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CN-121981520-A - Enterprise management consultation system based on big data

CN121981520ACN 121981520 ACN121981520 ACN 121981520ACN-121981520-A

Abstract

The invention belongs to the technical field of enterprise management consultation, in particular to an enterprise management consultation system based on big data, which comprises a data acquisition and integration module, a risk index system construction module, a real-time risk monitoring and early warning module, an intervention scheme generation and optimization module, an effect tracking and feedback module and a user interaction module, wherein the modules work cooperatively. The invention solves the problems of narrow data acquisition range, low processing efficiency, risk identification non-system, early warning lag, lack of scientificity of an intervention scheme and difficulty in tracking and evaluating effects in the traditional enterprise management consultation by arranging the data acquisition and integration module, the risk index system construction module, the real-time risk monitoring and early warning module, the intervention scheme generation and optimization module, the effect tracking and feedback module and the user interaction module, and provides the enterprise management consultation system based on big data, which can realize the overall process control of enterprise management risks.

Inventors

  • ZHANG BINGCHUN
  • ZHOU RONGTAO
  • CHENG BINBIN

Assignees

  • 东营汇英科技咨询有限公司

Dates

Publication Date
20260505
Application Date
20251222

Claims (7)

  1. 1. The enterprise management consultation system based on big data is characterized by comprising a data acquisition and integration module (1), a risk index system construction module (2), a real-time risk monitoring and early warning module (3), an intervention scheme generation and optimization module (4), an effect tracking and feedback module (5) and a user interaction module (6), wherein the modules cooperatively realize the whole process management and control of enterprise management risks, and the specific functions are as follows: the data acquisition and integration module (1) is used for acquiring internal and external multisource data of an enterprise, and a unified enterprise management data pool is formed after standardized processing; the risk index system construction module (2) is used for establishing a standardized risk index system covering the enterprise full-service flow, and configuring weights and grading early warning thresholds for each index; the real-time risk monitoring and early warning module (3) is used for calculating a risk index value in real time based on an enterprise management data pool and a risk index system, identifying potential risks and triggering hierarchical early warning; the intervention scheme generation and optimization module (4) is used for intelligently generating a hierarchical intervention scheme aiming at early warning risks and supporting man-machine collaborative optimization; The effect tracking and feedback module (5) is used for tracking the execution progress and effect of the intervention scheme in real time, dynamically optimizing the scheme and updating the knowledge base; The user interaction module (6) is used for providing a visual operation interface and supporting data viewing, early warning processing and scheme management.
  2. 2. The enterprise management advisory system based on big data as claimed in claim 1, wherein the data acquisition and integration module (1) comprises a multi-source data access sub-module, a data cleaning and standardization sub-module and a data fusion and storage sub-module; The multisource data access sub-module adopts an API interface docking, batch file importing and real-time crawler capturing mode to dock an enterprise internal business system and an external public data platform, wherein the internal business system comprises ERP, CRM, MES, HRM and a financial system, the external public data platform comprises an industry association database, a macroscopic economic database, a bid monitoring platform and a weather data platform, and supports real-time synchronization of data for 1 min/time at the minimum, and external data is captured and validity verification is carried out by adopting a desensitization crawler technology; The data fusion and storage submodule adopts a distributed data warehouse to store structured and unstructured data, and establishes an association relation of multi-source data through a data association engine to form a global enterprise management data pool.
  3. 3. The enterprise management advisory system based on big data as claimed in claim 1, wherein the risk index system construction module (2) comprises a risk dimension dividing sub-module, an index screening and defining sub-module and an index weight and threshold configuration sub-module; The risk dimension dividing sub-module divides enterprise management risks into four major dimensions including financial risks, manpower risks, operation risks and market risks, and subdivides specific risk types under each dimension, wherein the financial risks comprise cash flow breakage risks, accounts receivable overdue risks and cost runaway risks, the manpower risks comprise core staff loss risks, staff efficiency low risks and talent reserve shortage risks, the operation risks comprise production equipment failure risks, product quality unqualified risks and supply chain interruption risks, and the market risks comprise bid price war risks, customer loss risks and industry policy fluctuation risks.
  4. 4. The enterprise management advisory system based on big data as claimed in claim 1, wherein the real-time risk monitoring and early warning module (3) comprises a real-time index calculation sub-module, a risk identification and classification sub-module and a multi-channel early warning notification sub-module; The real-time index calculation sub-module adopts a stream calculation framework to read the data of an enterprise management data pool in real time and dynamically calculate the risk index value; The risk identification and grading sub-module compares the real-time index value with an early warning threshold value, judges the risk grade and the influence range, and simultaneously excavates risk root data through the association analysis engine to locate risk causes; The multi-channel early warning notification sub-module triggers different notification channels according to risk grades, namely, a light risk notifies a department responsible person through a system station internal message and an enterprise WeChat/nail message, a moderate risk increase short message notification and a risk brief mail, a severe risk increase system voice telephone notifies an enterprise high-rise management and a system front page early warning popup window, and a risk emergency meeting is reserved automatically.
  5. 5. The enterprise management advisory system based on big data as claimed in claim 1, wherein the intervention scheme generation and optimization module (4) comprises an intervention scheme knowledge base sub-module, an intelligent scheme generation sub-module and a human-computer collaborative optimization sub-module; the intelligent scheme generation submodule generates a hierarchical intervention scheme through 'rule matching and machine learning' based on risk types, risk grades and enterprise characteristics, and the scheme is used for clearly implementing steps, responsibility departments, expected targets and required resources; The man-machine collaborative optimization submodule supports a manual adjustment scheme, predicts the change of risk indexes through an effect simulation algorithm after adjustment and gives adjustment suggestions, and finally generates a risk intervention execution scheme to be synchronized to a responsible department.
  6. 6. The enterprise management advisory system based on big data as claimed in claim 1, wherein the effects tracking and feedback module (5) comprises an execution progress tracking sub-module, an effects evaluation sub-module, and a solution iteration and knowledge base update sub-module; The execution progress tracking sub-module disassembles the intervention scheme into quantifiable execution tasks, sets the completion time limit and acceptance criteria, tracks the task completion condition through ' task punching and ' progress reporting ', and automatically sends a prompt for an out-of-date task; The scheme iteration and knowledge base updating sub-module updates the effective scheme as the preferred scheme to the knowledge base, adds the applicable scene label, analyzes the reason of the insufficient effect scheme, generates the optimization suggestion, and feeds back the execution data to the risk index system construction module (2), and dynamically optimizes the index weight and the early warning threshold.
  7. 7. The enterprise management advisory system based on big data as claimed in claim 1, wherein the user interaction module (6) comprises an early warning processing center, a scheme management interface and a personalized configuration interface; The scheme management interface supports checking, editing, exporting schemes and checking effect reports, and supports searching historical schemes according to risk types and implementation time; The personalized configuration interface supports user-defined risk indexes, adjusts early warning thresholds and notification channels, and sets user permission.

Description

Enterprise management consultation system based on big data Technical Field The invention relates to the technical field of enterprise management consultation, in particular to an enterprise management consultation system based on big data. Background In the current digital age, the business operation scale is continuously enlarged, the business process is increasingly complex, the facing internal and external environments are also increasingly changeable, and the enterprise management risk presents the characteristics of diversification, complexity, strong concealment and the like. Meanwhile, the traditional management consultation lacks systematic index system support on risk identification, early warning is delayed, the best intervention opportunity is missed by enterprises usually after risks occur and influence the enterprises to a certain extent, in addition, the subjectivity of intervention schemes aiming at risk formulation is stronger, scientific data support and intelligent optimization means are lacking, the scheme execution effect is difficult to effectively track and evaluate, a closed loop of risk management cannot be formed, and the requirements of enterprises on fine and real-time risk management are difficult to meet, so that an enterprise management consultation system based on big data is needed to solve the problems. Disclosure of Invention (One) solving the technical problems Aiming at the defects of the prior art, the invention provides the enterprise management consultation system based on the big data, which solves the problems of narrow data acquisition range, low processing efficiency, no risk identification system, early warning lag, lack of scientificity of an intervention scheme and difficulty in tracking and evaluating effects in the traditional enterprise management consultation, and provides the enterprise management consultation system based on the big data, which can realize the whole process management and control of the enterprise management risks. The invention adopts the following technical scheme for realizing the purposes: An enterprise management consultation system based on big data comprises a data acquisition and integration module, a risk index system construction module, a real-time risk monitoring and early warning module, an intervention scheme generation and optimization module, an effect tracking and feedback module and a user interaction module, wherein all the modules work cooperatively to jointly realize the overall process management and control of enterprise management risks. Further, the data acquisition and integration module comprises a multi-source data access sub-module, a data cleaning and standardization sub-module and a data fusion and storage sub-module. The multi-source data access sub-module adopts an API interface docking, batch file importing and real-time crawler capturing mode to dock an enterprise internal business system and an external public data platform, wherein the internal business system covers ERP, CRM, MES, HRM and a financial system, the external public data platform comprises an industry association database, a macroscopic economic database, a bid monitoring platform and a weather data platform, and supports data real-time synchronization of the lowest 1 minute/time, for external data, a desensitization crawler technology is adopted to capture and perform validity check, compliance and safety of data acquisition are guaranteed, the data cleaning and standardization sub-module processes the acquired multi-source data, duplicate, missing and abnormal data are removed, and the data is converted according to a unified standard, data isomerism is eliminated, the data fusion and storage sub-module adopts a distributed data warehouse to store structured and unstructured data, and a data association relation of the multi-source data is established through a data association engine, so that a global enterprise management data pool is formed. Further, the risk index system construction module comprises a risk dimension dividing sub-module, an index screening and defining sub-module and an index weight and threshold value configuration sub-module. The risk dimension dividing sub-module divides enterprise management risks into four major dimensions including financial risks, manpower risks, operation risks and market risks, wherein the financial risks comprise cash flow breaking risks, accounts receivable overdue risks and cost out-of-control risks, the manpower risks comprise core employee loss risks, employee efficiency low risks and talent reserve shortage risks, the operation risks comprise production equipment failure risks, product quality unqualified risks and supply chain interruption risks, the market risks comprise bid price war risks, customer loss risks and industry policy fluctuation risks, the index screening and defining sub-module screens representative and quantifiable easily-acquired indexes for each subdivision risk ty