Python-based automatic enterprise credit monitoring system

Abstract

The invention relates to the field of enterprise credit management, and provides a Python-based automatic enterprise credit monitoring system, which comprises an information acquisition module, an information summarizing and displaying module, a database module and an alarm pushing module, and is characterized in that the information acquisition module automatically completes information acquisition of an authoritative credit website based on python, so that the workload of previous manual query is remarkably reduced; the information summarization and display module provides a window for interaction between a user and the system, enterprise credit information is classified and summarized in a table form, and operations such as searching, statistics, sorting and detail viewing of the enterprise credit information are realized through buttons and an input box on a system U I; the database module provides a data processing service and completes data storage of the information acquisition module and information input of the information summarization display module; and the alarm pushing module is used for carrying out enterprise WeChat alarm pushing on the enterprise with the abnormal credit, so that the response time of the enterprise credit risk is effectively reduced, and the enterprise credit management level is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of enterprise credit management, and specifically to an automated enterprise credit monitoring system based on Python, aiming to improve the efficiency of enterprise credit information collection, aggregation, display and risk response through automated technology, thereby enhancing the level of enterprise credit management. Background Art

[0002] With the in-depth development of the market economy, enterprise credit plays an increasingly important role in market competition. It is not only a yardstick to measure the operation status of an enterprise, but also a key factor reflecting the strength of an enterprise's market competitiveness. Good enterprise credit can enhance consumers' trust in the enterprise, improve the enterprise's brand image, and thus promote the long-term development of the enterprise. How to timely discover and handle enterprise credit problems and quickly eliminate the impact is a research topic worthy of study. However, traditional enterprise credit monitoring methods face many challenges.

[0003] Traditional enterprise credit monitoring mainly relies on manual collection and analysis. This method is not only inefficient, consuming a large amount of manpower, material resources and time, but also difficult to ensure the comprehensiveness and accuracy of information. Since enterprise credit information is scattered on multiple authoritative credit websites, manual monitoring often fails to cover all websites, resulting in information omission and untimely update. In addition, manual monitoring is also easily affected by human factors, such as information entry errors and analysis judgment errors, further reducing the reliability of monitoring results.

[0004] Therefore, it is particularly important to develop an automated and intelligent enterprise credit monitoring system. Such a system can use advanced technical means, such as Python, big data analysis, machine learning, etc., to automatically collect, integrate and analyze enterprise credit information on multiple authoritative credit websites. Through intelligent algorithms and models, the system can accurately judge the credit status of an enterprise, timely discover potential credit risks, provide timely and accurate credit monitoring services for enterprise management, and also gain valuable time for the enterprise to handle and eliminate the impact in a timely manner. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention provides an automated enterprise credit monitoring system based on Python, which includes the following modules:

[0006] An information collection module, which automatically collects enterprise credit information from credit websites based on Python technology;

[0007] Information summary and display module. The information summary and display module serves as the interface for users to interact with the system. It classifies, summarizes, and displays the enterprise credit information that users are concerned about in tabular form. Users can perform operations such as searching, statistics, sorting, and viewing details of enterprise credit information through buttons and input boxes on the interaction interface;

[0008] Database module. The database module provides data processing services, is responsible for storing the data collected by the information collection module, and provides the required enterprise credit information to the information summary and display module;

[0009] Alarm push module. When detecting enterprise credit anomalies, the alarm push module pushes alarm information to users through communication tools to shorten the response time of enterprise credit risks and improve the level of enterprise credit management.

[0010] Furthermore, the information collection module includes a data cleaning unit for preprocessing the collected enterprise credit information to improve the accuracy and readability of the information;

[0011] For the processing of missing values in enterprise credit information, the data cleaning unit fills in the missing values based on the corresponding values of similar enterprises;

[0012] For the processing of conflicting values in enterprise credit information, when there are inconsistent values for the same attribute in different data sources, the data cleaning unit uses the weighted average method to process the conflicting values.

[0013] Furthermore, the information summary and display module includes a visualization function. It creates charts according to the information through a Python visualization database to display the statistical results of enterprise credit information in the form of charts, facilitating users to intuitively understand the enterprise credit status.

[0014] Furthermore, the alarm push module includes custom alarm rules that allow users to set alarm conditions according to actual needs to achieve precise monitoring of enterprise credit risks;

[0015] The custom alarm rules are constructed based on the enterprise's credit score, overdue records, and credit limit.

[0016] Furthermore, the enterprise risk levels are divided according to the financial status of the enterprise, into four levels: low, medium, high, and extremely high; the comprehensive score range corresponding to the low risk level is 80 points and above, the medium risk level is 60 - 79 points, the high risk level is 40 - 59 points, and the extremely high risk level is below 40 points.

[0017] The financial status parameters include solvency indicators, profitability indicators, and operating capacity indicators;

[0018] The solvency indicator calculates the asset - liability ratio of the enterprise:

[0019]

[0020] The profitability index calculates the return on equity (ROE) of an enterprise:

[0021]

[0022] The operation ability index calculates the total asset turnover rate of an enterprise:

[0023]

[0024] By calculating the above three indexes, the risk level of the enterprise is scored according to the index situation, and the weights of each index are adjusted according to the actual situation of the enterprise. For example, for an asset-heavy enterprise, the weight of the debt-paying ability index is greater than the other two indexes.

[0025] Furthermore, the system further includes a log recording module for recording the system operation status, user operations or historical information of alarm push.

[0026] Compared with the prior art, the present invention has the following beneficial effects:

[0027] The present invention realizes operations such as searching, statistics, sorting and viewing details of enterprise credit information through buttons and input boxes on the system UI; the database module provides data processing services to complete data storage of the information collection module and information input of the information summary display module; the alarm push module is used to push enterprise WeChat alarms for enterprises with credit anomalies, effectively reducing the response time of enterprise credit risks and improving the enterprise credit management level. Brief Description of the Drawings

[0028] Figure 1 is the overall design diagram of the system of the present invention. Detailed Embodiments

[0029] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and embodiments. The following embodiments are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0030] Embodiment:

[0031] As Figure 1 shown, the present invention proposes an automated enterprise credit monitoring system based on Python, including:

[0032] An information collection module, which automatically collects information from authoritative credit websites based on Python, significantly reducing the workload of manual queries in the past;

[0033] An information summary and display module, which provides a window for users to interact with the system, classifies and summarizes the credit information of enterprises concerned by users on various authoritative websites in the form of a table, and realizes operations such as searching, statistics, sorting, and viewing details of enterprise credit information through buttons and input boxes on the system UI;

[0034] A database module, which provides data processing services and completes the data storage of the information collection module and the information input of the information summary and display module;

[0035] An alarm push module, which is used to push enterprise WeChat alarms for enterprises with credit anomalies, effectively reducing the response time of enterprise credit risks and improving the enterprise credit management level.

[0036] This system is an automated enterprise credit monitoring system written in Python programs, which includes functions such as automatic collection of enterprise credit information, summary display of credit information, automatic analysis of credit anomalies, and alarm push.

[0037] The information collection module includes the collection of the "Credit China" website and the "Credit Energy" website. Different web crawler technologies are adopted for different website structures and anti-crawler measures. The "Credit China" website itself uses the sixth-generation product of RuiShu Information's Dynamic Security Botgate (robot firewall). This product takes "dynamic security" technology as the core and provides strong security protection for various Web and HTML5 through a series of technical means. This product includes anti-crawler measures such as dynamic loading, User-Agent detection, IP blacklist, random verification code verification, and parameter encryption. The information collection module of the enterprise credit automatic monitoring system adopts different bypass strategies for different measures. For example, for dynamic loading and parameter encryption, it uses the web automation tool DrissionPage based on Python; for User-Agent detection, it modifies the value of the User-Agent request header and disguises it as a browser to initiate a request to deceive the anti-crawler program; for the IP blacklist, it uses a proxy IP pool to hide the real IP address and uses a different IP address for each access to avoid being blocked; for random verification code verification, it uses the open-source OCR (Optical Character Recognition) library ddddocr based on deep learning for automatic verification code recognition. The "Credit Energy" website itself uses the method of server-side rendering (SSR), dynamically generates complete page content on the server side, and then returns the generated page content as a response to the client browser, and adopts anti-crawler measures to limit the access frequency of a unified IP. The information collection module of the enterprise credit automatic monitoring system uses the regular expression re library of Python for information matching for the server-side rendering mode, and at the same time adds a random access time interval to the collection program to bypass the access frequency. The lightweight Python timing task library APScheduler is used to regularly call the information collection module to collect data every day to ensure the timely update of the data. The information collected by the information collection module is classified and stored in the local MySQL database using the database connection library Flask-SQLAlchemy for other modules to analyze, process, and use.

[0038] The information summary and display module provides a window for information display and interaction between users and the system. General situation of the system interface: It includes TAB tabs classified by data source websites, a search bar, and tabular data summary display.

[0039] TAB tab: Each TAB tab displays the summary information of a data source website, and clicking on the TAB tab switches. The red digital badge beside the TAB tab title indicates the number of discredited enterprises in the corresponding tab page, which is simple and eye-catching.

[0040] Search bar: After entering keywords, it can filter the list of enterprise credit information whose names contain the keywords, supporting fuzzy search.

[0041] Data table: Displays all enterprise credit information centrally. Clicking on the field name can sort in ascending or descending order according to the fields that users are interested in. Clicking on the company name can link to the data source website to view the detailed credit information of the enterprise, eliminating the cumbersome operation of searching on the source website.

[0042] The information summary display module adopts the B / S architecture. Users do not need to install specific client software and can access the system only through a browser, greatly improving the usability of the system. The backend uses the web application framework Flask based on Python, which is lightweight, flexible and easy to use. The front end uses HTML technology and the Layui UI component library. The backend server uses the Jinja2 template engine to render data into templates to generate dynamic HTML pages and return them to users to complete the display of credit information.

[0043] The data source of the information summary display module is a local MySQL database, which is connected and called through the database connection library Flask-SQLAlchemy.

[0044] The alarm push module provides a WeChat message push service for credit alarms. The alarm push module uses Python to analyze the enterprise credit data in the database and screen out enterprises with abnormal credit status. For example, on the "Credit China" website, it shows "Administrative Penalty", "Serious Dishonesty", "Business Abnormality", "Judicial Judgment", and on the "Credit Energy" website, it shows "Administrative Penalty", "Key Attention List", "Dishonesty Punishment", etc. Use the f-string method of Python to form an alarm report according to a custom template. Use the HTTP library requests of Python to call the enterprise WeChat API and push the alarm report to a specific WeChat group to remind relevant credit management personnel to pay attention in time.

[0045] At the same time, combine the financial data of the enterprise to score the enterprise risk. According to the risk level of the enterprise, and then combine the website information for comparison. If there is a large deviation, mark the enterprise credit information, and then calculate the enterprise risk level again. If it is still inconsistent, confirm the enterprise credit according to the risk level.

[0046] At the same time, use the lightweight Python timing task library APScheduler to call the alarm push module to push WeChat messages regularly every day to inform relevant credit management personnel of the daily credit situation.

[0047] Embodiments of the present invention are given for purposes of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. An automated enterprise credit monitoring system based on Python, characterized in that: include: An information collection module, which automatically collects corporate credit information from credit websites based on Python technology; An information summary and display module, which serves as an interface for users to interact with the system, and summarizes and displays the corporate credit information of interest to users in a table format. Users can search, count, sort and view details of corporate credit information through buttons and input boxes on the interactive interface; A database module, which provides data processing services, is responsible for storing the data collected by the information collection module, and provides the required corporate credit information to the information summary and display module; The alarm push module pushes alarm information to users through communication tools when detecting corporate credit anomalies, so as to shorten the response time of corporate credit risk and improve the level of corporate credit management.

2. The Python-based automated enterprise credit monitoring system according to claim 1, characterized in that: The information collection module includes a data cleaning unit for preprocessing the collected corporate credit information to improve the accuracy and readability of the information; For the missing value processing of enterprise credit information, the data cleaning unit fills the missing values ​​based on the corresponding values ​​of similar enterprises; Regarding the processing of conflicting values ​​of enterprise credit information, when there are inconsistent values ​​for the same attribute in different data sources, the data cleaning unit uses a weighted average method to process the conflicting values.

3. The Python-based automated enterprise credit monitoring system according to claim 1, characterized in that: The information summary and display module includes a visualization function, which creates charts based on information through a Python visualization database to display the statistical results of corporate credit information in the form of charts, making it easier for users to intuitively understand the corporate credit status.

4. The Python-based automated enterprise credit monitoring system according to claim 1, characterized in that: The alarm push module includes custom alarm rules, allowing users to set alarm conditions according to actual needs to achieve accurate monitoring of corporate credit risks; The custom alarm rules are constructed based on the enterprise's credit score, overdue record, and credit limit.

5. The Python-based automated enterprise credit monitoring system according to claim 4, characterized in that: Enterprise risk levels are divided into four levels: low, medium, high and very high according to the company's financial status.

6. The Python-based automated enterprise credit monitoring system according to any one of claims 1 to 5, characterized in that: The system also includes a log recording module for recording system operating status, user operations or alarm push history information.

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