Enterprise digital management auxiliary method and system based on artificial intelligence

Abstract

The invention relates to the field of enterprise management, and discloses an artificial intelligence-based enterprise digital management auxiliary method and system, and the method comprises the following steps: S1, enabling an enterprise to collect and analyze a large amount of real and accurate data, and carrying out the data treatment and management; s2, realizing automation of a business process through an artificial intelligence technology, and performing intelligent monitoring on machine equipment by using an AI technology; s3, providing personalized recommendation service by the customer; s4, establishing an intelligent customer service system by using an NLP technology; and S5, optimizing a business process by using an artificial intelligence technology, and enabling enterprises to quickly adapt to market changes and customer requirements through digital process reconstruction. S6, realizing data visualization and analysis through an artificial intelligence technology; and a mobile terminal application is provided, and data access and management anytime and anywhere are supported. According to the method, real-time business insight can be provided, the business process of an enterprise is optimized, the working efficiency is improved, and powerful support is provided for operation and development of the enterprise.

Description

Technical Field

[0001] The present invention relates to the field of enterprise management, and specifically to an artificial intelligence-based enterprise digital management assistance method and system. Background Art

[0002] An enterprise digital management system (Digital Management System, DMS) is an integrated system that uses digital technology to improve enterprise management efficiency and business decision-making capabilities. It usually includes multiple modules, covering different aspects from data management, business processes to decision support.

[0003] Existing enterprise digital management systems have played an important role in improving enterprise operation efficiency, optimizing resource allocation, etc., but there are still some significant disadvantages and challenges in actual applications. The following are some of the main disadvantages:

[0004] 1. Due to the lack of a unified data management mechanism, data in different systems may have redundancy and inconsistency problems, affecting data quality and decision-making accuracy;

[0005] 2. Many existing systems mainly provide basic data management and reporting functions, lacking advanced data analysis and prediction functions; it is difficult for enterprises to extract valuable information and insights from massive data to support decision-making; some systems cannot provide real-time data analysis and decision support, resulting in enterprises being unable to respond promptly to market changes and emergencies. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to overcome the above technical defects and provide an artificial intelligence-based enterprise digital management assistance method and system.

[0007] To solve the above problems, the technical solution of the present invention is: an artificial intelligence-based enterprise digital management assistance method, including the following steps:

[0008] S1. An enterprise collects and analyzes a large amount of real and accurate data, which covers multiple aspects such as market trends, customer needs, and competitive situations, and conducts data governance and management to ensure data quality and security; through big data analysis technology, an enterprise can gain insights into market changes and customer needs, providing strong support for decision-making; by using artificial intelligence technology, an enterprise can perform predictive analysis on historical data to predict future market trends, customer needs, etc., which helps the enterprise formulate coping strategies in advance and seize the market initiative;

[0009] S2. Through artificial intelligence technology, an enterprise can automate business processes, such as automated procurement, automated production, etc.; an enterprise can use AI technology to intelligently monitor machinery and equipment, predict equipment failures in advance, and reduce production downtime losses;

[0010] S3. By leveraging artificial intelligence technology, enterprises can provide personalized recommendation services for customers, such as product recommendations, content recommendations, etc., which can enhance the user experience, improve customer satisfaction and loyalty;

[0011] S4. By using NLP technology, enterprises can establish an intelligent customer service system to achieve 24 / 7 round-the-clock customer service. The intelligent customer service can quickly respond to customer needs and provide an efficient and convenient customer service experience;

[0012] S5. Enterprises can use artificial intelligence technology to optimize business processes, improve production efficiency and service quality. Through digital process reengineering, enterprises can quickly adapt to market changes and customer needs;

[0013] S6. Through artificial intelligence technology, enterprises can achieve data visualization and analysis, enabling decision-makers to more intuitively understand the enterprise operation status and market trends, which helps enterprises formulate more accurate decision-making strategies; Provide mobile applications to support data access and management anytime, anywhere.

[0014] An enterprise digital management assistance system based on artificial intelligence, characterized by including:

[0015] Data management platform: including a data collection and integration module, a data cleaning module, and a data analysis and mining module;

[0016] Intelligent application module: including an intelligent customer service system, a customer management module, a predictive analysis module, an optimization algorithm module, a business process automation module, an intelligent workflow module, and an anomaly detection and early warning system;

[0017] User interface and experience module: including a dashboard and report module and a mobile application module;

[0018] Artificial intelligence engine: including a machine learning platform and a deep learning framework, and providing support for the data management platform, the intelligent application module, and the user interface and experience module.

[0019] Furthermore, the machine learning platform provides an environment for training, evaluating, and deploying machine learning models, supporting multiple algorithms and frameworks; The deep learning framework supports the development and application of deep learning models, such as neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), etc.

[0020] Furthermore, the data collection and integration module: collects data from within the enterprise (such as ERP, CRM, SCM, etc. systems) and externally (such as market data, social media, Internet of Things devices, etc.), and through technologies such as data lakes and data warehouses, converges the collected data together to form a unified data resource;

[0021] Data cleaning module: Use AI algorithms to clean data, remove redundant and incorrect data, and ensure the reliability and accuracy of data;

[0022] Data analysis and mining module: Use statistical analysis and machine learning techniques to extract valuable information and insights from data, and conduct in-depth analysis and mining. Based on the results of data analysis, support decision-making.

[0023] Furthermore, intelligent customer service system: Use NLP technology to provide intelligent customer service functions, answer customer questions, handle complaints and requests; conduct sentiment analysis and topic modeling on text data such as customer feedback, social media comments, and emails, and extract valuable information;

[0024] Customer management module: Use artificial intelligence technology to deeply analyze customer data, understand customer preferences and needs; provide personalized services and product recommendations to improve customer satisfaction and loyalty.

[0025] Predictive analysis module: Through methods such as time series analysis, regression analysis, and deep learning, provide prediction functions for market demand, inventory levels, sales trends, etc., and support decision-making;

[0026] Optimization algorithm module: Use optimization techniques such as linear programming, integer programming, and genetic algorithms to provide optimization functions for resource allocation, production planning, logistics distribution, etc., and improve operational efficiency;

[0027] Business process automation module: Through RPA (Robotic Process Automation) technology, automatically execute repetitive tasks such as data entry, report generation, and order processing;

[0028] Intelligent workflow module: Automatically trigger and execute specific workflows according to preset rules and conditions to improve efficiency and accuracy;

[0029] Anomaly detection and warning system: Real-time monitor data, identify patterns and trends in data, support market analysis, risk assessment, etc., detect abnormal situations in data such as fraud and equipment failures, and give early warnings and handle them in a timely manner.

[0030] Furthermore, dashboard and report module: Provide an intuitive data visualization interface, support custom reports and dashboards, and facilitate users to view and analyze data;

[0031] Mobile application module: Provide a mobile application to support data access and management anytime, anywhere.

[0032] The advantages of the present invention compared with the existing technologies are as follows: The enterprise digital management assistance system based on artificial intelligence of the present invention utilizes artificial intelligence technologies such as machine learning and deep learning to deeply integrate and analyze various data resources of the enterprise. It can automatically process a large amount of data, provide real-time business insights, optimize the business processes of the enterprise, improve work efficiency, reduce operating costs, and has functions such as customer relationship management, decision support, and intelligent customer service, providing strong support for the operation and development of the enterprise. Brief Description of the Drawings

[0033] Figure 1 It is the system architecture diagram of the present invention. Detailed Embodiment

[0034] The technical solutions in the embodiments of the present invention are described clearly and completely below. Apparently, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments; based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0035] An enterprise digital management assistance method based on artificial intelligence includes the following steps:

[0036] S1. The enterprise collects and analyzes a large amount of real and accurate data, which covers multiple aspects such as market trends, customer needs, and competitive situations, and conducts data governance and management to ensure the quality and security of the data; through big data analysis technology, the enterprise can gain insights into market changes and customer needs, providing strong support for decision-making; by using artificial intelligence technology, the enterprise can perform predictive analysis on historical data to predict future market trends, customer needs, etc., which helps the enterprise formulate countermeasures in advance and seize the market opportunity;

[0037] S2. Through artificial intelligence technology, the enterprise can automate business processes, such as automated procurement, automated production, etc.; the enterprise can use AI technology to intelligently monitor machine equipment, predict equipment failures in advance, and reduce production downtime losses;

[0038] S3. By using artificial intelligence technology, the enterprise can provide personalized recommendation services for customers, such as product recommendations, content recommendations, etc., which can enhance the user experience, improve customer satisfaction and loyalty;

[0039] S4. By using NLP technology, the enterprise can establish an intelligent customer service system to achieve 24 / 7 all-weather customer service. The intelligent customer service can quickly respond to customer needs and provide an efficient and convenient customer service experience;

[0040] S5. Enterprises can use artificial intelligence technology to optimize business processes, improve production efficiency and service quality. Through digital process reengineering, enterprises can quickly adapt to market changes and customer needs;

[0041] S6. Through artificial intelligence technology, enterprises can achieve data visualization and analysis, enabling decision-makers to more intuitively understand the enterprise operation status and market trends, which helps enterprises formulate more accurate decision-making strategies; Provide mobile applications to support data access and management anytime, anywhere.

[0042] An enterprise digital management assistance system based on artificial intelligence, characterized by including:

[0043] Data management platform: including data collection and integration module, data cleaning module, and data analysis and mining module;

[0044] Intelligent application module: including intelligent customer service system, customer management module, predictive analysis module, optimization algorithm module, business process automation module, intelligent workflow module, and anomaly detection and warning system;

[0045] User interface and experience module: including dashboard and report module and mobile application module;

[0046] Artificial intelligence engine: including machine learning platform and deep learning framework, and providing support for data management platform, intelligent application module, and user interface and experience module.

[0047] Furthermore, the machine learning platform provides an environment for training, evaluating, and deploying machine learning models, supporting multiple algorithms and frameworks; The deep learning framework supports the development and application of deep learning models, such as neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), etc.

[0048] Furthermore, the data collection and integration module: collects data from within the enterprise (such as ERP, CRM, SCM, etc. systems) and outside (such as market data, social media, Internet of Things devices, etc.), and through technologies such as data lakes and data warehouses, converges the collected data together to form a unified data resource;

[0049] Data cleaning module: uses AI algorithms to clean the data, removing redundant and incorrect data to ensure the reliability and accuracy of the data;

[0050] Data analysis and mining module: uses statistical analysis and machine learning technologies to extract valuable information and insights from the data, and conducts in-depth analysis and mining. Based on the results of data analysis, it supports decision-making.

[0051] Furthermore, the intelligent customer service system: Utilizes NLP technology to provide intelligent customer service functions, answer customer questions, handle complaints and requests; Conducts sentiment analysis and topic modeling on text data such as customer feedback, social media comments, and emails to extract valuable information;

[0052] Customer management module: Utilizes artificial intelligence technology to deeply analyze customer data, understand customer preferences and needs; Provides personalized services and product recommendations to improve customer satisfaction and loyalty.

[0053] Predictive analysis module: Through methods such as time series analysis, regression analysis, and deep learning, provides prediction functions for market demand, inventory levels, sales trends, etc., to support decision-making;

[0054] Optimization algorithm module: Utilizes optimization technologies such as linear programming, integer programming, and genetic algorithms to provide optimization functions for resource allocation, production planning, logistics distribution, etc., to improve operational efficiency;

[0055] Business process automation module: Through RPA (Robotic Process Automation) technology, automatically executes repetitive tasks such as data entry, report generation, and order processing;

[0056] Intelligent workflow module: Automatically triggers and executes specific workflows according to preset rules and conditions to improve efficiency and accuracy;

[0057] Anomaly detection and warning system: Real-time monitors data, identifies patterns and trends in the data, supports market analysis, risk assessment, etc., detects abnormal situations in the data such as fraud behaviors and equipment failures, and gives early warnings and handles them in a timely manner.

[0058] Furthermore, the dashboard and report module: Provides an intuitive data visualization interface, supports custom reports and dashboards, and facilitates users to view and analyze data;

[0059] Mobile application module: Provides a mobile application to support data access and management anytime, anywhere.

[0060] The enterprise digital management assistance system based on artificial intelligence of the present invention utilizes artificial intelligence technologies such as machine learning and deep learning to deeply integrate and analyze various data resources of the enterprise. It can automatically process a large amount of data, provide real-time business insights, optimize the business processes of the enterprise, improve work efficiency, reduce operating costs, and has functions such as customer relationship management, decision support, and intelligent customer service, providing strong support for the operation and development of the enterprise.

[0061] The unpublicized parts in the present invention are all prior arts, and their specific structures and working principles will not be elaborated further.

[0062] It should be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or elements inherent to such process, method, article or device.

[0063] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

[0064] The above description of the present invention and its embodiments is not restrictive. In general, if those of ordinary skill in the art are inspired by it and, without departing from the gist of the present invention, design similar structural forms and embodiments to this technical solution without creative efforts, they should all fall within the protection scope of the present invention.

Claims

1. An enterprise digital management assistance method based on artificial intelligence, characterized in that, It includes the following steps: S1. The enterprise collects and analyzes a large amount of real and accurate data, and conducts data governance and management; S2. Through artificial intelligence technology, the enterprise realizes the automation of business processes, and at the same time the enterprise uses AI technology to conduct intelligent monitoring of machinery and equipment; S3. Using artificial intelligence technology, the enterprise provides personalized recommendation services for customers; S4. Using NLP technology, the enterprise establishes an intelligent customer service system; S5. The enterprise uses artificial intelligence technology to optimize business processes. Through digital process reengineering, the enterprise can quickly adapt to market changes and customer needs. S6. Through artificial intelligence technology, data visualization and analysis are realized; a mobile application is provided to support data access and management anytime and anywhere.

2. An enterprise digital management assistance system based on artificial intelligence, characterized in that, It includes: Data management platform: including a data collection and integration module, a data cleaning module, and a data analysis and mining module; Intelligent application module: including an intelligent customer service system, a customer management module, a predictive analysis module, an optimization algorithm module, a business process automation module, an intelligent workflow module, and an anomaly detection and early warning system; User interface and experience module: including a dashboard and report module and a mobile application module; Artificial intelligence engine: including a machine learning platform and a deep learning framework, and providing support for the data management platform, the intelligent application module, and the user interface and experience module.

3. The auxiliary method and system for enterprise digital management based on artificial intelligence according to claim 2, wherein: Machine learning platform: providing an environment for training, evaluating, and deploying machine learning models, and supporting multiple algorithms and frameworks; Deep learning framework: supporting the development and application of deep learning models, such as neural networks, convolutional neural networks, and recurrent neural networks.

4. The auxiliary method and system for enterprise digital management based on artificial intelligence according to claim 2, wherein: Data collection and integration module: collecting data from inside and outside the enterprise, and through technologies such as data lakes and data warehouses, aggregating the collected data together to form a unified data resource; Data cleaning module: using AI algorithms to clean the data, removing redundant and incorrect data, and ensuring the reliability and accuracy of the data; Data analysis and mining module: using statistical analysis and machine learning technologies to extract valuable information and insights from the data, and conducting in-depth analysis and mining. Based on the results of data analysis, it supports decision-making.

5. The auxiliary method and system for enterprise digital management based on artificial intelligence according to claim 2, wherein: Intelligent customer service system: using NLP technology to provide intelligent customer service functions, answering customer questions, handling complaints and requests; conducting sentiment analysis and topic modeling on text data such as customer feedback, social media comments, and emails, and extracting valuable information; Customer management module: using artificial intelligence technology to conduct in-depth analysis of customer data, understand customer preferences and needs; providing personalized services and product recommendations to improve customer satisfaction and loyalty. Predictive analysis module: providing prediction functions such as market demand, inventory level, and sales trend through methods such as time series analysis, regression analysis, and deep learning, and supporting decision-making; Optimization Algorithm Module: Utilize optimization techniques of linear programming, integer programming, and genetic algorithms to provide optimization functions for resource allocation, production planning, and logistics distribution, improving operational efficiency; Business Process Automation Module: Automatically execute repetitive tasks, such as data entry, report generation, and order processing, through RPA technology; Intelligent Workflow Module: Automatically trigger and execute specific workflows according to preset rules and conditions, improving efficiency and accuracy; Anomaly Detection and Early Warning System: Real-time monitor data, identify patterns and trends in the data, support market analysis, risk assessment, etc., detect anomalies in the data, such as fraud behavior and equipment failures, and give early warnings and handle them in a timely manner.

6. The method and system for assisting enterprise digital management based on artificial intelligence according to claim 2, wherein: Dashboard and Report Module: Provide an intuitive data visualization interface, support custom reports and dashboards, and facilitate users to view and analyze data; Mobile Application Module: Provide a mobile application to support data access and management anytime, anywhere.

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