Marketing scene dynamic simulation and strategy optimization system

Abstract

The invention relates to the technical field of management systems, and particularly discloses a marketing scene dynamic simulation and strategy optimization system, which comprises a multi-source data fusion module, a dynamic scene simulation engine, a multi-agent strategy game module and a strategy iterative optimization and deployment module, and a closed loop from market environment perception to optimal marketing strategy generation and execution is realized. According to the invention, through a microcosmic consumer heterogeneity model, in combination with deep survival analysis and an attention mechanism, decision laws of individual consumers and differentiated responses to marketing stimulation can be accurately captured, the limitation of macroscopic analysis of a traditional system is broken through, full-dimension conversion prediction from individuals to groups is realized, and the prediction efficiency is improved. Refined data support is provided for strategy making, and the marketing conversion efficiency is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of management system technology, specifically a marketing scenario dynamic simulation and strategy optimization system. Background Technology

[0002] In the field of digital marketing, the market environment is characterized by dynamism, complexity, and multi-party competition. Consumer demand is highly heterogeneous, competitor strategies are constantly changing, and data from multiple sources is diverse. These factors place extremely high demands on the accuracy and adaptability of marketing strategies. Traditional marketing models often rely on experience-based judgment or static data analysis to generate strategies, lacking the ability to perceive and simulate dynamic market changes in real time. This makes it difficult to accurately capture the correlation between marketing stimuli and consumer behavior, leading to blind strategy formulation and problems such as wasted resources and low conversion rates. While existing marketing simulation and optimization technologies attempt to incorporate modeling methods, they still have several limitations: First, most systems focus on macro-market trend analysis, neglecting the heterogeneity of individual consumer decisions at the micro level, and cannot accurately predict the differentiated responses of different consumers to marketing stimuli; second, they lack effective simulation of the dynamic game process among multiple stakeholders, making it difficult to quantify the impact of competitors' strategies on our marketing effectiveness, resulting in insufficient anti-competitive capability of the strategy; third, their data integration capabilities are weak, and the mining and utilization of unstructured data such as social media images and text, and customer conversation recordings are insufficient, failing to comprehensively depict the characteristics of the market environment; fourth, there is a lack of a closed-loop feedback mechanism between simulation and actual implementation, resulting in a large deviation between simulation results and real market effects, and the strategy optimization process is difficult to adapt to dynamic market changes, leading to poor implementation and robustness of the optimized strategy. Furthermore, existing technologies do not adequately address the interpretability and robustness of strategies. Optimized strategies are difficult to trace their operational logic and are prone to failure in the face of sudden market fluctuations, failing to meet enterprises' core needs for the scientific rigor, reliability, and interpretability of marketing strategies. Therefore, developing an integrated system capable of deep fusion of multi-source data, accurate simulation of dynamic scenarios, multi-party game theory deduction, and iterative optimization of strategies has become a key technical challenge in the current marketing technology field. Summary of the Invention

[0003] To address the shortcomings of existing technologies, this invention provides a dynamic simulation and strategy optimization system for marketing scenarios, which solves the problems mentioned in the background technology.

[0004] To achieve the above objectives, the present invention is implemented through the following technical solution: a marketing scenario dynamic simulation and strategy optimization system, comprising a multi-source data fusion module, a dynamic scenario simulation engine, a multi-agent strategy game module, and a strategy iteration optimization and deployment module that work in sequence to achieve a closed loop from market environment perception to the generation and execution of the optimal marketing strategy; The multi-source data fusion module is configured to collect and structure multi-dimensional data from the market, consumers, competing brands and channels in real time, and generate a unified dynamic environmental feature vector set. The dynamic scene simulation engine receives the dynamic environment feature vector set and generates an interactive, parameterized dynamic virtual market environment based on the macro market dynamics model and the micro consumer heterogeneity model. The multi-agent strategy game module is a multi-agent system scheduled and managed by a central game coordinator, deployed in the dynamic virtual market environment; the central game coordinator is configured to schedule our marketing strategy agent, at least one competitor behavior simulation agent and consumer group simulation agent to conduct dynamic game simulation, and generate multiple candidate strategy sequences for our agents and their simulation effect evaluation. The strategy iteration optimization and deployment module receives the candidate strategy sequence and its simulation effect evaluation, performs strategy screening and parameter tuning based on multi-objective optimization algorithm and Bayesian optimization framework, outputs Pareto optimal strategy set, and deploys the highest priority strategy to real marketing channels for execution.

[0005] Preferably, the micro-consumer heterogeneity model in the dynamic scene simulation engine employs an individual decision-making model based on deep survival analysis and attention mechanisms to predict the conversion probability and conversion time of an individual consumer under marketing stimuli; for consumers At any moment Affected by marketing strategies Post-stimulus conditional transformation risk function Defined as: ; in, Using the baseline risk function, the Weibull distribution is used to fit the natural conversion trend of consumers without marketing incentives; For consumers The static feature vectors have dimensions of 128-256. For consumers At the time The interaction history sequence up to date, covering browsing, clicking, purchasing, and complaint-related behavior records and corresponding timestamps; For attention networks, a 3-layer Transformer encoder structure is used, which calculates the relationship between historical interaction behaviors and the current policy. The semantic relevance output weight value ranges from [0,1]. The time-varying effect function of the strategy, using a piecewise function form, captures the decay or enhancement of the marketing strategy's effect over time. The formula is as follows: ,in This is the initial effect coefficient. For decay rate, This is the critical point in time when the strategy takes effect. This is the adjustment coefficient for the critical aftereffect; consumers within the time window Internal conversion probability Derived from the cumulative risk function, specifically: .

[0006] Preferably, the consumer group simulated agent is composed of a group of agents driven by the micro-consumer heterogeneity model. The number of agents is configured at a ratio of 1:100 to the actual consumer size in the target market, and stratified sampling ensures that the characteristic distribution of the agent group is consistent with the real market. During the deduction process, the central game coordinator calculates the strategy to be adopted by our strategy agent by aggregating individual decisions. Then, during the simulation cycle Group-level key performance indicators, including total conversion cost. Net new customers and changes in customer lifetime value ; Among them, the net number of new customers The calculation introduces the competing loss factor The formula is: ; For the target potential customer group, This is a collection of our existing clients; For our existing clients At the same time, affected by competitors' strategies The probability of churn under the influence is obtained by mapping the competitive intensity output by the competitor behavior simulation agent. The mapping relationship is fitted by a logistic regression model, with the inputs being the intensity of the competitive strategy, the level of customer loyalty, and the product substitutability. This is the attrition factor, representing the resource competition between acquiring new customers and retaining existing customers. Its value is dynamically adjusted by the company's resource allocation ratio; when resources are tilted towards new customers... Use 0.3-0.5, and 0.6-0.8 when favoring regular customers.

[0007] Preferably, the competitor behavior simulation agent employs an adversarial policy generation network based on deep reinforcement learning. The network structure includes 6 fully connected layers and 2 LSTM layers. The input is a market state feature vector and the player's historical policy sequence, and the output is a probabilistic distribution of competitive policies. Its objective function is... Defined as maximizing its own gains while minimizing the core gain metric of our policy agent in a simulated environment: ; in, Network parameters for the intelligent agent; for The real-time market status is provided by a dynamic scenario simulation engine; , The competitor's intelligent agent and our intelligent agent respectively. Actions to be taken at any time; The immediate revenue of competitors is calculated by subtracting marketing and operating costs from sales revenue. This represents the change in our agent's gains before and after the competition. , This indicates that there is no competing action; The resistance intensity coefficient has a value range of [0.5, 1.5], which can be adjusted according to the intensity of industry competition. It is a non-linear scaling factor. The default value is 1.8, which is used to amplify the penalty for high-profit losses on our side; The competitor behavior simulation agent can dynamically generate adversarial competitive strategies against our historical strategy patterns through simulation learning. During the learning process, an experience replay mechanism is used to update the strategy synchronously with the target network. The experience replay buffer capacity is set to 100,000 entries, and the target network synchronizes the main network parameters once every 100 steps.

[0008] Preferably, the central game coordinator of the multi-agent strategy game module calculates the strategy game equilibrium index after each round of deduction. This is used to quantify the stability of the current simulated market state and the degree of mutual constraint between the strategies of various agents: ; in, The set of all intelligent agents participating in the game; For intelligent agents In this round of the game, the normalized utility value is obtained by mapping the original utility value to the [0,1] interval. The utility values ​​of our side and the competitor are calculated based on the revenue, and the utility value of the consumer group is calculated based on the satisfaction score. For average utility, i.e. ; The mean KL divergence between the policy distribution of each agent in this round and the policy distribution in the previous round reflects the magnitude of policy changes. This is the attenuation coefficient, with a value range of [0.8, 1.2], and a default value of 1.0; It is the minimum value. This is used to avoid the denominator being zero; when Above the threshold When the game reaches a Nash equilibrium, the coordinator stops the current branch deduction and records the strategies of each party and the market outcome in that state; if three consecutive rounds of deduction occur... All below If 70% of the time is reached, the policy diversity enhancement mechanism is triggered, adding random perturbation terms to the policy space of each agent.

[0009] Preferably, the multi-objective optimization algorithm in the strategy iteration optimization and deployment module maximizes the simulated net benefit. Maximize market share growth rate and minimizing strategy volatility risk To achieve this goal, construct a Pareto frontier; Among them, simulated net income Synthesized from group-level indicators: ; in, Let be the weighting coefficient, satisfying The default values ​​are 0.6, 0.3, and 0.1, which can be adjusted according to the company's strategic goals. For strategy The resulting change in average customer lifetime value is calculated by predicting customer spending, repurchase frequency, and retention rate over the next 36 months. This is based on our existing customer base; For strategy Total execution cost; The penalty items for policy compliance and feasibility are calculated from the preset business rule base. Each violation or infeasibility item corresponds to a penalty value of 0.05-0.2. When the cumulative penalty value exceeds 0.5, the policy is directly eliminated.

[0010] Preferably, the strategy iteration optimization and deployment module further includes a strategy robustness evaluation unit for stress testing each strategy in the Pareto optimal strategy set; the evaluation method is to introduce random perturbation variables into the dynamic scenario simulation engine. The random disturbance variables cover market demand fluctuations, sudden changes in raw material prices, and policy and regulatory adjustments. The probability of occurrence for each disturbance type is set based on historical data. The game is re-executed in finite rounds, and the robustness score of the strategy performance is calculated. : ; The average simulated net return with disturbances, i.e. , For the first The disturbance variable of the wheel; This represents the benchmark return under undisturbed conditions. The standard deviation coefficient of returns when disturbances exist, i.e. ; System priority deployment The highest-scoring strategy, when multiple strategies When the score difference is less than 0.03, the execution costs of the strategies are further compared, and the strategy with the lower cost is selected.

[0011] Preferably, after the marketing scenario dynamic simulation and strategy optimization system is deployed, it includes an online learning and feedback loop; real-world strategy execution. The resulting performance data is processed by a multi-source data fusion module and compared with the simulated prediction values ​​to generate a simulation fidelity error. : ; in, The number of key performance indicators; and These are the actual value and the simulated value, respectively. It is the minimum value. This is used to avoid the denominator being zero; when If the error exceeds the threshold for three consecutive evaluation periods, fine-tuning of the relevant models in the dynamic scenario simulation engine and multi-agent strategy game module is triggered. Fine-tuning employs incremental training, using the difference between real-world and simulated data as the loss signal. The optimizer used is AdamW, with a learning rate of [missing information]. The training sessions consist of 5-8 rounds. Meanwhile, new market phenomena and competitive behavior patterns from real-world scenarios are added to the system's scenario and strategy libraries to continuously improve the realism and accuracy of the simulation.

[0012] Preferably, the multi-source data fusion module integrates an unstructured data understanding unit, which uses a multimodal large model to analyze social media images and text, short video content, and customer service dialogue recordings to extract public sentiment trends, popularity of trending topics, and features of competitors' new products, and quantifies them into feature vectors that can be input into a dynamic scene simulation engine. The specific processing procedure is as follows: S.11 Data Preprocessing: Unstructured data is format-standardized, noise is removed, and segments are processed. Image data is uniformly scaled to 224×224 pixels, audio data is converted to 16kHz mono WAV format, and text data is segmented and stop words are removed. S.12 Feature Extraction: A visual encoder is used to extract product appearance and scene element features from the image, an audio encoder is used to extract speech emotion and keyword features, and a text encoder is used to extract semantic information and emotional tendency features. The feature vector of each modality has a dimension of 768. S.13 Cross-modal fusion: Multimodal features are fused through an attention mechanism, the correlation weights between each modal feature and the marketing scenario are calculated, and a 256-dimensional unified feature vector is generated after weighted fusion. S.14 Quantization Mapping: Public sentiment is mapped to a sentiment index in the range of [-1,1], the popularity of trending topics is mapped to a popularity index in the range of [0,1], and the features of competitors' new products are transformed into feature vectors that combine Boolean and numerical types, which are ultimately integrated into the components of the dynamic environment feature vector set.

[0013] Preferably, the marketing scenario dynamic simulation and strategy optimization system provides a strategy explanatory report generation function; for the output optimal strategy, the system can backtrack the simulation and deduction process, locate the key game turning point that led to the success of the strategy, the core consumer segment group that has an impact, and the main response mode of competitors, and present it in the form of a visual narrative report; The specific implementation process is as follows: S.21 Key Node Location: By analyzing the KPI change curves of each round in the strategic game, identify the rounds with sudden KPI changes as key game turning points, and extract the strategic actions of each party and changes in market status in that round. S.22 Consumer Group Analysis: Clustering algorithms are used to segment the consumer agent group, calculate the conversion contribution of each segment to the optimal strategy, select the top 3 groups with the highest contribution as the core influence groups, and output their feature profiles and behavioral patterns. S.23 Competitor Behavior Analysis: Statistically analyze the frequency of competitors' strategy selection and response delay time during the game process, summarize their main response patterns, and analyze the effectiveness of the optimal strategy in resisting each response pattern. S.24 Report Generation: Integrate the above analysis results with the specific content of the strategy, expected effects, and implementation recommendations to generate a visual report that includes text descriptions, data charts, and flowcharts.

[0014] This invention provides a marketing scenario dynamic simulation and strategy optimization system, which has the following beneficial effects: 1. Enhance the accuracy and targeting of marketing strategies: This invention, through a micro-consumer heterogeneity model combined with in-depth survival analysis and attention mechanisms, can accurately capture the decision-making patterns of individual consumers and their differentiated responses to marketing stimuli. It breaks through the limitations of traditional macro-analysis and achieves full-dimensional conversion prediction from individuals to groups, providing refined data support for strategy formulation and effectively improving marketing conversion efficiency.

[0015] 2. Enhance the realism and dynamic adaptability of simulated scenarios: The system integrates a multi-source data fusion module, which can deeply mine the value of structured and unstructured data and generate a comprehensive set of dynamic environmental feature vectors. At the same time, it constructs a virtual market environment based on macro market dynamics models and micro consumer models, which can replicate market dynamic changes and multi-entity interaction relationships in real time, making the simulation results closer to the real market scenario and providing a reliable foundation for strategy deduction.

[0016] 3. Enhance the anti-competitive capability and game adaptability of the strategy: The multi-agent strategy game module conducts dynamic game simulation by scheduling our own, competitors' and consumers' group agents. It can accurately simulate the competitor's adversarial strategy, quantify the impact of competitive behavior on our marketing effect, and make the optimized strategy more resilient to competition and adaptable to the complex and ever-changing market competition landscape.

[0017] 4. Achieve closed-loop iteration and robust implementation of strategy optimization: The system constructs a complete closed loop of simulation, optimization selection, deployment and execution, and feedback fine-tuning. It selects the Pareto optimal strategy set through multi-objective optimization algorithms and Bayesian optimization framework, and conducts stress tests on the strategies in conjunction with robustness evaluation units to ensure the feasibility and resilience to fluctuations of the strategies. At the same time, it continuously adapts to real market changes through an online learning mechanism to continuously improve the simulation fidelity and strategy optimization effect.

[0018] 5. Enhance the explainability and business adaptability of strategies: The system has the function of generating strategy explanation reports, which can trace back the process, locate the key nodes of strategy effectiveness, core impact groups and competitors' response patterns, and provide clear logical support for strategy implementation; at the same time, it takes into account compliance and business needs, avoids non-compliant strategies through penalty mechanisms, adapts to different strategic goals of enterprises, and enhances the practical application value of the system.

[0019] 6. Reduce marketing costs and resource waste: Through precise simulation and optimization, the system can effectively avoid the blindness of traditional marketing, achieve reasonable allocation of resources, reduce unnecessary marketing costs and operational losses while improving marketing effectiveness, and create a higher marketing input-output ratio for enterprises. Attached Figure Description

[0020] Figure 1 This is a principle block diagram of a marketing scenario dynamic simulation and strategy optimization system according to the present invention; Figure 2 This is a flowchart of the multi-source data fusion module described in this invention. Figure 3 This is a flowchart illustrating the strategy explanation report generation process described in this invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] like Figures 1-3 As shown, the present invention provides a technical solution: a marketing scenario dynamic simulation and strategy optimization system, including a multi-source data fusion module, a dynamic scenario simulation engine, a multi-agent strategy game module, and a strategy iteration optimization and deployment module that work in sequence to achieve a closed loop from market environment perception to the generation and execution of the optimal marketing strategy; The multi-source data fusion module is configured to collect and structure multi-dimensional data from the market, consumers, competing brands, and channels in real time, generating a unified dynamic environment feature vector set. The dynamic scenario simulation engine receives the dynamic environment feature vector set and generates an interactive, parameterized dynamic virtual market environment based on a macro-market dynamics model and a micro-consumer heterogeneity model. The multi-agent strategy game module is a multi-agent system scheduled and managed by a central game coordinator, deployed in the dynamic virtual market environment. The central game coordinator is configured to schedule our marketing strategy agent, at least one competitor behavior simulation agent, and consumer group simulation agent to conduct dynamic game simulation, generating multiple candidate strategy sequences for our agents and their simulation effect evaluation. The strategy iteration optimization and deployment module receives the candidate strategy sequences and their simulation effect evaluation, performs strategy screening and parameter tuning based on multi-objective optimization algorithms and Bayesian optimization frameworks, outputs a Pareto optimal strategy set, and deploys the highest priority strategy to real marketing channels for execution.

[0023] More specifically, the micro-consumer heterogeneity model in the dynamic scenario simulation engine employs an individual decision-making model based on deep survival analysis and attention mechanisms to predict the conversion probability and conversion time of individual consumers under marketing stimuli; for consumers At any moment Affected by marketing strategies Post-stimulus conditional transformation risk function Defined as: ; in, Using the baseline risk function, the Weibull distribution is used to fit the natural conversion trend of consumers without marketing incentives; For consumers The static feature vector has a dimension of 128-256 and contains fixed attributes such as demographics, consumption capacity, and historical preferences. For consumers At the time The interaction history sequence up to now, covering browsing, clicking, purchasing, complaint-related behavior records and corresponding timestamps; For attention networks, a 3-layer Transformer encoder structure is used, which calculates the relationship between historical interaction behaviors and the current policy. The semantic relevance output weight value ranges from [0,1]. The time-varying effect function of the strategy, using a piecewise function form, captures the decay or enhancement of the marketing strategy's effect over time. The formula is as follows: ,in This is the initial effect coefficient. For decay rate, This is the critical point in time when the strategy takes effect. This is the adjustment coefficient for the critical aftereffect; consumers within the time window Internal conversion probability Derived from the cumulative risk function, specifically: .

[0024] in, Consumers At the start time Until the end time Within a given time window, the probability of conversion behavior occurring after being stimulated by the corresponding marketing strategy during that time period, with a value range of [value missing]. This is used to quantify the conversion probability of a single individual under a specific marketing stimulus, providing micro-data support for subsequent aggregation of group-level conversion metrics; (Points item) Consumers Throughout the time window Accumulated conditions within the variable transform into risk, integral variable Using time as the variable, it iterates through every point in time within the time window to ensure full capture of dynamically changing marketing stimuli and consumer interaction history within the time period; the integrand function That is, the conditional transformation risk function defined above corresponds to any time within the time window. ,consumer Marketing strategies at this moment and the interaction history up to that moment Instantaneous transformation risk under the influence; the formula uses an exponential function Map the cumulative risk value to Interval, then through The final conversion probability value conforming to the definition of probability is obtained in the form of [formula missing]. This derivation logic not only follows the core principle of deep survival analysis, but also combines the time-varying characteristics of strategies and the heterogeneity of consumers in the marketing scenario, so as to achieve accurate quantitative prediction of individual conversion behavior.

[0025] This micro-level consumer heterogeneity model, through the fusion of deep survival analysis and attention mechanisms, not only captures the time dependence of consumer conversion behavior using the core logic of deep survival analysis, but also accurately quantifies the correlation strength between historical interactions and current marketing stimuli through attention networks. Furthermore, it dynamically characterizes the decay or enhancement patterns of marketing strategies by incorporating a time-varying effect function of the strategy. This effectively solves the technical pain points of traditional models that struggle to simultaneously consider individual consumer heterogeneity, the influence of historical interactions, and the dynamic effects of strategies. It can accurately output the conversion probability and conversion time prediction results for individual consumers within a specific time window, providing high-quality micro-level data support for the subsequent aggregation calculation of group-level conversion indicators (such as net new customers and total conversion cost), significantly improving the targeting and accuracy of marketing strategy formulation.

[0026] In this embodiment, for example, potential consumers of a certain e-commerce platform are selected. (A 28-year-old female with moderate spending power), the specific implementation of this micro-consumer heterogeneity model is as follows: Consumer static feature vectors It is set to 192 dimensions, including fixed attributes such as age, gender, city level, consumption level, historical category preference, and purchasing power score; interactive historical sequence. Covering consumers Deadline Behavioral records for the past three months (as of 00:00 on June 1, 2024) include 12 visits to electronic product pages, 5 clicks on promotional links, 2 additions to the shopping cart, and 1 after-sales inquiry. Each record is accompanied by a precise timestamp. The attention network adopts a 3-layer Transformer encoder structure with 4 attention heads. The hidden layer dimension of the Feed-Forward network is 1024 dimensions, and the activation function is the GELU function. The analysis calculates the relationship between historical interaction behavior and current marketing strategies. The semantic relevance of (618 discount promotion for electronic products) is calculated, with an output weight of 0.72; the time-varying effect function of the strategy is also considered. ( , The parameter setting is as follows: =0.9 (initial effect coefficient) =0.05 (attenuation rate) =3 days (strategy critical effective time point) =0.3 (adjustment coefficient for critical aftereffects); benchmark risk function A Weibull distribution was used for fitting, with a shape parameter of 2.1 and a scale parameter of 7; a time window was used. =7 days (i.e., from time) As of 00:00 on June 8, 2024, the conversion probability of consumer u within this time window is calculated using the cumulative risk function. This accurately reflects the consumer's conversion potential under the stimulus of the targeted marketing strategy.

[0027] More specifically, the consumer group simulated agent consists of a group of agents driven by a micro-consumer heterogeneity model. The number of agents is configured at a ratio of 1:100 to the actual consumer size in the target market, and stratified sampling ensures that the characteristic distribution of the agent group is consistent with the real market. In the deduction process, the central game coordinator calculates the strategy to be adopted by the agent by aggregating individual decisions. Then, during the simulation cycle Group-level key performance indicators, including total conversion cost. Net new customers and changes in customer lifetime value ; Among them, the net number of new customers The calculation introduces the competing loss factor The formula is: ; For the target potential customer group, This is a collection of our existing clients; For our existing clients At the same time, affected by competitors' strategies The probability of churn under the influence is obtained by mapping the competitive intensity output by the competitor behavior simulation agent. The mapping relationship is fitted by a logistic regression model, with the inputs being the intensity of the competitive strategy, the level of customer loyalty, and the product substitutability. This is the attrition factor, representing the resource competition between acquiring new customers and retaining existing customers. Its value is dynamically adjusted by the company's resource allocation ratio; when resources are tilted towards new customers... Use 0.3-0.5, and 0.6-0.8 when favoring regular customers.

[0028] The consumer group simulation agent is driven by a micro-level consumer heterogeneity model. By configuring agent groups proportionally and combining stratified sampling techniques, it achieves precise alignment between the agent groups and the distribution of consumer characteristics in the real market. This solves the technical problems of agent homogeneity and large deviation from the real market in traditional group simulations. At the same time, the central game coordinator calculates group-level indicators by aggregating individual conversion decisions. In particular, it introduces the competitive loss factor ξ in the calculation of net new customers, which accurately quantifies the resource competition relationship between new customer acquisition and old customer retention, as well as the impact of old customer churn caused by competitors' strategies. This breaks the limitations of traditional indicator calculations that ignore competitive interference and separate new and old customer management. It provides group-level data support that fits the real market scenario for subsequent strategy game deduction and multi-objective optimization, and improves the scientific nature of strategy evaluation and optimization.

[0029] In this embodiment, based on the 618 marketing scenario of electronic products on an e-commerce platform, a consumer group simulation intelligent agent and group-level indicator calculation are implemented, as follows: The target market has a real consumer scale of 1 million people, and an agent group is configured at a ratio of 1:100, that is, 10,000 consumer agents are constructed. Through stratified sampling, the agents are divided into levels according to four dimensions: age, consumption capacity, category preference, and city level. The proportion of agents in each level is consistent with the real market. Each agent is driven by the micro-consumer heterogeneity model mentioned above and has independent static feature vectors, interaction history sequences, and conversion probability prediction capabilities.

[0030] The simulation period T is set to 30 days (June 1st to June 30th, 2024). The strategy adopted by our policy agent... For electronics products, a cross-store discount + category coupon promotion strategy is employed; the central game coordinator aggregates all agent decisions and first calculates the target potential customer set. (500,000 agents) in strategy The sum of the conversion probabilities under the action, i.e. The result was 82,000; our existing customer base Competitive strategies output by intelligent agents that simulate the behavior of 500,000 agents and competitors. For direct price reductions and promotions within the same product category, a logistic regression model (inputting competitive strategy strength, customer loyalty level, and product substitutability) is used to map and obtain the average churn probability of existing customers. The loss rate is 3.5%, corresponding to 17,500 lost agents. Because this marketing effort prioritizes customer retention, the loss coefficient ξ is set at 0.7. Substituting this value into the net new customer calculation formula: ; Calculated =8.2 - 0.7 × 1.75 = 70,750; simultaneously, the central game coordinator calculates the total conversion cost. For 1.2 million yuan, changes in customer lifetime value This increases the average revenue per customer by 280 yuan, providing core metrics support for subsequent strategy optimization.

[0031] More specifically, the competitor behavior simulation agent employs an adversarial policy generation network based on deep reinforcement learning. The network structure consists of 6 fully connected layers and 2 LSTM layers. The inputs are the market state feature vector and the player's historical policy sequence, and the output is a probabilistic distribution of competitive policies. Its objective function is... Defined as maximizing its own gains while minimizing the core gain metric of our policy agent in a simulated environment: ; in, Network parameters for the intelligent agent; for The real-time market status is provided by a dynamic scenario simulation engine, including more than 20 dimensions of features such as market size, growth rate, price level, and channel coverage. , The competitor's intelligent agent and our intelligent agent respectively. The actions taken at any time encompass various strategies such as pricing adjustments, promotional activities, advertising, and channel expansion. The immediate revenue of competitors is calculated by subtracting marketing and operating costs from sales revenue. This represents the change in our agent's gains before and after the competition. , This indicates that there is no competing action; The resistance intensity coefficient has a value range of [0.5, 1.5], which can be adjusted according to the intensity of industry competition. It is a non-linear scaling factor. The default value is 1.8, which is used to amplify the penalty for high-profit losses on our side; The competitor behavior simulation agent can dynamically generate adversarial competitive strategies against our historical strategy patterns through simulation learning. During the learning process, an experience replay mechanism is used to update the strategy synchronously with the target network. The experience replay buffer capacity is set to 100,000 entries, and the target network synchronizes the main network parameters once every 100 steps.

[0032] This competitor behavior simulation agent constructs an adversarial policy generation network using deep reinforcement learning. Its combined structure of "6 fully connected layers + 2 LSTM layers" accurately captures static market state features while also uncovering the temporal dependencies in the player's historical policy sequences. This addresses the technical pain points of rigid policy generation in traditional competition simulations, which cannot adapt to dynamic adjustments. Its objective function accurately replicates the adversarial logic of real-world business competition by balancing maximizing its own profit with minimizing the player's profit, and incorporates a non-linear scaling factor. It further strengthens the targeted suppression of our high-yield scenarios, making the generated strategies more adversarial; at the same time, the design of the experience replay mechanism and the synchronous update of strategies by the target network effectively improves the stability and convergence speed of the agent's learning, ensuring that the generated competitive strategies can dynamically adapt to changes in our strategies, providing real and reliable competitive inputs for multi-agent game simulation, helping the system to more accurately evaluate the actual effect of our strategies, and enhancing the scientific nature of the overall simulation and optimization.

[0033] In this embodiment, based on the aforementioned e-commerce platform's 618 marketing scenario for electronic products (simulated period 30 days, our strategy) To implement a competitor behavior simulation agent for cross-store discounts and category coupon stacking promotions, the following is a detailed description: The adversarial strategy generation network uses a combination of 6 fully connected layers and 2 LSTM layers. The number of neurons in each fully connected layer is 1024, 512, 512, 256, 256, and 128 respectively, with ReLU activation function for all layers. The 2 LSTM layers have a hidden layer dimension of 256 and a dropout probability of 0.2. The network input includes a 22-dimensional market state feature vector (covering market size, year-on-year growth rate, industry price level, and coverage of various channels) and our historical strategy sequence for the past 90 days (including information on promotional intensity, distribution channels, and pricing adjustments). The output is the probability distribution of four types of strategies: pricing adjustment, promotional activities, advertising, and channel expansion. Objective function parameter settings: adversarial strength coefficient. (Adapted for intense competition during the 618 shopping festival), non-linear scaling factor During the learning process, the experience replay buffer capacity is set to 100,000 records to store market status, actions of both parties, and profit feedback data. The target network synchronizes the main network parameters every 100 steps. The optimizer used is Adam, and the learning rate is... .

[0034] After 2000 rounds of iterative training, the agent generates targeted adversarial strategies. The strategy involves: direct price reductions on similar products (5% lower than our average price after discounts) + exclusive advertising on the platform's homepage (4 hours daily), with dynamic adjustments to the price reduction and advertising time to mitigate our strategic advantage. Calculations show the change in our revenue under this competitive strategy: ; objective function By maximizing its own gains through this confrontational action, it successfully suppresses our gains, forming a closed loop with the 3.5% churn probability of our existing customers calculated in the previous group-level indicator, accurately replicating the real competitive scenario.

[0035] More specifically, the central game coordinator of the multi-agent strategy game module calculates the strategy game equilibrium index after each round of deduction. This is used to quantify the stability of the current simulated market state and the degree of mutual constraint between the strategies of various agents: ; in, The set of all intelligent agents participating in the game (including our side, competitors, and consumer groups). For intelligent agents In this round of the game, the normalized utility value is obtained by mapping the original utility value to the [0,1] interval. The utility values ​​of our side and the competitor are calculated based on the revenue, and the utility value of the consumer group is calculated based on the satisfaction score. For average utility, i.e. ; The mean KL divergence between the policy distribution of each agent in this round and the policy distribution in the previous round reflects the magnitude of policy changes. This is the attenuation coefficient, with a value range of [0.8, 1.2], and a default value of 1.0; It is the minimum value. This is used to avoid the denominator being zero; when Above the threshold (The default threshold is 0.75, which can be adjusted according to business needs.) When the game tends towards Nash equilibrium, the coordinator stops the current branch deduction and records the strategies of each party and the market outcome in this state. If there are 3 consecutive rounds of deduction... All below If 70% of the time is reached, the policy diversity enhancement mechanism is triggered, adding random perturbation terms to the policy space of each agent.

[0036] The central game coordinator of the multi-agent strategy game module calculates the strategy game equilibrium index. This solves the technical pain points of traditional multi-agent game simulation, such as the difficulty in quantifying market stability and the inefficiency caused by blindly pushing the simulation forward. By comprehensively considering the differences in normalized utility among various agents and the magnitude of policy distribution changes, the equilibrium of payoffs for each party in the game is quantified through the standard deviation of utility. The mean of KL divergence reflects the dynamic changes in policies, and the weights of both are adjusted by a decay coefficient to achieve a precise characterization of the game state stability. Simultaneously, based on... The established dual-judgment mechanism can stop branch deduction in time when the game tends to Nash equilibrium, reduce ineffective calculations and improve deduction efficiency. It can also trigger the strategy diversity enhancement mechanism when the equilibrium index is continuously low, so as to avoid the strategies of each agent from falling into a rigid cycle, ensure the comprehensiveness and objectivity of the game deduction, and provide more valuable game results to support subsequent strategy iteration and optimization.

[0037] In this embodiment, based on the aforementioned e-commerce platform's 618 marketing scenario for electronic products (simulated period 30 days, our strategy) Competitor strategies to combine cross-store discounts and category coupons for promotional purposes (For direct price reductions within the same product category + homepage advertising), a central game coordinator and strategic game equilibrium index calculation are implemented, as detailed below: The set of intelligent agents participating in the game It includes three types of agents: our marketing strategy agent, one competitor behavior simulation agent, and one consumer group simulation agent. After a round of simulation, the normalized utility value of each agent is calculated: our agent... (Based on net income calculations), competitor's intelligent agent (Based on its own revenue calculations), consumer group agency (Based on group satisfaction rating mapping); average utility .

[0038] Calculate the KL divergence of the policy distributions of each agent in this round and the previous round. The KL divergences for our side, competitors, and consumer agents are 0.12, 0.15, and 0.08, respectively, with a mean of... The parameter value is: attenuation coefficient. (Default value), Minimum value Equilibrium threshold Substitute into the formula The calculation yields: , , ; final .because The coordinator determines that the game tends towards Nash equilibrium, stops the current branch deduction, and records the market results in this state, such as our strategy, the competitor's countermeasure strategy, the probability of our existing customers churning by 3.5%, and the net increase of 70,750 customers, to provide closed-loop game data for subsequent strategy optimization.

[0039] More specifically, the multi-objective optimization algorithm in the strategy iteration optimization and deployment module aims to maximize the simulated net profit. Maximize market share growth rate and minimizing strategy volatility risk To achieve this goal, construct a Pareto frontier; Among them, simulated net income Synthesized from group-level indicators: ; in, Let be the weighting coefficient, satisfying The default values ​​are 0.6, 0.3, and 0.1, which can be adjusted according to the company's strategic goals. For strategy The resulting change in average customer lifetime value is calculated by predicting customer spending, repurchase frequency, and retention rate over the next 36 months. This is based on our existing customer base; For strategy The total execution cost includes advertising costs, promotional subsidy costs, channel cooperation costs, etc. The penalty items for strategy compliance and feasibility are calculated from a preset business rule base. The rule base includes advertising law compliance clauses, channel cooperation restrictions, budget limit constraints, etc. Each violation or infeasibility item corresponds to a penalty value of 0.05-0.2. When the cumulative penalty value exceeds 0.5, the strategy is directly eliminated.

[0040] This multi-objective optimization algorithm constructs a Pareto front with simulated net income, market share growth rate, and strategy volatility risk as the core optimization objectives. It solves the technical pain point of traditional single-objective optimization algorithms that unilaterally pursue returns while ignoring growth potential and risk control. By dynamically adapting the weight coefficients to corporate strategic objectives, it can flexibly balance the relationship between revenue acquisition, market expansion, and risk avoidance, meeting the needs of different business scenarios.

[0041] The formula for simulating net income integrates customer lifetime value, total execution cost, and compliance feasibility penalties. This achieves deep integration of group-level indicators and avoids non-compliant and infeasible strategies through the penalty mechanism. It ensures that the optimized strategy is both economical and compliant, provides a scientific basis for selecting the Pareto optimal strategy set, and helps the system output high-quality marketing strategies that are more in line with the actual needs of enterprises.

[0042] In this embodiment, based on the aforementioned e-commerce platform's 618 marketing scenario for electronic products (simulated period of 30 days, our strategy under game equilibrium state), The combined promotion of cross-store discounts and category coupons resulted in a net increase of 70,750 customers, with a total conversion cost of [missing information]. 10,000 yuan, average per customer (yuan), and implement a multi-objective optimization algorithm, as follows: The optimization objective is set to maximize the simulated net return. Maximize market share growth rate Minimize strategy volatility risk The weighting coefficients are set according to the strategic objective of prioritizing returns. , , The sum of the three conditions is 1.

[0043] Our existing customer base Ten thousand, substitute into the simulated net income formula Calculation: First, calculate the compliance penalties. After verification using the pre-set business rule base, the strategy was found to have no violations of advertising laws, no restrictions on channel cooperation, and no budget overruns; therefore, the penalty value was set to 0. Ten thousand yuan.

[0044] Simultaneous calculation of market share growth rate Strategy volatility risk (Based on the calculation of the change range of strategy parameters), the three indicators of this strategy and other candidate strategies were incorporated into the Pareto frontier construction. Ultimately, this strategy was included in the Pareto optimal strategy set because of its best overall performance, providing a core candidate for subsequent robustness evaluation and deployment.

[0045] More specifically, the strategy iteration optimization and deployment module also includes a strategy robustness evaluation unit, used to stress test each strategy in the Pareto optimal strategy set; the evaluation method is to introduce random perturbation variables into the dynamic scenario simulation engine. (Simulating sudden market events), random disturbance variables cover market demand fluctuations (fluctuation range ±15%), raw material price mutations (mutation range ±20%), and policy and regulatory adjustments (tightening or relaxing compliance requirements). The probability of occurrence for each type of disturbance is set based on historical data statistics. A finite-round game is re-executed (default rounds are 50), and the robustness score of the strategy performance is calculated. : ; The average simulated net return with disturbances, i.e. , For the first The disturbance variable of the wheel; This represents the benchmark return under undisturbed conditions. The standard deviation coefficient of returns when disturbances exist, i.e. ; System priority deployment The highest-scoring strategy, when multiple strategies When the score difference is less than 0.03, the execution costs of the strategies are further compared, and the strategy with the lower cost is selected.

[0046] This multi-objective optimization algorithm constructs a Pareto front with simulated net income, market share growth rate, and strategy volatility risk as the core optimization objectives. It solves the technical pain point of traditional single-objective optimization algorithms that unilaterally pursue returns while ignoring growth potential and risk control. By dynamically adapting the weight coefficients to corporate strategic objectives, it can flexibly balance the relationship between revenue acquisition, market expansion, and risk avoidance, meeting the needs of different business scenarios.

[0047] The formula for simulating net income integrates customer lifetime value, total execution cost, and compliance feasibility penalties. This achieves deep integration of group-level indicators and avoids non-compliant and infeasible strategies through the penalty mechanism. It ensures that the optimized strategy is both economical and compliant, provides a scientific basis for selecting the Pareto optimal strategy set, and helps the system output high-quality marketing strategies that are more in line with the actual needs of enterprises.

[0048] In this embodiment, based on the aforementioned e-commerce platform's 618 marketing scenario for electronic products (simulated period of 30 days, our strategy under game equilibrium state), The combined promotion of cross-store discounts and category coupons resulted in a net increase of 70,750 customers, with a total conversion cost of [missing information]. 10,000 yuan, average per customer (yuan), and implement a multi-objective optimization algorithm, as follows: The optimization objective is set to maximize the simulated net return. Maximize market share growth rate Minimize strategy volatility risk The weighting coefficients are set according to the "priority return" strategic objective. , , The sum of the three conditions must be 1. Our existing customer base... Ten thousand, substitute into the simulated net income formula Calculation: First, calculate the compliance penalties. After verification using the pre-set business rule base, the strategy was found to have no violations of advertising laws, no restrictions on channel cooperation, and no budget overruns; therefore, the penalty value was set to 0. Final calculation: Ten thousand yuan; Simultaneous calculation of market share growth rate Strategy volatility risk (Based on the calculation of the change range of strategy parameters), the three indicators of this strategy and other candidate strategies were incorporated into the Pareto frontier construction. Ultimately, this strategy was included in the Pareto optimal strategy set because of its best overall performance, providing a core candidate for subsequent robustness evaluation and deployment.

[0049] More specifically, after the marketing scenario dynamic simulation and strategy optimization system is deployed, it includes an online learning and feedback loop; real-world strategy execution. The resulting performance data is processed by a multi-source data fusion module and compared with the simulated prediction values ​​to generate a simulation fidelity error. : ; in, The number of key performance indicators (KPIs) includes core metrics such as sales revenue, conversion rate, market share, and customer retention rate, ranging from 5 to 10. and These are the actual value and the simulated value, respectively. It is the minimum value. This is used to avoid the denominator being zero; when If the value exceeds a threshold (default threshold is 0.15) for three consecutive evaluation periods, fine-tuning of the relevant models in the dynamic scenario simulation engine and multi-agent strategy game module is triggered. Fine-tuning employs incremental training, using the difference between real-world and simulated data as the loss signal. The optimizer used is AdamW, with a learning rate of [missing value]. The training sessions consist of 5-8 rounds. Meanwhile, new market phenomena and competitive behavior patterns from real-world scenarios are added to the system's scenario and strategy libraries to continuously improve the realism and accuracy of the simulation.

[0050] This online learning and feedback loop constructs a continuous optimization chain encompassing deployment execution, data feedback, model iteration, and capability upgrades. It effectively addresses the technical pain points of traditional marketing simulation systems being disconnected from the real market and having fixed, rigid models after deployment. By comprehensively quantifying the deviation between the real and simulated values ​​of multiple core KPIs through simulation fidelity error (Errfidelity), it avoids the one-sidedness of single-indicator evaluation and provides precise targets for model optimization.

[0051] When the error continues to exceed the standard, the incremental training method takes into account both the efficiency and adaptability of model fine-tuning. It can quickly fit the real scenario without full retraining, and at the same time, it adds new market phenomena and competitive patterns to the system library, so that the simulation capability can dynamically iterate with the real environment, continuously narrowing the gap between simulation and reality, and ensuring the long-term accuracy and reliability of the strategy output.

[0052] In this embodiment, based on the aforementioned e-commerce platform's 618 marketing scenario for electronic products, we deploy the optimal strategy selected through robustness evaluation and multi-objective optimization. (Cross-store discounts + category coupons combined promotion), implement an online learning and feedback loop, specifically as follows: Set the number of key performance indicators M=8 (covering sales, conversion rate, market share, customer retention rate, average customer lifetime value, marketing ROI, new customer acquisition cost, and repurchase rate), with a minimum value. The default threshold for simulation fidelity error is 0.15, and the evaluation period is 7 days. After the strategy is implemented, real-world performance data is collected continuously for 3 evaluation periods. Compared with simulated predicted values (Examples of key metrics: Sales revenue: 81.5 million yuan (actual), 83.64 million yuan (simulated); Conversion rate: 7.9% (actual), 8.2% (simulated); Customer retention rate: 77.5% (actual), 80% (simulated); New customer acquisition cost: 185 yuan (actual), 172 yuan (simulated).

[0053] Substitute into the error formula The calculated errors for the three cycles were 0.11, 0.16, and 0.18, respectively. The latter two cycles consistently exceeded the threshold of 0.15, triggering model fine-tuning. Fine-tuning employed incremental training, using the difference between real and simulated data as the loss signal. The optimizer chosen was AdamW, and the learning rate was set to [value missing]. The training rounds were set at 7. Simultaneously, new phenomena and corresponding competitive behaviors observed in real-world scenarios, such as competitors urgently adding live-streaming sales promotions and the shift in consumer preferences towards lightweight products among young people in first-tier cities, were added to the system's scenario and strategy libraries. After fine-tuning, the simulation fidelity error in the next evaluation cycle decreased to 0.10, significantly improving the consistency between simulated and real data, providing more accurate support for subsequent marketing strategy iterations.

[0054] More specifically, such as Figure 2As shown, the multi-source data fusion module integrates an unstructured data understanding unit, uses a multimodal large model to analyze social media images and text, short video content, and customer service dialogue recordings, extracts public sentiment trends, popularity of trending topics, and features of competitors' new products, and quantifies them into feature vectors that can be input into a dynamic scene simulation engine. The specific processing procedure is as follows: S.11 Data Preprocessing: Unstructured data is format-standardized, noise is removed, and segments are processed. Image data is uniformly scaled to 224×224 pixels, audio data is converted to 16kHz mono WAV format, and text data is segmented and stop words are removed. S.12 Feature Extraction: A visual encoder is used to extract product appearance and scene element features from the image, an audio encoder is used to extract speech emotion and keyword features, and a text encoder is used to extract semantic information and emotional tendency features. The feature vector of each modality has a dimension of 768. S.13 Cross-modal fusion: Multimodal features are fused through an attention mechanism, the correlation weights between each modal feature and the marketing scenario are calculated, and a 256-dimensional unified feature vector is generated after weighted fusion. S.14 Quantization Mapping: Public sentiment is mapped to a sentiment index in the range of [-1,1], the popularity of trending topics is mapped to a popularity index in the range of [0,1], and the features of competitors' new products are transformed into feature vectors that combine Boolean and numerical types, which are ultimately integrated into the components of the dynamic environment feature vector set.

[0055] Furthermore, the unstructured data understanding unit integrates unstructured data such as social media images and text, short videos, and customer service voice messages through a multimodal large model, solving the technical pain points of traditional data fusion that rely solely on structured data and provide only a partial portrayal of the market environment. The standardized four-step processing flow enables unified adaptation and efficient processing of multiple data types, and the attention mechanism-driven cross-modal fusion can accurately filter core features strongly related to marketing scenarios, avoiding information redundancy. Through quantitative mapping, abstract public sentiment and topic popularity are transformed into standardized feature vectors, which are then added to the dynamic environment feature vector set, making market state perception more comprehensive and accurate. This provides rich and high-quality underlying data support for subsequent dynamic scenario simulation and multi-agent game, improving the overall simulation accuracy and strategy optimization capabilities of the system.

[0056] Based on the e-commerce platform's 618 marketing scenario for electronic products, the unstructured data understanding unit of the multi-source data fusion module is implemented. The specific process is as follows: S.11 Data Preprocessing: Collect 2800 images and texts related to electronic products, 1200 short videos, and 900 customer service dialogue recordings from Douyin and Xiaohongshu platforms in the past 30 days; images are uniformly scaled to 224×224 pixels and denoised and watermarked; audio is converted to 16kHz mono WAV format and segmented into 20-40 second segments; text is standardized by jieba word segmentation and stop word removal. S.12 Feature Extraction: Image, audio, and text features are extracted using ViT-B / 16, Wav2Vec2-Base, and BERT-base-Chinese encoders, respectively. The feature vectors of the three modalities are all 768-dimensional. S.13 Cross-Modal Fusion: The text, image, and audio modalities are assigned weights of 0.48, 0.32, and 0.20 respectively through a multi-layer attention mechanism, and weighted fusion is used to generate a 256-dimensional unified feature vector. S.14 Quantization Mapping: Public positive sentiment is mapped to a sentiment index of 0.58 (range [-1,1]), and the topic of "618 cost-effective products" is mapped to a popularity index of 0.83 (range [0,1]). The features of competing new products are converted into Boolean and numerical vectors and integrated into a dynamic environment feature vector set to provide market environment input for the dynamic scene simulation engine.

[0057] More specifically, such as Figure 3 As shown, the marketing scenario dynamic simulation and strategy optimization system provides the function of generating strategy explanatory reports. For the output optimal strategy, the system can backtrack the simulation and deduction process, locate the key game turning point that led to the success of the strategy, the core consumer segments that have an impact, and the main response modes of competitors, and present them in the form of a visual narrative report. The specific implementation process is as follows: S.21 Key Node Location: By analyzing the KPI change curves of each round in the strategic game, identify the rounds with sudden KPI changes as key game turning points, and extract the strategic actions of each party and changes in market status in that round. S.22 Consumer Group Analysis: Clustering algorithms are used to segment the consumer agent group, calculate the conversion contribution of each segment to the optimal strategy, select the top 3 groups with the highest contribution as the core influence groups, and output their feature profiles and behavioral patterns. S.23 Competitor Behavior Analysis: Statistically analyze the frequency of competitors' strategy choices and response delays during the game process, summarize their main response modes (such as price following, differentiated promotion, channel blockade, etc.), and analyze the effectiveness of the optimal strategy in resisting each response mode. S.24 Report Generation: Integrate the above analysis results with the specific content of the strategy, expected effects, and implementation recommendations to generate a visual report containing text descriptions, data charts, and flowcharts. It supports exporting in multiple formats such as PDF and HTML, and also provides an online report sharing function.

[0058] Furthermore, the strategy explanation report generation function effectively addresses the technical pain points of traditional marketing optimization systems, such as the lack of traceability in strategy effectiveness logic and the ambiguity of decision-making basis. By retrospectively simulating and extrapolating the entire process, it accurately breaks down the core elements of strategy success, transforming black-box decisions into an explainable logical chain. The visual narrative report integrates key game nodes, core consumer groups, competitor response patterns, and other core information, along with data charts and flowcharts. This lowers the barrier for enterprises to understand strategies and provides clear execution guidance for strategy implementation. It also supports multi-format export and online sharing, improving cross-departmental collaboration efficiency and further enhancing the system's business adaptability and practical application value.

[0059] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A marketing scenario dynamic simulation and strategy optimization system, characterized in that, It includes a multi-source data fusion module, a dynamic scenario simulation engine, a multi-agent strategy game module, and a strategy iteration optimization and deployment module that work in sequence to achieve a closed loop from market environment perception to the generation and execution of the optimal marketing strategy; The multi-source data fusion module is configured to collect and structure multi-dimensional data from the market, consumers, competing brands and channels in real time, and generate a unified dynamic environmental feature vector set. The dynamic scene simulation engine receives the dynamic environment feature vector set and generates an interactive, parameterized dynamic virtual market environment based on the macro market dynamics model and the micro consumer heterogeneity model. The multi-agent strategy game module is a multi-agent system scheduled and managed by a central game coordinator, and is deployed in the dynamic virtual market environment. The central game coordinator is configured to schedule our marketing strategy agent, at least one competitor behavior simulation agent and consumer group simulation agent to conduct dynamic game simulation, and generate multiple candidate strategy sequences for our agent and their simulation effect evaluation. The strategy iteration optimization and deployment module receives the candidate strategy sequence and its simulation effect evaluation, performs strategy screening and parameter tuning based on multi-objective optimization algorithm and Bayesian optimization framework, outputs Pareto optimal strategy set, and deploys the highest priority strategy to real marketing channels for execution.

2. The marketing scenario dynamic simulation and strategy optimization system according to claim 1, characterized in that, The micro-consumer heterogeneity model in the dynamic scenario simulation engine adopts an individual decision-making model based on deep survival analysis and attention mechanism to predict the conversion probability and conversion time of a single consumer under marketing stimuli. For consumers At any moment Affected by marketing strategies Post-stimulus conditional transformation risk function Defined as: ； in, Using the baseline risk function, the Weibull distribution is used to fit the natural conversion trend of consumers without marketing incentives; For consumers The static feature vectors have dimensions of 128-256. For consumers At the time The interaction history sequence up to now, covering browsing, clicking, purchasing, complaint-related behavior records and corresponding timestamps; For attention networks, a 3-layer Transformer encoder structure is used, which calculates the relationship between historical interaction behaviors and the current policy. The semantic relevance output weight value ranges from [0,1]. The time-varying effect function of the strategy, using a piecewise function form, captures the decay or enhancement of the marketing strategy's effect over time. The formula is as follows: ,in This is the initial effect coefficient. For decay rate, This is the critical point in time when the strategy takes effect. This is the adjustment coefficient for the critical aftereffect; consumers within the time window Internal conversion probability Derived from the cumulative risk function, specifically: .

3. The marketing scenario dynamic simulation and strategy optimization system according to claim 2, characterized in that, The simulated consumer group consists of a group of agents driven by the micro-consumer heterogeneity model. The number of agents is configured at a ratio of 1:100 to the actual consumer size in the target market, and stratified sampling ensures that the characteristic distribution of the agent group is consistent with the real market. During the deduction process, the central game coordinator calculates the strategy to be adopted by the agent by aggregating individual decisions. Then, during the simulation cycle Group-level key performance indicators, including total conversion cost. Net new customers and changes in customer lifetime value ; Among them, the net number of new customers The calculation introduces the competing loss factor The formula is: ； For the target potential customer group, This is a collection of our existing clients; For our existing clients At the same time, affected by competitors' strategies The probability of churn under the influence is obtained by mapping the competitive intensity output by the competitor behavior simulation agent. The mapping relationship is fitted by a logistic regression model, with the inputs being the intensity of the competitive strategy, the level of customer loyalty, and the product substitutability. This is the attrition factor, representing the resource competition between acquiring new customers and retaining existing customers. Its value is dynamically adjusted by the company's resource allocation ratio; when resources are tilted towards new customers... Use 0.3-0.5, and 0.6-0.8 when favoring regular customers.

4. The marketing scenario dynamic simulation and strategy optimization system according to claim 3, characterized in that, The competitor behavior simulation agent employs an adversarial policy generation network based on deep reinforcement learning. The network structure consists of 6 fully connected layers and 2 LSTM layers. The inputs are the market state feature vector and the player's historical policy sequence, and the output is a probabilistic distribution of competitive policies. Its objective function is... Defined as maximizing its own gains while minimizing the core gain metric of our policy agent in a simulated environment: ； in, Network parameters for the intelligent agent; for The real-time market status is provided by a dynamic scenario simulation engine; , The competitor's intelligent agent and our intelligent agent respectively. Actions to be taken at any time; The immediate revenue of competitors is calculated by subtracting marketing and operating costs from sales revenue. This represents the change in our agent's gains before and after the competition. , This indicates that there is no competing action; The resistance intensity coefficient has a value range of [0.5, 1.5], which can be adjusted according to the intensity of industry competition. It is a non-linear scaling factor. The default value is 1.8, which is used to amplify the penalty for high-profit losses on our side; The competitor behavior simulation agent can dynamically generate adversarial competitive strategies against our historical strategy patterns through simulation learning. During the learning process, an experience replay mechanism is used to update the strategy synchronously with the target network. The experience replay buffer capacity is set to 100,000 entries, and the target network synchronizes the main network parameters once every 100 steps.

5. The marketing scenario dynamic simulation and strategy optimization system according to claim 4, characterized in that, The central game coordinator of the multi-agent strategy game module calculates the strategy game equilibrium index after each round of deduction. This is used to quantify the stability of the current simulated market state and the degree of mutual constraint between the strategies of various agents: ； in, The set of all intelligent agents participating in the game; For intelligent agents In this round of the game, the normalized utility value is obtained by mapping the original utility value to the [0,1] interval. The utility values ​​of our side and the competitor are calculated based on the revenue, and the utility value of the consumer group is calculated based on the satisfaction score. For average utility, that is ; The mean KL divergence between the policy distribution of each agent in this round and the policy distribution in the previous round reflects the magnitude of policy changes. This is the attenuation coefficient, with a value range of [0.8, 1.2], and a default value of 1.0; It is the minimum value. This is used to avoid the denominator being zero; when Above the threshold When the game reaches a Nash equilibrium, the coordinator stops the current branch deduction and records the strategies of each party and the market outcome in that state; if three consecutive rounds of deduction occur... All below If 70% of the time is reached, the policy diversity enhancement mechanism is triggered, adding random perturbation terms to the policy space of each agent.

6. The marketing scenario dynamic simulation and strategy optimization system according to claim 5, characterized in that, The multi-objective optimization algorithm in the strategy iteration optimization and deployment module aims to maximize the simulated net profit. Maximize market share growth rate and minimizing strategy volatility risk To achieve this goal, construct a Pareto frontier; Among them, simulated net income Synthesized from group-level indicators: ； in, Let be the weighting coefficient, satisfying The default values ​​are 0.6, 0.3, and 0.1, which can be adjusted according to the company's strategic goals. For strategy The resulting change in average customer lifetime value is calculated by predicting customer spending, repurchase frequency, and retention rate over the next 36 months. This is based on our existing customer base; For strategy Total execution cost; The penalty items for policy compliance and feasibility are calculated from the preset business rule base. Each violation or infeasibility item corresponds to a penalty value of 0.05-0.

2. When the cumulative penalty value exceeds 0.5, the policy is directly eliminated.

7. The marketing scenario dynamic simulation and strategy optimization system according to claim 6, characterized in that, The strategy iteration optimization and deployment module also includes a strategy robustness evaluation unit, used to perform stress tests on each strategy in the Pareto optimal strategy set; the evaluation method is to introduce random perturbation variables into the dynamic scenario simulation engine. The random disturbance variables cover market demand fluctuations, sudden changes in raw material prices, and policy and regulatory adjustments. The probability of occurrence for each disturbance type is set based on historical data. The game is re-executed in finite rounds, and the robustness score of the strategy performance is calculated. : ； The average simulated net return with disturbances, i.e. , For the first The disturbance variable of the wheel; This represents the benchmark return under undisturbed conditions. The standard deviation coefficient of returns when disturbances exist, i.e. ; System priority deployment The highest-scoring strategy, when multiple strategies When the score difference is less than 0.03, the execution costs of the strategies are further compared, and the strategy with the lower cost is selected.

8. The marketing scenario dynamic simulation and strategy optimization system according to claim 7, characterized in that, After the marketing scenario dynamic simulation and strategy optimization system is deployed, it is equipped with an online learning and feedback loop. Real-world execution strategy The resulting performance data is processed by a multi-source data fusion module and compared with the simulated prediction values ​​to generate a simulation fidelity error. : ； in, The number of key performance indicators; and These are the actual value and the simulated value, respectively. It is the minimum value. This is used to avoid the denominator being zero; when If the error exceeds the threshold for three consecutive evaluation periods, fine-tuning of the relevant models in the dynamic scenario simulation engine and multi-agent strategy game module is triggered. Fine-tuning employs incremental training, using the difference between real-world and simulated data as the loss signal. The optimizer used is AdamW, with a learning rate of [missing information]. The training sessions consist of 5-8 rounds. Meanwhile, new market phenomena and competitive behavior patterns from real-world scenarios are added to the system's scenario and strategy libraries to continuously improve the realism and accuracy of the simulation.

9. The marketing scenario dynamic simulation and strategy optimization system according to claim 8, characterized in that, The multi-source data fusion module integrates an unstructured data understanding unit, uses a multimodal large model to analyze social media images and text, short video content, and customer service dialogue recordings, extracts public sentiment trends, popularity of trending topics, and features of competitors' new products, and quantifies them into feature vectors that can be input into a dynamic scene simulation engine. The specific processing procedure is as follows: S.11 Data Preprocessing: Unstructured data is format-standardized, noise is removed, and segments are processed. Image data is uniformly scaled to 224×224 pixels, audio data is converted to 16kHz mono WAV format, and text data is segmented and stop words are removed. S.12 Feature Extraction: A visual encoder is used to extract product appearance and scene element features from the image, an audio encoder is used to extract speech emotion and keyword features, and a text encoder is used to extract semantic information and emotional tendency features. The feature vector of each modality has a dimension of 768. S.13 Cross-modal fusion: Multimodal features are fused through an attention mechanism, the correlation weights between each modal feature and the marketing scenario are calculated, and a 256-dimensional unified feature vector is generated after weighted fusion. S.14 Quantization Mapping: Public sentiment is mapped to a sentiment index in the range of [-1,1], the popularity of trending topics is mapped to a popularity index in the range of [0,1], and the features of competitors' new products are transformed into feature vectors that combine Boolean and numerical types, which are ultimately integrated into the components of the dynamic environment feature vector set.

10. A marketing scenario dynamic simulation and strategy optimization system according to claim 9, characterized in that, The marketing scenario dynamic simulation and strategy optimization system provides a strategy explanatory report generation function; for the output optimal strategy, the system can backtrack the simulation and deduction process, locate the key game turning point that led to the success of the strategy, the core consumer segment that had an impact, and the main response mode of competitors, and present it in the form of a visual narrative report. The specific implementation process is as follows: S.21 Key Node Location: By analyzing the KPI change curves of each round in the strategic game, identify the rounds with sudden KPI changes as key game turning points, and extract the strategic actions of each party and changes in market status in that round. S.22 Consumer Group Analysis: Clustering algorithms are used to segment the consumer agent group, calculate the conversion contribution of each segment to the optimal strategy, select the top 3 groups with the highest contribution as the core influence groups, and output their feature profiles and behavioral patterns. S.23 Competitor Behavior Analysis: Statistically analyze the frequency of competitors' strategy selection and response delay time during the game process, summarize their main response patterns, and analyze the effectiveness of the optimal strategy in resisting each response pattern. S.24 Report Generation: Integrate the above analysis results with the specific content of the strategy, expected effects, and implementation recommendations to generate a visual report that includes text descriptions, data charts, and flowcharts.

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