CN-121457594-B - Digital transformation maturity evaluation data management method and system for coal industry

CN121457594BCN 121457594 BCN121457594 BCN 121457594BCN-121457594-B

Abstract

The application discloses a method and a system for managing digital transformation maturity evaluation data in coal industry, which relate to the field of digital transformation maturity evaluation data management in coal industry, and are used for generating event identifiers containing preset causal chains for each event by responding to original event data acquired in an industrial field and actively pushing the event identifiers to associated receiving equipment to trigger preset actions so as to realize intelligent identification and response of the event data. More importantly, the method can intelligently detect the conflict between the physical timestamp in the original event data and the causal logic indicated by the preset causal chain in the process of constructing the event causal graph. The application can remarkably improve the authenticity and reliability of the digital transformation maturity evaluation data in the coal industry, provides firm data support for strategic decisions of enterprises, avoids error decisions caused by data deviation, and has remarkable excellent technical effects.

Inventors

GUO JIANLI
LI XIUDONG
LIU NI
LI LIYING
ZHENG YINGYING
SUN YI
LIU MENGMENG
XUE GUOCHUN
SUN LIUWEI
ZHANG XUELIANG
CAO ZHENG
ZHU SHUANCHENG
YUAN JUNLIN
QIN RONGJUN
ZHAO LUZHENG
MA SHENGNAN
GAO YUNZENG

Assignees

煤炭工业规划设计研究院有限公司

Dates

Publication Date: 20260508
Application Date: 20251217

Claims (7)

1. The method for managing the digitized transformation maturity evaluation data in the coal industry is characterized by comprising the following steps of: generating event identifiers containing preset causal chains for each event in response to original event data acquired by an industrial field, wherein the preset causal chains are generated based on event type association rules; constructing an event causal graph based on preset causal link information contained in the event identifier; In the event causal graph construction process, if a conflict exists between a physical timestamp in the original event data and causal logic indicated by the preset causal link information, correcting the physical timestamp of the conflict event according to the causal logic to obtain a logic calibration timestamp; the raw event data at least comprises untreated data records collected by a gas sensor, a temperature sensor, a pressure sensor and a belt conveyor running state sensor, wherein the data records comprise any one of occurrence time, event type and related parameter values; Before the step of generating an event identifier containing a preset causal chain for each event in response to raw event data collected at an industrial site, further comprising: receiving original event data from sensor units in an industrial field, and carrying out space-time correlation aggregation on all original event data generated by the same sensor unit in a sliding time window aiming at each sensor unit; extracting corresponding physical parameters for each event in the aggregation time window, and calculating the instantaneous variation amplitude and variation rate of the physical parameters to obtain instantaneous variation characteristics; based on the transient variation characteristics, identifying key events with strong causal potential, and quantifying causal transmission efficiency of the key events to related events in combination with the variation rate; analyzing the lead-lag relation and the correlation degree between different physical parameter changes in the same aggregation time window to obtain the time sequence correlation between the physical parameters; and if the physical timestamp of the collision event is detected to have a collision with the causal logic indicated by the preset causal link information, correcting the physical timestamp of the collision event according to the causal logic, wherein the step of obtaining the logic calibration timestamp comprises the following steps of: When the preset causal link description in the original event data is fuzzy or contradictory, dynamically giving causal weight to each potential cause event according to the transient variation characteristics and the time sequence relativity; Identifying a potential cause event with the highest causal weight as a leading cause, and correcting a physical time stamp of the conflict event according to the leading cause to obtain a logic calibration time stamp; The step of dynamically assigning causal weights to each potential causal event according to the temporal variation characteristics and the timing correlation when a preset causal link description in the raw event data is ambiguous or contradictory comprises: acquiring a plurality of linkage response modes of physical parameters associated with the potential cause event in real time; comparing the variability of the linkage response modes; Identifying a dominant causative event based on the variability; each of the potential causal events is dynamically assigned a causal weight based on the identified dominant causal event.
2. The method of coal industry digitized transformation maturity evaluation data management of claim 1, wherein when a preset causal link description in the raw event data is ambiguous or contradictory, dynamically assigning causal weights to each potential causal event based on the temporal variation characteristics and the temporal correlation, comprises: acquiring current operation environment parameters in real time; According to the current operation environment parameters, adjusting a baseline value and a change threshold value for calculating the instantaneous change characteristics, and adjusting a time lag range and a correlation threshold value for analyzing time sequence correlation; calculating the transient variation characteristic of the physical parameter and the time sequence relevance according to the adjusted baseline value, the variation threshold value, the time delay range and the relevance threshold value; and dynamically giving causal weight to each potential cause event according to the transient variation characteristics and the time sequence relativity.
3. The method for managing digitized transformation maturity evaluation data of coal industry of claim 1, wherein said step of obtaining in real time a linked response pattern of a plurality of physical parameters associated with said potential causative event comprises: performing coal mine impulse noise-oriented filtering treatment on the physical parameters to obtain treated physical parameter data; performing time sequence alignment on the processed physical parameter data to obtain time sequence aligned physical parameter data; extracting a physical parameter sequence associated with the potential cause event from time sequence aligned physical parameter data according to a preset linkage rule; and constructing a linkage pattern map of the cooperative response of the equipment group according to the physical parameter sequence.
4. The method for managing digitized transformation maturity evaluation data of coal industry of claim 3, wherein said step of performing coal mine impulse noise-oriented filtering processing on said physical parameters to obtain processed physical parameter data comprises: Identifying a noise type and outlier characteristic of the physical parameter; Selecting a filtering processing method for the physical parameter according to the identified noise type and abnormal value characteristics; And combining the selected filtering processing methods, and performing filtering processing on the physical parameters to obtain processed physical parameter data.
5. The method for managing digitized transformation maturity evaluation data of coal industry of claim 4, wherein said step of identifying noise type and outlier characteristics of said physical parameter comprises: decomposing the physical parameters into a plurality of frequency band components and residual components; Analyzing energy distribution, statistical characteristics and time correlation of the plurality of frequency band components and the residual component to distinguish different types of noise; detecting whether abnormal fluctuation exists in the residual component, and carrying out association analysis by combining the occurrence time of the abnormal fluctuation and the noise intensity variation in the frequency band components to obtain an analysis result; And determining the type of the composite noise and the abnormal value characteristic existing in the physical parameter according to the analysis result.
6. The method for managing digitized transformation maturity evaluation data of coal industry of claim 4, wherein said step of selecting a filtering processing method for said physical parameters based on the identified noise type and outlier characteristics comprises: identifying low-frequency drift noise caused by transient impulse noise and formation stress change caused by starting and stopping of electromechanical equipment; adopting a cascade adaptive filter to inhibit interference components in different frequency bands in stages; the signal fidelity is monitored in real time in the filtering process, and the filtering strategy recombination is triggered when the equipment fault characteristic waveform is detected.
7. A coal industry digital transformation maturity evaluation data management system, comprising: The system comprises an identification generation module, a control module and a control module, wherein the identification generation module is used for responding to original event data acquired by an industrial field and generating an event identifier containing a preset causal chain for each event, wherein the preset causal chain is generated based on an event type association rule; The data transmission module is used for actively pushing the event identifier of each event to the associated receiving equipment through the industrial network and triggering the receiving equipment to execute a preset action; The cause and effect construction module is used for constructing an event cause and effect diagram based on preset cause and effect chain information contained in the event identifier; The time adjustment module is used for correcting the physical time stamp of the conflict event according to the causal logic when the conflict exists between the physical time stamp in the original event data and the causal logic indicated by the preset causal link information in the event causal graph construction process so as to obtain a logic calibration time stamp; Before generating an event identifier containing a preset causal chain for each event in response to the raw event data collected at the industrial site, further comprising: receiving original event data from sensor units in an industrial field, and carrying out space-time correlation aggregation on all original event data generated by the same sensor unit in a sliding time window aiming at each sensor unit; extracting corresponding physical parameters for each event in the aggregation time window, and calculating the instantaneous variation amplitude and variation rate of the physical parameters to obtain instantaneous variation characteristics; based on the transient variation characteristics, identifying key events with strong causal potential, and quantifying causal transmission efficiency of the key events to related events in combination with the variation rate; analyzing the lead-lag relation and the correlation degree between different physical parameter changes in the same aggregation time window to obtain the time sequence correlation between the physical parameters; The time adjustment module is further configured to: When the preset causal link description in the original event data is fuzzy or contradictory, dynamically giving causal weight to each potential cause event according to the transient variation characteristics and the time sequence relativity; Identifying a potential cause event with the highest causal weight as a leading cause, and correcting a physical time stamp of the conflict event according to the leading cause to obtain a logic calibration time stamp; when the preset causal link description in the original event data is ambiguous or contradictory, dynamically assigning causal weights to each potential causal event according to the transient variation characteristics and the time sequence relativity comprises: acquiring a plurality of linkage response modes of physical parameters associated with the potential cause event in real time; comparing the variability of the linkage response modes; Identifying a dominant causative event based on the variability; each of the potential causal events is dynamically assigned a causal weight based on the identified dominant causal event.

Description

Digital transformation maturity evaluation data management method and system for coal industry Technical Field The application relates to the field of coal industry digital transformation maturity evaluation data management, in particular to a coal industry digital transformation maturity evaluation data management method and system. Background In the digitalized transformation of coal industry, enterprises need a data management system which accurately reflects progress, and data time consistency of cross-system and cross-environment is a long-term pain point, namely, related technical difference and is strongly related to underground operation environment and complex integration. In the early stage of transformation, enterprises introduce multi-provider heterogeneous systems for the rapid online function, wherein a production control system uses millisecond-level time stamps, an environment monitoring system relies on second-level recording of an operating system, and hidden danger is buried due to time mechanism differences. In a severe underground environment, the aging of the quartz crystal oscillator of an old system causes frequency drift, the deviation of a timestamp and a standard source is changed from constant to continuously increased, and inconsistency is aggravated. Operation and maintenance are to reduce the synchronization failure alarm, deliberately widen the 'time difference tolerance', short-term alarm drop, and cover the problem, so that the data with deviation flows into the central repository. Finally, the accumulated deviations burst in transformation maturity evaluation, namely, the sequence of events is disordered, causal analysis fails, early warning is inaccurate, even decision is misled, and the reliability of an evaluation report is damaged. Therefore, the method effectively manages and corrects complex time deviation and ensures the reality of data, and becomes a specific technical problem to be solved urgently. Improvements are needed in the art. Disclosure of Invention The application discloses a method and a system for managing digital transformation maturity evaluation data in coal industry, and aims to solve the problem that data time stamps are inconsistent due to factors such as heterogeneous systems, hardware aging, operation and maintenance strategies and the like in the digital transformation process in the coal industry, so that accuracy and reliability of digital transformation maturity evaluation are affected. The technical scheme of the application is as follows: in a first aspect, the application discloses a method for managing digital transformation maturity evaluation data in the coal industry, which comprises the following steps: Generating, for each event, an event identifier comprising a preset causal link, the preset causal link being predefined by event type association rules, in response to raw event data collected at an industrial site; actively pushing event identifiers of each event to associated receiving equipment through an industrial network, and triggering the receiving equipment to execute preset actions; constructing an event causal graph based on preset causal link information contained in the event identifier; In the event causal graph construction process, if a conflict exists between a physical timestamp in original event data and causal logic indicated by preset causal link information, correcting the physical timestamp of the conflict event according to the causal logic to obtain a logic calibration timestamp; The raw event data at least comprises untreated data records collected by a gas sensor, a temperature sensor, a pressure sensor and a belt conveyor running state sensor, wherein the data records comprise any one of occurrence time, event type and related parameter values. Optionally, before the step of generating an event identifier containing a preset causal chain for each event in response to raw event data collected at the industrial site, the method further comprises: receiving original event data from sensor units in an industrial field, and carrying out space-time correlation aggregation on all original event data generated by the same sensor unit in a sliding time window aiming at each sensor unit; extracting corresponding physical parameters for each event in the aggregation time window, and calculating the instantaneous variation amplitude and variation rate of the physical parameters to obtain instantaneous variation characteristics; Based on the transient change characteristics, identifying key events with strong causal potential, and quantifying causal transmission efficiency of the key events to related events by combining the change rate; And analyzing the lead-lag relation and the degree of correlation between different physical parameter changes in the same aggregation time window to obtain the time sequence correlation between the physical parameters. Optionally, when a conflict is detected, correcting the physical t