CN-121981379-A - Intelligent business management method and system for enterprise investment number

CN121981379ACN 121981379 ACN121981379 ACN 121981379ACN-121981379-A

Abstract

The invention discloses an enterprise investment number intelligent operation management method and system, which integrate global supply chain data through a multi-source heterogeneous data fusion technology to form a standardized information set, and combine with visual monitoring to dynamically capture node state deviation of a process, trigger an alarm in time to identify potential risk points. Aiming at risk points, a classification algorithm is adopted to analyze multidimensional risk factors, interrupt probability distribution is predicted, high risk links are extracted, a path is optimized through logic rules, and an integrated scheme covering standby supplier selection and inventory buffering is formulated. And meanwhile, the global branch institutions are connected, resource allocation and fund management are optimized based on the transnational operation log, risk early warning signals are derived, and data driving basis is provided for decision making through dynamic bulletin board aggregate operation efficiency indexes. According to the invention, the accurate prevention and control of the supply chain risk and the resource optimization configuration are realized through data fusion and intelligent prediction, and the response speed and the operation efficiency of the supply chain are obviously improved.

Inventors

LIU LINA
LONG TAO
ZHONG JINGLEI
LIU EYING

Assignees

湖南涉外经济学院

Dates

Publication Date: 20260505
Application Date: 20251226

Claims (10)

1. An enterprise investment number intelligent operation management method is characterized by comprising the following steps: s100, integrating real-time inventory and logistics information of a supply chain through a global data source, and fusing the integrated real-time inventory and logistics information by adopting an integrated learning algorithm to obtain a standardized supply chain information set; S200, performing visual monitoring according to the standardized supply chain information set, acquiring a dynamic process node state, triggering an alarm if the deviation of the dynamic process node state exceeds a preset threshold value, and determining a potential risk event point; S300, intelligent prediction is carried out on the potential risk event points, and a classification algorithm is adopted to process multidimensional risk factors to obtain predicted outage probability distribution, wherein the multidimensional risk factors comprise provider delays and market demand fluctuations; S400, extracting a high risk link from the predicted outage probability distribution, acquiring related purchasing manufacturing plan data, adjusting an optimization path through a logic rule chain, covering standby supplier selection and inventory buffering strategies, and obtaining an integrated management scheme; S500, connecting a global branch office system according to the integrated management scheme, acquiring a national-crossing collaborative operation log, and if the capital flows are not matched, re-distributing resources based on the national-crossing collaborative operation log to determine a compliance capital management sequence; And S600, deriving a risk early warning signal from the compliance fund management sequence, and aggregating operation efficiency indexes through a dynamic billboard to obtain a data-driven decision basis, wherein the operation efficiency indexes comprise delivery timing rate and cost control level.
2. The method for intelligent management of the investment count of enterprises according to claim 1, wherein the data source interface comprises an inventory data interface and a logistics interface, the real-time inventory and logistics information comprises real-time inventory data and logistics transportation information, and the step S100 comprises: S110, acquiring real-time inventory data and logistics transportation information in a supply chain through a plurality of global data source interfaces, performing preliminary cleaning on the real-time inventory data and the logistics transportation information, and performing format conversion by adopting a data mapping tool to obtain a standardized initial data set; s120, if a missing value or an abnormal value exists in the initial data set, processing the initial data set through a preset threshold rule to obtain a complete integrated data set; s130, extracting dynamic characteristics of a supply chain by utilizing a specific algorithm aiming at the integrated data set, acquiring key information from the integrated data set, and determining real-time state data of each link; and S140, converting the real-time state data into a standardized information set by adopting a data storage tool, and carrying out classification and index processing on the information set to obtain a standardized supply chain information set.
3. The method for intelligent operation management of investment count for enterprises as set forth in claim 1, wherein step S200 comprises: s210, constructing a dynamic monitoring interface by adopting a visualization tool according to a standardized supply chain information set, and refreshing state data of flow nodes in real time to obtain a node state display result; S220, acquiring current state data of each flow node according to the node state display result, comparing the current state data of each flow node with a preset threshold value, and judging the current state data to be abnormal state data if the deviation of the current state data exceeds the preset threshold value; and S230, aiming at the abnormal state data, triggering an alarm signal by adopting a pre-established early warning mechanism, extracting key node information from the abnormal state data, and determining a potential risk event point.
4. The method for intelligent operation management of investment count for enterprises as set forth in claim 1, wherein step S300 comprises: s310, intelligent prediction is carried out aiming at potential risk event points, and a classification processing tool is adopted to analyze provider delays and inventory turnover rates and determine influence weights on outage probability; s320, if the influence weight exceeds a preset threshold, carrying out association matching on related data of market demand fluctuation and demand prediction deviation through a data integration tool to obtain a comprehensive risk assessment result; S330, generating an outage probability distribution chart of supplier delay and market demand fluctuation by adopting a visualization tool according to the comprehensive risk assessment result, and obtaining a predicted outage probability distribution.
5. The method for intelligent operation management of investment count for enterprises as set forth in claim 1, wherein step S400 comprises: S410, extracting data of high-risk links from predicted interruption probability distribution, and classifying key nodes of supply chain interruption by adopting a data screening tool to obtain at least one high-risk link identifier; S420, acquiring corresponding purchasing plan and manufacturing plan data according to the high risk link identifier, and performing association matching on standby provider information through a data integration tool to obtain a preliminary resource allocation scheme; S430, if the data in the preliminary resource allocation scheme exceeds a preset threshold, adjusting an optimized path by adopting a logic rule chain, and sequencing priority for the delivery cycle to judge an adaptive standby provider selection scheme; S440, fusing the adjusted optimized path and inventory buffer data by adopting the standby provider selection scheme by adopting a data mapping tool, and generating an integrated management scheme covering a purchase plan and a manufacturing plan.
6. The method for intelligent operation management of investment count for enterprises as set forth in claim 1, wherein step S500 comprises: s510, acquiring data connection information of global branches from an integrated management scheme, and classifying and processing the nationwide collaborative records of each branch by adopting a data integration tool to obtain a unified collaborative operation data set; s520, acquiring the content of a corresponding nationwide collaborative operation log according to the collaborative operation data set, comparing the fund flowing records item by item through a log analysis tool, and generating a fund flowing abnormal identifier if a mismatch record is found; s530, aiming at the fund flow abnormality identification, rearranging related data in the cross-country cooperative operation log by adopting a resource allocation tool, adjusting a resource allocation scheme by combining with a preset threshold value, and determining an optimized allocation detail; S540, comparing the regulation result compliance management requirements through the optimized distribution details by adopting a data mapping tool, and generating a fund flow scheme which accords with the management sequence to obtain a compliance fund management sequence.
7. The method for intelligent operation management of investment count for enterprises as set forth in claim 1, wherein step S600 comprises: S610, acquiring related data of risk early warning signals from a compliance fund management sequence, classifying and sorting signal contents by adopting a data extraction tool, comparing the classified data with a preset threshold value, and generating an abnormal mark if the classified data exceeds the threshold value to obtain an abnormal mark set; a decision rule for generating a set of anomaly markers is formulated by: ; Wherein, the Represent the first The abnormal signature status of the data points, Represent the first The actual observed value of the data point, Represent the first Preset threshold corresponding to data point when When equal to 1, this data point is marked as abnormal, when A value equal to 0 indicates that the data point is normal; S620, according to the abnormal mark set, carrying out association matching on the mark data and the operation efficiency value in the dynamic billboard by adopting a data integration tool, obtaining specific numerical values of the delivery time rate and the cost control level, and determining an efficiency index data set; The control effect of budget execution is reflected by the following formula for calculating a specific value of the cost control level: Wherein, the The cost control rate is indicated as a function of the cost, Representing the total number of cost control monitoring items, Represent the first The cost of the budget of the individual items, Represent the first The actual occurrence cost of the individual items; s630, carrying out multidimensional presentation on the delivery time accuracy and the cost control level by adopting a data visualization tool through the efficiency index data set, and if the presentation result does not accord with the preset compliance requirement, generating an adjustment suggestion record to obtain an adjustment suggestion list; And S640, according to the adjustment suggestion list, adopting a data mapping tool to carry out corresponding processing on the suggestion content and the fund flow record, and combining the branch office cooperative data to dynamically adjust the resource allocation scheme to obtain a data driving decision basis.
8. The method for intelligent management of the investment in enterprises according to claim 7, wherein in step S630, the delivery timing rate is obtained by the following formula: ; Wherein, the Represents the comprehensive evaluation index of the delivery time rate, Indicating the total number of delivery lots in the evaluation period, Represent the first The actual delivery time of the lot, Represent the first The planned delivery time of the lot, Represent the first The weight coefficient of the batch; The adjustment advice list is derived by the following formula: ; Wherein, the Indicating that the recommendation score is to be adjusted, Representing the total number of compliance required items, Represent the first The importance coefficients required by the term compliance are, Represent the first The actual measured value of the term index, Represent the first The target threshold value of the term index, Represent the first Priority weights of the term indicators.
9. The method for intelligent management of the investment in enterprises of claim 8, wherein in step S640, the corresponding processing results of the recommended content and the record of the flow of funds in the data mapping tool are obtained by the following formula: ; Wherein, the Representing suggested content in a data mapping tool Record with funding Is used to determine the corresponding processing result of the corresponding processing result, Represent the first The term adjusts the suggested weight coefficient(s), Represent the first The value of the item funds flow record, Representing the total number of funds flow records, Represent the first The weight factor of the item record, Represent the first A risk assessment value for the item record, Representing suggested content With funds recording A coefficient of matching degree between the two; the data driven decision is based on the following formula: ; Wherein, the A composite assessment indicator representing a data driven decision, Representing the total number of decision factors, Represent the first The importance weight of the individual decision factors, Represent the first The data value of the individual factors is used, Represent the first The quality score of the individual factors is calculated, Representing the total number of uncertainty factors, Represent the first The influence coefficient of the individual uncertainty factors, Represent the first The value of the one uncertainty factor is, Representing decision confidence adjustment parameters.
10. An enterprise investment count intelligent operation management system for executing the enterprise investment count intelligent operation management method according to any one of claims 1 to 9, characterized by comprising: The standardized supply chain information collection acquisition module (10) is used for integrating the real-time inventory and logistics information of the supply chain through a global data source, and integrating the integrated real-time inventory and logistics information by adopting an integrated learning algorithm to obtain a standardized supply chain information collection; The potential risk event point determining module (20) is used for performing visual monitoring according to the standardized supply chain information set to acquire a dynamic flow node state, triggering an alarm if the deviation of the dynamic flow node state exceeds a preset threshold value, and determining a potential risk event point; a predicted outage probability distribution acquisition module (30) for performing intelligent prediction on the potential risk event points, and processing multidimensional risk factors by using a classification algorithm to obtain a predicted outage probability distribution, wherein the multidimensional risk factors comprise provider delays and market demand fluctuations; The integrated management scheme acquisition module (40) is used for extracting high risk links from the predicted interruption probability distribution, acquiring related purchasing manufacture plan data, adjusting an optimization path through a logic rule chain, covering standby supplier selection and inventory buffering strategies, and obtaining an integrated management scheme; The compliance fund management sequence determining module (50) is used for connecting a global branch office system according to the integrated management scheme, acquiring a nationwide collaborative operation log, and if the fund flows are not matched, re-distributing resources based on the nationwide collaborative operation log to determine a compliance fund management sequence; and the data-driven decision basis acquisition module (60) is used for deriving a risk early warning signal from the compliance fund management sequence, and acquiring a data-driven decision basis by aggregating operation efficiency indexes through a dynamic billboard, wherein the operation efficiency indexes comprise delivery timing rate and cost control level.

Description

Intelligent business management method and system for enterprise investment number Technical Field The invention relates to the technical field of enterprise operation management number intelligence, and particularly discloses an enterprise investment number intelligence operation management method and system. Background In the current global economic background, the intellectualization of enterprise management numbers has become an important direction for promoting the efficient operation and competitive advantage construction of transnational businesses. This field is not only concerned with the efficient configuration of enterprise resources, but also directly affects the ability to respond quickly in the global marketplace, the criticality of which is self-evident. With the popularization of digital technology, more and more enterprises begin to explore how to realize cross-regional and cross-cultural business integration and management optimization through technical means. However, many current solutions often have difficulty balancing the need for centralized management with localized adaptation in coping with the complexity of globalization operations. When facing multiple service environments, a plurality of methods lack flexible adaptation capability to rules and demands of different areas, so that enterprises always sacrifice quick response capability of local markets while uniformly managing the services. This contradiction is particularly pronounced in transnational business, especially where multiple language environments, different currency systems, and tax and accounting rules differences across sites need to be handled simultaneously, the existing methods are of great concern. The technical difficulty is how to integrate and cooperate with the global service data in real time. The factor directly determines whether an enterprise can make smooth information and timely decisions in a complex globalization environment. Due to the wide and decentralized sources of data, and the different systems involved in multiple countries and regions, enterprises often face problems of inconsistent data formats, transmission delays, and insufficient inter-system compatibility when attempting to unify such information into one platform. These problems further lead to the formation of information islands, which makes it difficult for the manager to fully grasp the general view of the global business and to respond to the potential risk in a timely manner. For example, in the case of supply chain management, an enterprise may need to monitor data of hundreds of suppliers and logistics nodes at the same time in the global scope, but due to different standards of systems in each area, the data cannot be summarized in real time, and when a manager faces an emergency, such as a logistics interrupt in a certain area, the manager can only passively wait for information feedback, so that resource allocation cannot be early-warned or quickly adjusted in advance. This asymmetry of information and response lag directly affects the overall efficiency and stability of the supply chain. Therefore, how to realize seamless integration and real-time collaboration of distributed data under a globalization architecture, ensure the flexibility of local operation while centralized management of enterprises, and become a key problem to be solved currently. Disclosure of Invention The invention provides an enterprise investment number intelligent operation management method and system, and aims to solve at least one defect in the prior art. One aspect of the invention relates to an enterprise investment number intelligent operation management method, which comprises the following steps: s100, integrating real-time inventory and logistics information of a supply chain through a global data source, and fusing the integrated real-time inventory and logistics information by adopting an integrated learning algorithm to obtain a standardized supply chain information set; s200, performing visual monitoring according to a standardized supply chain information set, acquiring a dynamic process node state, triggering an alarm if the deviation of the dynamic process node state exceeds a preset threshold value, and determining a potential risk event point; S300, intelligent prediction is carried out on potential risk event points, and a classification algorithm is adopted to process multidimensional risk factors to obtain prediction interruption probability distribution, wherein the multidimensional risk factors comprise provider delays and market demand fluctuations; S400, extracting a high risk link from the predicted outage probability distribution, acquiring related purchasing manufacturing plan data, adjusting an optimization path through a logic rule chain, covering standby supplier selection and inventory buffering strategies, and obtaining an integrated management scheme; S500, connecting a global branch system according to an integrated