Strategic dynamic management-driven ai business management decision module

Abstract

A strategic, dynamic management-driven AI-powered business management decision-making module is installed on a server and includes: a plurality of business management modules, each corresponding to different business management areas; each business management module includes a management database, an AI data analysis unit, an AI decision suggestion unit, and an AI risk warning unit; and an AI module including an AI database, an AI training unit, an AI learning unit, and an AI control unit. This invention integrates data analysis from various levels of business management using machine learning and deep learning technologies, thereby automatically generating optimized decision suggestions and risk warnings through AI, and enabling continuous monitoring and dynamic adjustment and rolling optimization of data analysis, decision suggestions, and risk warnings through AI.

Description

[Technical Field]

[0001] This invention relates to business operation and management systems, and in particular to a strategic dynamic management driven AI business management decision-making module. [Previous Technology]

[0002] Note: With the evolution of computer hardware computing and data integration technologies, today's business operations have entered the era of big data. Companies are drowning in a sea of ​​data, trying to extract useful information. However, too much data, incomplete data, and data falsification all lead to increased blind spots in business management decisions, increasing the uncertainty of the future. At the same time, the subjectivity and limitations of human perception and cognition mean that in the complex ocean of data, the past methods of manually organizing and analyzing data are no longer sufficient to accurately predict the future. Therefore, businesses today need new scientific forecasting methods so that they can allocate resources in advance and adopt more effective business strategies to develop their operations.

[0003] Currently, common business analysis tools on the market collect data from various business IT systems of enterprises, such as ERP and CRM, and perform tabular analysis and processing of the data. Using corresponding query and analysis tools, they output reports for presentation and analysis, providing data analysis support for enterprises.

[0004] However, the data analysis generated by the aforementioned business analysis tools is merely a result, simply telling corporate decision-makers what the current data results are for the company. It cannot analyze the reasons for the data's generation, what problems led to its occurrence, or reveal the patterns and rules behind the data. Therefore, how to provide an innovative business management system that can automatically collect and analyze data and provide decision-making suggestions or early warnings is one of the urgent issues that scholars and researchers in related fields need to address. [Summary of the Invention]

[0005] The main purpose of this invention is to provide a strategic dynamic management driven AI business management decision-making module, which can use artificial intelligence technology to analyze enterprise data in various fields of business management and automatically generate optimized decision suggestions and risk warnings.

[0006] To achieve the above-mentioned objective, the present invention provides a strategic dynamic management-driven AI business management decision-making module, which is installed on a server and includes: a plurality of business management modules, each corresponding to a different business management field; each business management module includes: a management database storing at least one internal data or at least one external data; an artificial intelligence data analysis unit for performing an internal status analysis based on the internal data in the management database, or performing an external status analysis based on the external data in the management database, and generating an artificial intelligence analysis result accordingly; an artificial intelligence decision suggestion unit for automatically generating an artificial intelligence decision suggestion based on the artificial intelligence analysis result generated by the artificial intelligence data analysis unit; and an artificial intelligence risk warning unit for automatically generating a risk assessment level or a risk assessment report by comparing the artificial intelligence analysis result generated by the artificial intelligence data analysis unit with a predetermined risk assessment standard; wherein when the artificial intelligence risk warning unit determines that the risk assessment level is higher than a risk threshold, it further issues a risk warning message; and an artificial intelligence module, which is connected to... The system is connected to the management modules; the artificial intelligence module includes an artificial intelligence database, an artificial intelligence training unit, an artificial intelligence learning unit, and an artificial intelligence control unit; the artificial intelligence database pre-stores a plurality of training data; the artificial intelligence training unit is used to input the training data from the artificial intelligence database into the artificial intelligence data analysis unit, the artificial intelligence decision suggestion unit, and the artificial intelligence risk warning unit of the management modules, so that they are trained using machine learning or deep learning to build corresponding AI models; the artificial intelligence learning unit is used to train the system using machine learning or deep learning based on at least one feedback data generated by the user to the decision suggestions or warning messages of the artificial intelligence data analysis unit, the artificial intelligence decision suggestion unit, and the artificial intelligence risk warning unit, so that the calculation accuracy of the artificial intelligence calculation module can be manually corrected.

[0007] The strategic dynamic management-driven AI business management decision-making module provided by this invention integrates data analysis from various fields of business management through artificial intelligence, machine learning, and deep learning technologies. Based on relevant data, it automatically generates optimized annual plans, various indicators, and decision-making recommendations for the enterprise. Simultaneously, it analyzes enterprise risks based on relevant data and issues early warnings to business decision-makers before the enterprise encounters risks. Furthermore, during each cycle of the enterprise's implementation of the aforementioned decision recommendations, this invention simultaneously collects relevant data, performs real-time effectiveness analysis through artificial intelligence, and continuously corrects the aforementioned operational decision recommendations. This allows the enterprise to continuously optimize its business operation strategy. Therefore, this invention can establish an intelligent, efficient, objective, continuously optimizing, and ESG-oriented business management system for enterprises.

[0008] The following are preferred embodiments based on the purpose, effects and structural configuration disclosed in this invention, and are described in detail with reference to the drawings.

Implementation Method

[0010] Please refer to Figure 1. A preferred embodiment of the present invention provides a strategic dynamic management-driven AI business management decision-making module, which is installed on at least one server 1. This module uses AI to collect and analyze data on the internal / external status of an enterprise, and further uses AI to automatically generate strategic / decision recommendations and provide early warnings for enterprise risks through machine learning and deep learning algorithms. The server 1 may include a cloud server or a physical server. This strategic dynamic management-driven AI business management decision-making module mainly includes a plurality of business management modules 10 and an artificial intelligence module 20. Wherein:

[0011] These management modules 10 correspond to different business areas of the enterprise and are interconnected and integrated through deep learning technology of artificial intelligence. Each management module 10 includes a dynamic management database 11, at least one artificial intelligence data analysis unit 12, at least one artificial intelligence decision suggestion unit 13, and at least one artificial intelligence risk warning unit 14.

[0012] The dynamic business management database 11 stores at least one type of internal data and / or at least one type of external data. The internal data includes, but is not limited to, the company's internal business data, sales data, financial data, cost data, human resource data, etc., in a specific business area. The external data includes, but is not limited to, publicly available business data, sales data, financial data, cost data, human resource data, etc., from at least one competitor company in a specific business area. It is worth noting that the internal and external data stored in the dynamic business management database 11 can be collected and uploaded manually, for example, by employees collecting data daily and uploading it to the database 11. In another embodiment, data collection and storage can also be performed using a data extraction unit. This data extraction unit is communicatively connected to the dynamic business management database and is used to crawl at least one website on the internet and extract data corresponding to the company or its competitors. Specifically, the data extraction unit includes crawling code. The crawling code is a web crawler computer program used to automatically browse specific websites or text content on the Internet according to a pre-set schedule, extracting and storing data from them. This action is also called "crawling". The data extraction unit crawls multiple relevant data from at least one website and stores one piece of data information from each of these relevant data in the dynamic management database 11.

[0013] The artificial intelligence data analysis unit 12 is used to perform an internal status analysis based on the internal data of the dynamic business management database 11, or an external status analysis based on the external data of the dynamic business management database 11, through AI machine learning or deep learning technology, and generate an artificial intelligence analysis result accordingly. The internal status analysis focuses on the internal situation of the enterprise, including but not limited to human resources (salary, recruitment, promotion, etc.), production (production efficiency, utilization rate, cost ratio, etc.), and business (performance, market development, accounts receivable collection rate, etc.). The external status analysis includes, but is not limited to, comparisons between the enterprise and its competitors (market share, operational capabilities, profitability, etc.).

[0014] The AI ​​decision-making suggestion unit 13 is used to automatically generate AI decision-making suggestion data based on the AI ​​analysis results generated by the AI ​​data analysis unit 12, using AI machine learning or deep learning techniques. The AI ​​decision-making suggestion data may take the form of, but is not limited to, reports, charts, proposals, summaries, or any other text format that can be read by business professionals. For example, the AI ​​decision-making suggestion unit 13 can automatically generate a market environment overview table containing information about its own company and competitors based on the AI ​​analysis results generated by the external situation analysis conducted by the AI ​​data analysis unit 12. Alternatively, the AI ​​decision-making suggestion unit can automatically generate a decision-making suggestion proposal containing information about its own company's production aspects based on the AI ​​analysis results generated by the internal situation analysis conducted by the AI ​​data analysis unit.

[0015] The AI ​​risk warning unit 14 uses deep learning analysis technology to compare the AI ​​analysis results generated by the AI ​​data analysis unit 12 with a predetermined risk assessment standard, and generates a risk assessment level and / or a risk assessment report accordingly. When the risk assessment level is higher than a risk threshold, the AI ​​risk warning unit 14 will further issue a risk warning message to remind the enterprise's management decision-makers. By automatically analyzing the relevant risks of the enterprise through the AI ​​risk warning unit 15, the threats and vulnerabilities faced by the enterprise can be effectively and objectively judged, as well as the potential harm caused by a risk event.

[0016] The artificial intelligence module 20 is communicatively connected to the management modules 10 and includes an artificial intelligence database 21, an artificial intelligence training unit 22, an artificial intelligence control unit 23, and an artificial intelligence learning unit 24. The artificial intelligence database 21 pre-stores a plurality of training data. These training data are relevant information collected in advance for training the artificial intelligence model, including but not limited to historical business data, sales data, financial data, cost data, human resource data, etc., from within the enterprise and / or other enterprises. The artificial intelligence training unit 22 is used to input the corresponding training data stored in the artificial intelligence database 21 into the artificial intelligence data analysis unit 13, the artificial intelligence decision suggestion unit 14, and the artificial intelligence risk warning unit 15 of the management modules 10, so that they are trained in the form of machine learning or deep learning, thereby establishing the AI ​​model corresponding to the aforementioned AI unit. The AI ​​learning unit 23 is used to train and revise the AI ​​models of the AI ​​data analysis unit 13, the AI ​​decision suggestion unit 14, and the AI ​​risk warning unit 15 using machine learning or deep learning methods, based on feedback data generated after the user uses the decision suggestions and / or warning suggestions of the AI ​​data analysis unit 13, the AI ​​decision suggestion unit 14, and the AI ​​risk warning unit 15, thereby optimizing the computational accuracy of the management modules. In other words, the AI ​​learning unit 23 optimizes the AI ​​model by communicating back and forth with the user and dynamically revising the AI ​​model. The AI ​​control unit 24 is used to adjust the relevant parameters of the AI ​​data analysis unit 13, the AI ​​decision suggestion unit 14, and the AI ​​risk warning unit 15 of the management modules 10, so that the computational accuracy of the aforementioned AI units can be manually corrected.

[0017] Furthermore, as shown in Figure 2, in this embodiment, each of the operation management modules 10 includes at least one or a combination of a planning and budget management module 30, a financial management module 40, a business management module 50, a human resources management module 60, an operation management module 70, a procurement management module 80, an inventory management module 90, an equipment management module 100, and an energy management module 110.

[0018] The planning and budget management module 30 includes a planning and budget management database 31, a planning and budget data analysis unit 32, a planning and budget decision-making suggestion unit 33, a planning and budget performance analysis unit 34, a planning and budget execution control unit 35, and a planning and budget risk early warning unit 36. The planning and budget management database 31 stores at least one year's planning data and at least one budget data, including but not limited to annual plans, annual performance indicators, quarterly KPIs, annual budget amounts, special budget amounts, etc. The planning and budget data analysis unit 32 is used to perform a rationality analysis based on the planning and budget data in the planning and budget management database, thereby generating a planning analysis result and a budget analysis result. The planning and budget decision-making suggestion unit 33 automatically generates integrated planning and budget decision-making suggestion data based on the planning analysis results and budget analysis results generated by the planning and budget data analysis unit 32. This includes, but is not limited to, optimization decisions, implementation suggestions, or improvement suggestions for the company's planning and budget. The planning and budget performance analysis unit 34 compares and analyzes the actual performance data and target performance data collected by the company at each predetermined time period (monthly / quarterly / semi-annual) after implementing the integrated planning and budget decision-making suggestion data, and generates a planning performance analysis result and a budget performance analysis result respectively. The planning and budget execution control unit 35 automatically generates optimized integrated planning and budget decision-making suggestion data based on the planning performance analysis results and budget analysis results generated by the planning and budget performance analysis unit 34. This includes, but is not limited to, optimization decisions, implementation suggestions, or improvement suggestions for the company's planning and budget in the next time period, thus forming a rolling optimization planning and budget decision-making function. The planning and budget risk early warning unit 36 ​​is used to analyze the planning analysis results and budget analysis results generated by the planning and budget data analysis unit 32, and compare them with a predetermined planning risk assessment standard and a predetermined budget risk assessment standard, respectively, and generate a planning risk assessment level, a budget risk assessment level and / or a planning risk assessment report and a budget risk assessment report accordingly. When the planning risk assessment level is higher than a planning risk threshold, or the budget risk assessment level is higher than a budget risk threshold, the planning and budget risk early warning unit 36 ​​further issues a corresponding planning risk early warning message or a budget risk early warning message to remind the enterprise's management decision-makers.

[0019] The financial management module 40 includes a financial management database 41, a financial data analysis unit 42, a financial decision-making suggestion unit 43, a financial performance analysis unit 44, a financial execution control unit 45, and a financial risk early warning unit 46. The financial management database 41 stores at least one type of financial data, including but not limited to financial structure, cash flow, capital, liabilities, etc. The financial data analysis unit 42 analyzes the financial data in the financial management database 41 to generate a financial analysis result. The financial decision-making suggestion unit 43 automatically generates financial decision-making suggestion information based on the financial analysis result generated by the financial data analysis unit 42, including but not limited to optimization decisions, implementation suggestions, or improvement suggestions regarding the company's financial activities. The financial performance analysis unit 44 compares and analyzes the differences between actual financial performance data collected by the company at each predetermined time period (monthly / quarterly / semi-annual) after implementing the financial decision-making suggestion information and target financial performance data to generate a financial performance analysis result. The financial execution control unit 45 automatically generates optimized financial decision-making suggestions based on the financial performance analysis results generated by the financial performance analysis unit 44. These suggestions include, but are not limited to, optimization decisions, execution recommendations, or improvement suggestions for the company's financial activities in the next time period, thus enabling a rolling optimization of financial decision-making. The financial risk early warning unit 46 compares the financial analysis results generated by the financial data analysis unit 42 with a predetermined financial risk assessment standard, and generates a financial risk assessment level and / or a financial risk assessment report accordingly. When the financial risk assessment level exceeds a financial risk threshold, the financial risk early warning unit 46 further issues a financial risk early warning message to remind the company's management decision-makers.

[0020] The business management module 50 includes a business management database 51, a business data analysis unit 52, a business decision suggestion unit 53, a business performance analysis unit 54, a business execution control unit 55, and a business risk early warning unit 56. The business management database 51 stores at least one set of internal business data, including but not limited to performance, market development, accounts receivable collection rate, selling price ratio, customer complaint rate, etc.; and external business data, including but not limited to market demand, market information, competitor order volume, etc. The business data analysis unit 52 is used to perform internal innovation analysis and external competitiveness analysis based on the internal and external business data in the business management database 51, thereby generating an internal innovation analysis result and an external competitiveness analysis result, respectively. The business decision suggestion unit 53 is used to automatically generate business decision suggestion data based on the internal innovation analysis result and the external competitiveness analysis result generated by the business data analysis unit 52, including but not limited to optimization decisions, execution suggestions, or improvement suggestions for the enterprise's business. The business performance analysis unit 54 compares and analyzes the actual business performance data collected by the enterprise at each predetermined time period (monthly / quarterly / semi-annual / annual) after implementing the business decision-making recommendations, and generates a business performance analysis result. The business execution control unit 55 automatically generates revised business decision-making recommendations based on the business performance analysis results generated by the business performance analysis unit, including but not limited to optimization decisions, execution recommendations, or improvement suggestions, thus forming a rolling optimization function for business decisions. The business risk early warning unit 56 analyzes and compares the business analysis results (including internal innovation analysis results and external competitiveness analysis results) generated by the business data analysis unit 52 with a predetermined business risk assessment standard, and generates a business risk assessment level and / or a business risk assessment report. When the business risk assessment level is higher than a business risk threshold, the business risk early warning unit 56 further transmits a business risk early warning message to remind the enterprise's management decision-makers.

[0021] The human resource management module 60 includes a human resource management database 61, a human resource data analysis unit 62, a human resource decision-making suggestion unit 63, a human resource performance analysis unit 64, a human resource execution control unit 65, and a human resource risk early warning unit 66. The human resource management database 61 stores at least one type of human resource data, including but not limited to talent information, salary structure, performance bonuses, education and training, and talent promotion information. The human resource data analysis unit 62 is used to analyze the various human resource data in the human resource management database 61 to generate a human resource analysis result. The human resource decision-making suggestion unit 63 is used to automatically generate human resource decision-making suggestion information based on the human resource analysis result generated by the human resource data analysis unit 62, including but not limited to optimization decisions, implementation suggestions, or improvement suggestions for the enterprise in human resources. The human resource performance analysis unit 64 compares and analyzes the differences between performance data collected by the enterprise at each predetermined time period (monthly / quarterly / semi-annual) after implementing the human resource decision-making suggestion information and target performance data to generate a human resource performance analysis result. The human resources execution control unit 65 automatically generates optimized human resources decision-making suggestions based on the human resources effectiveness analysis results generated by the human resources effectiveness analysis unit 64. These suggestions include, but are not limited to, optimization decisions, implementation recommendations, or improvement suggestions for the company's human resources in the next time period, thus enabling a rolling optimization of human resources decision-making. The human resources risk early warning unit 66 analyzes at least one key data point from the human resources analysis results generated by the human resources data analysis unit 62 and compares it with a predetermined human resources risk assessment standard, generating a human resources risk assessment level and / or a human resources risk assessment report. When the human resources risk assessment level exceeds a human resources risk threshold, the human resources risk early warning unit 66 further issues a human resources risk early warning message to remind the company's management decision-makers.

[0022] The operations management module 70 includes an operations management database 71, an operations data analysis unit 72, an operations decision-making suggestion unit 73, an operations performance analysis unit 74, an operations execution control unit 75, and an operations risk early warning unit 76. The operations management database 71 stores at least one type of operations data, including but not limited to the enterprise's operating capacity, solvency, profitability, growth capacity, etc. The operations data analysis unit 72 is used to analyze the various types of operations data in the operations management database 71 to generate an operations analysis result. The operations decision-making suggestion unit 73 is used to automatically generate operations decision-making suggestion data based on the operations analysis result generated by the operations data analysis unit 72, including but not limited to optimization decisions, implementation suggestions, or improvement suggestions related to the enterprise's operating conditions. The operational performance analysis unit 74 compares and analyzes the actual performance data collected by the enterprise at each predetermined time period (monthly / quarterly / semi-annual / annual) after implementing the operational decision-making recommendations, and generates an operational performance analysis result. The operational execution control unit 75 automatically generates optimized operational decision-making recommendations based on the operational performance analysis results generated by the operational performance analysis unit 74. This includes, but is not limited to, optimization decisions, implementation recommendations, or improvement suggestions for the enterprise's operational status in the next time period, forming a rolling optimization operational strategy. The operational risk warning unit 76 compares the operational analysis results generated by the operational data analysis unit 72 with a predetermined operational risk assessment standard, and generates an operational risk assessment level and / or an operational risk assessment report. When the operational risk assessment level is higher than an operational risk threshold, the operational risk warning unit 76 further transmits an operational risk warning message to remind the enterprise's management decision-makers.

[0023] The procurement management module 80 includes a procurement management database 81, a procurement data analysis unit 82, a procurement decision suggestion unit 83, a procurement performance analysis unit 84, a procurement execution control unit 85, and a procurement risk warning unit 86. The procurement management database 81 stores at least one type of procurement data, including but not limited to the company's procurement price, procurement quantity, and suppliers. The procurement data analysis unit 82 analyzes the procurement data in the procurement management database 81 to generate a procurement analysis result. The procurement decision suggestion unit 83 automatically generates procurement decision suggestion information based on the procurement analysis result generated by the procurement data analysis unit 82, including but not limited to the company's optimization decisions, execution suggestions, or improvement suggestions in procurement. The procurement performance analysis unit 84 compares and analyzes the differences between actual performance data collected by the company at each predetermined time period (monthly / quarterly / semi-annual) after implementing the procurement decision suggestion information and target performance data to generate a procurement performance analysis result. The procurement execution control unit 85 automatically generates optimized procurement decision-making suggestions based on the procurement performance analysis results generated by the procurement performance analysis unit 82, including but not limited to optimal decisions, implementation suggestions, or improvement suggestions for the company's procurement in the next time period. The procurement risk warning unit 86 analyzes and compares the procurement analysis results generated by the procurement data analysis unit 82 with a predetermined procurement risk assessment standard, and generates a procurement risk assessment level and / or a procurement risk assessment report accordingly. When the procurement risk assessment level is higher than a procurement risk threshold, the procurement risk warning unit 86 will further issue a procurement risk warning message to remind the company's management decision-makers.

[0024] The inventory management module 90 includes an inventory management database 91, an inventory data analysis unit 92, an inventory decision suggestion unit 93, an inventory performance analysis unit 94, an inventory execution control unit 95, and an inventory risk early warning unit 96. The inventory management database 91 stores at least one type of inventory data, including but not limited to the efficiency, utilization rate, cost rate, quality rate, and loss rate of the enterprise's inventory products. The inventory data analysis unit 92 analyzes the inventory data in the inventory management database 91 to generate an inventory analysis result. The inventory decision suggestion unit 93 automatically generates inventory decision suggestion data based on the inventory analysis result generated by the inventory data analysis unit 92, including but not limited to optimization decisions, execution suggestions, or improvement suggestions for the enterprise's inventory. The inventory performance analysis unit 94 compares and analyzes the differences between actual performance data collected by the enterprise at each predetermined time period (monthly / quarterly / semi-annual) after implementing the inventory decision suggestion data and target performance data to generate an inventory performance analysis result. The inventory execution control unit 95 automatically generates optimized inventory decision-making suggestions based on the inventory performance analysis results generated by the inventory performance analysis unit 94. This includes, but is not limited to, optimization decisions, implementation suggestions, or improvement suggestions for the enterprise's inventory in the next time period, thus forming a rolling inventory optimization strategy. The inventory risk early warning unit 96 analyzes the inventory analysis results generated by the inventory data analysis unit 92 and compares them with a predetermined inventory risk assessment standard, generating an inventory risk assessment level and / or an inventory risk assessment report accordingly. When the inventory risk assessment level is determined to be higher than an inventory risk threshold, the inventory risk early warning unit 96 will further issue an inventory risk early warning message to remind the enterprise's management decision-makers.

[0025] The equipment management module 100 includes an equipment management database 101, an equipment data analysis unit 102, an equipment decision suggestion unit 103, an equipment performance analysis unit 104, an equipment execution control unit 105, and an equipment risk warning unit 106. The equipment management database 101 stores at least one piece of equipment data, including but not limited to the efficiency, utilization rate, cost rate, quality rate, and wear rate of software or hardware equipment. The equipment data analysis unit 102 is used to analyze the equipment data in the equipment management database 101 to generate an equipment analysis result. The equipment decision suggestion unit 103 is used to automatically generate equipment decision suggestion data based on the equipment analysis result generated by the equipment data analysis unit 102, including but not limited to optimization decisions, implementation suggestions, or improvement suggestions for the enterprise regarding equipment. The equipment performance analysis unit 104 compares and analyzes the actual performance data collected by the enterprise at each predetermined time period (monthly / quarterly / semi-annual / annual) after implementing the equipment decision-making recommendations, and generates an equipment performance analysis result. The equipment execution control unit 105 automatically generates optimized equipment decision-making recommendations based on the equipment performance analysis results generated by the equipment performance analysis unit 102, including but not limited to the enterprise's optimal decisions, implementation recommendations, or improvement suggestions regarding equipment in the next period. The equipment risk warning unit 106 analyzes and compares the equipment analysis results generated by the equipment data analysis unit 102 with a predetermined equipment risk assessment standard, and generates an equipment risk assessment level and / or an equipment risk assessment report. If the equipment risk assessment level is determined to be higher than an equipment risk threshold, the equipment risk warning unit 106 will further transmit an equipment risk warning message to remind the enterprise's management decision-makers.

[0026] The energy management module 110 includes an energy management database 111, an energy data analysis unit 112, an energy decision-making suggestion unit 113, an energy performance analysis unit 114, an energy execution control unit 115, and an energy risk early warning unit 116. The energy management database 111 stores at least one type of energy data, including but not limited to the enterprise's carbon emissions, carbon credits, product carbon footprint, energy consumption, etc. The energy data analysis unit 112 analyzes the energy data in the energy management database 111 to generate an energy analysis result. The energy decision-making suggestion unit 113 automatically generates energy decision-making suggestion data based on the energy analysis result generated by the energy data analysis unit, including but not limited to the enterprise's energy optimization decisions, implementation suggestions, or improvement suggestions. The energy performance analysis unit 114 compares and analyzes the actual performance data collected by the enterprise at each predetermined time period (monthly / quarterly / semi-annual) after implementing the energy decision-making suggestion data with a target performance data to generate an energy performance analysis result. The energy execution control unit 115 automatically generates optimized energy decision-making recommendations based on the energy performance analysis results generated by the energy performance analysis unit 114, including but not limited to the enterprise's optimal energy decisions, implementation recommendations, or improvement suggestions for the next time period. The energy risk early warning unit 116 analyzes the energy analysis results generated by the energy data analysis unit 112 and compares them with a predetermined energy risk assessment standard, generating an energy risk assessment level and / or an energy risk assessment report accordingly. If the energy risk assessment level is determined to be higher than an energy risk threshold, the energy risk early warning unit 116 will further transmit an energy risk early warning message to remind the enterprise's management decision-makers.

[0027] In summary, the strategic dynamic management-driven AI business management decision-making module provided by this invention integrates data analysis from various fields of business management through artificial intelligence, machine learning, and deep learning technologies. Based on relevant data, it automatically generates optimized annual plans, various indicators, and decision-making recommendations for the enterprise. Simultaneously, it analyzes enterprise risks based on relevant data and issues early warnings to business decision-makers before the enterprise encounters risks. Furthermore, during each cycle of the enterprise's implementation of the aforementioned decision recommendations, this invention simultaneously collects relevant data, performs real-time effectiveness analysis through artificial intelligence, and continuously corrects the aforementioned operational decision recommendations. This allows the enterprise to continuously optimize its business operation strategy. Therefore, this invention can establish an intelligent, efficient, objective, continuously optimizing, and ESG-oriented business management system for enterprises.

[0028] The above embodiments are merely illustrative of the technology and effects of the present invention and are not intended to limit the present invention. Any person skilled in the art can modify and change the above embodiments without departing from the technical principles and spirit of the present invention. Therefore, the scope of protection of the present invention should be as described in the following patent application scope. [Simplified Explanation of the Diagram]

[0009] Figure 1 is a schematic diagram of the architecture of a preferred embodiment of the present invention. Figure 2 is a block diagram of a preferred embodiment of the present invention.

Claims

1. A strategically dynamic management-driven AI business management decision-making module, installed on a server, comprising: a plurality of business management modules, each corresponding to a different business management field; each business management module comprising: a dynamic business management database storing at least one internal data or at least one external data; and an artificial intelligence data analysis unit for performing an internal status analysis based on the internal data in the management database, or performing an external status analysis based on the external data in the management database, and generating an artificial intelligence analysis result accordingly; An AI decision-making suggestion unit is configured to automatically generate AI decision-making suggestion data based on the AI ​​analysis results generated by the AI ​​data analysis unit using internal and external data; an AI risk warning unit is configured to automatically generate a risk assessment level or a risk assessment report by comparing the AI ​​analysis results generated by the AI ​​data analysis unit using internal and external data with a predetermined risk assessment standard; wherein when the AI ​​risk warning unit determines that the risk assessment level is higher than a risk threshold, it further issues a risk warning message; and an AI module is communicatively connected to the management modules; the AI ​​module includes an AI database, an AI training unit, an AI learning unit, and an AI control unit; the AI ​​database pre-stores a plurality of training data; the AI ​​training... The unit is used to input the training data of the artificial intelligence database into the artificial intelligence data analysis unit, the artificial intelligence decision suggestion unit, and the artificial intelligence risk warning unit of the management module, so that they are trained by machine learning or deep learning to build corresponding AI models; the artificial intelligence learning unit is used to train the management module by machine learning or deep learning based on at least one feedback data generated by the user to the decision suggestions or warning messages of the artificial intelligence data analysis unit, the artificial intelligence decision suggestion unit, and the artificial intelligence risk warning unit, so as to optimize the calculation accuracy of the management module; the artificial intelligence control unit is used to adjust the relevant parameters of the artificial intelligence data analysis unit, the artificial intelligence decision suggestion unit, and the artificial intelligence risk warning unit of the management module for operation, so that the calculation accuracy of the artificial intelligence calculation module can be manually corrected.

2. The strategic dynamic management-driven AI business management decision-making module as described in claim 1, wherein the business management module includes at least one or a combination of a planning and budget management module, a financial management module, a business management module, a human resources management module, an operations management module, a procurement management module, an inventory management module, an equipment management module and an energy management module.

3. The strategic dynamic management-driven AI business management decision-making module as described in claim 2, wherein the planning and budget management module includes a planning and budget management database, a planning and budget data analysis unit, a planning and budget decision suggestion unit, a planning and budget performance analysis unit, a planning and budget execution control unit, and a planning and budget risk early warning unit; the planning and budget management database stores at least one year's planning data and at least one budget data; the planning and budget rationality analysis unit is used to analyze each of the planning data and budget data in the planning and budget management database, and generate a planning analysis result and a budget analysis result respectively; the planning and budget decision suggestion unit is used to automatically generate a planning and budget integration decision suggestion based on the planning and budget analysis results generated by the planning and budget data analysis unit; the planning and budget performance analysis unit is based on the enterprise's implementation of the planning and budget integration decision suggestion. The system compares and analyzes the actual performance data collected at each predetermined time period after the meeting with the target performance data, thereby generating a plan performance analysis result and a budget performance analysis result. The plan and budget execution control unit automatically generates an optimized plan and budget integration decision recommendation based on the plan performance analysis result and the budget analysis result generated by the plan and budget performance analysis unit. The plan and budget risk early warning unit analyzes and compares the plan analysis result and the budget analysis result generated by the plan and budget data analysis unit with a predetermined plan risk assessment standard and a predetermined budget risk assessment standard, respectively, and generates a plan risk assessment level and a budget risk assessment level. When it is determined that the plan risk assessment level is higher than a plan risk threshold or the budget risk assessment level is higher than a budget risk threshold, the plan and budget risk early warning unit issues a corresponding plan risk early warning message or a budget risk early warning message.

4. The strategic dynamic management-driven AI business management decision-making module as described in claim 2, wherein the financial management module includes a financial management database, a financial data analysis unit, a financial decision suggestion unit, a financial performance analysis unit, a financial execution control unit, and a financial risk early warning unit; the financial management database stores at least one type of financial data; the financial data analysis unit is used to analyze the financial data in the financial management database to generate a financial analysis result; the financial decision suggestion unit is used to automatically generate financial decision suggestion information based on the financial analysis result generated by the financial data analysis unit; the financial performance analysis unit is based on the enterprise... The financial performance analysis unit compares and analyzes actual financial performance data collected at each predetermined time period after the implementation of the financial decision-making recommendation data with target financial performance data to generate a financial performance analysis result. The financial execution control unit automatically generates an optimized financial decision-making recommendation data based on the financial performance analysis result generated by the financial performance analysis unit. The financial risk early warning unit compares and analyzes the financial analysis result generated by the financial data analysis unit with a predetermined financial risk assessment standard to generate a financial risk assessment level. When the financial risk assessment level is determined to be higher than a financial risk threshold, the financial risk early warning unit issues a financial risk early warning message.

5. The strategic dynamic management-driven AI business management decision-making module as described in claim 2, wherein the business management module includes a business management database, a business data analysis unit, a business decision suggestion unit, a business performance analysis unit, a business execution control unit, and a business risk early warning unit; the business management database stores at least one type of internal business data and at least one type of external business data; the business data analysis unit is used to perform internal innovation analysis and external competitiveness analysis based on each of the internal business data and each of the external business data in the business management database, thereby generating an internal innovation analysis result and an external competitiveness analysis result respectively; the business decision suggestion unit is used to generate an internal innovation analysis result and an external competitiveness analysis result based on the internal innovation analysis result and the external competitiveness analysis result generated by the business data analysis unit. The system automatically generates business decision-making recommendations. The business performance analysis unit compares actual business performance data collected at each predetermined time period after the implementation of the recommendations with target business performance data to generate a business performance analysis result. The business execution control unit automatically generates optimized business decision-making recommendations based on the business performance analysis result generated by the business performance analysis unit. The business risk warning unit compares the business analysis result generated by the business data analysis unit with a predetermined business risk assessment standard and generates a business risk assessment level and / or a business risk assessment report. When the business risk assessment level is determined to be higher than a business risk threshold, the business risk warning unit issues a business risk warning message.

6. The strategic dynamic management-driven AI business management decision-making module as described in claim 2, wherein the business management module includes an business management database, a business data analysis unit, a business decision suggestion unit, a business performance analysis unit, a business execution control unit, and a business risk early warning unit; the business management database stores at least one type of business data; the business data analysis unit is used to analyze each type of business data in the business management database to generate a business analysis result; the business decision suggestion unit is used to automatically generate business decision suggestion information based on the business analysis result generated by the business data analysis unit; the business performance analysis unit is used to analyze the business data generated by the business data analysis unit to generate a business decision suggestion; the business performance analysis unit is used to analyze the business data generated by the business data analysis unit to generate a business decision suggestion; the business performance analysis unit is used to analyze the business data generated by the business data analysis unit to generate a business decision suggestion. The system compares and analyzes the actual operational performance data collected at each predetermined time period after the operational decision-making recommendation data with a target operational performance data to generate an operational performance analysis result. The operational execution control unit automatically generates an optimized operational decision-making recommendation data based on the operational performance analysis result generated by the operational performance analysis unit. The operational risk warning unit analyzes and compares the operational analysis result generated by the operational data analysis unit with a predetermined operational risk assessment standard to generate an operational risk assessment level and / or an operational risk assessment report. When the operational risk assessment level is higher than an operational risk threshold, the operational risk warning unit issues an operational risk warning message.

7. The strategic dynamic management-driven AI business management decision-making module as described in claim 2, wherein the procurement management module includes a procurement management database, a procurement data analysis unit, a procurement decision suggestion unit, a procurement performance analysis unit, a procurement execution control unit, and a procurement risk early warning unit; the procurement management database stores at least one procurement data; the procurement data analysis unit is used to analyze the procurement data in the procurement management database to generate a procurement analysis result; the procurement decision suggestion unit is used to automatically generate procurement decision suggestion information based on the procurement analysis result generated by the procurement data analysis unit; the procurement performance analysis unit is used to analyze the enterprise's performance in executing the procurement... The procurement decision-making and control unit compares and analyzes the actual procurement performance data collected at each predetermined time period after the procurement decision-making and control unit with a target procurement performance data to generate a procurement performance analysis result. Based on the procurement performance analysis result generated by the procurement performance analysis unit, the procurement execution and control unit automatically generates optimized procurement decision-making and control data. The procurement risk warning unit analyzes and compares the procurement analysis result generated by the procurement data analysis unit with a predetermined procurement risk assessment standard, and generates a procurement risk assessment level and / or a procurement risk assessment report. When the procurement risk assessment level is higher than a procurement risk threshold, the procurement risk warning unit issues a procurement risk warning message.

8. The strategic dynamic management-driven AI business management decision-making module as described in claim 2, wherein the inventory management module includes an inventory management database, an inventory data analysis unit, an inventory decision suggestion unit, an inventory performance analysis unit, an inventory execution control unit, and an inventory risk early warning unit; the inventory management database stores at least one type of inventory data; the inventory data analysis unit is used to analyze each type of inventory data in the inventory management database to generate an inventory analysis result; the inventory decision suggestion unit is used to automatically generate inventory decision suggestion information based on the inventory analysis result generated by the inventory data analysis unit; the inventory performance analysis unit is used to analyze the inventory data generated by the enterprise in executing the inventory management decision; The inventory performance analysis unit compares and analyzes the actual inventory performance data collected at each predetermined time period after storing decision-making recommendation data with a target inventory performance data to generate an inventory performance analysis result. The inventory execution control unit automatically generates an optimized inventory decision-making recommendation based on the inventory performance analysis result generated by the inventory performance analysis unit. The inventory risk warning unit analyzes and compares the inventory analysis result generated by the inventory data analysis unit with a predetermined inventory risk assessment standard, and generates an inventory risk assessment level and / or an inventory risk assessment report. When the inventory risk assessment level is higher than an inventory risk threshold, the inventory risk warning unit issues an inventory risk warning message.

9. The strategic dynamic management-driven AI business management decision-making module as described in claim 2, wherein the equipment management module includes an equipment management database, an equipment data analysis unit, an equipment decision suggestion unit, an equipment performance analysis unit, an equipment execution control unit, and an equipment risk warning unit; the equipment management database stores at least one piece of equipment data; the equipment data analysis unit is used to analyze the equipment data in the equipment management database to generate an equipment analysis result; the equipment decision suggestion unit is used to automatically generate equipment decision suggestion information based on the equipment analysis result generated by the equipment data analysis unit; the equipment performance analysis unit is used to analyze the equipment data generated by the enterprise during the execution of the equipment management database. The system compares and analyzes the actual equipment performance data collected at each predetermined time period after the preparation of decision-making recommendation data with the target equipment performance data to generate an equipment performance analysis result. The equipment execution control unit automatically generates an optimized equipment decision-making recommendation data based on the equipment performance analysis result generated by the equipment performance analysis unit. The equipment risk warning unit analyzes and compares the equipment analysis result generated by the equipment data analysis unit with a predetermined equipment risk assessment standard, and generates an equipment risk assessment level and / or an equipment risk assessment report. When the equipment risk assessment level is higher than an equipment risk threshold, the equipment risk warning unit issues an equipment risk warning message.

10. The strategic dynamic management-driven AI business management decision-making module as described in claim 2, wherein the energy management module includes an energy management database, an energy data analysis unit, an energy decision suggestion unit, an energy performance analysis unit, an energy execution control unit, and an energy risk early warning unit; the energy management database stores at least one type of energy data; the energy data analysis unit is used to analyze each energy data in the energy management database to generate an energy analysis result; the energy decision suggestion unit is used to automatically generate energy decision suggestion information based on the energy analysis result generated by the energy data analysis unit; the energy performance analysis unit is used to analyze the energy data in the database to generate an energy analysis result; the energy performance analysis unit is used to analyze the energy data in the database to generate an energy decision suggestion; the energy performance analysis unit is used to analyze the energy data in the database to generate an energy decision suggestion. The energy decision-making recommendation unit compares and analyzes the actual energy performance data collected at each predetermined time period with a target energy performance data to generate an energy performance analysis result. The energy execution control unit automatically generates optimized energy decision-making recommendation data based on the energy performance analysis result generated by the energy performance analysis unit. The energy risk warning unit analyzes and compares the energy analysis result generated by the energy data analysis unit with a predetermined energy risk assessment standard, and generates an energy risk assessment level and / or an energy risk assessment report. When the energy risk assessment level is higher than an energy risk threshold, the energy risk warning unit issues an energy risk warning message.

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