CN-122022481-A - Enterprise digital operation real-time data analysis and early warning method based on big data

CN122022481ACN 122022481 ACN122022481 ACN 122022481ACN-122022481-A

Abstract

The invention discloses a method for analyzing and early warning enterprise digital operation real-time data based on big data, which relates to the technical field of data processing and comprises the following steps that S1, multisource data during enterprise operation are collected in real time, an operation data set is constructed, and the operation data set is preprocessed; and S2, carrying out feature extraction and fusion based on the preprocessed operation data set, and constructing a feature model for representing the comprehensive operation state of the enterprise. According to the enterprise digital operation real-time data analysis and early warning method based on big data, various regulation strategies are pre-evaluated, optimized and selected through online simulation deduction, and executable instructions are automatically generated to directly act on production, supply chains and service systems, so that accurate business flow regulation is implemented before risk dominance or in the early stage of spreading, and the active defense capacity and intervention timeliness of enterprises on operation risks are effectively improved.

Inventors

NIU PENGFEI
WU GUANG
LIU SHAODONG
MA LINA
WANG JINGJING

Assignees

山西鼎胜科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260202

Claims (10)

1. The enterprise digital operation real-time data analysis and early warning method based on big data is characterized by comprising the following steps: s1, acquiring multisource data in an enterprise operation period in real time, constructing an operation data set, and preprocessing the operation data set; S2, carrying out feature extraction and fusion based on the preprocessed operation data set, and constructing a feature model for representing the comprehensive operation state of the enterprise; s3, based on the characteristic model, carrying out real-time analysis and future trend prediction on enterprise comprehensive benefit indexes, and identifying potential operation risks; s4, inputting the identified potential operation risk into a regulation and control closed loop module, wherein the regulation and control closed loop module is generated through online simulation deduction and optimized selection based on the potential operation risk and the real-time state of each business link of the current enterprise, and then outputs a regulation and control strategy aiming at a specific business link, converts the regulation and control strategy into an executable instruction and transmits the executable instruction to a corresponding business execution system so as to dynamically adjust related business flows; And S5, visually displaying one or more information of future operation trend, comprehensive benefit index, identified risk, generated regulation strategy and execution effect of the enterprise.
2. The method for analyzing and pre-warning real-time data of digitized operation of enterprise based on big data of claim 1, wherein in step S1, said multi-source data comprises equipment operation data, enterprise financial data, customer behavior data, human resource data, and external environment data.
3. The method for analyzing and pre-warning the enterprise digital operation real-time data based on the big data according to claim 2, wherein the step S2 specifically comprises the following steps: S21, extracting equipment running state characteristics, enterprise financial characteristics, customer behavior characteristics, human resource characteristics and external environment characteristics from the preprocessed running data set; S22, performing fusion processing on the extracted various features to form a comprehensive feature vector; s23, constructing a dynamic feature model capable of reflecting the multidimensional operation state of the enterprise based on the comprehensive feature vector.
4. The method for analyzing and pre-warning the enterprise digital operation real-time data based on the big data as set forth in claim 3, wherein the step S3 specifically comprises the following steps: s31, calculating enterprise comprehensive benefit indexes at the current and future time points by using a time sequence prediction model based on the dynamic characteristic model; S32, evaluating the overall trend of enterprise operation according to the historical data and the forecast data of the enterprise comprehensive benefit index; And S33, analyzing abnormal variation amounts of the running state, the supply chain state and the client state of the equipment based on the dynamic characteristic model, and identifying the equipment as potential operation risk when the abnormal variation amounts exceed a preset threshold or the enterprise comprehensive benefit index shows unexpected descending trend.
5. The method for analyzing and early warning the enterprise digital operation real-time data based on the big data according to claim 4, wherein the regulation closed-loop module comprises a risk prediction and preliminary regulation coupling unit, an online simulation deduction unit and an adaptive service flow regulation unit; The step S4 specifically comprises the following steps: s41, the risk prediction and preliminary regulation and control coupling unit receives the potential operation risk information output in the step S3 and the state vector of each current business link, and generates a group of preliminary regulation and control parameter suggestions; S42, the online simulation deduction unit parallelly simulates the execution process of various regulation and control strategy combinations in a future period based on the current enterprise operation state digital model and the preliminary regulation and control parameter suggestion, and evaluates the effect and the execution cost of each strategy combination on improving the enterprise comprehensive benefit index; s43, the online simulation deduction unit selects an optimized target regulation strategy from a plurality of regulation strategy combinations according to the evaluation result; s44, the self-adaptive service flow regulating and controlling unit receives the target regulating and controlling strategy, converts the target regulating and controlling strategy into an instruction sequence which can be identified by a specific service execution system and transmits the instruction sequence through a preset interface; And S45, continuously collecting new data of related business links after the self-adaptive business flow regulating and controlling unit issues the instruction, verifying the regulating and controlling effect by comparing the actual business state change with the change trend predicted by the online simulation deduction unit, and triggering a new round of regulating and controlling strategy generation and optimization flow if the deviation exceeds the allowable range.
6. The method for analyzing and early warning the digitized operation real-time data of the enterprise based on big data as set forth in claim 5, wherein in step S41, the risk prediction and preliminary regulation coupling unit is a two-channel deep learning model; The first channel takes the real-time output of the dynamic characteristic model as input and is used for predicting comprehensive benefit indexes and various abnormal variation of enterprises; The second channel takes the potential operation risk information and a state vector formed by real-time state data of equipment, a supply chain and a client link as inputs, and outputs a preliminary regulation parameter set containing one or more of suggested production regulation parameters, supply link regulation parameters and client service resource allocation parameters; The first channel and the second channel share part of bottom layer characteristic representation and perform joint training, so that risk prediction results can be directly coupled to generate preliminary regulation response suggestions.
7. The method for analyzing and pre-warning the enterprise digital operation real time data based on big data as set forth in claim 6, wherein in step S42, the online simulation deduction unit performs deduction by constructing and maintaining a lightweight digital twin simulation environment; The lightweight digital twin simulation environment is instantiated based on an enterprise operation state digital model characterized by the dynamic feature model constructed in the step S2, and a simplified business logic rule base is built in, wherein the business logic rule base comprises production scheduling logic, material and information circulation rules among supply chain nodes and customer request response logic; the parallel simulation process comprises the steps of combining different preliminary regulation parameter suggestions, applying the combined preliminary regulation parameter suggestions as input disturbance to corresponding business logic rules of the lightweight digital twin simulation environment, and driving the simulation environment to operate for a preset future time period; And the evaluation process is that after the simulation operation is finished, the comprehensive effect score of each regulation strategy combination is calculated based on the recorded change track, the resource consumption and the state disturbance degree.
8. The method for analyzing and pre-warning the digitized operation real-time data of enterprise based on big data as set forth in claim 7, wherein in step S44, a configurable instruction interface mapping table corresponding to the enterprise manufacturing execution system, the enterprise resource planning system and the customer relationship management system is preset in the adaptive service flow regulating unit; analyzing specific regulation parameters in the target regulation strategy, matching a target service execution system and a corresponding application programming interface according to the configurable instruction interface mapping table, and packaging the regulation parameters into specific instructions or data messages according to interface specifications; the process of issuing the instruction is realized by calling a matched application programming interface, and the packaged instruction or data message is sent to a corresponding service execution system; In step S45, the process of verifying the regulation effect comprises the steps of directionally acquiring new state data of the regulated business link from a data acquisition source after the command is issued, inputting the new state data into the first channel of the risk prediction and preliminary regulation coupling unit to obtain an actual enterprise comprehensive benefit index change amount, comparing the actual change amount with the corresponding change amount of the simulation prediction in step S42, and calculating a deviation value.
9. The method for analyzing and pre-warning the enterprise digital operation real-time data based on big data according to claim 8, wherein when the deviation value calculated in the step S45 continuously exceeds a preset tolerance threshold, the adaptive traffic flow regulating and controlling unit sends a re-planning signal to the risk prediction and preliminary regulation and control coupling unit and the online simulation deduction unit; The risk prediction and preliminary regulation coupling unit updates potential operation risk information and preliminary regulation parameter suggestions according to the latest business state data; the online simulation deduction unit re-executes parallel simulation and evaluation based on the updated information to generate a new target regulation strategy; The self-adaptive business flow regulating and controlling unit generates and issues a new instruction sequence according to a new target regulating and controlling strategy to form a self-adaptive regulating and controlling closed loop based on feedback.
10. The method for analyzing and early warning the enterprise digital operation real-time data based on the big data according to claim 9, wherein the visual display in the step S5 is realized by an interactive instrument panel, and the interactive instrument panel at least comprises a trend prediction chart, a comprehensive benefit index instrument, a risk topological relation chart, an executed regulation strategy list and a state indication area thereof.

Description

Enterprise digital operation real-time data analysis and early warning method based on big data Technical Field The invention relates to the technical field of data processing, in particular to an enterprise digital operation real-time data analysis and early warning method based on big data. Background Along with the rapid development of technologies such as big data, internet of things and artificial intelligence, enterprises accumulate massive, multi-source and heterogeneous data resources in daily operation, and the enterprises cover multiple dimensions such as equipment operation, financial flow, customer behaviors, human resources and external environments. How to extract valuable information from the data, and realize real-time sensing, intelligent analysis and accurate early warning of the operation state of enterprises, has become a key problem of enterprise digital transformation and upgrading. The traditional data analysis method is often focused on single data source or static data analysis, and is difficult to realize deep fusion and dynamic association analysis of multi-source data, so that comprehensive operation benefit evaluation of enterprises is not comprehensive enough, prediction is not accurate enough, and potential risks are difficult to discover in time and respond effectively. Although some researches are attempted to introduce a multi-source data fusion technology, such as an enterprise digital operation real-time data analysis method based on multi-source data fusion, which is proposed in patent document with an authority publication number of CN120430560A, the method realizes analysis and risk identification of enterprise comprehensive benefit indexes by constructing a three-dimensional vector space, embedding a knowledge graph and combining a dynamic graph neural network. However, the method still has certain limitations that on one hand, a closed loop is not formed on the real-time performance and response mechanism of risk early warning, dynamic tracking and active intervention capability on a risk propagation path is lacked, and on the other hand, in the early warning decision process driven by data, the actual requirements of a service scene are not fully combined, so that the interpretation and operability of an early warning result are insufficient, and an enterprise is difficult to be supported to quickly formulate and execute a risk coping strategy. In addition, most of the existing big data analysis systems still depend on an offline or batch processing mode, cannot adapt to the characteristics of real-time generation and dynamic change of data in the enterprise operation process, so that analysis results are lagged, and real-time decision support in a real sense cannot be provided for managers. Especially, when facing to equipment abnormality, supply chain fluctuation, customer loss and other multidimensional risks, a set of early warning and response system capable of deeply fusing multi-source data, identifying risk symptoms in real time and dynamically optimizing resource allocation is lacking. Therefore, it is highly desirable to provide a more efficient, real-time and intelligent enterprise digital operation data analysis and early warning method, which can realize early identification, dynamic evaluation and active prevention and control of operation risk on the basis of multi-source data fusion, so as to improve the stability and comprehensive benefit of enterprise operation. Disclosure of Invention The invention aims to provide a real-time data analysis and early warning method for enterprise digital operation based on big data, so as to solve the problems in the background technology. In order to solve the technical problems, the invention provides the technical scheme that the enterprise digital operation real-time data analysis and early warning method based on big data comprises the following steps: s1, acquiring multisource data in an enterprise operation period in real time, constructing an operation data set, and preprocessing the operation data set; S2, carrying out feature extraction and fusion based on the preprocessed operation data set, and constructing a feature model for representing the comprehensive operation state of the enterprise; s3, based on the characteristic model, carrying out real-time analysis and future trend prediction on enterprise comprehensive benefit indexes, and identifying potential operation risks; s4, inputting the identified potential operation risk into a regulation and control closed loop module, wherein the regulation and control closed loop module is generated through online simulation deduction and optimized selection based on the potential operation risk and the real-time state of each business link of the current enterprise, and then outputs a regulation and control strategy aiming at a specific business link, converts the regulation and control strategy into an executable instruction and transmits the executable inst