CN111047273A - Enterprise operation decision method and system based on physical options - Google Patents
Enterprise operation decision method and system based on physical options Download PDFInfo
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Abstract
The invention provides a method, a system and a medium for enterprise operation decision based on a physical option, which comprises the following steps: step S1: recording enterprise information and enterprise financial data input by a user, and selecting an operation decision to be investigated; step S2: processing and calculating enterprise information and enterprise financial data aiming at different operation decisions to obtain a judgment conclusion of the operation decisions; step S3: and outputting a judgment conclusion of the operation decision. The invention adopts the user to input information and calls the model calculation in real time, thereby solving the problem of answering the user question in real time; the problem that a user cannot scientifically judge the enterprise operation decision is solved through a background entity option calculation model related to the enterprise operation decision.
Description
Technical Field
The invention relates to the field of enterprise operation decision-making, in particular to an enterprise operation decision-making method and system based on a physical option.
Background
At present, the transaction amount and the transaction speed of a plurality of real-time service applications are very high, and when a fault occurs, a continuous service transaction request in the time window is often not processed in the switching action process of an application server. That is to say, the dual-computer hot standby or other redundancy methods inevitably lose part of the processing of the service during the switching process, resulting in service failure and make-up processing performed by the application service background.
The closest technology to the patent: an enterprise decision point mining method and system based on mass data.
The invention relates to a method and a system for mining enterprise decision points based on mass data, wherein the method comprises the steps of classifying and layering enterprises needing service to obtain enterprise sub-items; associating the enterprise sub-items with various decision requirements of the enterprise by combining enterprise business characteristics to form a decision point classification model; dynamically adjusting the decision point classification model based on the special data source and the mass data; and matching the adjusted decision point classification model with the products and services operated by the enterprises to obtain the enterprise decision points. The invention realizes the combination of mass data and actual operation analysis of enterprises, provides active, dynamic and timely data support for enterprise operation decision-making, can predict enterprise development demand and operation change to a certain extent, has more predictive and normative analysis, improves the grasping accuracy of enterprise demand, ensures accurate decision-making, and can effectively serve various types of enterprises through large data analysis and machine learning of enterprises in different industry fields
The difference is as follows: the model does not consider financial data, the model considers; the model is based on massive enterprise decision data and is difficult to obtain.
Patent document CN110188949A (application number: 201910463520.9) discloses a dynamic model of an enterprise operation decision simulation system and a realization method thereof, including a power supply enterprise operation decision simulation model based on value amount operation target constraints and a power supply enterprise operation decision simulation model based on grid index operation target constraints.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a method and a system for enterprise operation decision based on physical options.
The enterprise operation decision method based on the physical options provided by the invention comprises the following steps:
step S1: recording enterprise information and enterprise financial data input by a user, and selecting an operation decision to be investigated;
step S2: processing and calculating enterprise information and enterprise financial data aiming at different operation decisions to obtain a judgment conclusion of the operation decisions;
step S3: and outputting a judgment conclusion of the operation decision.
Preferably, the step S1 includes:
step S101: recording enterprise information and enterprise financial data input by a user; the enterprise information includes: enterprise establishment time, enterprise industry type, main business, location and number scale; the financial data includes: annual asset liability statement, cash flow statement, income statement;
step S102: selecting an operational decision to be investigated, comprising: whether the enterprise needs to borrow money in the future or not, and the amount of money to be borrowed is proper, whether the enterprise needs to release dividends or not, and how many dividends are proper, whether the enterprise needs a new round of directional increase of capital, whether the increase of the capital is proper or not, and what the increase of the capital is proper is optimal, and whether the enterprise needs to increase or decrease the fixed assets or how much the increase of the fixed assets is proper or not;
step S103: and storing the enterprise information, the enterprise financial data and the operation decision to be investigated in a database by corresponding enterprise names or numbers.
Preferably, the step S2 includes:
step S201: reading operation decision selection data to be investigated stored in a database, proposing an assumption to an investigation item, and simulating enterprise operation data and an end-of-term financial report through big data analysis and machine learning according to a previous-stage enterprise report, a corporate enterprise report on the market and macroscopic economic data;
step S202: judging whether the simulated terminal financial newspaper is to be adopted or not by utilizing a judgment function;
if the simulated end-of-term financial statement is judged not to be adopted, returning to the step S201, and adjusting the hypothesis put forward on the investigation item until the simulated end-of-term financial statement is judged to be adopted;
if the simulated end-of-term financial statement is judged to be adopted, outputting the hypothesis proposed for the investigation item in the step S201;
preferably, the judgment function judges the simulated end-of-term financial reports by big data analysis and machine learning in combination with the financial conditions of the market, the industry and the enterprise.
The invention provides an enterprise operation decision-making system based on a physical option, which comprises:
module S1: recording enterprise information and enterprise financial data input by a user, and selecting an operation decision to be investigated;
module S2: processing and calculating enterprise information and enterprise financial data aiming at different operation decisions to obtain a judgment conclusion of the operation decisions;
module S3: and outputting a judgment conclusion of the operation decision.
Preferably, the module S1 includes:
a module S101: recording enterprise information and enterprise financial data input by a user; the enterprise information includes: enterprise establishment time, enterprise industry type, main business, location and number scale; the financial data includes: annual asset liability statement, cash flow statement, income statement;
a module S102: selecting an operational decision to be investigated, comprising: whether the enterprise needs to borrow money in the future or not, and the amount of money to be borrowed is proper, whether the enterprise needs to release dividends or not, and how many dividends are proper, whether the enterprise needs a new round of directional increase of capital, whether the increase of the capital is proper or not, and what the increase of the capital is proper is optimal, and whether the enterprise needs to increase or decrease the fixed assets or how much the increase of the fixed assets is proper or not;
a module S103: and storing the enterprise information, the enterprise financial data and the operation decision to be investigated in a database by corresponding enterprise names or numbers.
Preferably, the module S2 includes:
a module S201: reading operation decision selection data to be investigated stored in a database, proposing an assumption to an investigation item, and simulating enterprise operation data and an end-of-term financial report through big data analysis and machine learning according to a previous-stage enterprise report, a corporate enterprise report on the market and macroscopic economic data;
a module S202: judging whether the simulated terminal financial newspaper is to be adopted or not by utilizing a judgment function;
if the simulated end-of-term financial statement is judged not to be adopted, calling the module S201, and adjusting the hypothesis put forward on the investigation item until the simulated end-of-term financial statement is judged to be adopted;
if the simulated end-of-term financial statement is judged to be adopted, the hypothesis put forward for the investigation item in the module S201 is output;
preferably, the judgment function judges the simulated end-of-term financial reports by big data analysis and machine learning in combination with the financial conditions of the market, the industry and the enterprise.
According to the present invention, there is provided a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method of any of the above.
Compared with the prior art, the invention has the following beneficial effects:
(1) by adopting the user to input information and calling model calculation in real time, the problem of answering the user question in real time is solved.
(2) The problem that a user cannot scientifically judge the enterprise operation decision is solved through a background entity option calculation model related to the enterprise operation decision.
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Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments with reference to the following drawings:
fig. 1 is a schematic flow chart of an enterprise operation decision method based on physical options according to the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the invention, but are not intended to limit the invention in any way. It should be noted that it would be obvious to those skilled in the art that various changes and modifications can be made without departing from the spirit of the invention. All falling within the scope of the present invention.
The enterprise operation decision method based on the physical options provided by the invention comprises the following steps:
step S1: recording enterprise information and enterprise financial data input by a user, and selecting an operation decision to be investigated;
step S2: processing and calculating enterprise information and enterprise financial data aiming at different operation decisions to obtain a judgment conclusion of the operation decisions;
step S3: and outputting a judgment conclusion of the operation decision.
Specifically, the step S1 includes:
step S101: recording enterprise information and enterprise financial data input by a user; the enterprise information includes: enterprise establishment time, enterprise industry type, main business, location and number scale; the financial data includes: annual asset liability statement, cash flow statement, income statement;
step S102: selecting an operational decision to be investigated, comprising: whether the enterprise needs to borrow money in the future or not, and the amount of money to be borrowed is proper, whether the enterprise needs to release dividends or not, and how many dividends are proper, whether the enterprise needs a new round of directional increase of capital, whether the increase of the capital is proper or not, and what the increase of the capital is proper is optimal, and whether the enterprise needs to increase or decrease the fixed assets or how much the increase of the fixed assets is proper or not;
step S103: and storing the enterprise information, the enterprise financial data and the operation decision to be investigated in a database by corresponding enterprise names or numbers.
Specifically, the step S2 includes:
step S201: reading operation decision selection data to be investigated stored in a database, proposing an assumption to an investigation item, and simulating enterprise operation data and an end-of-term financial report through big data analysis and machine learning according to a previous-stage enterprise report, a corporate enterprise report on the market and macroscopic economic data;
step S202: judging whether the simulated terminal financial newspaper is to be adopted or not by utilizing a judgment function;
if the simulated end-of-term financial statement is judged not to be adopted, returning to the step S201, and adjusting the hypothesis put forward on the investigation item until the simulated end-of-term financial statement is judged to be adopted;
if the simulated end-of-term financial statement is judged to be adopted, outputting the hypothesis proposed for the investigation item in the step S201;
specifically, the judgment function judges the simulated end-of-term financial reports by combining the financial conditions of markets, industries and enterprises through big data analysis and machine learning.
The invention provides an enterprise operation decision-making system based on a physical option, which comprises:
module S1: recording enterprise information and enterprise financial data input by a user, and selecting an operation decision to be investigated;
module S2: processing and calculating enterprise information and enterprise financial data aiming at different operation decisions to obtain a judgment conclusion of the operation decisions;
module S3: and outputting a judgment conclusion of the operation decision.
Specifically, the module S1 includes:
a module S101: recording enterprise information and enterprise financial data input by a user; the enterprise information includes: enterprise establishment time, enterprise industry type, main business, location and number scale; the financial data includes: annual asset liability statement, cash flow statement, income statement;
a module S102: selecting an operational decision to be investigated, comprising: whether the enterprise needs to borrow money in the future or not, and the amount of money to be borrowed is proper, whether the enterprise needs to release dividends or not, and how many dividends are proper, whether the enterprise needs a new round of directional increase of capital, whether the increase of the capital is proper or not, and what the increase of the capital is proper is optimal, and whether the enterprise needs to increase or decrease the fixed assets or how much the increase of the fixed assets is proper or not;
a module S103: and storing the enterprise information, the enterprise financial data and the operation decision to be investigated in a database by corresponding enterprise names or numbers.
Specifically, the module S2 includes:
a module S201: reading operation decision selection data to be investigated stored in a database, proposing an assumption to an investigation item, and simulating enterprise operation data and an end-of-term financial report through big data analysis and machine learning according to a previous-stage enterprise report, a corporate enterprise report on the market and macroscopic economic data;
a module S202: judging whether the simulated terminal financial newspaper is to be adopted or not by utilizing a judgment function;
if the simulated end-of-term financial statement is judged not to be adopted, calling the module S201, and adjusting the hypothesis put forward on the investigation item until the simulated end-of-term financial statement is judged to be adopted;
if the simulated end-of-term financial statement is judged to be adopted, the hypothesis put forward for the investigation item in the module S201 is output;
specifically, the judgment function judges the simulated end-of-term financial reports by combining the financial conditions of markets, industries and enterprises through big data analysis and machine learning.
According to the present invention, there is provided a computer readable storage medium having a computer program stored thereon, which, when executed by a processor, performs the steps of the method of any of the above.
The present invention will be described more specifically below with reference to preferred examples.
Preferred example 1:
as shown in fig. 1, a schematic flow chart of the enterprise operation decision method based on the physical option provided by the present invention includes:
step 1: the system records enterprise information and enterprise financial data input by a user and selected specific contents of the operation decision to be investigated;
step 2: calling different sub-methods according to different operation decisions; the sub-method refers to: and (3) processing and calculating the enterprise information and the enterprise financial data to obtain a specific judgment conclusion of the operation decision (step 3).
And step 3: in the sub-method, enterprise information and enterprise financial data are processed and operated to obtain a specific judgment conclusion of the operation decision;
and 4, step 4: and outputting a judgment conclusion of the system on the operation decision in the user interface.
The step 1 comprises the following steps:
step 1.1: a user inputs enterprise information and enterprise financial data according to questions of a system interface, wherein the enterprise information comprises enterprise establishment time, enterprise industry types, main and business services, places, number scale and the like, and the financial data comprises a debt table, a cash flow table and a income table of assets for a plurality of years;
step 1.2: the user selects the operation decision needed to be investigated, which comprises the following steps: whether the enterprise needs to borrow money in the future or not, and the amount of money to be borrowed is proper, whether the enterprise needs to release dividends and how many dividends are proper, whether the enterprise needs a new round of directional increase of capital, whether the increase of the capital is proper, and how the increase of the capital is optimal (the amount of increase in the stockbook and the stockbook sum respectively), whether the enterprise needs to increase/decrease the fixed assets, and how much the increase/decrease of the fixed assets is proper;
step 1.3: the data, including enterprise information data, enterprise financial data, and the operation decision selection to be investigated, are stored in the database with the corresponding enterprise name or number.
The step 2 comprises the following steps:
step 2.1: reading the 'operation decision selection needed to be investigated' data stored in the database, and calling the sub-methods corresponding to different selections.
Step 2.2: the sub-model provides hypothesis for the investigation item, and simulates the enterprise operation data through the previous enterprise report, the enterprise reports of other companies in the industry in the market and the macroscopic economic data through big data analysis and machine learning to predict the final financial reports;
step 2.3: judging the simulated terminal financial reports by using a judgment function, wherein the judgment function is a conclusion of judging whether the financial reports are good or not by combining the financial conditions of markets, industries and enterprises through big data analysis and machine learning, and outputting the simulated financial reports to be accepted or not through the judgment function;
step 2.4: and if the simulated financial report is judged not to be adopted, returning to the step 2.2 to adjust the hypothesis put forward by the investigation item, repeating the step 2.3 until the simulated financial report is judged to be adopted, and if the simulated financial report is judged to be adopted, outputting the hypothesis of the investigation item input in the step 2.2.
Those skilled in the art will appreciate that, in addition to implementing the systems, apparatus, and various modules thereof provided by the present invention in purely computer readable program code, the same procedures can be implemented entirely by logically programming method steps such that the systems, apparatus, and various modules thereof are provided in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Therefore, the system, the device and the modules thereof provided by the present invention can be considered as a hardware component, and the modules included in the system, the device and the modules thereof for implementing various programs can also be considered as structures in the hardware component; modules for performing various functions may also be considered to be both software programs for performing the methods and structures within hardware components.
The foregoing description of specific embodiments of the present invention has been presented. It is to be understood that the present invention is not limited to the specific embodiments described above, and that various changes or modifications may be made by one skilled in the art within the scope of the appended claims without departing from the spirit of the invention. The embodiments and features of the embodiments of the present application may be combined with each other arbitrarily without conflict.
Claims (9)
1. An enterprise operation decision-making method based on a physical option is characterized by comprising the following steps:
step S1: recording enterprise information and enterprise financial data input by a user, and selecting an operation decision to be investigated;
step S2: processing and calculating enterprise information and enterprise financial data aiming at different operation decisions to obtain a judgment conclusion of the operation decisions;
step S3: and outputting a judgment conclusion of the operation decision.
2. The physical option-based business operation decision method of claim 1, wherein the step S1 comprises:
step S101: recording enterprise information and enterprise financial data input by a user; the enterprise information includes: enterprise establishment time, enterprise industry type, main business, location and number scale; the financial data includes: annual asset liability statement, cash flow statement, income statement;
step S102: selecting an operational decision to be investigated, comprising: whether the enterprise needs to borrow money in the future or not, and the amount of money to be borrowed is proper, whether the enterprise needs to release dividends or not, and how many dividends are proper, whether the enterprise needs a new round of directional increase of capital, whether the increase of the capital is proper or not, and what the increase of the capital is proper is optimal, and whether the enterprise needs to increase or decrease the fixed assets or how much the increase of the fixed assets is proper or not;
step S103: and storing the enterprise information, the enterprise financial data and the operation decision to be investigated in a database by corresponding enterprise names or numbers.
3. The physical option-based business operation decision method of claim 1, wherein the step S2 comprises:
step S201: reading operation decision selection data to be investigated stored in a database, proposing an assumption to an investigation item, and simulating enterprise operation data and an end-of-term financial report through big data analysis and machine learning according to a previous-stage enterprise report, a corporate enterprise report on the market and macroscopic economic data;
step S202: judging whether the simulated terminal financial newspaper is to be adopted or not by utilizing a judgment function;
if the simulated end-of-term financial statement is judged not to be adopted, returning to the step S201, and adjusting the hypothesis put forward on the investigation item until the simulated end-of-term financial statement is judged to be adopted;
if the simulated end-of-term financial statement is judged to be adopted, the hypothesis proposed for the investigation item in the step S201 is output.
4. The physical option-based business operation decision method of claim 3, wherein the decision function combines the financial status of market, industry and the business itself to decide the simulated end-of-term financial reports through big data analysis and machine learning.
5. An enterprise operation decision-making system based on a physical option, comprising:
module S1: recording enterprise information and enterprise financial data input by a user, and selecting an operation decision to be investigated;
module S2: processing and calculating enterprise information and enterprise financial data aiming at different operation decisions to obtain a judgment conclusion of the operation decisions;
module S3: and outputting a judgment conclusion of the operation decision.
6. The physical option-based enterprise business decision system of claim 5, wherein said module S1 comprises:
a module S101: recording enterprise information and enterprise financial data input by a user; the enterprise information includes: enterprise establishment time, enterprise industry type, main business, location and number scale; the financial data includes: annual asset liability statement, cash flow statement, income statement;
a module S102: selecting an operational decision to be investigated, comprising: whether the enterprise needs to borrow money in the future or not, and the amount of money to be borrowed is proper, whether the enterprise needs to release dividends or not, and how many dividends are proper, whether the enterprise needs a new round of directional increase of capital, whether the increase of the capital is proper or not, and what the increase of the capital is proper is optimal, and whether the enterprise needs to increase or decrease the fixed assets or how much the increase of the fixed assets is proper or not;
a module S103: and storing the enterprise information, the enterprise financial data and the operation decision to be investigated in a database by corresponding enterprise names or numbers.
7. The physical option-based enterprise business decision system of claim 5, wherein said module S2 comprises:
a module S201: reading operation decision selection data to be investigated stored in a database, proposing an assumption to an investigation item, and simulating enterprise operation data and an end-of-term financial report through big data analysis and machine learning according to a previous-stage enterprise report, a corporate enterprise report on the market and macroscopic economic data;
a module S202: judging whether the simulated terminal financial newspaper is to be adopted or not by utilizing a judgment function;
if the simulated end-of-term financial statement is judged not to be adopted, calling the module S201, and adjusting the hypothesis put forward on the investigation item until the simulated end-of-term financial statement is judged to be adopted;
if the fact that the simulated end-of-term financial reports should be adopted is judged, the hypothesis put forward for the investigation item in the module S201 is output.
8. The physical option-based business operations decision system of claim 7, wherein the decision function combines market, industry and corporate financial status to decide on the simulated end-of-term financial reports through big data analysis and machine learning.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 4.
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