CN-122019543-A - Digital twinning-oriented dynamic data mapping method and system

CN122019543ACN 122019543 ACN122019543 ACN 122019543ACN-122019543-A

Abstract

The invention relates to the technical field of digital twinning and discloses a digital twinning-oriented dynamic data mapping method and a digital twinning-oriented dynamic data mapping system, wherein the digital twinning-oriented dynamic data mapping method comprises the steps of collecting real-time data flow of an equipment operation process and preprocessing, extracting samples and analyzing feature importance, identifying a current operation mode, dynamically selecting feature subsets and mapping parameters according to the modes, executing dynamic mapping, analyzing feature correlation and grouping, constructing a mapping structure and executing mapping calculation layer by layer, calculating mapping residual errors and analyzing error evolution trend, triggering an updating process to re-identify the operation mode and updating mapping parameters, counting and analyzing mapping error characteristics, carrying out error feature decomposition and carrying out deviation compensation and smoothing processing, detecting data abnormality and labeling quality labels, carrying out abnormal data restoration, executing mapping and labeling sources, and carrying out abnormal generation maintenance early warning on a statistical sensor.

Inventors

SUN LINGYU
ZHANG YAO
Sun lingkun
WANG CHAO

Assignees

山东翼动智能科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260130

Claims (10)

1. The digital twinning-oriented dynamic data mapping method is characterized by comprising the following steps of: Acquiring real-time data flow of the equipment operation process based on the multi-source sensor and preprocessing to obtain standardized data flow; based on the standardized data flow, extracting samples, analyzing and identifying the current operation mode, dynamically selecting a feature subset and mapping parameters according to the mode, and executing dynamic mapping to obtain a dynamic mapping result; analyzing and grouping the characteristic correlation based on the dynamic mapping result, constructing a mapping structure, and executing mapping calculation layer by layer to obtain a layering mapping result; Calculating a mapping residual error and analyzing an error evolution trend based on the layering mapping result, triggering an updating flow to re-identify an operation mode and update mapping parameters, and obtaining an optimized and updated mapping result; based on the mapping result after optimization and updating, carrying out statistics analysis on the mapping error characteristics, carrying out error characteristic decomposition, carrying out deviation compensation and smoothing treatment, and obtaining a mapping result after error self-correction; Based on the standardized data flow and the mapping result after error self-correction, detecting data abnormality and labeling quality labels, repairing abnormal data, executing mapping and labeling sources, counting abnormal generation maintenance early warning of the sensor, and obtaining an abnormal fault-tolerant mapping result and sensor health management information.
2. A digital twinning oriented dynamic data mapping method according to claim 1, wherein the step of obtaining a normalized data stream comprises: Each data source device is connected to a clock synchronization network to synchronize clocks to a time server, and a global time stamp is obtained during data acquisition; Calculating the greatest common divisor of the sampling periods of all data sources to obtain a basic time unit, setting space-time anchor points at intervals of the basic time unit, and dividing a time axis into time slicing sequences; calculating statistical features in the time slices by adopting a sliding window method, and taking the statistical features as attribute values of the time slices; Filling the data value into the subsequent time slicing until the next update by adopting a state holding strategy for the low-frequency data stream; transforming the position data in the local coordinate system of each device to a global coordinate system through a coordinate transformation matrix; integrating the time alignment and the space alignment results to construct a unified space-time data stream taking the time slicing index as a main key.
3. The method for mapping dynamic data to digital twinning according to claim 1, wherein the step of obtaining the dynamic mapping result includes: Selecting a time window from the standardized data stream, extracting a data sequence of each sensor to form a feature matrix, extracting target parameters of a virtual model to form a target vector, and obtaining a mapping learning sample set; Calculating mutual information values between each input feature and the target parameter, and selecting features with the mutual information values exceeding a preset importance threshold as effective feature subsets; Extracting key parameters of the operation modes from the historical data for clustering, calculating the characteristic vector of each operation mode and storing the characteristic vector in an operation mode library; comparing the similarity between the key parameters of the current time window and the feature vectors of all modes in the operation mode library, and selecting the mode with the highest similarity as the current operation mode; inquiring a mapping model library according to the current operation mode label, loading mapping parameters if the mapping model library exists, and establishing and storing a mapping relation according to the effective feature subset if the mapping parameter does not exist; And inputting the feature matrix into a mapping model to calculate a predicted value of the target parameter, and fine-tuning the mapping parameter based on the mapping residual error.
4. The method for mapping digital twin-oriented dynamic data according to claim 1, wherein the step of obtaining the hierarchical mapping result comprises: Calculating correlation coefficients among the features, and considering strong correlation when the absolute value of the correlation coefficients is larger than a preset strong correlation threshold value to obtain a feature correlation matrix; taking the characteristic correlation matrix as an undirected graph, dividing the nodes into a plurality of subgraphs by adopting a graph clustering method, and obtaining a characteristic grouping result; Analyzing the mapping relation between each feature group and the virtual model target parameter to construct a multi-layer mapping structure; performing mapping calculation layer by layer according to the hierarchical structure, and performing weighted summation calculation on the feature group input mapping function of each layer; the mapping tasks of different feature groups are distributed to different computing cores to be executed in parallel, and the next layer is entered after the result is synchronized; and summarizing the final model parameters output by the mapping of the last layer and updating the model state.
5. The digital twinning-oriented dynamic data mapping method according to claim 1, wherein the step of obtaining the optimized updated mapping result comprises: comparing the predicted output of the virtual model with the actual measured value of the physical entity to obtain a mapping residual error, and organizing to obtain a mapping error sequence; the mapping error sequence is smoothed by a moving average method, then a slope is calculated, and when the absolute value of the slope is larger than a preset trend judgment threshold value or an error mutation is detected, the mapping relation is judged to evolve and an update flow is triggered; Collecting data samples of the latest time window, recalculating feature importance ranking, re-executing operation mode identification, and adding a mode library when a new operation mode is identified; Based on the latest sample and the updated feature importance, performing iterative optimization by using the new sample with the original parameter as an initial value to obtain updated mapping parameters; And recording the related information updated by the mapping relation to an evolution history database.
6. The method for mapping digital twin-oriented dynamic data according to claim 1, wherein the step of obtaining the error self-corrected mapping result comprises: extracting error data in a stable operation period, and calculating an error mean value and a standard deviation; The total error is decomposed into systematic deviation and random error, wherein the systematic deviation is the mean value of an error sequence, and the random error is the difference between the error value at each moment and the systematic deviation; Correcting systematic deviation by constructing a deviation compensation function, and smoothing random error by adopting a filtering method; and updating the model state of the corrected and smoothed mapping output, and feeding back residual information to the mapping relation learning module.
7. The digital twinning-oriented dynamic data mapping method according to claim 1, wherein the step of obtaining the mapping result of abnormal fault tolerance and the sensor health management information comprises: Performing anomaly detection on the data record, marking as statistical anomaly when the data value exceeds a normal value range, marking as physical anomaly when the data value violates physical constraint, and marking as dynamic anomaly when the change rate exceeds a preset threshold; When the abnormal data has the redundant sensor, replacing the abnormal data by a data value of the redundant sensor, and marking a data source in the quality label; when the redundant sensor does not exist, calculating a data value at an abnormal moment by adopting a time sequence interpolation method; For the situation that data is missing for a long time, deducing estimated values of missing parameters by using measured values of other sensors according to a physical model; executing a mapping flow on the repaired data, and marking a data source and a confidence level in a mapping result; And counting abnormal data frequency of each sensor, and generating a sensor health early warning message when the abnormal proportion exceeds a preset health early warning threshold value.
8. A digital twin dynamic data mapping method according to claim 3, wherein the length of the selected time window is set according to the complete machining cycle of the equipment, and the key parameters of the operation mode include spindle rotation speed interval, feed speed interval, cutting depth, tool type and process type, and the historical operation data is divided into a rough machining high speed mode, a rough machining low speed mode, a finish machining high precision mode, a drilling mode, a tapping mode, an idle mode, a fault mode and a transition mode by a clustering algorithm.
9. The digital twin oriented dynamic data mapping method of claim 4, wherein the multi-layer mapping structure comprises a first layer mapping, a second layer mapping and a third layer mapping, the first layer mapping maps the original feature set to the intermediate parameters, the second layer mapping maps the intermediate parameters and the feature set to the next intermediate parameters, the third layer mapping maps the intermediate parameters to the final model parameters, and when the absolute value of the slope of the mapping error sequence is greater than a preset trend decision threshold or an error mutation is detected, the feature importance analysis and the operation pattern recognition are re-performed to update the mapping parameters.
10. A digital twinning oriented dynamic data mapping system for performing a digital twinning oriented dynamic data mapping method according to any one of claims 1 to 9, comprising: the data acquisition module acquires real-time data flow of the equipment operation process based on the multi-source sensor and performs preprocessing to obtain standardized data flow; the mapping learning module is used for extracting samples based on the standardized data stream, analyzing and identifying the current operation mode, dynamically selecting a feature subset and mapping parameters according to the mode, and executing dynamic mapping to obtain a dynamic mapping result; The dimension reduction calculation module analyzes the characteristic correlation based on the dynamic mapping result and groups the characteristic correlation, constructs a mapping structure and executes mapping calculation layer by layer to obtain a layering mapping result; The evolution tracking module calculates a mapping residual error and analyzes an error evolution trend based on the layering mapping result, triggers an updating process to re-identify an operation mode and update mapping parameters, and obtains an optimized and updated mapping result; The error correction module is used for carrying out statistics analysis on the mapping error characteristics based on the mapping result after optimization and updating, carrying out error characteristic decomposition, carrying out deviation compensation and smoothing treatment, and obtaining a mapping result after error self-correction; and the robust processing module is used for detecting data anomalies and labeling quality labels based on the standardized data flow and the mapping result after error self-correction, repairing the abnormal data, executing mapping and labeling sources, counting abnormal generation maintenance early warning of the sensor, and obtaining the mapping result of abnormal fault tolerance and sensor health management information.

Description

Digital twinning-oriented dynamic data mapping method and system Technical Field The invention relates to the technical field of digital twinning, in particular to a dynamic data mapping method and a dynamic data mapping system for digital twinning. Background The digital twin technology realizes real-time monitoring, simulation analysis and optimization control of the running state of a physical system by constructing a virtual mapping model of a physical entity, and becomes a key enabling technology in the fields of intelligent manufacturing, smart city, energy management and the like. The core of the digital twin system is to establish a real-time, accurate and dynamic data mapping relation between the physical entity and the virtual model, so that the virtual model can synchronously reflect the real state and the behavior characteristics of the physical entity. In the field of intelligent manufacturing, production equipment is various, including digit control machine tools, industrial robots, sensor networks, visual detection systems and the like, and data generated by the equipment has obvious multi-source heterogeneous characteristics. The sampling frequency of the data sources varies greatly, from millisecond-level high frequency data of the machine tool controller to second-level low frequency data of the production management system, with time scales spanning orders of magnitude. The time references of the different data sources come from the respective clock systems and there are clock drift and synchronization errors. In addition, the distribution positions of the equipment in the physical space are different, and the local coordinate system established by the equipment has a translation and rotation relation relative to the global coordinate system of the production line. These spatiotemporal heterogeneous characteristics present challenges to data mapping. The traditional digital twin data mapping method mainly adopts static rule configuration or a mapping strategy based on a fixed template. In the system initialization stage, the corresponding relation between the data source and the model parameters is established through manual configuration, for example, the measured value of a certain sensor is multiplied by a fixed coefficient and then assigned to the model parameters. The static mapping method can meet basic requirements at the initial stage of system operation, but when the operation working conditions of physical entities change, such as processing technology switching, equipment state adjustment, product model replacement and the like, the association relationship between data characteristics and model parameters changes accordingly, and a fixed mapping rule is invalid, so that a virtual model cannot accurately reflect the real state of the physical entities. In addition, static mapping methods lack dynamic assessment of data quality and exception handling capability, and mapping processes are prone to interruption or false results when a sensor fails or data is missing. With the complexity and refinement of digital twin application scenarios, higher requirements are put on data mapping techniques. The method is used for solving the problem of space-time alignment of multi-source heterogeneous data, ensuring that data with different sampling frequencies and different time references can establish an accurate mapping relation under a unified space-time coordinate system, and realizing dynamic self-adaptive adjustment of the mapping relation so that a mapping strategy can be automatically optimized according to the state change of a physical entity and the mapping precision is maintained. Meanwhile, in a high-dimensional data space, the mapping calculation complexity needs to be reduced to meet the real-time requirement, and the robustness of the mapping process to data anomalies and deletions is enhanced. Therefore, a dynamic data mapping method capable of coping with the inconsistency of multi-source heterogeneous data time sequence, adapting to the dynamic change of mapping relation, reducing the calculation complexity of high-dimensional mapping and guaranteeing the mapping continuity under the condition of data abnormality is urgently needed so as to support the high-fidelity application of the digital twin system in complex industrial scenes. Disclosure of Invention The invention provides a digital twinning-oriented dynamic data mapping method and a digital twinning-oriented dynamic data mapping system, which solve the technical problems that the multi-source heterogeneous data timing sequence is inconsistent and the static mapping method lacks dynamic evaluation and exception handling on data quality in the related technology. The invention provides a digital twinning-oriented dynamic data mapping method, which comprises the following steps: Acquiring real-time data flow of the equipment operation process based on the multi-source sensor and preprocessing to obtain standardized d