AI-based enterprise growth profit management system and method thereof

Abstract

The invention relates to the technical field of enterprise management, in particular to an AI-based enterprise growth profit management system and method, and the system comprises a profit growth resource adjustment module, a profit prediction model development module, a pre-allocation resource optimization module, a customer value-added service optimization module, a dynamic service adjustment and feedback module, and a profit management efficiency analysis module. According to the invention, the artificial intelligence technology and the new scheme are comprehensively applied to optimize enterprise growth profit management, the task demand is dynamically analyzed and calculated, the resource use weight is adjusted, the real-time utilization rate and efficiency of resources are improved, the idle time is shortened through resource utilization optimization, and the adaptation to demand fluctuation is enhanced; accurate profit prediction and demand change diagrams help enterprises to optimize pricing and product strategies, adapt to market changes, deeply analyze customer data, convert potential demands into specific service proposals, and enhance customer satisfaction and loyalty, thereby improving market response speed and decision quality.

Description

Technical Field

[0001] The present invention relates to the field of enterprise management technology, and in particular to an AI-based enterprise growth profit management system and method thereof. Background Art

[0002] The field of enterprise management technology covers many methods and tools used to improve the efficiency and profitability of enterprise operations, including strategic planning, human resource management, financial analysis, marketing, and supply chain optimization. The application of technology in this field aims to enhance decision-making processes and operational efficiency through data analysis, automated processes, customer relationship management systems, and enterprise resource planning (ERP) software. With the development of artificial intelligence and machine learning technologies, enterprise management can realize real-time analysis of complex data and provide deeper business insights, thereby supporting enterprises to maintain competitiveness in a highly competitive market environment.

[0003] Among them, the enterprise growth profit management system is a tool designed to improve the profitability and sustainable growth of enterprises. The system identifies profit growth points by analyzing sales data, customer behavior, market trends and cost structure. The purpose of using this system is to assist enterprises in optimizing pricing strategies, improving product portfolios, reducing costs and enhancing customer satisfaction. It can also assist decision makers in formulating data-driven strategies to promote the long-term development of enterprises and expand market share. This system is an important application in the field of enterprise management technology, enabling enterprises to respond flexibly and effectively under cyclically changing market conditions.

[0004] Although the existing technology covers a wide range of management tools and strategies, common profit management systems lack the ability to quickly adapt to real-time market changes. Traditional profit management methods are relatively fixed and delayed in resource allocation and customer service, making it difficult to respond immediately to rapidly changing market demands. This static processing method limits the performance of enterprises in a highly competitive and cyclically changing market environment because it cannot effectively optimize resource utilization and adjust service strategies to attract and retain customers. For example, the lack of the ability to analyze customer behavior in real time leads to insufficient personalization of service proposals, affecting customer satisfaction and loyalty, thereby reducing the overall profit potential and market share of the enterprise. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings existing in the prior art and to propose an AI-based enterprise growth profit management system and method thereof.

[0006] In order to achieve the above objectives, the present invention adopts the following technical solutions: An AI-based enterprise growth profit management system includes: The profit growth resource adjustment module uses AI technology to analyze the needs of enterprise parallel computing tasks, dynamically adjust the computing resource usage weights, optimize resource utilization and efficiency, reallocate computing tasks, and generate resource profit optimization plans; The profit forecast model development module analyzes the resource profit optimization plan, analyzes the short-term computing demand changes, graphically displays the resource demand in the future time period, predicts the profit growth trend, and generates a profit growth trend forecast chart; The pre-allocated resource optimization module adjusts the resource allocation strategy in the tasks in the future time period based on the profit growth trend forecast chart, implements resource optimization configuration, and establishes a pre-allocated execution plan; The customer value-added service optimization module identifies value-added service opportunities based on the pre-allocated execution plan, converts potential needs into service proposals, adjusts customer service strategies, and builds customer service expansion strategies; The dynamic service adjustment and feedback module uses the customer service extension strategy to adjust service recommendations in real time, update recommendation strategies, refine and adjust strategies, and obtain dynamic update records of service recommendations; The profit management efficiency analysis module summarizes and analyzes the data of the service recommendation dynamic update record, compares it with the real-time operation data, evaluates the resource scheduling and service recommendation execution efficiency, and generates a profit management efficiency evaluation result.

[0007] As a further scheme of the present invention, the resource profit optimization plan includes a resource weight adjustment plan, a utilization rate optimization plan, and an efficiency improvement plan; the profit growth trend forecast chart includes a demand change chart, a resource demand chart, and a profit trend chart; the pre-allocation execution plan includes a task allocation strategy, a resource ratio adjustment plan, and optimization configuration details; the customer service expansion strategy includes a service opportunity analysis, service proposal content, and strategy optimization details; the service recommendation dynamic update record includes real-time adjustment data, customer feedback updates, and purchasing behavior analysis; the profit management efficiency evaluation results include recommendation efficiency analysis, scheduling efficiency analysis, and strategy adjustment evaluation.

[0008] As a further solution of the present invention, the profit growth resource adjustment module includes: The resource management analysis submodule uses AI technology to analyze the needs of enterprise parallel computing tasks, records key indicators of CPU usage and memory requirements, uses indicators to perform time point analysis, determines the peak and valley periods of resource usage, and generates resource consumption patterns; The resource optimization submodule resets the usage weight of computing resources based on the resource consumption pattern, including adjusting the resource allocation ratio, modifying the priority setting, analyzing and evaluating the impact of the differentiated configuration on resource utilization efficiency by simulating, verifying the effectiveness of the new configuration, and generating the adjusted resource configuration; The task reallocation submodule uses the adjusted resource configuration to reorder and allocate computing tasks, including updating the task queue according to the new resource weight, optimizing the task allocation logic to match the current resource configuration, tracking the task execution in real time to verify the effectiveness of the allocation strategy, and generating a resource profit optimization plan.

[0009] As a further solution of the present invention, the profit forecasting model development module includes: The log analysis submodule organizes the original logs of load and resource scheduling according to the resource profit optimization plan, extracts key resource usage data, analyzes resource utilization, identifies scheduling anomalies, marks log entries with timestamps, and counts frequency and duration to generate resource usage patterns; The demand forecasting submodule uses time series analysis based on the resource usage pattern to identify trends and detect fluctuations in computing demand, adjust sampling frequency and time interval, forecast demand for cycles in future time periods, and generate demand fluctuation forecast results; The graphic display submodule adopts the demand fluctuation forecast results to design a dynamic chart of resource demand in the future time period, adjusts chart parameters, including color depth, zoom ratio and update frequency, and combines the forecast data to generate a profit growth trend forecast chart.

[0010] As a further solution of the present invention, the pre-allocated resource optimization module includes: The profit growth prediction submodule extracts data from the profit growth trend prediction graph, identifies periodic demand peaks by quantitatively analyzing the time series of the data, subdivides the peaks, determines the key increase and decrease periods of resource demand, and generates a periodic demand graph; The resource adjustment submodule performs real-time adjustment of resource weights based on the periodic demand graph, including refining resource allocation ratios, optimizing allocation strategies and matching predicted demand changes, and generating a resource allocation strategy graph; The execution plan configuration submodule uses the resource allocation strategy diagram to formulate a task schedule and resource allocation instructions, adjust task priorities and resource allocation logic, and conduct real-time monitoring to generate a pre-allocated execution plan.

[0011] As a further solution of the present invention, the customer value-added service optimization module includes: The data analysis submodule analyzes the customer purchase data according to the pre-allocated execution plan, extracts key data points, analyzes consumption frequency and amount changes, including the identification of purchase cycles and the extraction of consumption peaks, and generates consumption behavior patterns; The service opportunity identification submodule analyzes consumption data based on the consumption behavior pattern, identifies potential opportunities for service upgrades and cross-selling, and converts potential needs into service proposals by analyzing target consumption events and customer feedback, thereby generating value-added service opportunities; The service strategy formulation submodule utilizes the value-added service opportunities and adopts a decision tree algorithm to analyze customers' current service needs, evaluate the impact of service proposals on current services, optimize service operations and match proposal requirements, continuously monitor the service implementation process and analyze feedback in real time, and generate customer service expansion strategies.

[0012] As a further solution of the present invention, the formula of the decision tree algorithm is as follows: ; Calculate the service impact index, where R represents the service impact index, represents the priority weight of service improvement, D represents the current value of customer satisfaction, M represents the industry average customer satisfaction value, V represents the coefficient of variation of customer feedback data, and C represents the estimate of service change cost.

[0013] As a further solution of the present invention, the dynamic service adjustment and feedback module includes: The service adjustment submodule uses real-time data streams to monitor customer purchase behavior according to the customer service expansion strategy, dynamically adjusts service parameters, including service ratios and time windows, matches customer needs, and generates service adjustment dynamics; The feedback analysis submodule adjusts the dynamics of the service, analyzes customer feedback, adjusts the priority of items in the recommendation list, including the adjustment of item sorting and recommendation timing, and obtains strategy optimization records; The record updating submodule optimizes the records based on the strategy and synchronously updates the recommended records in the service database, including adding new adjustment records and deleting outdated records, to obtain a service recommendation dynamic update record.

[0014] As a further solution of the present invention, the profit management efficiency analysis module includes: The data comparison submodule dynamically updates records based on the service recommendations, synchronizes real-time operation data, compares records and real-time data item by item, compares differences and anomalies in key data points, readjusts comparison parameters, and obtains difference analysis results; Based on the difference analysis results, the efficiency optimization submodule checks the execution path of current resource scheduling and service recommendations, modifies configuration parameters and policy thresholds, rearranges the recommendation logic and matches the updated data trends, and constructs policy adjustment feedback results; The strategy verification submodule utilizes the strategy adjustment feedback results to conduct strategy analysis, integrate the adjusted data, evaluate and verify the effectiveness of the strategy, and generate profit management efficiency evaluation results.

[0015] A method for managing enterprise growth and profit based on AI, wherein the method for managing enterprise growth and profit based on AI is executed based on the above-mentioned enterprise growth and profit management system based on AI, and comprises the following steps: S1: Analyze the needs of enterprise parallel computing tasks through AI technology, monitor the current resource usage in real time, dynamically adjust the computing resource weights according to resource utilization data and task demand data, adaptively adjust resource allocation, and obtain resource profit optimization solutions; S2: Based on the resource profit optimization solution, the original load and resource scheduling log are analyzed, and the change of computing demand in the future time period is predicted through quantitative analysis. The predicted data is graphically processed and visualized to generate a profit growth trend prediction chart; S3: using the profit growth trend forecast chart, comparing the forecast demand with the current resource allocation, adjusting the proportion of resources in tasks in the future time period according to the forecast demand, optimizing resource allocation, reducing idle resources, and building a pre-allocated execution plan; S4: Analyze the original customer purchase data according to the pre-allocated execution plan, identify service expansion opportunities through cluster analysis, convert the analysis results into service proposals, adjust the service execution method, and formulate customer service expansion strategies; S5: Based on the customer service expansion strategy, update service recommendations in real time, use customer feedback and purchase behavior data to refine and adjust recommendation strategies, capture service recommendation update data, and generate profit management efficiency evaluation results.

[0016] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, through the comprehensive application of artificial intelligence technology, the deep integration and optimization of enterprise growth profit management is achieved. By dynamically analyzing the requirements of parallel computing tasks and adjusting the weight of computing resource usage, the real-time utilization rate and efficiency of resources are significantly improved. The optimization of resource utilization not only reduces idle time, but also increases the adaptability to resource demand fluctuations. With accurate profit forecasts and demand change graphs, enterprises can better formulate pricing and product portfolio strategies to adapt to future market changes. By deeply mining customer data and behavioral analysis, potential demand can be transformed into actual service proposals, customer satisfaction and customer loyalty can be enhanced, which directly affects long-term profitability. The strategic adjustment of customer service and the real-time feedback mechanism ensure that service recommendations are always synchronized with customer needs, improving market response speed and decision-making quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 is a system flow chart of the present invention; Figure 2 It is a schematic diagram of the system framework of the present invention; Figure 3 This is a flow chart of the profit growth resource adjustment module in the present invention; Figure 4 A flow chart of the profit forecasting model development module in the present invention; Figure 5 This is a flow chart of the pre-allocated resource optimization module in the present invention; Figure 6 A flow chart of the customer value-added service optimization module in the present invention; Figure 7 It is a flow chart of the dynamic service adjustment and feedback module in the present invention; Figure 8 It is a flow chart of the profit management efficiency analysis module in the present invention; Fig. 9 It is a schematic diagram of the method steps of the present invention. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0019] In the description of the present invention, it is necessary to interpret that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the accompanying drawings, which are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be interpreted as a limitation on the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0020] Embodiment 1 See also Figure 1 to Figure 2 , the present invention provides a technical solution: an AI-based enterprise growth profit management system comprising: The profit growth resource adjustment module uses AI technology to analyze the needs of enterprise parallel computing tasks, dynamically adjusts the usage weights of computing resources, optimizes the real-time utilization rate and efficiency of resources, reallocates computing tasks based on the adjustment results, and generates resource profit optimization plans; The profit forecast model development module uses AI technology to analyze the load and resource scheduling logs of the resource profit optimization plan, predict changes in computing demand in the short term, graphically display resource demand in the future time period, predict profit growth trends, and generate profit growth trend forecast charts; The pre-allocated resource optimization module adjusts the resource allocation strategy for tasks in the future time period based on the profit growth trend forecast chart, adjusts the resource allocation ratio according to the forecast demand, implements resource optimization configuration, reduces resource idle time, and establishes a pre-allocated execution plan; The customer value-added service optimization module is based on the pre-allocated execution plan. According to the analysis of the customer's original purchase data, it identifies the value-added service opportunities through data mining technology, converts potential demand into service proposals, adjusts and optimizes customer service strategies, and builds customer service expansion strategies; The dynamic service adjustment and feedback module uses customer service expansion strategies to adjust service recommendations in real time, uses customer feedback and purchase behavior data to update recommendation strategies, refines and adjusts strategies, keeps service recommendations synchronized with customer needs, and obtains dynamic update records of service recommendations; The profit management efficiency analysis module aggregates and analyzes the data of dynamically updated service recommendation records, compares it with real-time operation data, evaluates the execution efficiency of resource scheduling and service recommendations, optimizes and adjusts strategies, and generates profit management efficiency evaluation results.

[0021] The resource profit optimization plan includes the resource weight adjustment plan, the utilization rate optimization plan, and the efficiency improvement plan. The profit growth trend forecast chart includes the demand change chart, the resource demand chart, and the profit trend chart. The pre-allocation execution plan includes the task allocation strategy, the resource ratio adjustment plan, and the optimization configuration details. The customer service expansion strategy includes the service opportunity analysis, the service proposal content, and the strategy optimization details. The service recommendation dynamic update record includes real-time adjustment data, customer feedback updates, and purchasing behavior analysis. The profit management efficiency evaluation results include recommendation efficiency analysis, scheduling efficiency analysis, and strategy adjustment evaluation.

[0022] Specifically, if Figure 2 , 3 As shown, the profit growth resource adjustment module includes: The resource management analysis submodule uses AI technology to analyze the needs of enterprise parallel computing tasks, records key indicators of CPU usage and memory requirements, uses indicators to perform time point analysis, determines the peak and valley periods of resource usage, and generates the execution process of resource consumption patterns as follows; Through AI technology, the needs of enterprise parallel computing tasks are analyzed. By recording key indicators of CPU utilization and memory demand, the indicators are used for time point analysis. In the enterprise big data environment, the CPU and memory utilization records are realized through resource monitoring configuration. Resource usage data is extracted from operation configuration and application programs, and the data is aggregated and analyzed. The time series data of CPU utilization and memory demand are extracted. Data plays a vital role in time point analysis. Through historical data analysis, the peak and valley periods of resource utilization can be predicted, and resource consumption patterns can be generated accordingly. This pattern assists enterprises in analyzing the cyclical changes in their resource needs and provides support for resource management decisions.

[0023] The resource optimization submodule resets the usage weight of computing resources based on the resource consumption pattern, including adjusting the resource allocation ratio, modifying the priority setting, analyzing and evaluating the impact of differentiated configuration on resource utilization efficiency by simulating, verifying the effectiveness of the new configuration, and generating the execution process of the adjusted resource configuration as follows; By analyzing and evaluating the impact of simulated differentiated configuration on resource utilization efficiency, according to the formula: ; Calculate the effectiveness of the new configuration, where represents the efficiency under the new configuration, Represents the resource usage under the new configuration, Represents the performance index under the new configuration, and They represent the resource usage and performance indicators under the old configuration respectively; Set resource usage under the old configuration For 100 units, performance indicators 80%, the resource usage under the new configuration For 120 units, performance indicators is 95%, then the efficiency of the new configuration for: ; This means that the new configuration is 42.5% more efficient than the old one.

[0024] The task reallocation submodule uses the adjusted resource configuration to reorder and allocate computing tasks, including updating the task queue according to the new resource weight, optimizing the task allocation logic to match the current resource configuration, and real-time tracking of task execution to verify the effectiveness of the allocation strategy. The execution process of generating a resource profit optimization plan is as follows; Use the adjusted resource configuration to reorder and allocate computing tasks. Implement task reordering and resource allocation through the resource management system. Update the task queue based on the new resource weight configuration, re-evaluate the priority and required resources of each task to match the current resource configuration. Real-time monitoring system cyclically tracks task execution and verifies the effectiveness of the allocation strategy. It can adjust the task allocation strategy in real time to optimize resource utilization and generate a resource profit optimization plan. The plan can ensure that the enterprise achieves the best resource allocation, improves overall computing efficiency and reduces costs.

[0025] Specifically, if Figure 2 , 4 As shown, the profit forecasting model development module includes: The log analysis submodule organizes the original logs of load and resource scheduling according to the resource profit optimization plan, extracts key resource usage data, analyzes resource utilization, identifies scheduling anomalies, marks log entries with timestamps, and counts frequency and duration to generate resource usage patterns. The execution process is as follows; The process of collating the original logs of load and resource scheduling involves advanced data extraction and analysis techniques. Through professional log management tools, the data in the original logs are classified and labeled, and key resource usage data such as CPU usage, memory usage, and I / O operations are extracted. The data is captured at various time points by resource monitoring tools. Such details allow the system to analyze resource utilization and identify any scheduling anomalies. For identified anomalies, the system automatically adds timestamps to related log entries. Timestamps help track the specific time when an event occurs, and also count the frequency and duration of anomalies. Through comprehensive data analysis, the log analysis submodule can effectively generate resource usage patterns, which is crucial for optimizing future resource scheduling strategies.

[0026] The demand forecasting submodule uses time series analysis based on resource usage patterns to identify trends and detect fluctuations in computing demand, adjust sampling frequency and time interval, and forecast demand for cycles in future time periods. The execution process for generating demand fluctuation forecast results is as follows; Demand forecast for the cycle in the future time period is carried out according to the formula: ; Calculate the demand fluctuation forecast results, where D(t) represents the demand forecast at time t, and a, b, and c are coefficients obtained through time series analysis; Assume that in the past observation data, coefficients a=0.05, b=0.3, c=2.5 are obtained by least squares fitting, and the demand at time t=10 is to be predicted, then calculate D(t): ; This result indicates that at time t=10, the predicted demand will be 10.5 units.

[0027] The graphic display submodule uses the demand fluctuation forecast results to design a dynamic chart of resource demand in the future time period, adjusts the chart parameters, including color depth, scaling, and update frequency, and combines the forecast data to generate the profit growth trend forecast chart. The execution process is as follows; Using the demand fluctuation forecast results, a dynamic chart of resource demand in future time periods is designed. Advanced chart design tools are used to adjust chart parameters such as color depth, scaling and update frequency. The adjustment of parameters is based on a detailed analysis of the forecast data. By adjusting the color depth of the chart, the use of differentiated resources can be made more visually prominent. The adjustment of the scaling ensures that the chart can display data changes on differentiated time scales. The optimization of the update frequency ensures the real-time and accuracy of the data. Combining technical adjustments and forecast data, the graphic display submodule successfully generates a profit growth trend forecast chart. The chart not only provides an intuitive data display, but also assists decision makers in analyzing the changing trend of future resource demand.

[0028] Specifically, if Figure 2 , 5 As shown, the pre-allocated resource optimization module includes: The profit growth forecast submodule extracts data from the profit growth trend forecast graph, identifies the periodic demand peaks by quantitatively analyzing the time series of the data, subdivides the peaks, determines the key increase and decrease periods of resource demand, and generates the periodic demand graph. The execution process is as follows; Data is extracted from the profit growth trend forecast chart and quantitative time series analysis is performed using advanced data analysis software that can identify cyclical patterns in data and determine peak demand. Through cyclical data fluctuations, the submodule accurately identifies and segments the key periods of increase and decrease in resource demand. Cyclic analysis is based on the comprehensive pattern of historical data. This approach allows the submodule to effectively predict future changes in resource demand. The generated cyclical demand chart provides a visual display of future demand cycles. The process not only optimizes the efficiency of resource allocation, but also enhances the accuracy of predictions.

[0029] The resource adjustment submodule makes real-time adjustments to resource weights based on the periodic demand graph, including refining resource allocation ratios, optimizing allocation strategies, and matching predicted demand changes. The execution process for generating a resource allocation strategy graph is as follows; Make real-time adjustments to resource weights according to the formula: ; Compute resource allocation strategy diagram, where represents the new resource weight, represents the predicted demand, is the maximum value of the predicted demand, is the old resource weight; Set the maximum demand forecasted within a period For 500 units, the forecast demand Changes over time, at a certain point in time it is 350 units, the old resource weight is 0.7, then the new resource weight Calculated as: ; This indicates that at this point in time, the resource weight should be adjusted to 0.49 to optimize resource usage.

[0030] The execution plan configuration submodule uses the resource allocation strategy diagram to formulate the task schedule and resource allocation instructions, adjust the task priority and resource allocation logic, and monitor in real time to generate the execution process of the pre-allocated execution plan as follows; The resource allocation strategy diagram is used to formulate task schedules and resource allocation instructions. The task priority and resource allocation logic are adjusted through the real-time monitoring system. The advanced scheduling algorithm is used to automatically adjust resource allocation to ensure that each task receives reasonable resources based on priority and resource requirements. The schedule is formulated with reference to the urgency and importance of each task. The resource allocation instructions ensure that all tasks are executed according to the predetermined strategy. The results of real-time monitoring assist the configuration module to cyclically optimize the scheduling strategy. The generated pre-allocated execution plan lists in detail the resource usage and the time points of task execution, which not only improves the efficiency of task execution, but also reduces resource waste.

[0031] Specifically, if Figure 2 , 6 As shown, the customer value-added service optimization module includes: The data analysis submodule analyzes customer purchase data according to the pre-assigned execution plan, extracts key data points, analyzes consumption frequency and amount changes, including the identification of purchase cycles and the extraction of consumption peaks, and generates the execution process of consumption behavior patterns as follows; First, it receives customer purchase data from the pre-allocated execution plan. After the data is initially cleaned and formatted, it conducts in-depth mining of key data points, including the use of time series analysis technology to determine the purchase cycle of each customer, thereby detecting and recording periodic purchase behavior. This module also uses statistical analysis methods, such as moving average and standard deviation calculations, to determine the fluctuation range of consumption amounts. The data analysis submodule can identify consumption peaks, analyze the changing trends of consumption frequency and amount, and generate consumption behavior patterns for each customer based on this. The patterns not only include the customer's purchasing habits, but also provide in-depth analysis of consumption motivations and preferences, providing data support for subsequent market strategies.

[0032] The service opportunity identification submodule analyzes consumption data based on consumption behavior patterns, identifies potential opportunities for service upgrades and cross-selling, and converts potential needs into service proposals by analyzing target consumption events and customer feedback. The execution process for generating value-added service opportunities is as follows; Using consumption behavior patterns as a basis, further analysis of customer consumption data, including identification of customers' repeat purchase patterns and preference changes, including purchase behavior during specific holidays or events, and by comparing and analyzing customers' historical and current consumption data, this module can tap into potential opportunities for consumption upgrades and cross-selling. For example, if a customer has increased the frequency of purchases of high-end products in the past few months, the service opportunity identification submodule will identify this as a potential need for upgraded services. By analyzing the correlation between target consumption events and customer feedback, this module will convert potential customer needs into specific service proposals, generate value-added service opportunities, and provide execution suggestions to the sales team. This process not only enhances customer satisfaction, but also significantly improves the company's marketing efficiency and service quality.

[0033] The service strategy formulation submodule takes advantage of the value-added service opportunities and adopts the decision tree algorithm to analyze the current service needs of customers, evaluate the impact of service proposals on current services, optimize service operations and match proposal requirements, continuously monitor the service implementation process and analyze feedback in real time, and generate the execution process of customer service expansion strategy as follows; The formula for the decision tree algorithm is as follows: ; Calculate the service impact index, where R represents the service impact index, represents the priority weight of service improvement, D represents the current value of customer satisfaction, M represents the industry average customer satisfaction value, V represents the coefficient of variation of customer feedback data, and C represents the estimate of service change cost.

[0034] Detailed explanation of the formula and the process of formula calculation and derivation: The R in the formula stands for the service impact index, which is a quantitative assessment of the effectiveness of customer service improvement; Priority weights for service improvements are evaluated from historical data. Weights are calculated based on the average of urgency and impact of past service adjustment cases. For example, based on data from the past three years, The average rating was 0.8; D and M represent the current value of customer satisfaction and the industry average satisfaction, respectively, which are obtained through customer satisfaction surveys. For example, D is 75 in the most recent survey, while the industry average M is 70, which is obtained from industry reports; V represents the coefficient of variation of customer feedback, which is obtained by analyzing the ratio of the standard deviation and mean of customer feedback data. If the standard deviation is 10 and the mean is 50, the coefficient of variation V is 0.2; C is the estimated cost of the service change, which is obtained by the finance department based on the average cost of similar service adjustments in the past and is set at 5,000 yuan; Calculation flow example: Calculate the absolute difference : ; Compute the square root portion of the denominator: ; Substitute the above results into the main formula to calculate R: ; The results show that the service impact index is very low, suggesting that current customer satisfaction is very close to the industry average, while the cost of service change is high, which means that if further service improvements are to be implemented, it is necessary to refer to more efficient or cost-effective methods, or seek strategies to improve customer satisfaction. This numerical result guides us to be more cautious in service improvement decisions and analyze low-cost and effective improvement measures.

[0035] Specifically, if Figure 2 , 7 As shown, the dynamic service adjustment and feedback module includes: The service adjustment submodule uses real-time data streams to monitor customer purchase behavior based on customer service expansion strategies, dynamically adjusts service parameters, including service ratios and time windows, matches customer needs, and generates the following execution flow for service adjustment dynamics; Dynamically adjust service parameters according to the formula: ; Compute service adjustment dynamics, where Represents a new service ratio, For the old service ratio, represents the adjustment coefficient based on customer demand matching, and F is the demand response rate obtained by real-time data stream monitoring; Set old service ratio is 0.5, the demand response rate F is calculated from real-time monitoring and is 0.2, and the adjustment coefficient Set it to 0.05 and substitute it into the formula to calculate the new service ratio: ; This result suggests that the new service ratio should be adjusted to 0.505, reflecting a slight increase to better match customer needs.

[0036] The feedback analysis submodule adjusts the dynamics of services, analyzes customer feedback, and adjusts the priority of items in the recommendation list, including the adjustment of item sorting and recommendation timing. The execution process of obtaining strategy optimization records is as follows; By adjusting service dynamics and analyzing customer feedback, data analysis tools are used to adjust the priority of items in the recommendation list. The item sorting and recommendation timing are adjusted based on specific customer feedback. Analysis tools help identify which service items are more popular and which should be increased or decreased in priority. This dynamic adjustment helps the service meet customer needs more accurately. The final strategy optimization record includes all adjustment details and guides future service optimization strategies.

[0037] The record update submodule optimizes records based on the strategy and synchronously updates the recommended records in the service database, including adding new adjustment records and deleting outdated records. The execution process of obtaining the service recommendation dynamic update record is as follows; Based on the strategy optimization records, the recommendation records in the service database are updated synchronously. Through the automated database management system, new adjustment records are added to the database and outdated records are deleted. The update process ensures that the data of the service recommendation system is always kept up to date, effectively supporting the accuracy and responsiveness of the recommendation system. The generated service recommendation dynamic update records provide a continuous optimization basis for the service, ensuring that the recommendation system can reflect the latest market trends and consumer preferences.

[0038] Specifically, if Figure 2 , 8 As shown, the profit management efficiency analysis module includes: The data comparison submodule dynamically updates records based on service recommendations, synchronizes real-time operation data, compares records and real-time data item by item, compares differences and anomalies in key data points, readjusts comparison parameters, and obtains the execution process of difference analysis results as follows; Compare records and real-time data item by item, according to the formula: ; Calculate the difference analysis results, where represents the sum of data differences, represents a recorded data point, represents real-time data points, and n is the number of data points; Set up 5 data points, the recorded data is [10, 20, 30, 40, 50], and the real-time data is [12, 19, 29, 43, 48]; calculate : ; This result indicates that the total variability between the data points is 9 units.

[0039] Based on the difference analysis results, the efficiency optimization submodule checks the execution path of the current resource scheduling and service recommendation, modifies the configuration parameters and policy thresholds, re-arranges the recommendation logic and matches the updated data trends, and constructs the execution process of the policy adjustment feedback results as follows; Based on the results of the differential analysis, check the execution path of the current resource scheduling and service recommendations, modify the configuration parameters and policy thresholds, and use data analysis tools to identify which parameters or thresholds need to be adjusted to reduce execution errors. The sub-module automatically adjusts the recommendation logic based on the analysis to match the updated data trends. The re-arranged recommendation logic not only reflects the latest trends in the data, but also refers to historical efficiency data to ensure that each adjustment is based on comprehensive data analysis. The final constructed policy adjustment feedback results provide a dynamic adjustment mechanism for the system, which can continuously optimize system performance and respond quickly to future changes.

[0040] The strategy verification submodule uses the feedback results of strategy adjustment to conduct strategy analysis, integrate the adjusted data, evaluate and verify the effectiveness of the strategy, and generate the profit management efficiency evaluation results. The execution process is as follows; Utilize the feedback results of strategy adjustment to conduct strategy analysis. Through integrated analysis tools, integrate the adjusted data, conduct systematic strategy evaluation, and verify the effectiveness of the strategy and the accuracy of its execution. During the evaluation process, the submodule calculates various indicators, such as cost savings, resource utilization, and service response time, to ensure that each indicator meets the preset performance standards. The calculation results assist in verifying the actual effect of the strategy adjustment. The generated profit management efficiency evaluation results demonstrate the specific advantages brought by the adjustment strategy, such as cost reduction and efficiency improvement, thereby providing clear data support for management to make more accurate management decisions.

[0041] See also Fig. 9 , a method for managing enterprise growth and profit based on AI, the method for managing enterprise growth and profit based on AI is executed based on the above-mentioned management system for enterprise growth and profit based on AI, and includes the following steps: S1: Analyze the needs of enterprise parallel computing tasks through AI technology, monitor the current resource usage in real time, dynamically adjust the computing resource weights according to resource utilization data and task demand data, adaptively adjust resource allocation, and obtain resource profit optimization solutions; S2: Based on the resource profit optimization plan, analyze the original load and resource scheduling logs, predict the changes in computing demand in the future time period through quantitative analysis, process the predicted data graphically, and display it visually to generate a profit growth trend prediction chart; S3: Use the profit growth trend forecast chart to compare the predicted demand with the current resource allocation, adjust the proportion of resources in tasks in the future time period according to the predicted demand, optimize resource allocation, reduce idle resources, and build a pre-allocated execution plan; S4: Based on the pre-allocated execution plan, analyze the original customer purchase data, identify service expansion opportunities through cluster analysis, convert the analysis results into service proposals, adjust the service execution method, and formulate customer service expansion strategies; S5: Update service recommendations in real time based on customer service expansion strategies, use customer feedback and purchase behavior data to refine and adjust recommendation strategies, capture service recommendation update data, and generate profit management efficiency evaluation results.

[0042] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An AI-based enterprise growth profit management system, characterized in that: The system comprises: The profit growth resource adjustment module uses AI technology to analyze the needs of enterprise parallel computing tasks, dynamically adjust the computing resource usage weights, optimize resource utilization and efficiency, reallocate computing tasks, and generate resource profit optimization plans; The profit forecast model development module analyzes the resource profit optimization plan, analyzes the short-term computing demand changes, graphically displays the resource demand in the future time period, predicts the profit growth trend, and generates a profit growth trend forecast chart; The pre-allocated resource optimization module adjusts the resource allocation strategy in the tasks in the future time period based on the profit growth trend forecast chart, implements resource optimization configuration, and establishes a pre-allocated execution plan; The customer value-added service optimization module identifies value-added service opportunities based on the pre-allocated execution plan, converts potential needs into service proposals, adjusts customer service strategies, and builds customer service expansion strategies; The dynamic service adjustment and feedback module uses the customer service extension strategy to adjust service recommendations in real time, update recommendation strategies, refine and adjust strategies, and obtain dynamic update records of service recommendations; The profit management efficiency analysis module summarizes and analyzes the data of the service recommendation dynamic update record, compares it with the real-time operation data, evaluates the resource scheduling and service recommendation execution efficiency, and generates a profit management efficiency evaluation result.

2. The AI-based enterprise growth profit management system according to claim 1 is characterized by: The resource profit optimization plan includes a resource weight adjustment plan, a utilization rate optimization plan, and an efficiency improvement plan. The profit growth trend forecast chart includes a demand change chart, a resource demand chart, and a profit trend chart. The pre-allocation execution plan includes a task allocation strategy, a resource ratio adjustment plan, and optimization configuration details. The customer service expansion strategy includes a service opportunity analysis, service proposal content, and strategy optimization details. The service recommendation dynamic update record includes real-time adjustment data, customer feedback updates, and purchasing behavior analysis. The profit management efficiency evaluation results include recommendation efficiency analysis, scheduling efficiency analysis, and strategy adjustment evaluation.

3. The AI-based enterprise growth profit management system according to claim 1 is characterized by: The profit growth resource adjustment module includes: The resource management analysis submodule uses AI technology to analyze the needs of enterprise parallel computing tasks, records key indicators of CPU usage and memory requirements, uses indicators to perform time point analysis, determines the peak and valley periods of resource usage, and generates resource consumption patterns; The resource optimization submodule resets the usage weight of computing resources based on the resource consumption pattern, including adjusting the resource allocation ratio, modifying the priority setting, analyzing and evaluating the impact of the differentiated configuration on resource utilization efficiency by simulating, verifying the effectiveness of the new configuration, and generating the adjusted resource configuration; The task reallocation submodule uses the adjusted resource configuration to reorder and allocate computing tasks, including updating the task queue according to the new resource weight, optimizing the task allocation logic to match the current resource configuration, tracking the task execution in real time to verify the effectiveness of the allocation strategy, and generating a resource profit optimization plan.

4. The AI-based enterprise growth profit management system according to claim 1 is characterized by: The profit forecast model development module includes: The log analysis submodule organizes the original logs of load and resource scheduling according to the resource profit optimization plan, extracts key resource usage data, analyzes resource utilization, identifies scheduling anomalies, marks log entries with timestamps, and counts frequency and duration to generate resource usage patterns; The demand forecasting submodule uses time series analysis based on the resource usage pattern to identify trends and detect fluctuations in computing demand, adjust sampling frequency and time interval, forecast demand for cycles in future time periods, and generate demand fluctuation forecast results; The graphic display submodule adopts the demand fluctuation forecast results to design a dynamic chart of resource demand in the future time period, adjusts chart parameters, including color depth, zoom ratio and update frequency, and combines the forecast data to generate a profit growth trend forecast chart.

5. The AI-based enterprise growth profit management system according to claim 1 is characterized by: The pre-allocated resource optimization module includes: The profit growth prediction submodule extracts data from the profit growth trend prediction graph, identifies periodic demand peaks by quantitatively analyzing the time series of the data, subdivides the peaks, determines the key increase and decrease periods of resource demand, and generates a periodic demand graph; The resource adjustment submodule performs real-time adjustment of resource weights based on the periodic demand graph, including refining resource allocation ratios, optimizing allocation strategies and matching predicted demand changes, and generating a resource allocation strategy graph; The execution plan configuration submodule uses the resource allocation strategy diagram to formulate a task schedule and resource allocation instructions, adjust task priorities and resource allocation logic, and conduct real-time monitoring to generate a pre-allocated execution plan.

6. The AI-based enterprise growth profit management system according to claim 1 is characterized by: The customer value-added service optimization module includes: The data analysis submodule analyzes the customer purchase data according to the pre-allocated execution plan, extracts key data points, analyzes consumption frequency and amount changes, including the identification of purchase cycles and the extraction of consumption peaks, and generates consumption behavior patterns; The service opportunity identification submodule analyzes consumption data based on the consumption behavior pattern, identifies potential opportunities for service upgrades and cross-selling, and converts potential needs into service proposals by analyzing target consumption events and customer feedback, thereby generating value-added service opportunities; The service strategy formulation submodule utilizes the value-added service opportunities and adopts a decision tree algorithm to analyze customers' current service needs, evaluate the impact of service proposals on current services, optimize service operations and match proposal requirements, continuously monitor the service implementation process and analyze feedback in real time, and generate customer service expansion strategies.

7. The AI-based enterprise growth profit management system according to claim 6 is characterized by: The formula of the decision tree algorithm is as follows: ； Calculate the service impact index, where R represents the service impact index, represents the priority weight of service improvement, D represents the current value of customer satisfaction, M represents the industry average customer satisfaction value, V represents the coefficient of variation of customer feedback data, and C represents the estimate of service change cost.

8. The AI-based enterprise growth profit management system according to claim 1 is characterized by: The dynamic service adjustment and feedback module includes: The service adjustment submodule uses real-time data streams to monitor customer purchase behavior according to the customer service expansion strategy, dynamically adjusts service parameters, including service ratios and time windows, matches customer needs, and generates service adjustment dynamics; The feedback analysis submodule adjusts the dynamics of the service, analyzes customer feedback, adjusts the priority of items in the recommendation list, including the adjustment of item sorting and recommendation timing, and obtains strategy optimization records; The record updating submodule optimizes the records based on the strategy and synchronously updates the recommended records in the service database, including adding new adjustment records and deleting outdated records, to obtain a service recommendation dynamic update record.

9. The AI-based enterprise growth profit management system according to claim 1 is characterized by: The profit management efficiency analysis module includes: The data comparison submodule dynamically updates records based on the service recommendations, synchronizes real-time operation data, compares records and real-time data item by item, compares differences and anomalies in key data points, readjusts comparison parameters, and obtains difference analysis results; Based on the difference analysis results, the efficiency optimization submodule checks the execution path of current resource scheduling and service recommendations, modifies configuration parameters and policy thresholds, rearranges the recommendation logic and matches the updated data trends, and constructs policy adjustment feedback results; The strategy verification submodule utilizes the strategy adjustment feedback results to conduct strategy analysis, integrate the adjusted data, evaluate and verify the effectiveness of the strategy, and generate profit management efficiency evaluation results.

10. A method for managing enterprise growth and profit based on AI, characterized in that: According to any one of claims 1 to 9, the AI-based enterprise growth profit management system is implemented, comprising the following steps: Through AI technology, the company's parallel computing task requirements are analyzed, and current resource usage is monitored in real time. Based on resource utilization data and task demand data, the computing resource weights are dynamically adjusted, resource allocation is adaptively adjusted, and resource profit optimization solutions are obtained. Based on the resource profit optimization scheme, the original load and resource scheduling logs are analyzed, and the changes in computing demand in the future time period are predicted through quantitative analysis. The predicted data is graphically processed and visualized to generate a profit growth trend prediction chart; Using the profit growth trend forecast chart, compare the forecast demand with the current resource allocation, adjust the proportion of resources in tasks in the future time period according to the forecast demand, optimize resource allocation, reduce idle resources, and build a pre-allocated execution plan; According to the pre-allocated execution plan, analyze the original customer purchase data, identify service expansion opportunities through cluster analysis, convert the analysis results into service proposals, adjust the service execution method, and formulate customer service expansion strategies; According to the customer service expansion strategy, service recommendations are updated in real time, customer feedback and purchase behavior data are used to refine and adjust recommendation strategies, capture service recommendation update data, and generate profit management efficiency evaluation results.

Images