Intelligent examination and approval auxiliary system and method driven by relation chain analysis

Abstract

The invention provides an intelligent examination and approval auxiliary system and method driven by relation chain analysis. The system comprises: a data input and processing module supporting a plurality of data input modes and preprocessing input data; the relation chain analysis module performs deep analysis on data in the approval process through a relation chain analysis algorithm to generate a relation chain graph; the intelligent decision support module performs intelligent analysis on data in the approval process through an artificial intelligence algorithm and provides decision auxiliary suggestions; the risk identification and management module monitors data in the approval process in real time through an intelligent algorithm and relation chain analysis, and discovers business exceptions and potential risks; and the data visualization module provides a visual data analysis graph and supports an approver to comprehensively understand business conditions and decision-making backgrounds. The invention provides an intelligent examination and approval auxiliary system and method driven by relation chain analysis, and aims to improve the efficiency and accuracy of an examination and approval process and enhance the capability of identifying and managing enterprise risks.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing and intelligent decision support, and in particular to an intelligent approval auxiliary system and method driven by relationship chain analysis. Background Art

[0002] In modern enterprise management, the approval process is an important part of ensuring business compliance and risk control. With the expansion of enterprise scale and the increase of business complexity, the amount of data involved in the approval process is becoming increasingly large, and the traditional manual approval method can no longer meet the needs of efficient and accurate processing. Traditional approval processes usually rely on manual judgment, which is not only inefficient, but also easily affected by human factors, leading to errors.

[0003] Deficiencies of existing technology: 1. Efficiency issues: Traditional approval processes often require a lot of manual review work, especially when faced with complex approval requests, which makes the entire process very time-consuming; in addition, approvers may need to review a large amount of documents and records to make a decision, which further reduces the speed of approval.

[0004] 2. Accuracy issues: Since approval mainly relies on personal experience and intuition, it is difficult to ensure the consistency and accuracy of each decision; there may be different standards among different approvers, which will affect the reliability of the approval results.

[0005] 3. Weak risk management capabilities: The existing approval system usually lacks effective means to monitor and warn of potential risks in real time; once abnormal situations or violations occur, they are often not discovered and corresponding measures cannot be taken in time, increasing the operational risks of the enterprise.

[0006] 4. Information island phenomenon: In actual operations, many companies have multiple independently operated information systems, and the data exchange between them is not smooth, forming information islands. In this case, it is difficult for approvers to fully obtain the required information, affecting the overall grasp of the business situation.

[0007] 5. Low level of visualization: Traditional approval reports are mostly presented in text form and lack intuitive data display methods; approvers need to spend extra time to understand and digest this information, which is not conducive to making quick decisions.

[0008] Therefore, the prior art has deficiencies and needs further improvement. Summary of the invention

[0009] In view of the problems existing in the prior art, the present invention provides an intelligent approval assistance system and method driven by relationship chain analysis.

[0010] To achieve the above object, the specific scheme of the present invention is as follows: The present invention provides an intelligent approval assistance system driven by relationship chain analysis, the system comprising: Data entry and processing module, which supports multiple data entry methods and pre-processes the entered data; The relationship chain analysis module uses the relationship chain analysis algorithm to conduct in-depth analysis of the data in the approval process and generate a relationship chain map; Intelligent decision support module, which uses artificial intelligence algorithms to intelligently analyze data in the approval process and provides intelligent decision suggestions based on historical and current data; The risk identification and management module monitors the data in the approval process in real time through intelligent algorithms and relationship chain analysis to discover business anomalies and potential risks, provides risk warning functions and automatically generates detailed risk reports and corresponding handling suggestions; The data visualization module provides intuitive data analysis graphs to support approvers in fully understanding the business situation and decision-making background. The module includes timeline trajectory generation, statistical information display, and multi-dimensional data relationship diagrams. Users can customize the data display method.

[0011] Furthermore, the data entry and processing module supports manual entry and automatic import, and performs data cleaning, format conversion and label generation on the entered data; Among them, the manual input interface is used for users to manually input data; Auto-import feature for automatically synchronizing data with external systems via API.

[0012] Furthermore, the relationship chain analysis module is used to identify the association relationship between different data in the approval process and dynamically update the relationship chain map to reflect the latest data changes.

[0013] Furthermore, the intelligent decision support module generates intelligent decision suggestions by combining historical data and current data; The intelligent decision support module includes a historical data analysis engine for analyzing historical approval data to identify common patterns; and a real-time decision suggestion generator for generating decision suggestions based on real-time data.

[0014] Furthermore, the risk identification and management module provides risk warning function and automatically generates detailed risk reports and corresponding treatment suggestions; The risk identification and management module also includes: a rule engine for identifying risks based on predefined business rules; an anomaly detection algorithm for identifying abnormal behaviors in the approval process through machine learning technology.

[0015] The present invention also provides an intelligent approval assistance method driven by relationship chain analysis. Based on the above system, the method comprises the following steps: S1, import approval process related data from various channels; S2, preprocessing the received data, including data cleaning, format conversion, and label generation; S3, using the relationship chain analysis algorithm to conduct in-depth analysis of the data in the approval process, identify the associations between the data, and generate a relationship chain map; S4, uses artificial intelligence algorithms to intelligently analyze data in the approval process, and combines historical data with current data to generate intelligent decision-making suggestions for approvers; S5, real-time monitoring of data in the approval process, automatic identification of business anomalies and potential risks, providing risk warnings, and issuing warning notifications when necessary; S6, automatically generates a risk report, which contains a detailed description of the anomalies and potential risks found and the corresponding treatment suggestions; S7 provides intuitive data analysis diagrams, including timeline trajectory generation, statistical information display, and multi-dimensional data relationship diagrams, so that approvers can fully understand the business situation and decision-making background.

[0016] Furthermore, step S1 of importing data also includes manually inputting or automatically importing data from an external system through an API interface to achieve data synchronization with the external system.

[0017] Furthermore, in step S3, a relationship chain analysis algorithm is used to reveal the associations between invoices and orders, expense reimbursements and budgets, and the relationship chain graph can be updated instantly as new data is added.

[0018] Furthermore, in step S4, the artificial intelligence algorithm in the intelligent analysis trains a model based on historical approval records, thereby being able to predict future approval trends and provide recommendations accordingly.

[0019] Furthermore, in step S5, an anomaly detection algorithm is used in the risk identification process to identify behaviors that deviate from the normal pattern, and an early warning mechanism is triggered immediately once an anomaly is found.

[0020] The technical solution of the present invention has the following beneficial effects: 1. Improve approval efficiency: Through automated relationship chain analysis and artificial intelligence algorithms, the system can quickly process large amounts of approval data and reduce the time for manual review; the intelligent decision support module can automatically identify key nodes and potential problems, and provide intelligent decision-making recommendations, thereby speeding up the approval process.

[0021] 2. Enhance decision-making accuracy: The system conducts in-depth analysis based on historical and current data, and combines relationship chain graphs and intelligent algorithms to provide data-driven decision-making assistance to approvers; this helps eliminate subjective biases in human judgment and improve the consistency and accuracy of decisions.

[0022] 3. Real-time risk identification and management: The system can monitor data changes in the approval process in real time, promptly identify business anomalies and potential risks, and automatically generate risk reports through a built-in rule engine and anomaly detection algorithm; this enables enterprises to take quick action and effectively reduce operational risks.

[0023] 4. Comprehensive data visualization: The data visualization module provides intuitive data analysis diagrams, including timeline trajectory generation, statistical information display, and multi-dimensional data relationship diagrams; these tools help approvers understand complex business situations more clearly and make more informed decisions.

[0024] 5. Improve user experience: Users can choose the data display method that best suits them through customization, and combine third-party map data to generate trajectories and statistical information within the timeline, enhancing the interactivity and usability of the user interface.

[0025] 6. Promote cross-departmental collaboration: The system can integrate data from different systems (such as ERP, CRM), break the information island phenomenon, enable approvers to examine the business situation from a global perspective, and promote communication and collaboration between departments.

[0026] 7. Compliance Enhancement: Through preset business rules and compliance standards, the system can automatically check and mark non-compliant behaviors, ensuring that all approval activities comply with the company's internal policies and external regulatory requirements, thereby improving the company's compliance level.

[0027] 8. Continuous learning and optimization: Artificial intelligence algorithms based on machine learning can learn from the continuously accumulated historical data and gradually optimize the decision-making model; this means that over time, the system will become smarter and the decision recommendations it provides will become more and more accurate.

[0028] 9. Strong adaptability: The system is flexibly designed, supports multiple data entry methods, and can be customized according to different business needs; both large enterprises and small and medium-sized enterprises can benefit from it and it has broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is a system block diagram of the present invention; Figure 2 It is the overall flow chart of the present invention; Figure 3 This is the financial twin front-end project architecture diagram of the present invention; Figure 4 It is the data flow diagram of the present invention. DETAILED DESCRIPTION

[0030] The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments; it can be understood that the specific embodiments described herein are only used to explain the present invention rather than to limit the present invention; it should also be noted that, for ease of description, only the parts related to the present invention rather than all are shown in the accompanying drawings.

[0031] Combination Figure 1-Figure 4 As shown, the present invention provides an intelligent approval assistance system driven by relationship chain analysis, the system comprising: Data entry and processing module, which supports multiple data entry methods and pre-processes the entered data; The relationship chain analysis module uses the relationship chain analysis algorithm to conduct in-depth analysis of the data in the approval process and generate a relationship chain map; Intelligent decision support module, which uses artificial intelligence algorithms to intelligently analyze data in the approval process and provides intelligent decision suggestions based on historical and current data; The risk identification and management module monitors the data in the approval process in real time through intelligent algorithms and relationship chain analysis to discover business anomalies and potential risks, provides risk warning functions and automatically generates detailed risk reports and corresponding handling suggestions; The data visualization module provides intuitive data analysis graphs to support approvers in fully understanding the business situation and decision-making background. The module includes timeline trajectory generation, statistical information display, and multi-dimensional data relationship diagrams. Users can customize the data display method.

[0032] The data entry and processing module supports manual entry and automatic import, and performs data cleaning, format conversion and label generation on the entered data; Among them, the manual input interface is used for users to manually input data; Auto-import feature for automatically synchronizing data with external systems via API.

[0033] The relationship chain analysis module is used to identify the associations between different data in the approval process and dynamically update the relationship chain map to reflect the latest data changes.

[0034] The intelligent decision support module generates intelligent decision suggestions by combining historical data and current data; The intelligent decision support module includes a historical data analysis engine for analyzing historical approval data to identify common patterns; and a real-time decision suggestion generator for generating decision suggestions based on real-time data.

[0035] The risk identification and management module provides risk warning function and automatically generates detailed risk reports and corresponding treatment suggestions; The risk identification and management module also includes: a rule engine for identifying risks based on predefined business rules; an anomaly detection algorithm for identifying abnormal behaviors in the approval process through machine learning technology.

[0036] The present invention also provides an intelligent approval assistance method driven by relationship chain analysis. Based on the above system, the method comprises the following steps: S1, import approval process related data from various channels; S2, preprocessing the received data, including data cleaning, format conversion, and label generation; S3, using the relationship chain analysis algorithm to conduct in-depth analysis of the data in the approval process, identify the associations between the data, and generate a relationship chain map; S4, uses artificial intelligence algorithms to intelligently analyze data in the approval process, combining historical data with current data to generate intelligent decision-making suggestions for approvers; S5, real-time monitoring of data in the approval process, automatic identification of business anomalies and potential risks, providing risk warnings, and issuing warning notifications when necessary; S6, automatically generates a risk report, which contains a detailed description of the anomalies and potential risks found and the corresponding treatment suggestions; S7 provides intuitive data analysis diagrams, including timeline trajectory generation, statistical information display, and multi-dimensional data relationship diagrams, so that approvers can fully understand the business situation and decision-making background.

[0037] Step S1 of importing data also includes manually inputting or automatically importing data from an external system through an API interface to achieve data synchronization with the external system.

[0038] In step S3, the relationship chain analysis algorithm is used to reveal the relationship between invoices and orders, expense reimbursements and budgets, and the relationship chain map can be updated in real time as new data is added.

[0039] In step S4, the artificial intelligence algorithm in the intelligent analysis trains a model based on historical approval records, so that it can predict future approval trends and provide recommendations accordingly.

[0040] In step S5, an anomaly detection algorithm is used in the risk identification process to identify behaviors that deviate from the normal pattern, and an early warning mechanism is triggered immediately once an anomaly is found.

[0041] Example: The present invention provides an intelligent approval assistance system driven by relationship chain analysis. By using relationship chain analysis and artificial intelligence algorithms, it provides data-driven decision assistance for the approval process, intelligently discovers business anomalies and potential risks, and provides intuitive data analysis graphs to support approvers in fully understanding business conditions and decision-making backgrounds.

[0042] Specific plan: 1. Intelligent decision support: 1. The system conducts in-depth analysis of data in the approval process through relationship chain analysis and artificial intelligence algorithms.

[0043] 2. Provide data-driven decision support to help approvers make more accurate and efficient decisions.

[0044] 3. The system can automatically identify key nodes and potential problems in the approval process and provide intelligent decision-making recommendations.

[0045] 2. Risk identification and management: 1. The system uses intelligent algorithms and relationship chain analysis to monitor data in the approval process in real time and identify business anomalies and potential risks.

[0046] 2. Provide risk warning and management functions to help business leaders identify and take measures in a timely manner to reduce business risks.

[0047] 3. The system can automatically generate risk reports, detailing the anomalies and potential risks found and providing corresponding handling suggestions.

[0048] 3. Data Visualization: 1. The system provides intuitive data analysis charts to support approvers in fully understanding the business situation and decision-making background.

[0049] 2. Data visualization functions include trajectory generation within the timeline, statistical information display, multi-dimensional data relationship diagrams, etc.

[0050] 3. Users can customize the data display method and combine third-party maps to generate tracks and statistical information within the timeline.

Claims

1. An intelligent approval assistance system driven by relationship chain analysis, characterized in that: The system includes: Data entry and processing module, which supports multiple data entry methods and pre-processes the entered data; The relationship chain analysis module uses the relationship chain analysis algorithm to conduct in-depth analysis of the data in the approval process and generate a relationship chain map; Intelligent decision support module, which uses artificial intelligence algorithms to intelligently analyze data in the approval process and provides intelligent decision suggestions based on historical and current data; The risk identification and management module monitors the data in the approval process in real time through intelligent algorithms and relationship chain analysis to discover business anomalies and potential risks, provides risk warning functions and automatically generates detailed risk reports and corresponding handling suggestions; The data visualization module provides intuitive data analysis graphs to support approvers in fully understanding the business situation and decision-making background. The module includes timeline trajectory generation, statistical information display, and multi-dimensional data relationship diagrams. Users can customize the data display method.

2. The intelligent approval assistance system driven by relationship chain analysis according to claim 1 is characterized in that: The data entry and processing module supports manual entry and automatic import, and performs data cleaning, format conversion and label generation on the entered data; Among them, the manual input interface is used for users to manually input data; Auto-import feature for automatically synchronizing data with external systems via API.

3. The intelligent approval assistance system driven by relationship chain analysis according to claim 1 is characterized in that: The relationship chain analysis module is used to identify the associations between different data in the approval process and dynamically update the relationship chain map to reflect the latest data changes.

4. The intelligent approval assistance system driven by relationship chain analysis according to claim 1 is characterized in that: The intelligent decision support module generates intelligent decision suggestions by combining historical data and current data; The intelligent decision support module includes a historical data analysis engine that analyzes historical approval data to identify common patterns; A real-time decision suggestion generator is used to generate decision suggestions based on real-time data.

5. The intelligent approval assistance system driven by relationship chain analysis according to claim 1 is characterized in that: The risk identification and management module provides risk warning function and automatically generates detailed risk reports and corresponding treatment suggestions; The risk identification and management module also includes: a rule engine for identifying risks based on predefined business rules; an anomaly detection algorithm for identifying abnormal behaviors in the approval process through machine learning technology.

6. An intelligent approval assistance method driven by relationship chain analysis, based on the approval assistance system according to any one of claims 1 to 5, characterized in that: The method comprises the following steps: S1, import approval process related data from various channels; S2, preprocessing the received data; S3, using the relationship chain analysis algorithm to conduct in-depth analysis of the data in the approval process, identify the associations between the data, and generate a relationship chain map; S4, uses artificial intelligence algorithms to intelligently analyze data in the approval process, combining historical data with current data to generate intelligent decision-making suggestions for approvers; S5, real-time monitoring of data in the approval process, automatic identification of business anomalies and potential risks, providing risk warnings, and issuing warning notifications when necessary; S6, automatically generates a risk report, which contains a detailed description of the anomalies and potential risks found and the corresponding treatment suggestions; S7 provides intuitive data analysis diagrams, including timeline trajectory generation, statistical information display, and multi-dimensional data relationship diagrams, so that approvers can fully understand the business situation and decision-making background.

7. The method according to claim 6, characterized in that Step S1 of importing data also includes manually inputting or automatically importing data from an external system through an API interface to achieve data synchronization with the external system.

8. The intelligent approval assistance method driven by relationship chain analysis according to claim 6 is characterized in that: In step S3, the relationship chain analysis algorithm is used to reveal the relationship between invoices and orders, expense reimbursements and budgets, and the relationship chain map can be updated in real time as new data is added.

9. The intelligent approval assistance method driven by relationship chain analysis according to claim 6 is characterized in that: In step S4, the artificial intelligence algorithm in the intelligent analysis trains a model based on historical approval records, so that it can predict future approval trends and provide recommendations accordingly.

10. The intelligent approval assistance method driven by relationship chain analysis according to claim 6, characterized in that: In step S5, an anomaly detection algorithm is used in the risk identification process to identify behaviors that deviate from the normal pattern, and an early warning mechanism is triggered immediately once an anomaly is found.

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