A ternary dynamic growth method and system based on AI intelligent driving

Abstract

The present application relates to the technical field of the integration of artificial intelligence and business intelligence, and particularly relates to a ternary dynamic growth method and system based on AI intelligence driving, which comprises three modules of value potential, system kinetic energy and growth efficiency linked and configured through a ternary intelligence driving interaction interface, uses an AI algorithm containing a nonlinear coupling term to calculate a comprehensive growth index in real time, and combines multi-source data initialization, simulation stress testing, senior management intervention, online learning and a closed-loop feedback mechanism to realize adaptive optimization and dynamic evolution of enterprise growth strategies; the present application can break through the static and fragmented limitations of traditional business growth mode design, and improve the effect, intelligence and self-iteration of the growth path.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence and intelligent decision-making technology, specifically a three-element dynamic growth method and system based on AI intelligent drive. Background Technology

[0002] Business model optimization is a core element for enterprises to achieve sustainable growth, spanning the entire lifecycle from startup to maturity. In today's highly competitive and rapidly changing market environment, enterprises urgently need a scientific, systematic, and dynamically adaptable methodology to accurately anchor their value proposition, efficiently integrate internal and external resources, and continuously drive business evolution. However, traditional business model design methods often rely on static, modular analytical frameworks (such as the classic business canvas), treating elements such as customer segmentation, value proposition, and distribution channels in isolation. This lack of modeling and quantification of the dynamic coupling relationships between these dimensions hinders the release of synergistic effects and limits overall innovation capabilities and responsiveness. For example, customer segmentation results often cannot be fed back to the iteration of the value proposition in real time, and resource investment is difficult to link with channel efficiency in a closed loop, thus weakening the overall resilience and growth potential of the business model.

[0003] In recent years, some studies have attempted to introduce digital tools to improve the efficiency of business model design, but most remain at the level of single-point optimization, failing to effectively capture and utilize the "multiplicative effect" between multi-dimensional factors—that is, the non-linear interaction relationships such as the driving role of customer segmentation on value proposition, the supporting ability of resource leverage for channel construction, and the amplifying effect of relationship networks on profit models. At the same time, existing tools generally lack real-time verification mechanisms and dynamic optimization capabilities, making it difficult to achieve rapid strategy calibration and system adaptive evolution in the face of market fluctuations, changes in user behavior, or sudden changes in the competitive landscape. Although some scholars have proposed dynamic adjustment approaches based on industry trends or life cycle theories, these methods have not yet built a complete feedback loop, nor have they deeply integrated artificial intelligence technology as the underlying engine for cognition, verification, and evolution.

[0004] Therefore, there is an urgent need for a new business model construction method and system that integrates systems thinking, nonlinear coupling theory, and AI-driven intelligence. This system should be able to break down traditional modular barriers, achieve dynamic integration and synergistic emergence of three dimensions: value potential, system kinetic energy, and growth efficiency, and support enterprises to achieve scientific and intelligent sustainable growth in complex and uncertain environments through a calculable, verifiable, and evolvable mechanism. This technological gap is precisely the core problem that this invention aims to solve. Summary of the Invention

[0005] Therefore, the first objective of this invention is to propose a three-dimensional dynamic growth method based on AI intelligence, which nonlinearly and dynamically couples the three core dimensions of value potential energy, system kinetic energy and growth efficiency. It utilizes artificial intelligence as a cognitive engine, verification system and evolution mechanism to construct a calculable, verifiable and evolvable scientific business operating system, breaking through the limitations of modular fragmentation, static planning and linear superposition in traditional business model design, and realizing the intelligent generation and adaptive optimization of enterprise growth path.

[0006] Therefore, a second objective of the present invention is to provide a computer device.

[0007] Therefore, a third objective of the present invention is to provide a computer-readable storage medium.

[0008] To achieve the above objectives, a first aspect of the present invention proposes an AI-driven three-dimensional dynamic growth method, comprising: displaying a three-dimensional intelligent driving interactive interface to a business strategist, wherein the three-dimensional intelligent driving interactive interface integrates a value potential energy panel, a system kinetic energy panel, and a growth efficiency panel that can be linked; receiving a first configuration instruction triggered by the strategist on the three-dimensional intelligent driving interactive interface to set parameters or adjust the structure of the customer segmentation, value proposition, or competitive positioning modules in the value potential energy panel; receiving a second configuration instruction triggered by the strategist on the three-dimensional intelligent driving interactive interface to configure resources or optimize processes for the resource leverage, channel construction, or relationship design modules in the system kinetic energy panel; receiving a third configuration instruction triggered by the strategist on the three-dimensional intelligent driving interactive interface to perform strategy modeling for the profit model, business system, or evolution path modules in the growth efficiency panel; calculating the synergistic effect between the three panels in real time based on a preset AI dynamic coupling algorithm and generating a comprehensive growth index; dynamically adjusting the weights of each module according to the comprehensive growth index and outputting an executable growth strategy scheme. As described above, the AI ​​dynamic coupling algorithm uses a nonlinear activation function to perform a product-composite operation on the three dimensions of scores, and its mathematical expression is: Where G is the comprehensive growth index, and V, S, and E represent the standardized scores of value potential energy, system kinetic energy, and growth efficiency, respectively. , , λ is the initial weight coefficient, λ is the coupling strength factor, and σ(⋅) is a Sigmoid-type nonlinear activation function used to simulate the system's emergent characteristics caused by the multiplication effect between dimensions. The method described above, after generating the comprehensive growth index, further includes: receiving the simulation run command triggered by the strategy maker on the three-dimensional intelligent interactive interface; calling historical market data and enterprise operation data to conduct multi-scenario stress tests on the current growth strategy; dynamically correcting module parameters based on simulation results and recalculating the comprehensive growth index; freezing the current strategy and generating the final execution plan when the comprehensive growth index's improvement rate is lower than a preset threshold for three consecutive iterations. The method described above, before receiving the first configuration command triggered by the strategy maker, further includes: automatically collecting multi-source heterogeneous data from the enterprise's ERP, CRM, and external market monitoring platforms; extracting features from unstructured data using natural language processing and graph neural networks to construct a three-dimensional customer-product-channel relationship map; and initializing the customer segmentation and value proposition modules in the value potential panel based on the relationship map. The method described above further includes: displaying a dynamic growth dashboard to senior executives, wherein the dashboard presents the collaborative status and bottleneck nodes of the three dimensions in real time in the form of a heatmap; receiving intervention instructions triggered by senior executives on the dynamic growth dashboard, and manually adjusting the specified modules; feeding back the manually adjusted parameters to the AI ​​dynamic coupling algorithm to trigger a new round of strategy optimization cycle. The method described above also includes: continuously collecting user behavior logs and financial performance indicators during the strategy execution phase; updating the weight coefficients and coupling factors in the AI ​​dynamic coupling algorithm through an online learning mechanism; automatically triggering a model recalibration process and generating an emergency evolution path when a sudden external environmental event (such as policy adjustments or competitor product launches) is detected. After generating the final execution plan, the method described above further includes: decomposing the strategy plan into atomic-level task units and assigning them to corresponding business system interfaces; monitoring the execution status and output quality of each task unit; and adjusting the business system modules and evolution path modules in the growth efficiency panel in reverse according to the actual execution deviation, forming a closed-loop feedback mechanism. The method described above also includes: reconstructing the enterprise capability matrix based on the updated comprehensive growth index; matching the optimal market opportunity window according to the capability matrix, and automatically recommending the next stage of value potential focus.

[0009] To achieve the above objectives, a computer device according to a second aspect of the present invention includes: a processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing the AI-driven ternary dynamic growth method described in the first aspect.

[0010] To achieve the above objectives, a computer-readable storage medium according to a third aspect of the present invention stores a computer program thereon, which, when executed by a processor, implements the AI-driven ternary dynamic growth method described in the first aspect.

[0011] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0012] Figure 1 The system architecture diagram of the AI-driven three-element dynamic growth method provided in the embodiments of the present invention shows the integration relationship of the value potential energy panel, the system kinetic energy panel and the growth efficiency panel in the three-element intelligent driving interactive interface, as well as the connection and interaction process between the AI ​​dynamic coupling algorithm, the data acquisition module, the simulation operation module and the closed-loop feedback mechanism. Figure 2 The dynamic growth dashboard and strategy optimization loop diagram provided in this embodiment of the invention presents the collaborative status and bottleneck nodes of the three dimensions in the form of a heat map, and shows the closed loop path of senior management intervention instruction input, manual coverage adjustment, parameter feedback to AI algorithm, and a new round of strategy optimization. Detailed Implementation

[0013] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of protection of the present invention. Figure 1The diagram shown illustrates the system architecture of the AI-driven ternary dynamic growth method provided in this embodiment of the invention. The system includes a ternary intelligent driving interactive interface 100, an AI dynamic coupling algorithm module 200, a multi-source data acquisition and preprocessing module 300, a simulation operation module 400, a closed-loop feedback mechanism 500, and a dynamic growth dashboard 600. The three-dimensional intelligent interactive interface 100 integrates a value potential energy panel 110, a system kinetic energy panel 120, and a growth efficiency panel 130 that can be linked; the AI ​​dynamic coupling algorithm module 200 is used to calculate the synergistic effect between the three panels in real time and generate a comprehensive growth index G; the multi-source data acquisition and preprocessing module 300 is used to automatically collect multi-source heterogeneous data from the enterprise's internal ERP system, CRM system, and external market monitoring platform, and construct a three-dimensional relationship map of customers-products-channels through natural language processing (NLP) and graph neural networks (GNN); the simulation operation module 400 is used to conduct multi-scenario stress tests on the current solution during the strategy formulation stage; the closed-loop feedback mechanism 500 is used to continuously monitor the execution status of task units during the strategy execution stage and adjust the business system and evolution path module in the growth efficiency panel 130 in reverse; and the dynamic growth dashboard 600 presents the synergistic status and bottleneck nodes of the three dimensions to the enterprise's senior management in real time in the form of a heat map.

[0015] In the specific implementation process, the multi-source data acquisition and preprocessing module 300 first automatically connects to the enterprise's ERP system, CRM system, and third-party market data platforms (such as Tianyancha, iResearch, Google Trends, etc.) to acquire mixed structured and unstructured data, including but not limited to customer transaction records, user review texts, social media sentiment, competitor pricing information, and industry policy documents. Subsequently, the system uses natural language processing technology to perform entity recognition, sentiment analysis, and keyword extraction on the unstructured text, and combines graph neural networks to construct a three-dimensional relationship map of "customer-product-channel". For example, consumer electronics company A obtains nearly 2 million customer interaction logs from its CRM system over the past year. Using an NLP model, it identifies high-frequency demand keywords such as "fast charging," "thin and light," and "long battery life." Then, using a Generative Neural Network (GNN), it establishes associations between these keywords and product SKUs and sales channels (online / offline / social e-commerce), automatically generating initial customer segment clusters and value proposition suggestions. These serve as the initial input for the customer segmentation and value proposition modules in the Value Potential Panel 110. This module receives product feature data (such as ingredients, functions, and price) and customer pain point data (from surveys or review mining). Through Natural Language Processing (NLP) technology, it calculates the semantic similarity between product selling points and customer pain points. For example, it uses the BERT model to vectorize the text and calculates the matching score M using the cosine similarity formula. Where A is the customer pain point vector and B is the product selling point vector. Through this method, the module can quantify the fit between the product and customer needs and output the "value proposition matching degree" to determine whether the product hits the user pain points, thereby guiding enterprises to optimize market positioning, value proposition design and product design.

[0016] Next, the system presents the Tri-Element Intelligent Interactive Interface 100 to business strategists. This interface employs a visual drag-and-drop design. The Value Potential Panel 110 includes three sub-modules: Customer Segmentation 111, Value Proposition 112, and Competitive Positioning 113. This module receives data on competitors' pricing strategies, channel layouts, and market share. By constructing a game theory matrix and using the TOPSIS (Top-Solution Ranking) algorithm, it calculates the company's relative advantage distance in the target market. : ;in The distance from the ideal solution, To determine the distance from the negative ideal solution, the module generates a "competitive positioning score" to help companies determine their differentiated entry points and strategies (such as low-price penetration or high-end positioning) to find a unique value proposition in a highly competitive market. The system dynamics panel 120 includes resource leverage 121 (this module receives data on the company's capital, human resources, inventory, supply chain response, and other resources, and establishes an input-output ratio (ROI) optimization model. Through a linear programming algorithm, it solves the problem under limited resource constraints). Next, how to allocate resources? Maximize total output Z: , Where Z is the benefit indicator pursued by the enterprise resource allocation plan, which can be total profit, total revenue, or comprehensive benefit value. The amount of resources allocated to the i-th business unit. The marginal contribution or rate of return generated by each unit of resource invested in the i-th business unit, compared with the advantage distance mentioned above. These are different definitions. This represents the maximum amount of available resources. Here, 'st' represents the unit resource consumption coefficient for the i-th business unit, indicating "constrained by". The "optimal resource allocation scheme" generated by the module can guide enterprises to invest funds in the business links with the highest return, thereby achieving efficient resource utilization. Channel Construction 122 (This module receives traffic data, conversion rates, and customer acquisition costs from various channels (such as online e-commerce, offline stores, and social media), and constructs a multi-channel attribution model (such as the Shapley value model) to analyze the contribution of each channel to the final conversion and identify traffic bottlenecks. The module outputs "optimal channel traffic".) and Relationship Design 123 (This module receives social interaction data, recommendation relationships, and repurchase data between users, and constructs a user relationship graph). The Graph module utilizes Graph Neural Networks (GNNs) to calculate the influence score and intimacy of nodes (users). The "Relationship Network Maintenance Strategy" generated by this module can identify high-influence Key Opinion Users (KOCs) and trigger automated interaction mechanisms (such as points rewards and exclusive benefits) through algorithms, thereby increasing user stickiness. Additionally, this module can extend the relationship objects to a broader range of corporate stakeholders, calculate the influence relationship between these stakeholders and the company, and trigger automated changes in relationship and interaction mechanisms through algorithms. The Growth Efficiency Panel 130 includes three sub-modules: Profit Model 131 (this module receives cost structure data, pricing strategies, and expected sales forecasts, and builds a financial forecast model. Combining Monte Carlo simulation algorithms and considering market fluctuations, the module can predict the profit margin range and break-even point for the next 12 months and generate a "Profit Forecast Report," providing companies with quantitative financial feasibility analysis of growth strategies and a basis for optimizing different pricing and charging strategies), and Business System 132 (this module receives execution data of current business processes, automation tool usage rates, and manual intervention ratios, and defines a "Replicability Coefficient"). The calculation formula is: Where α and β are weighting coefficients, their values ​​can be determined "through training with historical data". The module evaluates whether the current business process meets the "replicable" standard (e.g., ...). >0.8), if the target is not met, a prompt will be made to standardize the process first to ensure that the company has the ability to expand rapidly) and evolution path 133 (this module receives market capacity data, competitor expansion speed and internal capacity data of the company, and predicts the market growth inflection point based on time series analysis algorithms (such as ARIMA model). The "growth evolution roadmap" generated by the module can suggest when the company should start regional replication and when to extend the product line, so as to achieve self-operation and ensure that the company can maintain the momentum of continuous growth at different stages of development) are three sub-modules. The strategist can trigger the first configuration command in the interface, such as adjusting the target group profile weight in customer segment 111, or changing the value proposition 112 from "cost-effectiveness-oriented" to "experience-oriented"; at the same time, the second configuration command can be triggered to optimize the channel construction 122 strategy in the system momentum panel 120, such as increasing the investment ratio of live e-commerce channels; and the third configuration command can be triggered to reconstruct the profit model 131 in the growth efficiency panel 130, such as shifting from one-time sales to subscription services.

[0017] Once the above configuration is complete, the system invokes the AI ​​dynamic coupling algorithm module 200 to calculate the comprehensive growth index G in real time based on a preset nonlinear dynamic coupling formula. The complete expression of this formula is as follows: Where G is the comprehensive growth index, with a value range of [0,1]. The higher the value, the stronger the overall synergy and growth potential of the strategy. V, S, and E represent the standardized scores of value potential, system kinetic energy, and growth efficiency, respectively. The original indicators of each module are normalized to the [0,1] interval by Z-score or Min-Max method. , , The initial weighting coefficients can be preset by the company's strategic preferences (e.g., startups may assign weights based on these preferences). =0.5 emphasizes value potential energy, and can also be obtained through reverse learning from historical successful cases; λ is the coupling strength factor, used to regulate the influence of cross terms between dimensions, usually initialized to 0.3, and can be dynamically adjusted through online learning; σ(⋅) is the Sigmoid-type nonlinear activation function, defined as Its function is to simulate the "barrel effect," where the overall growth index is still limited even if the third dimension scores very high when the scores of any two dimensions are low, thus reflecting the emergent characteristics of the system.

[0018] After calculating G, the system determines whether it has received a simulation run command triggered by the strategy maker. If so, it starts the simulation run module 400, calling historical market data (such as quarterly sales data from the past three years and macroeconomic indicators) and enterprise operational data (such as inventory turnover rate and customer service response time) to build a multi-scenario stress test environment. For example, three scenarios are set: baseline scenario (maintaining current market conditions), pessimistic scenario (prices of major raw materials rise by 20% + competitors release similar new products), and optimistic scenario (government introduces consumer subsidy policies + social media virality). Under each scenario, the system simulates the strategy execution path 1000 times using Monte Carlo simulation, and calculates the mean, variance, and 95% confidence interval of the comprehensive growth index G. If the simulation results show that G decreases by more than 30% under the pessimistic scenario, the system automatically suggests reducing the proportion of high-risk investments in resource leverage 121 and recalculating G. This process is iterated until the rate of increase of G in three consecutive iterations (i.e., If the growth rate falls below a preset threshold (e.g., 0.5%), the system freezes the current strategy parameters and generates the final executable growth strategy.

[0019] During the strategy execution phase, the system continuously collects user behavior logs (such as app clickstream, page dwell time, and conversion funnel data) and financial performance indicators (such as gross profit margin, customer lifetime value (LTV), and customer acquisition cost (CAC)) through a closed-loop feedback mechanism. This real-time data is input into the online learning module to dynamically update the weight coefficients in the AI ​​dynamic coupling algorithm. , , Coupling factor λ. For example, if the actual LTV of a newly launched subscription service (belonging to profit model 131) is significantly higher than expected, the system will automatically increase... The weighting of a particular emerging social platform in Channel Construction 122 is adjusted; if the ROI of a certain emerging social platform remains consistently low, its contribution to the system's dynamic score S is reduced. Furthermore, the system has a built-in event detection engine that can identify sudden changes in the external environment, such as the enactment of the National Data Security Law or the release of a disruptive product by a major competitor, Company B. Once such an event is detected, the system immediately triggers a model recalibration process: re-collecting the latest policy texts or competitor product manuals, updating the three-dimensional correlation graph, and generating an emergency evolution path 133, such as suggesting strengthening data compliance capabilities or accelerating the development of differentiated features in the short term.

[0020] Meanwhile, the Dynamic Growth Kanban 600 (such as...) Figure 2The dashboard (shown below) displays the real-time collaboration status across three dimensions to senior executives. It uses a heatmap format, with the horizontal axis representing time and the vertical axis representing the three dimensions and their sub-modules. Color intensity indicates collaboration strength (red for high collaboration, blue for low collaboration or bottlenecks). For example, if the heatmap shows "Value Proposition 112" and "Channel Building 122" consistently in blue, it indicates that the current value proposition is failing to effectively reach target customers through existing channels. Executives can then directly click on this area to trigger an intervention command, manually adjusting the channel strategy. This manual adjustment parameter is immediately fed back to the AI ​​dynamic coupling algorithm module 200, triggering a new round of strategy optimization and achieving dynamic decision-making through "human-machine collaboration."

[0021] After generating the final execution plan, the system decomposes the strategy into atomic task units. For example, if the strategy includes "launching a KOL co-branded product on the Douyin platform," it is decomposed into several atomic tasks such as "contacting 10 vertical KOLs," "designing co-branded packaging," "configuring exclusive coupons," and "setting up UTM tracking links," and is automatically assigned to the corresponding business systems (such as marketing automation platforms, ERP production modules, and BI analysis tools) through API interfaces. The closed-loop feedback mechanism continuously monitors the execution status of each task unit (such as KOL contract completion rate, packaging sampling cycle, and coupon redemption rate). If actual execution deviations are found (such as the redemption rate being lower than expected by 50%), the system will adjust the business system configuration in the growth efficiency panel 130 (such as increasing the frequency of SMS reminders) or the evolution path 133 (such as launching the candidate KOL list in advance) to form a complete PDCA (Plan, Do, Check, Act) closed loop.

[0022] Finally, the system reconstructs the enterprise capability matrix based on the updated comprehensive growth index G. This matrix uses value potential, system kinetic energy, and growth efficiency as coordinate axes to map various enterprise capabilities (such as supply chain response speed, brand awareness, and data platform maturity) to specific locations in a three-dimensional space. The system identifies the current capability focus through clustering algorithms and combines this with external market opportunity windows (such as a surge in demand from a specific demographic or a sharp drop in the cost of a certain technology) for matching analysis, automatically recommending the next stage's value potential focus. For example, if the capability matrix shows that the enterprise scores highly in "rapid iteration," while market monitoring data shows that "Generation Z's demand for personalized customization is increasing by 40% annually," the system suggests shifting value proposition 112 from "standardized products" to "C2M flexible customization," and simultaneously adjusting resource leverage 121 (increasing investment in flexible production lines) in the system kinetic energy panel 120 and evolution path 133 (planning a 6-month product customization roadmap) in the growth efficiency panel 130.

[0023] In summary, this invention achieves a leap from a static business model canvas to a dynamic intelligent growth operating system by constructing a three-element dynamic growth system integrating data-driven approaches, AI coupling, human-machine collaboration, and closed-loop feedback. The entire implementation process relies on... Figure 1 and Figure 2 The system architecture shown can significantly improve the scientific nature of strategy formulation, the agility of execution, and the adaptability to evolution in real enterprise scenarios.

Claims

1. A three-element dynamic growth method based on AI intelligent drive, characterized in that, include: The system displays a three-dimensional intelligent interactive interface (100) to business strategists, wherein the three-dimensional intelligent interactive interface (100) integrates a value potential energy panel (110), a system kinetic energy panel (120), and a growth efficiency panel (130) that can be linked; it receives a first configuration command triggered by the strategist on the three-dimensional intelligent interactive interface (100) to set parameters or adjust the structure of the customer segmentation (111), value proposition (112), or competitive positioning (113) modules in the value potential energy panel (110); it receives a second configuration command triggered by the strategist on the three-dimensional intelligent interactive interface (100) to adjust the system kinetic energy panel (110) and growth efficiency panel (130). The resource leverage (121), channel construction (122), or relationship design (123) modules in panel (120) are configured or optimized for resources; the third configuration instruction triggered by the strategist on the three-element intelligent interactive interface (100) is received to perform strategy modeling on the profit model (131), business system (132), or evolution path (133) modules in the growth efficiency panel (130); based on the preset AI dynamic coupling algorithm module (200), the synergistic effect between the three panels is calculated in real time and a comprehensive growth index is generated; the weight of each module is dynamically adjusted according to the comprehensive growth index, and an executable growth strategy scheme is output.

2. The method as described in claim 1, characterized in that, The AI ​​dynamic coupling algorithm module (200) uses a nonlinear activation function to perform a product-composite operation on the scores of the three dimensions. Its mathematical expression is: Where G is the comprehensive growth index, and V, S, and E represent the standardized scores of value potential energy, system kinetic energy, and growth efficiency, respectively. , , λ is the initial weight coefficient, λ is the coupling strength factor, and σ(⋅) is the Sigmoid-type nonlinear activation function.

3. The method as described in claim 1, characterized in that, After generating the comprehensive growth index, the process also includes: receiving the simulation operation command triggered by the strategy maker on the three-element intelligent interactive interface (100); calling historical market data and enterprise operation data, and conducting multi-scenario stress tests on the current growth strategy scheme through the simulation operation module (400); dynamically correcting the module parameters according to the simulation results, and recalculating the comprehensive growth index; when the improvement rate of the comprehensive growth index is lower than the preset threshold for three consecutive iterations, freezing the current strategy and generating the final execution scheme.

4. The method as described in claim 1, characterized in that, Before receiving the first configuration instruction triggered by the strategy maker, the system also includes: automatically collecting multi-source heterogeneous data from the enterprise ERP system, CRM system and external market monitoring platform through the multi-source data acquisition and preprocessing module (300); extracting features from unstructured data through natural language processing and graph neural networks to construct a three-dimensional relationship map of customer-product-channel; and initializing the customer segmentation (111) and value proposition (112) modules in the value potential panel (110) based on the relationship map.

5. The method as described in claim 1, characterized in that, Also includes: The system displays a dynamic growth dashboard (600) to senior executives, whereby the dynamic growth dashboard (600) presents the collaborative status and bottleneck nodes of the three dimensions in real time in the form of a heat map; it receives intervention instructions triggered by senior executives on the dynamic growth dashboard (600) and manually adjusts the specified modules; it feeds back the manually adjusted parameters to the AI ​​dynamic coupling algorithm module (200) to trigger a new round of strategy optimization cycle.

6. The method as described in claim 1, characterized in that, Also includes: During the strategy execution phase, user behavior logs and financial performance indicators are continuously collected through a closed-loop feedback mechanism (500); the weight coefficients and coupling factors in the AI ​​dynamic coupling algorithm module (200) are updated through an online learning mechanism; when an external environmental mutation event is detected, the model recalibration process is automatically triggered to generate an emergency evolution path (133).

7. The method as described in claim 1, characterized in that, After generating the final execution plan, the following steps are also included: decomposing the strategy plan into atomic-level task units and assigning them to the corresponding business system interfaces; monitoring the execution status and output quality of each task unit; and adjusting the business system (132) module and evolution path (133) module in the growth efficiency panel (130) in reverse according to the actual execution deviation to form a closed-loop feedback mechanism (500).

8. The method as described in claim 1, characterized in that, Also includes: The enterprise capability matrix is ​​reconstructed based on the updated comprehensive growth index; the optimal market opportunity window is matched according to the capability matrix, and the value potential focus point for the next stage is automatically recommended.

9. A computer device, characterized in that, include: A processor and a memory; wherein the processor runs a program corresponding to the executable program code by reading executable program code stored in the memory, for implementing the AI-driven ternary dynamic growth method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the AI-driven ternary dynamic growth method as described in any one of claims 1 to 8.

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