TW202133003A - Application method of intelligent analysis system allowing a user to realize intelligent operation and management through a chart, a model or a table - Google Patents

Abstract

The present invention relates to an application method of an intelligent analysis system, which includes the steps of: retrieving an open data from a server, and obtaining a closed data from a client end; pre-processing the open data and the closed data in a processing unit, and outputting a first data source and a second data source; storing the first data source and the second data source in a database; analyzing the first data source and the second data source, and outputting an analysis result; and outputting a chart, a model, or a table on an interface according to the analysis result, so that a user realizes intelligent operation and management through the chart, the model or the table.

Description

Application method of intelligent analysis system

The present invention relates to an application method of an analysis system, in particular to an application method of a smart analysis system by collecting, front-end analysis, in-depth analysis and outputting analysis results.

With the development of network technology, artificial intelligence has become the most popular industry nowadays. The combination of artificial intelligence with traditional industries can greatly help the data processing of traditional industries. Artificial intelligence (AI) is a field of computer science dedicated to solving common cognitive problems related to human intelligence, such as learning, problem solving, and pattern recognition.

Similarly, in business or academia, the related fields of BI&A (Business Intelligence & Analytics) have become more and more important, and there have been major breakthroughs and developments in many industrial studies. Therefore, in addition to obtaining information, how to store and analyze data is also a technology that needs to be broken and developed. Simply put, the place where data is stored is the work that a general database needs to achieve, and it is further targeted at a collection of related data in the database. Perform analysis. The data stored in the database can generally be divided into open data and closed data, that is, open data can be accessed by the public, while closed data are data restricted to access within private enterprises or government agencies.

Generally, the applications of open data open to the outside world are mainly non-text data materials, such as maps, gene sequences, neural network bodies (or Connections), chemical molecular data, mathematical functions, scientific formulas, medical data, and Applications, life sciences, biodiversity, and other public information that has no privacy restrictions and no copyright restrictions. For private enterprises or government agencies, the public information provided by them without access restrictions is also open information, but it is also open There are closed data for internal access only. For example, the results of private enterprises' business activities are basically fixed reports and other modes. The business report stage is the initial stage of data analysis. With the advent of database technology, especially nowadays, companies have begun to build informatization. The informatization of business processes has deposited a large amount of digital business data. In fact, everyone has always had the need for data analysis. Now that there is data accumulation, through these The need for report statistics and data analysis of data naturally arises.

However, as the database stores a large amount of digitized data, it is inevitable that there will be redundant data or too detailed, which makes data analysis need to spend more time, so how to effectively remove the above data and extract effective Data is the research direction of the present invention.

In view of the above-mentioned problems, the present invention provides an application method of an intelligent analysis system, and combines modern technologies such as artificial intelligence analysis and deep learning to quickly convert the above-mentioned data into practical technical data.

One purpose of the present invention is to provide an application method of an intelligent analysis system, by analyzing internal and external structured data or semi-structured data of an enterprise, after data extraction, conversion, and loading, artificial intelligence analysis technology is used to integrate The knowledge of business management is externalized into a set of management system to help managers operate wisely.

In order to achieve the above objective, the present invention discloses an application method of a smart analysis system, which includes the following steps: a processing unit of a server retrieves an open data, and a client obtains a closed data; the processing The unit pre-processing the open data and the closed data to generate a first data source and a second data source; store the first data source and the second data source in a data In the library; the processing unit executes an artificial intelligence program and analyzes the first data source and the second data source according to at least one analysis parameter to generate an analysis result; displays a chart and a model on a user interface according to the analysis result Or a table, the user realizes smart operation and management through the chart, the model or the table.

In an embodiment of the present invention, it is further disclosed that the pre-processing includes data cleaning, missing value processing, and normalization processing.

In an embodiment of the present invention, it further discloses that the open data and the closed data are subjected to Extract-Transform-Load (ETL) processing.

In an embodiment of the present invention, it further discloses the use of PDI (Pentaho Data Integration) tools to analyze the open data and the closed data.

In an embodiment of the present invention, it further discloses performing statistical induction, regression analysis, decision tree analysis, and deep learning on the first data source and the second data source.

In an embodiment of the present invention, it further discloses that the deep learning is Long Short-Term Memory (LSTM).

In an embodiment of the present invention, it further discloses that the open data is economic indicator open data or industry financial document data.

In an embodiment of the present invention, it further discloses that the closed data is structured data or semi-structured data.

In an embodiment of the present invention, it further discloses that the model includes a regression model.

In order to enable your reviewer to have a further understanding and understanding of the features of the present invention and the effects achieved, the examples and detailed explanations are provided here. The explanations are as follows:

The present invention is an application method of a smart analysis system, which retrieves open data on the server and obtains closed data on the client, and then analyzes the above data, after data extraction, conversion, and loading. Artificial intelligence analysis technology externalizes business management knowledge into a set of management system to help managers operate smartly.

Hereinafter, the application method of a smart analysis system of the present invention will be further explained:

Please refer to Figure 1, which is a flowchart of the intelligent analysis system of the present invention. As shown in the figure, the application method of the intelligent analysis system of the present invention includes the following steps:

S1: Retrieve open data and obtain closed data;

S3: Pre-process open data and closed data;

S9: Generate the first data source and the second data source;

S11: Store the first data source and the second data source;

S13: Analyze the first data source and the second data source;

S17: output analysis results;

S19: Display a chart, model or table on the user interface according to the analysis result.

Next, in order to achieve an intelligent analysis system of the invention, please continue to refer to Figure 1 and Figure 2 together, which is a schematic diagram of the intelligent analysis system of the present invention. As shown in the figure, an intelligent analysis system of the present invention includes: a server 10, a client 20, a processing unit 30, a database 40, and an operation interface 50. The server 10 and the client 20 are connected to the processing unit 30, the database 40 is connected to the processing unit 30, and the operation interface 50 further includes a display unit for displaying an analysis result of the database 40. Wherein, the server 10 is a web server, the server 10 stores an open data, the client 20 is an enterprise internal client, and the customer order 20 stores a closed data. The specific steps of a smart analysis system of the present invention are as follows:

As shown in step S1, an open data is retrieved, and a closed data is obtained. In this step, firstly, an open data is retrieved from the server 10, and a closed data is obtained from the client 20. Among them, the open materials are related materials such as open economic indicators or industrial financial documents. The open data collection methods include cross-platform data connection and conversion, open data collection and retrieval, and web crawler components written in Python programming language. In this embodiment, the web crawler components are used to capture the server 10 The open information. The collection of the closed data is the data provided by the customer, which is structured data or semi-structured data. The data must go through the data quality verification process before it becomes the required closed data.

As shown in step S3, the open data and the closed data are pre-processed. In this embodiment, after the step of capturing an open data and obtaining a closed data, it further includes step S5: extract-transform the open data and the closed data. -Load, ETL) processing, in other words, capture-transform-load processing is the process of extracting, transforming, and loading data from the source to the destination. In other words, after extracting an open data and obtaining a closed data step, first do some simple analysis work. The pre-processing includes data cleaning, missing value processing, normalization processing, etc., such as: 1. Extract (Extract): read data, the data source may be txt, excel or database; 2. Transform: analyze the data, such as verifying whether the data format is correct; 3. Load: The processed data may be exported into txt, excel, written or updated to the specified database.

Examples of open information are shown in Table 1: Function Main external data source (Input) Processing method (Process) Output External statistical indicators: l comprehensive economic indicators, l labor wages and productivity, l industrial management indicators l National Development Conference Prosperity Index Query System l Yahoo Stock Market Observation Station l Juheng.com l China Statistical Information Network of the Republic of China 1. Data grouping and classification 2. Statistical calculation 3. Graph presentation According to the characteristics of the indicators, it is presented to users in the form of appropriate charts, aiming to make users understand the performance indicators of other companies in the industry, as a reference for company operations. Industry News: l Summary analysis, l Keyword analysis l Fun with stocks l MoneyDJ l Juheng.com Industry News Network l Taiwan Economic Research Institute 1. Text pre-processing analysis 2. Keyword frequency analysis 3. Abstract text exploration Artificial intelligence algorithm is used to calculate the important keywords and summary content of the collected external industry news, so that users can quickly grasp the external information related to the business. Trend analysis: l exchange rate trends, l commodity prices l Yingwei Finance l (investing.com) l Srock-AI l Finance M Square l (MacoMicro) 1. Pre-analysis of numerical data 2. Multivariate dimensional analysis 3. Regression/DL model For the raw materials or exchange rates related to the relatively high cost of the enterprise, it provides a basic forecast of future trends for the enterprise as a reference for financial planning, sales or purchasing decisions. Current situation analysis l Internal information of the company l External information of the first three items above 1. Knowledge base (KB) model 2. Time series data analysis 3. Statistical analysis 4. Pattern analysis The analysis results obtained from the company's internal data and external data are comprehensively analyzed to give users a basic analysis of the current situation, especially reminders of some abnormal phenomena. Future forecast l Company's internal data l The results of the analysis of the first three external data above 1. Knowledge base (KB) model 2. Time series data analysis 3. Statistical analysis 4. Pattern analysis Exploring and analyzing the future prediction results of the company's internal data and external data, and predicting the future of the company's business management indicators, if any abnormal phenomenon is found, it can give the operator an early warning. Table I

Examples of open information of private companies are shown in Table 2 below: Comprehensive Prosperity Index Leading indicators Leading indicator composite index, export order trend index, total currency count M1B, stock price index, industrial and service industries, the net entry rate of employees, the floor area of the building construction, and the import value of semiconductor equipment Simultaneous indicators At the same time index composite index, industrial production index, industrial production index, total electricity consumption (enterprise), manufacturing sales volume index, non-agricultural sector employment, customs export value, machinery and electrical equipment import value Lagging indicators Backward index composite index, unemployment rate, manufacturing unit output labor cost index, financial industry overnight loan interest rate, lending and investment of all financial institutions, manufacturing inventory value Wage labor productivity Salary level Total monthly salary per person, monthly recurring salary per person, non-recurring monthly salary per person Human growth Entry rate, exit rate, flow rate Productivity cost Labor productivity index, labor cost index per unit output Industrial management indicators Profitability Turnover per share, earnings per share after tax, gross profit margin after tax, net profit before tax, net profit after tax, return on assets business performance Revenue growth rate, operating profit growth rate, net profit growth rate before tax, net profit growth rate after tax Management capacity Inventory turnover rate, account payable turnover rate, account receivable turnover rate, total asset turnover rate Financial structure Interest protection multiple, current ratio, quick ratio Solvency Leverage ratio, debt ratio Table II

Examples of closed data are shown in Table 3 below: Production Output, production yield, productivity per capita, inventory amount, inventory age, inventory turnover days, overdue production ratio analysis, outsourcing delay ratio analysis Sales Analysis of total revenue, customer sales ranking, product sales ranking, and shipment delay HR Staff turnover rate, salary cost rate, human output value R&D R&D expense rate, plan achievement rate finance Gross profit margin, accounts payable turnover rate, accounts receivable turnover rate, debt ratio, operating expense ratio Table Three

In addition, it also includes step S7: Use Pentaho Data Integration (PDI) to process the open data and the closed data. The so-called Pentaho Data Integration (PDI) is a Spoon-based data integration development environment. Pentaho data integration supports deployment on a single host with a cloud server architecture or a distributed cluster server architecture.

As shown in step S9, a first data source and a second data source are output. Wherein, after pre-processing the open data and the closed data, the first data source and the second data source are output.

As shown in step S11, the first data source and the second data source are stored. Wherein, after outputting the first data source and the second data source, the first data source and the second data source are stored in the database 40, in other words, the open data and the closed data pass through the previous After the processing, only the valuable data is retained, and the above-mentioned data is stored for further analysis in order to obtain more valuable information.

As shown in step S13, the first data source and the second data source are analyzed. In this step, first extract the required data from the database 40 or directly perform the next stage of analysis in the database. After step S13, it further includes step 15: performing statistical induction, regression analysis, decision tree analysis, and deep learning on the first data source and the second data source. Wherein, in this embodiment, the deep learning is an analysis method such as Long Short-Term Memory (LSTM). However, this analysis method is not limited.

As shown in step S17, an analysis result is output. Wherein, in this step, after further analysis is performed on the first data source and the second data source, the analysis result is output.

As shown in step S19, a chart, a model or a table is output on an operation interface according to the analysis result. Wherein, in this embodiment, the graph is a statistical graph, the model is a regression model, etc., but it is not limited to this.

In one embodiment, the analysis of the first data source and the second data source is based on an indicator characteristic, and the analysis result is output in the form of a suitable chart, so that the user can understand the performance indicators of other companies in the industry and serve as the company Operational reference.

In another embodiment, an artificial intelligence algorithm is used to calculate the important keywords and abstract content of the first data source of the open data, and provide the user with relevant external information.

In another embodiment, the analysis of the first data source and the second data source is to provide a basic forecast of future trends for the relevant raw materials or exchange rates with higher costs for the company for financial planning, sales or purchase. Reference for decision-making.

In another embodiment, the closed data of the company and the future forecast results of the open data are explored and analyzed to predict the future of the company's business management indicators, and if an abnormal phenomenon is found, an early warning message is sent to the operator.

To sum up, the intelligent analysis system of the present invention retrieves the open data on the server and obtains the closed data on the client, and then analyzes the above data, through data extraction, conversion, After loading, it is stored in the database, and secondly, artificial intelligence analysis technology is used to externalize the business management knowledge into a set of management system to help managers operate smartly.

Therefore, the present invention is really novel, progressive, and available for industrial use. It should meet the patent application requirements of my country's patent law. Undoubtedly, I filed an invention patent application in accordance with the law. I pray that the Bureau will grant the patent as soon as possible.

However, the above are only the preferred embodiments of the present invention, and are not used to limit the scope of implementation of the present invention. For example, the shapes, structures, features and spirits described in the scope of the patent application of the present invention are equally changed and modified. , Should be included in the scope of patent application of the present invention.

10: Server 20: client 30: processing unit 40: database 50: Operation interface S1~S19: steps

Figure 1: It is the first flow chart of the application method of the intelligent analysis system of the present invention; and Figure 2: It is a schematic diagram of the application method of the intelligent analysis system of the present invention.

10: Server

20: client

30: The first application

40: The second application

50: The second authentication mechanism

Claims (9)

An application method of a smart analysis system, the steps include: A processing unit of a server retrieves an open data, and obtains a closed data from a client; The processing unit performs a pre-processing on the open data and the closed data to generate a first data source and a second data source; The processing unit stores the first data source and the second data source in a database; The processing unit executes an artificial intelligence program and analyzes the first data source and the second data source according to at least one analysis parameter to generate an analysis result; and According to the analysis result, a chart, a model or a table is displayed on a user interface. For the application method of the intelligent analysis system described in item 1 of the scope of the patent, the pre-processing includes data cleaning, missing value processing, and normalization processing. The application method of the smart analysis system as described in claim 1, wherein the processing unit performs the pre-processing step of the open data and the closed data, and further includes the following steps: Perform Extract-Transform-Load (ETL) processing on the open data and the closed data. For the application method of the intelligent analysis system described in the third item of the patent scope, in the step of extract-transform-load (ETL) processing the open data and the closed data, More steps: Use the PDI (Pentaho Data Integration) tool to analyze the open data and the closed data. According to the application method of the intelligent analysis system described in claim 1, wherein in the step, the processing unit executes an artificial intelligence program and analyzes the first data source and the second data source according to at least one analysis parameter. Contains steps: The artificial intelligence program is executed to perform statistical induction, regression analysis, decision tree analysis, and deep learning on the first data source and the second data source. For the application method of the smart analysis system described in item 5 of the scope of the patent, the deep learning is Long Short-Term Memory (LSTM). Such as the application method of the smart analysis system described in item 1 of the patent scope, wherein the open data is open data of economic indicators or industrial financial documents. For the application method of the intelligent analysis system described in item 1 of the patent scope, the closed data is structured data or semi-structured data. According to the application method of the intelligent analysis system described in claim 1, wherein the model includes a regression model.

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