval manner, and the write priority of data in the dilution time window is automatically reduced when the succession time window is adjacent to the dilution time window.

8. The lifecycle management method for precision pulley molds according to claim 6, characterized in that, Based on the summary results of anti-coverage, time-driven management and control are implemented. When the precursor anchor chain continues to appear, the data release rhythm is adjusted to suppress data input within the dilution time window, and the running data is released in a pulse-silent interleaved sequence. The steps are as follows: After the summary results of the anti-coverage measures are generated, the time correlation identification of the recorded acceptance time window and dilution time window is performed, the release management logic is established according to the time sequence, and a release identifier is assigned to each time period. After completing the release marking, the time period during which the precursor anchor chain continuously appears in the anti-coverage summary results is detected. The continuously appearing precursor anchors are marked, high-sensitivity sections are defined, and controlled release is implemented. After identifying highly sensitive sections, the data release rhythm is dynamically adjusted based on the summary results of anti-coverage and release indicators. The operation data is released in a pulse-silent staggered sequence, and data input within the dilution time window is suppressed. After completing the pulse silence interleaved release, the data link controlled by the timing traction is continuously integrated to ensure that the precursor anchor link maintains a temporal sequence in the data link.

9. A precision pulley mold lifecycle management system, used to implement the precision pulley mold lifecycle management method according to any one of claims 1-8, characterized in that, It includes a time base data construction module, a precursor anchor point generation module, a sequential inheritance constraint module, an anti-overlay aggregation control module, and a time-series traction scheduling module: The time-base data construction module continuously collects design version change data, machining process record data, operating condition waveform data, and maintenance and handling trajectory data during the entire life cycle management of precision pulley molds. The collected data is uniformly written into a data chain with the same time base for time-series management, and a time alignment index is generated at the end of the data chain. The precursor anchor point generation module solidifies and manages abnormal segments with continuous changing trends in chronological order, and generates a precursor anchor point chain. The sequential acceptance constraint module introduces prior priority constraints in the subsequent data association process of the data chain based on the precursor anchor chain, so that the running data is accepted only segment by segment along the time order of the precursor anchor chain, and a sequential acceptance rule table is generated based on the prior priority constraints. The anti-overlay aggregation control module rearranges the data aggregation method of the data chain based on the sequential acceptance rule table, divides the acceptance time window and dilution time window before and after the precursor anchor point chain, and applies restricted write management control to the data entering the dilution time window to generate anti-overlay aggregation results. The timing-driven scheduling module performs timing-driven management and control based on the anti-coverage summary results. When the precursor anchor chain continues to appear, it adjusts the data release rhythm, suppresses and dilutes the data input within the time window, and releases the running data in the pulse silence interleaved sequence.

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