Intelligent auditing and monitoring system for group enterprise operation management data
By designing an intelligent audit monitoring system in a group enterprise and using artificial intelligence and machine learning algorithms for automatic auditing, the problems of traditional auditing are solved, and efficient and accurate data auditing and monitoring are achieved.
Patent Information
- Application Number
- CN202411373141.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-29
- Publication Date
- 2024-12-27
AI Technical Summary
Group companies face inefficiency, omissions and errors in data audits. Existing information audit tools cannot process semi-structured or unstructured data, and cannot adapt to rapidly changing data formats and sources.
An intelligent audit and monitoring system for group enterprises was designed, including task management module, data call module, intelligent audit module and information list module, and automatic audit and real-time monitoring is used to use algorithms such as artificial intelligence technology, machine learning and computer vision. Supports audits of multiple data types.
It improves the accuracy and efficiency of data audits, realizes comprehensive monitoring and management of different types of data, reduces the cost and error of manual audits, and adapts to rapidly changing data needs.
Smart Images
Figure CN119204574A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of intelligent information processing technology, and in particular to an intelligent audit monitoring system for group enterprises that integrates a task management module, a data call module, an intelligent audit module, an intelligent monitoring module, and an information list module. The system aims to improve the automation level of data processing and auditing, and improve the accuracy and efficiency of auditing. Background Art
[0002] In group management companies or companies with multiple holdings, enterprise management data audit and monitoring have become key factors in enterprise decision-making. Group companies face a complex market environment and a huge amount of data. Traditional enterprise management data audits use paper forms and manual audits. With the development of information technology, information-based audit methods have begun to appear, but there are many shortcomings in the monitoring methods. On the one hand, manual audits are inefficient and prone to omissions and errors. Auditors need to spend a lot of time and energy to check massive amounts of data one by one, which is not only time-consuming and labor-intensive, but also difficult to ensure the comprehensiveness and accuracy of the audit. On the other hand, the audit process of information-based audit tools is fixed, and the data audit function is single. For the situation where a new department is temporarily added to the audit due to a certain matter during the audit process, the existing information-based audit tools do not support it. Data audits are only applicable to structured data, and semi-structured or unstructured data cannot be automatically and accurately audited. Usually in group management companies or multiple holdings, data sources are wide and in various formats, including structured digital data in financial statements, text data from sales departments, and image or video data from production departments (such as product quality inspection images and production process monitoring videos). Existing information-based audit tools can often only process one type of data and cannot conduct comprehensive audits on different types of data. With the continuous expansion and innovation of corporate business, new data formats and data sources continue to emerge, and existing audit monitoring methods are difficult to adapt to rapidly changing needs. Therefore, group management companies or headquarters companies of multi-holding companies need a technology that can flexibly, efficiently and accurately audit and monitor the data quality in the process of group enterprise management data investigation. Summary of the invention
[0003] The present invention focuses on solving the problems of data collection and processing and approval monitoring management in business management of group companies or multi-holding companies, optimizing the approval process, and promoting the standardization and efficiency of business operations.
[0004] In order to achieve the above-mentioned purpose, the present invention provides an intelligent audit and monitoring system for group enterprise management data, and the system includes a task management module, a data call module, an intelligent audit module, an intelligent monitoring module and an information list module. The task management module is responsible for overall arrangement, including task allocation and progress tracking. The data call module can conveniently obtain data from various departments within different enterprises, such as the financial department, procurement department, sales department, production department, etc., to provide a basis for audit monitoring. The intelligent audit module uses artificial intelligence technology to automatically audit data to improve accuracy and efficiency. The intelligent monitoring module monitors the approval process and data quality in real time, finds problems in time and feeds them back to the operator. The information list module clearly lists various data and approval status information, which is convenient for users to query and manage, and realizes standardized and efficient operation and optimization of the approval process.
[0005] The main functions of the task management module are task allocation and progress tracking. The task allocation function module is used by managers to allocate data collection and approval tasks. They can flexibly select and allocate tasks according to the departments involved in the task process. According to the responsibilities of different departments and the professional capabilities of personnel, specific data collection and approval tasks can be accurately assigned to specific employees or teams. The progress tracking function module mainly tracks the progress of audit tasks in real time. The department to which the task belongs, the task progress, and the task description are displayed through an intuitive interface. Managers can understand the progress of each task at any time, discover possible delays or problems in a timely manner, and take appropriate measures to make adjustments.
[0006] The data call module is mainly responsible for supporting the call of the required audit data from different sources such as multi-source heterogeneous databases, cloud storage and real-time data streams, and has multi-department data acquisition functions and data basic functions. By integrating these data from different departments, the intelligent audit monitoring system for group enterprises can fully understand the business status of the enterprise and provide strong support for approval decisions.
[0007] The intelligent audit module mainly relies on machine learning and deep learning artificial intelligence technologies to conduct comprehensive automatic audits on the called data. Machine learning algorithms are used to learn a large amount of historical data and establish audit models to automatically identify anomalies and potential problems in the data. Natural language processing technology is used to analyze text data, extract key information, and improve the accuracy and efficiency of audits. The intelligent audit module automatically generates an audit result list based on the audit results, providing managers with detailed audit opinions and suggestions to facilitate decision-making.
[0008] The intelligent monitoring module is mainly responsible for real-time monitoring of data quality, system performance, and potential security risks. By working in collaboration with the task management module and the intelligent audit module, the intelligent monitoring module can keep track of the progress of approval tasks and the results of data audits. It monitors the quality of data in real time and promptly detects errors, omissions, or anomalies in the data. It immediately notifies relevant personnel to handle the situation. By real-time monitoring and timely handling of problems, the intelligent monitoring module can ensure that the approval process and data quality of the enterprise are always in good condition.
[0009] The information list module is mainly used to realize the intuitive display of problem classification, optimize user experience, and simplify the operation process. Through such a design, users can understand all aspects of the task, so as to operate and manage more conveniently and improve work efficiency. Innovation Intelligent audit algorithm: The intelligent audit module uses artificial intelligence algorithms at the forefront of the industry. The natural language processing (NLP) algorithm used in the module conducts in-depth semantic understanding and sentiment analysis operations on text data, accurately mines key information in the text, and realizes intelligent audit of text data. The computer vision (CV) algorithm performs efficient target detection and image recognition processing on image data, and quickly and accurately judges the compliance of image content. The machine learning (ML) algorithm provides powerful learning and adaptability for the entire intelligent audit module. Through training and learning of large amounts of data, it continuously optimizes the audit model and improves the accuracy and efficiency of audits of different types of data. The module can also perform comprehensive intelligent analysis and audits of different types of data such as text, pictures, audio and video at the same time, breaking the limitations of single data type audits in traditional audit methods, bringing new ideas and methods to data audit work, and greatly improving the intelligence level and overall efficiency of audit work. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] Figure 1 This is a general flow chart of the intelligent audit and monitoring system for group enterprise management data according to an embodiment of the present invention; Figure 2 This is a functional module diagram of the intelligent audit and monitoring system for group enterprise business management data according to an embodiment of the present invention; DETAILED DESCRIPTION
[0011] In order to make the above-mentioned objects, features and advantages of the present invention clearer and easier to understand, Figure 1 —2 A detailed description is given of the specific implementation method of an intelligent audit and monitoring system for group enterprise management data involved in the present invention.
[0012] like Figure 1As shown, the present invention is a general flow chart of an intelligent audit monitoring system for group enterprise management data, which generates audit tasks after data collection and sorting. The audited task is called, and in this process, if there is redundant data, it is cleaned. Artificial intelligence algorithms are used to perform intelligent analysis, audit and intelligent detection of data. Throughout the audit, the quality of the data is monitored in real time. After the audit is completed, an information list is generated, which records important contents including task information, audit results, and problem records.
[0013] like Figure 2 As shown, the present invention is a functional module diagram of an intelligent audit monitoring system for group enterprise business management data, which is mainly composed of a task management module, a data call module, an intelligent audit module, an intelligent detection module and an information list module.
[0014] like Figure 2 As shown, the task management module is closely connected with the data call module, providing the data call module with relevant information of the audit task. The data call module is interconnected with the task management module and the intelligent audit module, calling data according to the requirements of the task management module and providing audit data to the intelligent audit module. The intelligent audit module is connected with the data call module, receiving the data provided by the data call module for intelligent audit. The intelligent monitoring module and the intelligent audit module are connected with the data call module at the same time, monitoring the audit process of the intelligent audit module throughout the process, and accurately passing the relevant information obtained to the information list module for detailed recording and proper maintenance. The information list module is connected with the intelligent monitoring module, recording the information of all modules, and generating an information list covering the entire process accordingly.
[0015] During this implementation, task management was placed at the front end of the process, and forward-looking planning was carried out in terms of business management. The system established connections with various data sources (databases, file systems, network interfaces) to obtain the data that needs to be reviewed.
[0016] The main functions of the task management module are task allocation and progress tracking. The task allocation function module is used by managers to allocate data collection and approval tasks. Flexible selection and allocation can be made according to the departments involved in the task process, thereby solving the problem that the approval process of the current information-based audit tool is fixed and cannot be reviewed by multiple departments. According to the responsibilities of different departments and the professional capabilities of personnel, specific data collection and approval tasks are accurately assigned to specific employees or teams. For approval tasks involving financial data, they can be assigned to professionals in the financial department for processing. The progress tracking function module mainly tracks the progress of audit tasks in real time. The department to which the task belongs, the task progress, and the task description are displayed through an intuitive interface. Managers can keep abreast of the progress of each task, promptly discover possible delays or problems, and take appropriate measures to adjust them. If the progress of a task lags behind, the manager can urge the person in charge of the task to speed up the approval progress, or reallocate the task to ensure the smooth progress of the overall approval process.
[0017] The data calling module calls the required data from multi-source heterogeneous databases, cloud storage or real-time data streams according to the task requirements assigned by the task management module. The module has built-in efficient data retrieval and preprocessing algorithms, which can quickly and accurately find the target data, and perform data cleaning and format conversion preprocessing, thereby providing a high-quality data source for subsequent intelligent audits. Multi-department data acquisition can easily obtain data from various departments within a group company or a holding company. These departments include but are not limited to the financial department, procurement department, sales department and production department. The data of each department has an important impact on the decision-making and operation of the enterprise, so the data calling module needs to ensure that it can seamlessly connect with the systems of these departments and efficiently obtain the required data. Data provides a basis for audit monitoring. The data of the financial department is used to audit the financial status and capital flow of the enterprise; the data of the procurement department is used to audit the compliance and cost control of the procurement process; the data of the sales department is used to audit sales performance and market share; the data of the production department is used to audit production efficiency and product quality. By integrating these data from different departments, the intelligent audit monitoring system for group enterprises can fully understand the operating status of the enterprise and provide strong support for approval decisions.
[0018] The intelligent audit module is the core part of the system. It receives data from the data call module and uses artificial intelligence algorithms for intelligent analysis and auditing. A large amount of historical data is learned using machine learning algorithms to establish an audit model, thereby automatically identifying anomalies and potential problems in the data. This core module contains three submodules: a text audit submodule, a picture audit submodule, and an audio and video audit submodule. Among them, the text audit submodule refers to the use of natural language processing technology to accurately identify illegal words and sensitive information in the text; the picture audit submodule refers to the use of computer vision technology to detect prohibited content and abnormal phenomena in the picture; the audio and video audit submodule refers to the combination of voice recognition and image recognition technology to conduct a comprehensive and integrated audit of audio and video content. In addition, the intelligent audit module supports custom audit rule settings to meet the specific needs of different business scenarios. The intelligent audit module automatically generates an audit result list based on the audit results, providing managers with detailed audit opinions and suggestions to facilitate decision-making.
[0019] The intelligent detection module plays an important role in real-time monitoring of data quality, system performance and potential security risks during the audit process. The module has built-in detection algorithms and monitoring mechanisms, which can timely detect and warn of data errors, system bottlenecks, and network attack problems. Once a problem is detected, the system will automatically generate a prompt message and notify relevant personnel to properly handle it, so as to ensure the stable operation of the system and the accuracy of the audit results. For example, if a delay in the approval process is found, the intelligent monitoring module can remind relevant personnel to speed up the approval process; if problems are found in data quality, the intelligent monitoring module can guide data collectors online to correct or supplement data, and only after the intelligent monitoring data quality meets the requirements can it enter the next approval link.
[0020] The information list module is mainly responsible for recording audit information, realizing intuitive display of problem classification, optimizing user experience, and simplifying operation procedures. Specifically, it includes the source of tasks, distribution status, execution progress, and audit problem content. Among them, the source of tasks mainly clarifies the initiating department or person of the task; the distribution status of tasks mainly shows which personnel or teams the tasks are specifically assigned to; tracking the execution progress reflects the completion stage and progress of the task in real time; and collating the audit problem list, listing in detail the problems and errors found during the audit process. This module provides query and export functions, and users can easily query the details of the task, audit results, and problem record information at any time. It supports information classification management, which helps users quickly locate information points that need attention.
[0021] The present invention is an improvement on the intelligent audit monitoring system for group enterprise management data. Compared with the traditional audit mode, this system significantly improves the intelligence level of the audit process and realizes comprehensive monitoring and management of various audit data. By creating audit tasks, it can flexibly adapt to different audit needs, providing great convenience for various business scenarios. The data call module ensures the accurate acquisition and preprocessing of data, laying a solid and reliable foundation for subsequent audits and analyses. The intelligent audit module uses a variety of artificial intelligence algorithms to quickly and accurately discover problems in the data, greatly improving the quality and efficiency of the audit. The intelligent monitoring module monitors data quality in real time, further ensuring the accuracy and reliability of the audit, and providing a strong guarantee for the stable operation of the system. The recording function of the information list module facilitates subsequent inquiries and tracing, and provides users with a convenient means of information management.
[0022] Compared with the existing technology, the present invention has the following beneficial effects: it can realize efficient and accurate review and monitoring of enterprise management data, flexibly configure the review process, greatly improve the review efficiency and quality, reduce the cost and error of manual review, and provide solid and reliable support for related businesses.
Claims
1. An intelligent audit and monitoring system for group enterprise management data, characterized by: It includes task management module, data call module, intelligent audit module, intelligent monitoring module and information list module.
2. The intelligent audit monitoring system according to claim 1 is characterized in that: The task management module is mainly used to allocate intelligent audit tasks and monitor the progress of audit tasks; The data calling module calls required data from multi-source heterogeneous databases, cloud storage or real-time data streams according to the task requirements assigned by the task management module; The intelligent audit module receives data from the data call module and uses artificial intelligence algorithms to perform intelligent analysis and audit; The intelligent detection module plays a role in real-time monitoring of data quality, system performance and potential security risks during the audit process; The information list module is mainly responsible for recording audit information, realizing intuitive display of problem classification, optimizing user experience, and simplifying operation procedures.
3. The intelligent audit monitoring system according to claim 2 is characterized in that: The task management module is used by managers to allocate data collection and approval tasks. They can flexibly select and allocate tasks according to the departments involved in the task process. According to the responsibilities of different departments and the professional capabilities of personnel, specific data collection and approval tasks can be accurately assigned to specific employees or teams. The progress tracking function module mainly tracks the progress of audit tasks in real time.
4. The intelligent audit monitoring system according to claim 2 is characterized in that: The data call module has the function of acquiring data from multiple departments and the basic function of data. By integrating these data from different departments, the intelligent audit monitoring system for group enterprises can fully understand the business status of the enterprise and provide strong support for approval decisions; The data calling module has built-in efficient data retrieval and preprocessing algorithms, which can quickly and accurately find target data, and perform preprocessing work such as data cleaning and format conversion, thereby providing a high-quality data source for subsequent intelligent audits.
5. The intelligent audit monitoring system according to claim 2 is characterized in that: The intelligent audit module mainly relies on artificial intelligence technologies such as machine learning and deep learning to conduct comprehensive automatic audits on the called data. Machine learning algorithms are used to learn a large amount of historical data and establish audit models to automatically identify anomalies and potential problems in the data. Natural language processing technology is used to analyze text data, extract key information, and improve the accuracy and efficiency of the audit. The intelligent audit module automatically generates an audit result list based on the audit results, and provides managers with detailed audit opinions and suggestions to facilitate decision-making; The natural language processing technology in the intelligent review module includes in-depth semantic understanding and sentiment analysis operations on text data, accurately mining key information in the text, and realizing intelligent review of text data; The computer vision technology in the intelligent audit module includes efficient target detection and image recognition processing of image data to quickly and accurately determine the compliance of image content; The machine learning algorithm in the intelligent audit module provides the entire intelligent audit module with powerful learning and adaptability. By training and learning a large amount of data, the audit model is continuously optimized to improve the accuracy and efficiency of auditing different types of data. The text review submodule in the intelligent review module can use natural language processing technology to accurately identify illegal words and sensitive information in the text; The image review submodule can detect prohibited content and abnormal phenomena in images with the help of computer vision technology; The audio and video review sub-module can combine voice recognition and image recognition technology to conduct a comprehensive review of audio and video content.
6. The intelligent audit monitoring system according to claim 2 is characterized in that: The intelligent monitoring module works in collaboration with the task management module and the intelligent audit module. The intelligent monitoring module can keep track of the progress of the approval task and the results of the data audit at any time. It monitors the quality of the data in real time and promptly detects errors, omissions or abnormalities in the data. It immediately notifies relevant personnel to handle the situation. By real-time monitoring and timely handling of problems, the intelligent monitoring module can ensure that the approval process and data quality of the enterprise are always in good condition. The intelligent monitoring module has built-in detection algorithms and monitoring mechanisms, which can promptly detect and warn of data errors, system bottlenecks, network attacks and other problems, and automatically generate prompt information to notify relevant personnel to handle them properly, thereby ensuring the stable operation of the system and the accuracy of the audit results.
7. The intelligent audit monitoring system according to claim 2 is characterized in that: The information list module is mainly used to realize the intuitive display of problem classification, optimize user experience, and simplify the operation process. Through such a design, users can understand all aspects of the task, so as to operate and manage more conveniently and improve work efficiency; The task source in the information list module mainly clarifies the department or person who initiated the task; the task allocation status mainly shows which personnel or teams the task is specifically allocated to; the execution progress reflects the completion stage and progress of the task in real time; the audit issue list lists in detail the problems and errors found during the audit process.
8. The intelligent audit monitoring system according to claims 1 and 2 is characterized in that: The task management module is closely connected with the data call module, providing the data call module with relevant information of the audit task. The data call module is interconnected with the task management module and the intelligent audit module, calling data according to the requirements of the task management module and providing audit data to the intelligent audit module. The intelligent audit module is connected with the data call module, receiving the data provided by the data call module for intelligent audit. The intelligent monitoring module and the intelligent audit module are connected with the data call module at the same time, monitoring the audit process of the intelligent audit module throughout the process, and accurately passing the relevant information obtained to the information list module for detailed recording and proper maintenance. The information list module is connected with the intelligent monitoring module, recording the information of all modules, and generating an information list covering the entire process accordingly.