A product ecological value conversion optimization method based on big data analysis

Abstract

The present application belongs to the field of big data processing and data analysis, and particularly relates to a product ecological value conversion optimization method based on big data analysis. The method comprises: constructing a structured user behavior feature set through multi-source data acquisition and processing; constructing a multi-dimensional product ecological value conversion graph based on graph theory, and giving a multi-dimensional attribute vector to the directed edge; using a graph neural network to perform global representation learning and automatically mine high-value conversion path patterns; simultaneously performing reverse churn analysis on the graph to construct negative value decay path patterns; establishing a real-time optimization decision model coupled with positive path reinforcement and negative path blocking, and dynamically generating resource allocation weights and intervention strategies for each node. The present application realizes dynamic identification of conversion paths and accurate allocation of resources through the construction of a heterogeneous graph and a coupled optimization mechanism, inhibits user churn while improving the overall value transmission efficiency of the ecological system.

Description

Technical Field

[0001] This invention belongs to the field of big data processing and data analysis, specifically relating to a method for optimizing the transformation of product ecosystem value based on big data analysis. Background Technology

[0002] With the widespread application of big data analytics, building a robust product ecosystem has become a core strategy for enterprises to enhance user stickiness and unlock potential value. By deeply mining massive amounts of user behavior data, enterprises can integrate diverse elements such as hardware, software, and services to form a closed-loop value conversion chain, achieving optimal resource allocation and business growth.

[0003] In a complex product ecosystem, accurately identifying user interaction logic across different product nodes and optimizing value delivery efficiency accordingly has become a key indicator for measuring big data-driven decision-making capabilities.

[0004] Product ecosystem value conversion optimization aims to analyze the behavioral sequences throughout the user lifecycle, uncover the relationships between products, and formulate targeted guidance strategies. This process typically involves pattern recognition of historical user data, establishing recommendation algorithms or association rules to predict the user's next conversion trend, and using this as a basis for resource allocation or feature guidance within the ecosystem. The value conversion mechanism not only enhances the contribution of individual products but also leverages the synergistic effects of the ecosystem, maximizing the overall value throughout the user lifecycle.

[0005] Existing technologies in the product ecosystem value conversion process typically employ static association rules or collaborative filtering models based on user historical behavior. These methods can only identify simple associations at a single time point and struggle to dynamically capture the complete behavioral paths of users moving between products within the ecosystem. This point-to-point optimization strategy ignores the temporal and causal chains of value conversion, causing the system to fail to identify key conversion nodes. Furthermore, due to the lack of precise attribution analysis across product types, it is difficult to quantify the catalytic effect of preceding products on high-value conversions, easily leading to resource misallocation. Existing optimization logic often focuses on the one-way enhancement of positive value, lacking mechanisms to identify and block negative value decay paths. It cannot intervene in a coordinated manner to address the risks of user churn or value decline, making it difficult to guarantee net growth in ecosystem value. Moreover, the neglect of non-linear path dependencies limits overall resource utilization efficiency. Summary of the Invention

[0006] The purpose of this invention is to provide a product ecosystem value transformation and optimization method based on big data analysis, which can solve the problems in the background technology mentioned above.

[0007] To achieve the above objectives, the technical solution adopted by this invention is: a product ecosystem value transformation and optimization method based on big data analysis, comprising the following specific steps:

[0008] Step 1: Obtain the full amount of raw user interaction data within the product ecosystem through multi-source data acquisition interfaces, and clean, denoise, and standardize the raw data to construct a structured set of user behavior features;

[0009] Step 2: Based on the user behavior feature set, construct a multi-dimensional product ecosystem value conversion graph using graph theory principles. Define the interaction between each user and product node as a node and directed edge in the graph, and assign a multi-dimensional attribute vector containing timestamp, action attribute and value weight to each directed edge.

[0010] Step 3: Use graph neural networks to perform global representation learning on the product ecosystem value transformation map, extract high-dimensional feature representations of nodes, and automatically mine high-value transformation path patterns in the global map space through dynamic programming algorithm;

[0011] Step 4: Simultaneously perform reverse churn analysis on the product ecosystem value conversion map to identify key triggering nodes for user value decline or behavior interruption, and construct a negative value decay path pattern.

[0012] Step 5: Establish a real-time optimization decision model that couples positive path reinforcement with negative path blocking. Based on the identified high-value conversion path patterns and negative value decay path patterns, dynamically generate resource allocation weights and intervention strategies for each product node.

[0013] Step 6: Feed back the resource allocation weights and intervention strategies to the execution module of the product ecosystem, adjust the user reach logic in real time, and perform closed-loop iteration of the product ecosystem value conversion map based on conversion feedback data.

[0014] Preferably, in step 1, obtaining the full amount of raw user interaction data covers all log information from the hardware terminal layer, application software layer, and cloud service layer. This raw data includes, but is not limited to, unstructured data such as user device activation records, click frequency of functional modules, page dwell time, service subscription status changes, and user feedback. During the standardization process, the system aligns data from different sources and formats to a unified time base and uses a discretization method to transform continuous numerical features into categorical features suitable for graph modeling.

[0015] Preferably, in step 1, the cleaning and denoising process is implemented using an outlier detection algorithm based on statistical probability distribution. The system automatically identifies invalid records with abnormally high or low frequencies in the interaction data, such as forged requests generated by automated scripts or duplicate reports due to network latency. For missing values, the system imputes them based on the mean or mode of the user's historical behavior trajectory, ensuring the continuity and integrity of the subsequently constructed value conversion chain in the time dimension.

[0016] Preferably, in step 2, the process of constructing the product ecosystem value conversion graph employs heterogeneous graph modeling technology. In this graph, nodes are categorized into three types: user nodes, product nodes, and service nodes. Directed edges represent user flow behavior between different nodes. The value weights are determined by a preset value evaluation function. This function comprehensively considers the direct contribution of the behavior to the user's lifetime value and its potential to guide subsequent high-value behaviors. When a user completes a conversion from a free trial to a paid subscription, the corresponding edge is assigned a higher positive weight; conversely, when a user performs operations such as uninstalling or turning off push notifications, the corresponding edge is assigned a specific negative weight.

[0017] Preferably, in step 2, the timestamp information assigned to each directed edge is used to accurately characterize the sequence of actions. The system maintains a global clock vector to record the precise physical time and logical step size of user jumps between nodes. This time-constrained graph structure can distinguish between static relationships and dynamic causal paths. Furthermore, the action attributes cover more than ten specific behavior types, including browsing, clicking, deep usage, sharing, and purchasing. Each behavior type corresponds to a different decay factor to reflect the characteristic that the impact of a behavior weakens over time.

[0018] Preferably, in step 3, the process of global representation learning using graph neural networks includes multi-layer aggregation processing. In each layer, each node automatically learns the importance weights of its neighboring nodes through an attention mechanism, and performs a weighted summation of the feature vectors of its neighboring nodes to update its own hidden state. This mechanism enables the system to capture deep implicit relationships across multiple product nodes. After multiple rounds of feature iteration, each node contains topological and attribute information of its local subgraph, providing a high-precision mathematical description for subsequent path pattern mining.

[0019] Preferably, in step 3, when using dynamic programming to mine high-value conversion path patterns, the system defines a conversion path as a set of ordered nodes. The goal of the search is to find one or more optimal paths that maximize the cumulative value score of the path. During the search process, the system uses a forward recursive approach, combined with the node features output by the graph neural network, to calculate the probability distribution of all possible paths from the initial contact node to the final high-value conversion node. To improve computational efficiency, the system sets a path depth threshold and a probability pruning threshold, retaining only high-quality paths with a conversion success rate significantly higher than a preset benchmark.

[0020] Preferably, in step 4, the process of constructing the negative value decay path pattern involves in-depth analysis of early warning signs of user churn. The system identifies frequently occurring negative behavior sequences before churn by tracing back the historical behavioral patterns of churned users. These sequences, forming a negative value decay path, can reveal the core bottleneck nodes that lead to damage to the user's ecosystem value. For example, when the system detects that a user experiences a decrease in activity immediately after receiving a specific type of push notification multiple times, the directed edge between this push notification behavior and the activity decrease node will be marked as a core component of the negative value transmission chain.

[0021] Preferably, in step 5, the coupled optimization decision model for positive path reinforcement and negative path blocking employs a multi-task collaborative learning framework. This model simultaneously addresses two optimization objectives: the first objective is to maximize the trigger probability of high-value conversion paths; the second objective is to minimize the activation frequency of negative-value decay paths. By introducing a coupling factor, the system can make trade-offs under limited resources. For example, if a product node is on both a positive conversion path and a negative-value decay path, the system will dynamically decide whether to strengthen guidance or implement strategy blocking based on its net contribution to the overall ecosystem value.

[0022] Preferably, in step 5, when generating resource allocation weights, the system employs a non-linear optimization algorithm. The resources obtained by each product node, including but not limited to the exposure of recommended positions, the amount of marketing subsidies, and the priority of system notifications, are all positively correlated with its centrality in multiple high-value paths. Simultaneously, to suppress negative value conversion, the system generates blocking signals for preceding nodes in the negative value decay path. When a user's behavior trajectory falls within the monitoring range of the negative value decay path, the system automatically triggers an early warning mechanism and adjusts subsequent interaction processes to interrupt further transmission of negative value.

[0023] Preferably, in step 6, the real-time optimization strategy is deployed using a distributed streaming processing architecture. The system can respond to every new action taken by the user on the front end with millisecond-level latency. Each time a user interacts, the system updates the user's position in the product ecosystem value conversion graph in real time and reassesses the risk and value of their current path. Based on the latest assessment results, the execution module dynamically adjusts the interface elements or service content displayed to the user.

[0024] Preferably, in step 6, the closed-loop iterative process relies on a continuously running performance monitoring subsystem. This subsystem is responsible for collecting conversion rate change data, user retention rate data, and total ecosystem value growth data after strategy implementation. By comparing the data deviations before and after implementing the optimization strategy, the system uses reinforcement learning algorithms to automatically correct the parameters of the graph neural network and the weight coefficients of the dynamic programming. This self-evolving mechanism ensures that the optimization method can adapt to the ever-changing market environment and user preferences.

[0025] Preferably, the present invention also includes a visualized value flow monitoring dashboard. This dashboard graphically displays the evolution of the product ecosystem value transformation map in real time. Managers can clearly observe the direction of value flow between different product nodes, the traffic distribution of critical paths, and the distribution of negative value nodes. The interactive analysis tools provided by the dashboard allow managers to manually set constraints for specific nodes, further assisting the system in generating optimization strategies that align with business objectives.

[0026] Preferably, when processing massive amounts of user data, the method employs differential privacy protection technology. During the construction of the data graph and feature extraction, the system adds random noise of a preset intensity to the data to ensure that no third party can deduce a specific user's real sensitive information from the graph structure. This privacy protection mechanism ensures the efficiency of big data analysis while strictly adhering to data security compliance requirements.

[0027] Preferably, the product nodes in the product ecosystem include smart hardware devices, mobile applications, web-based service portals, and third-party collaborative service plugins. The system connects these isolated product nodes through a unified cross-platform account system, forming a complete value loop. This optimization method is not only applicable to the internal ecosystem of a single enterprise but can also be extended to a joint ecosystem composed of multiple enterprises, achieving broader value transformation collaboration through shared graph features.

[0028] Preferably, the high-value conversion path model mentioned in step 3 includes not only direct purchasing behavior, but also social behaviors that can generate long-term indirect value, such as knowledge sharing, community interaction, and brand recommendations. The system calculates the contribution of these social behaviors to the overall activity of the ecosystem and assigns them corresponding virtual value weights. In the optimization process, it not only focuses on short-term financial returns, but also pays more attention to the long-term health of the ecosystem.

[0029] Preferably, the warning trigger condition for churn risk in step 4 is based on a dynamic probability threshold. This threshold is automatically adjusted according to the user's lifecycle stage. For example, for newly registered users, the sensitivity of the churn warning is set at a higher level so as to intervene in a timely manner when the user gives slight negative feedback; while for mature users, the warning mechanism focuses more on identifying long-term value decline trends.

[0030] Preferably, the resource allocation weight adjustment logic also considers the load of hardware resources. When generating intervention strategies, the system retrieves the processing capacity data of the cloud server in real time. If the current system load exceeds a predetermined threshold, the system will automatically simplify some complex graph calculation processes, prioritize the real-time push of core high-value paths, and achieve a dynamic balance between technical performance and business effectiveness.

[0031] Preferably, the method also involves a multi-dimensional attribution analysis technique. When the system observes a final high-value conversion, it uses path mining results to back-allocate the contribution of the conversion to each preceding node on the path according to a non-linear proportion. This precise attribution analysis can identify lead-generating products that, while not directly generating value, have a strong catalytic effect, providing an objective basis for resource allocation in the ecosystem.

[0032] Preferably, this method also includes an abnormal behavior defense module. This module is specifically designed to identify and filter out fraudulent conversion paths that exploit system optimization rules for malicious traffic manipulation or subsidy fraud. By analyzing the abnormal density of node connections and the mechanical repetition of time series in the graph, the system can automatically cut off the resource supply to such invalid paths, protecting the purity of ecological value conversion.

[0033] Preferably, the real-time optimization decision model has cold start processing capabilities. For newly launched product nodes or newly entered users, the system uses similarity-based collaborative filtering technology to associate them with existing similar path patterns in the graph. Even in the initial stage where historical data is lacking, it can still generate relatively accurate initial optimization suggestions.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. Dynamic identification and global optimization of conversion paths are achieved. By constructing a heterogeneous graph that includes time dimensions and behavioral weights, this invention overcomes the limitations of traditional static association rules and can accurately capture complex user behavior paths across different product types within the product ecosystem. This global path view enables the system to identify hidden nodes that play a key supporting role in long-term value conversion, improving the overall value transfer efficiency of the ecosystem.

[0036] 2. A multiplier effect and precise allocation of resources were achieved. The coupled optimization mechanism introduced in this invention deeply combines positive path reinforcement with negative path blocking, enabling each resource input to simultaneously amplify positive gains and suppress negative losses. Since the optimization objective is no longer limited to the conversion of a single product but targets the entire value transmission chain, experiments have shown that, under the premise of equal resource input, the net growth of overall ecological value has achieved a superlinear increase within a specific range, improving the marginal benefits of resource utilization.

[0037] 3. An automated mechanism for suppressing negative value and providing early warning of churn has been established. This invention not only focuses on the generation of positive value but also takes the path of negative value decay as the core analysis object. Through accurate identification and real-time intervention of pre-churn behaviors, the system can automatically trigger protection strategies at key points before user churn occurs, controlling the user churn rate below a preset percentage. This two-way closed-loop optimization logic ensures the steady growth of ecosystem value and enhances the product ecosystem's resilience and user stickiness.

[0038] 4. This invention reveals the deep attribution logic of nonlinear value contribution. By leveraging the deep feature extraction capabilities of graph neural networks, it can discover high-value transformation patterns—composed of multiple low-value behaviors—that are undetectable by traditional statistical methods. This precise quantitative attribution of catalytic nodes solves the long-standing technical challenge of accurately measuring cross-product type transformations, providing scientific data support for product evolution and strategy formulation within the ecosystem. Attached Figure Description

[0039] Figure 1 The flowchart is based on the present invention;

[0040] Figure 2 This is a schematic diagram of data flow according to the present invention;

[0041] Figure 3 This is a flowchart illustrating the high-value and negative-value path pattern mining method based on graph neural network representation learning, dynamic programming algorithm, and reverse flow analysis according to the present invention.

[0042] Figure 4A flowchart is provided to generate resource allocation weights and intervention strategies by establishing a real-time optimization decision model that couples positive path reinforcement and negative path blocking according to the present invention.

[0043] Figure 5 This is a logical framework diagram of closed-loop iterative optimization based on feedback data and reinforcement learning algorithms according to the present invention. Detailed Implementation

[0044] Example 1: Please refer to the appendix Figure 1 To be continued Figure 5 To make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below with reference to specific embodiments.

[0045] In a product ecosystem value transformation optimization method based on big data analytics, the method achieves deep insights into user behavior and precise resource allocation within a complex product ecosystem by constructing a highly integrated digital analysis and execution architecture. Step 1 includes acquiring full-volume raw user interaction data within the product ecosystem through a multi-source data acquisition interface, and cleaning, denoising, and standardizing the raw data to construct a structured set of user behavior features. In specific implementation, the multi-source data acquisition interface connects to the hardware terminal layer, application software layer, and cloud service layer within the product ecosystem.

[0046] At the hardware terminal layer, the system uses sensor driver modules and system log modules integrated within various smart hardware devices to capture device activation records, hardware parameter status changes, and physical button trigger signals in real time. These hardware devices include, but are not limited to, smart home terminals, wearable devices, in-vehicle intelligent systems, and various industrial-grade sensor nodes. The data acquisition process uses a preset millisecond-level polling mechanism or event-driven mechanism to convert raw pulse signals, voltage changes, or command streams into binary data packets containing a unique device identifier, hardware version number, and physical timestamp.

[0047] At the application software layer, the system collects all user interface interaction data through embedded tracking code in mobile applications, web-based service entry points, and third-party partner plugins. This data covers the click frequency of functional modules, page dwell time, screen swipe trajectories, character length of input boxes, and playback progress of multimedia content. For each interaction, the collection module records its logical context information, such as the preceding path to the page, the visual hierarchy of the current interface, and the system load status when the action was triggered.

[0048] At the cloud service layer, the data acquisition interface obtains unstructured data such as service subscription status changes, order transaction records, account balance changes, and user evaluation information within the community by calling remote procedure call interfaces or subscribing to message middleware. The system aggregates this log information from different dimensions into a distributed storage cluster, forming a massive raw interactive corpus.

[0049] The system performs a rigorous cleaning and denoising process on the collected raw data. This cleaning and denoising process is implemented using an outlier detection algorithm based on statistical probability distribution. The system first performs sliding window statistics on the collected interaction frequencies, calculating the average number of operations and standard deviation for each user per unit time. If the value of a data point deviates from the average by more than three times the standard deviation, the system automatically identifies it as an invalid record with abnormally high or low frequency. Such records are usually caused by forged requests generated by automated scripts or duplicate reporting due to network fluctuations.

[0050] During the standardization process, to eliminate differences in format and dimension among data from different sources, the system introduces time benchmark alignment technology. By maintaining a globally unified Coordinated Universal Clock (COUC), the system normalizes all timestamps at the hardware, application, and cloud layers, ensuring absolute continuity of the subsequently constructed value conversion chain in the time dimension. For numerical features, the system employs a discretization method, converting continuous physical quantities (such as dwell time and spending amount) into categorical feature labels suitable for graph modeling based on preset quantiles, reducing computational complexity and enhancing feature representation capabilities. For missing values, the system uses a mean or mode imputation strategy based on the user's historical behavioral trajectory. For example, if the geographical location information of an intermediate step is missing in an interaction sequence, the system extracts the location center value of the user within 10 minutes before and after to fill in the missing value, ensuring the integrity of the value transmission path.

[0051] Step 2 involves constructing a multi-dimensional product ecosystem value conversion graph based on the user behavior feature set and using graph theory principles. Each user's interaction with a product node is defined as a node and a directed edge in the graph, and each directed edge is assigned a multi-dimensional attribute vector containing timestamps, action attributes, and value weights. In this step, the process of constructing the product ecosystem value conversion graph employs heterogeneous graph modeling technology, abstracting complex business logic into a mathematical graph structure. In this heterogeneous graph, nodes are explicitly divided into three categories: user nodes, product nodes, and service nodes.

[0052] User nodes store anonymized user identifiers, level weights, and historical activity characteristics; product nodes represent specific physical products or functional modules within the ecosystem, including their product category tags and resource usage levels; service nodes represent abstract value units such as value-added services, after-sales support, or social interactions. Directed edges are used to accurately describe the flow of user behavior between different nodes, with the starting point of the edge representing the trigger source of the behavior and the ending point representing the target direction of the behavior.

[0053] The value weight assigned to each directed edge is determined by a pre-defined value evaluation function. This function derives the weight value by comprehensively calculating the direct contribution and guiding potential.

[0054] Direct contribution reflects the immediate economic value or activity increment generated by the action. For example, when a user completes a conversion from a free trial to a paid subscription, the system calculates the difference between the subscription amount and the cost, and assigns a higher positive weight to that side by combining it with a preset positive gain coefficient.

[0055] Guiding potential reflects the catalytic effect of current behavior on subsequent high-value behaviors. The system analyzes all historical paths to calculate the conditional probability of a core conversion behavior occurring within the next 30 days after a user performs a specific action. If this conditional probability is greater than a preset probability threshold, the edge is given additional potential points. Conversely, when a user uninstalls, turns off push notifications, or reduces usage frequency, the system assigns a specific negative weight to the corresponding edge based on the expected loss of user lifetime value caused by this behavior, combined with a negative deduction coefficient.

[0056] Each directed edge also contains precise timestamp information and an action attribute vector. By maintaining a global clock vector, the system records not only the precise physical time of a user's transitions between nodes but also the logical step size between actions. This time-constrained graph structure can distinguish between static relationships and dynamic causal paths. Action attributes cover more than 10 specific behavior types, including browsing, clicking, deep usage, sharing, and purchasing, each corresponding to a different decay factor. These decay factors decrease exponentially over time to reflect the natural diminishing influence of historical behaviors on current decisions.

[0057] Step 3 includes using a graph neural network to perform global representation learning on the product ecosystem value transformation map, extracting high-dimensional feature representations of nodes, and automatically mining high-value transformation path patterns within the global map space using a dynamic programming algorithm. The process of using a graph neural network for global representation learning involves deep, multi-layer aggregation processing logic. In each layer of aggregation processing, the system constructs a local neighborhood set for each node.

[0058] By introducing an attention mechanism, the system automatically calculates the relevance score between the current node and each of its neighboring nodes. Specifically, the relevance score is obtained by calculating the dot product between the node feature vectors and then processing it through a non-linear activation function. The system uses a normalization method to transform all relevance scores into importance weights that sum to 1. The system multiplies the feature vector of each neighboring node with its corresponding importance weight and then performs a weighted summation of all the product results, achieving a deep integration of neighborhood information. After multiple rounds of feature iteration updates, the high-dimensional hidden state of each node incorporates the topological structure information and multi-dimensional attribute information of its local subgraph.

[0059] After obtaining the high-dimensional feature representations of nodes, the system uses a dynamic programming algorithm to mine high-value conversion path patterns. The system defines a conversion path as an ordered set of nodes and edges. The search objective is to find one or more optimal paths that maximize the cumulative value score of the path. During the search process, the system employs a forward recursive approach, combining the node embedding vectors output by the graph neural network, to calculate the transition probabilities of all possible paths from the initial contact node to the final high-value conversion node hop-by-hop.

[0060] To balance computational accuracy and processing efficiency, the system sets a path depth threshold and a probability pruning threshold. The path depth threshold limits the maximum search step size to avoid generating excessively long redundant paths; the probability pruning threshold is used to automatically filter out low-quality paths with a conversion probability lower than a preset benchmark at each search level. Only high-quality paths with a conversion success rate significantly higher than the preset benchmark are retained as high-value conversion path patterns. These patterns include not only direct purchase behavior but also social behaviors that can generate long-term indirect value, such as knowledge sharing, community interaction, and brand recommendations. The system calculates the contribution of these social behaviors to the overall activity of the ecosystem and multiplies it by a social value conversion factor, assigning each a corresponding virtual value weight.

[0061] Step 4 includes simultaneously performing reverse churn analysis on the product ecosystem value conversion map to identify key trigger points for user value decline or behavioral interruption, and constructing a negative value decay path pattern. This step involves in-depth mining of early warning signs of user churn. The system traces back the historical behavioral trajectories of users who have already exhibited churn behavior or experienced a significant decline in value, and uses sliding window technology to extract a specific time series before the churn occurred.

[0062] By comparing the behavioral differences between churned and continuously active users, the system identifies statistically significant negative behavioral sequences that frequently occurred before churn. These sequences, forming a negative value decay path, reveal the core bottleneck nodes leading to damage to the user ecosystem's value. For example, when the system detects that a user receives push notifications at a specific frequency or with specific text multiple times, followed by a decrease in activity or entering a do-not-disturb mode, the system marks the directed edge between this push notification behavior node and the activity decrease node as a core component of the negative value transmission chain.

[0063] The warning trigger conditions for churn risk are based on a dynamic probability threshold. This threshold is not fixed but automatically adjusts according to the user's current lifecycle stage. For newly registered users, the system sets the sensitivity of the churn warning at a high level, meaning that a warning is triggered when the churn probability exceeds the first preset threshold, allowing for timely intervention when the user generates even minor negative feedback. For mature users who have established strong usage habits, the warning mechanism focuses more on identifying long-term value decline trends; the corresponding intervention logic is only activated when the churn probability exceeds the second preset threshold and shows a continuous upward trend.

[0064] Step 5 involves establishing a real-time optimization decision-making model that couples positive path reinforcement with negative path blocking. Based on the identified high-value conversion path patterns and negative-value decay path patterns, the model dynamically generates resource allocation weights and intervention strategies for each product node. This decision-making model employs a multi-task collaborative learning framework aimed at maximizing global ecosystem value. The model simultaneously addresses two optimization objectives: the first objective is to maximize the trigger probability of high-value conversion paths; the second objective is to minimize the activation frequency of negative-value decay paths.

[0065] By introducing a coupling factor, the system can perform complex trade-off calculations with limited total resources. The coupling factor reflects the degree of correlation between positive gains and negative risks. For example, if a product node is simultaneously on a high-value conversion path and a negative-value decay path, the system will calculate the node's net contribution value. The net contribution value equals the node's expected value increment in the positive path minus its expected value loss in the negative path. Based on the magnitude of the net contribution value, the system will dynamically decide whether to strengthen guidance for the node or strategically shield it.

[0066] The system employs a non-linear optimization algorithm when generating resource allocation weights. The resources acquired by each product node, including but not limited to the exposure weight of homepage recommendations, the amount of marketing subsidies, and the priority of system push notifications, are all positively correlated with its centrality metric across multiple high-value paths. The centrality metric is calculated by summing the value weights of all high-value paths passing through that node.

[0067] Simultaneously, to suppress negative value conversion, the system generates blocking signals for upstream risk nodes in the negative value decay path. When a user's real-time behavior trajectory is determined to fall within the monitoring range of the negative value decay path, the system automatically triggers an early warning mechanism. At this time, the decision-making model immediately adjusts subsequent interaction processes, such as temporarily shutting down push tasks that cause resentment, or issuing exclusive coupons to users to appease them, thus blocking further transmission of negative value. The resource allocation weight adjustment logic also retrieves cloud server processing capacity data in real time. If the current system computing load exceeds a predetermined threshold, the system automatically activates a simplified mode, reducing the depth of graph calculation and prioritizing the real-time strategy delivery of core high-value paths, achieving a dynamic balance between technical performance and business effectiveness.

[0068] Step 6 includes feeding back the resource allocation weights and intervention strategies to the execution module of the product ecosystem, adjusting the user outreach logic in real time, and performing closed-loop iteration on the product ecosystem value conversion map based on conversion feedback data. The real-time optimization strategy is issued using a distributed streaming processing architecture to ensure that the system can respond to every new action generated by the user on the front end with low latency.

[0069] Each time a user interacts with the system, the execution module updates the user's position in the product ecosystem value conversion graph in real time and reassesses the risk of churn and conversion potential of the user's current path. Based on the latest assessment results, the execution module adjusts the order of interface elements, recommended product lists, or personalized service content displayed to the user through dynamic interface rendering technology or real-time message delivery technology.

[0070] The closed-loop iterative process relies on a continuously running performance monitoring subsystem. This subsystem is responsible for frequently collecting various core indicator data after the strategy implementation, including fluctuations in conversion rates, changes in user retention rates, and the cumulative growth in total ecosystem value. By comparing the data deviations before and after implementing the optimization strategy, the system uses reinforcement learning algorithms to automatically correct model parameters.

[0071] In reinforcement learning logic, the system uses the value increase after each policy issuance as a reward signal. If the reward signal is positive, the weight coefficients of the corresponding path and the connection strength of the graph neural network are increased; if the reward signal is negative, the trigger probability of the corresponding policy is weakened. This self-evolving mechanism ensures that the optimization method can sensitively capture changes in the market environment and shifts in user preferences.

[0072] This embodiment also involves a multi-dimensional attribution analysis technique. When the system observes a final high-value conversion event, it uses path mining results to back-allocate the total value generated by this conversion to each preceding node on the path according to a non-linear proportion. The allocation ratio is determined based on the node's contribution to increasing the probability of subsequent conversions. This precise attribution analysis can identify product nodes that, although not directly generating financial revenue, have a strong guiding and catalytic effect, providing objective data evidence for the resource allocation of the ecosystem.

[0073] This embodiment also includes an abnormal behavior defense module. This module is specifically designed to identify and filter out fraudulent conversion paths that exploit system optimization rules for malicious traffic boosting or subsidy fraud. By analyzing the abnormal density of node connections in the graph and the mechanical repetition characteristics of the time series, the system can automatically determine invalid paths and cut off the resource supply to such paths in real time.

[0074] When processing massive amounts of sensitive data, the method employs differential privacy protection technology. At each stage of graph construction and feature extraction, the system adds random noise of a specific intensity to the original data or feature vectors according to a preset privacy budget. This processing ensures that no external entity can deduce the true identity information of a specific user from the graph structure, while maximizing the preservation of the group statistical efficiency of big data analysis.

[0075] The system also provides a visual value flow monitoring dashboard. This dashboard graphically displays the dynamic evolution of the product ecosystem value transformation map in real time. Managers can clearly observe the direction of value flow between different product nodes, the traffic distribution of critical paths, and the spatial distribution of negative value nodes. The interactive analysis tools provided by the dashboard allow managers to manually set constraints for specific nodes, further assisting the system in generating in-depth optimization strategies that align with business objectives.

[0076] The real-time optimization decision model possesses cold-start processing capabilities. For newly launched product nodes or new users entering the ecosystem, due to the lack of sufficient historical interaction data, the system utilizes similarity-based collaborative filtering technology. The system calculates the cosine similarity between new nodes and existing nodes in the attribute vector space, associating them with the most similar path patterns in the graph that have the closest features. In the initial stage, it can still generate relatively accurate initial optimization suggestions, until sufficient interaction data is accumulated, at which point it automatically switches to deep graph learning mode.

[0077] Example 2: Building upon Example 1, this example provides a distributed value transformation path optimization scheme for a large-scale heterogeneous product ecosystem. In this example, the product ecosystem not only encompasses the internal resources of a single enterprise but also involves a joint ecosystem composed of multiple collaborating enterprises, achieving broader value transformation collaboration through sharing anonymized graph features.

[0078] In the multi-source data acquisition phase of step 1, the system introduces a cross-platform unified account mapping system. This system uses hash association to link anonymized unique identifiers provided by each partner, thus piecing together user behavior data from different enterprises and applications into a complete global lifecycle chain while ensuring privacy. During data acquisition, for sensor data at the hardware terminal layer, this embodiment uses an edge computing gateway for preprocessing. The edge gateway is responsible for localized feature extraction and preliminary cleaning of the raw high-frequency signals, uploading only the processed feature summary to the cloud-based graph construction engine, significantly reducing network bandwidth pressure.

[0079] In step 2, when constructing the product ecosystem value transformation graph, this embodiment adopts a hierarchical and partitioned graph storage architecture. The system divides the global graph into multiple subgraph partitions based on product category or geographical location. Each partition is maintained by an independent distributed graph computing node. To handle value transfer across partitions, the system establishes a virtual proxy node mechanism. When a user node flows from subgraph A to subgraph B, the virtual proxy node in subgraph A records the outgoing state vector and timestamp and synchronously updates it to subgraph B.

[0080] In this embodiment, the value weighting evaluation function is further refined into a weighted sum of three sub-items: short-term profitability, medium-term retention, and long-term brand value. The system dynamically adjusts the weighting coefficients of these three sub-items based on the strategic priorities set by the business departments. For example, during the ecosystem expansion phase, the system automatically increases the weighting coefficient of long-term brand value, encouraging pathways that generate word-of-mouth marketing and social media viral growth.

[0081] In step 3, when using graph neural networks for representation learning, this embodiment employs an asynchronous stochastic gradient descent algorithm to optimize network parameters. Due to the large scale of the heterogeneous graph, the system uses subgraph sampling technology. In each training iteration, the system randomly extracts small-scale connected subgraphs containing key business nodes as training samples, updates local parameters, and gradually accumulates them into the global model.

[0082] To address the virtual value generated by social behaviors, this embodiment introduces a social influence propagation model. The system evaluates a user's ability to radiate value to surrounding nodes by calculating the centrality indicators (such as degree centrality and proximity centrality) of a user node in the social subgraph. If a social behavior is triggered by a user with high influence, the directed edge corresponding to that behavior will be assigned a higher virtual value weight and receive higher priority during path mining.

[0083] In the reverse churn analysis in step 4, this embodiment introduces a churn prediction model based on survival analysis theory. The system not only identifies key triggering nodes but also calculates the expected lifetime value of each user along their current path. When the expected lifetime value shows a significant downward trend, the system determines that the user has entered a high-risk churn range. Furthermore, by analyzing the user's historical sensitivity to different types of intervention strategies, the system constructs a churn intervention sensitivity feature vector, providing decision support for subsequent precise blocking.

[0084] In the real-time optimization decision-making stage of step 5, this embodiment employs a multi-objective evolutionary algorithm to solve for the optimal resource allocation scheme. Since resource allocation involves constraints across multiple dimensions (such as total budget limits, single push frequency limits, and server response time limits), the system constructs a Pareto optimal solution set to find a balance among multiple conflicting objectives. When generating intervention strategies, the system considers the complementarity and substitutability between product nodes. For example, if a user has already purchased product A, the system will automatically reduce the recommendation weight of product B, which has overlapping functions with product A, and instead increase the exposure of product C, which has strong complementarity with product A.

[0085] In the closed-loop iteration process of step 6, this embodiment employs a multi-armed bandit algorithm to manage the balance between strategy exploration and utilization. The system randomly allocates a small portion of traffic (e.g., 5%) to newly generated, untested experimental strategies. By observing the conversion feedback of this traffic in real time, the effectiveness of the new strategies is quickly evaluated. If the new strategy outperforms the current best strategy, the system gradually increases its traffic allocation ratio. This mechanism avoids the optimization process from falling into local optima.

[0086] This embodiment also significantly enhances the functionality of the visual monitoring dashboard. The dashboard supports multi-dimensional slice analysis, allowing managers to drag and drop elements to view value transformation graphs for specific regions, age groups, or terminal types. The dashboard also integrates a simulation engine, enabling managers to simulate and predict potential ecological value changes caused by strategy adjustments before actual strategy implementation by modifying edge weights or node attributes in the graph, thus aiding in informed business decision-making.

[0087] To protect differential privacy, this embodiment employs a dynamic noise injection mechanism. The system adjusts the amount of Laplace noise added in real time based on the sensitivity and aggregation level of the query request. For high-level macroscopic statistical queries, smaller noise is added to ensure accuracy; for fine-grained microscopic node queries, larger noise is added to strictly protect individual privacy.

[0088] Example 3: Building upon the above examples, this example further discloses an implementation of a value transformation optimization system with high fault tolerance and adaptive load balancing capabilities. In this example, the system focuses on maintaining the stability and accuracy of the optimization strategy under extreme loads or data quality fluctuations.

[0089] In step 1, the system introduces a data quality grading and labeling mechanism. When acquiring raw data, the data acquisition interface assigns a quality score to each record based on the stability of the data source, transmission latency, and format integrity. In the subsequent cleaning and noise reduction stages, data with lower quality scores will undergo more stringent verification logic. If the quality score still does not reach the preset threshold after processing, the system will isolate that portion of data to the pending processing area and trigger automatic data replenishment or re-acquisition commands.

[0090] In step 2, during the graph construction process, the system introduces a dynamic edge aging mechanism. Because user preferences shift rapidly over time, the system sets a half-life parameter for each directed edge. When an edge's creation time exceeds its half-life, its associated value weight will automatically decay according to a preset ratio. The weight will only be compensated again when the path is repeatedly activated by the user. This mechanism ensures that the graph can reflect the latest market trends and user preferences in real time, preventing outdated data from interfering with optimization decisions.

[0091] In step 3, when mining path patterns, this embodiment introduces the concept of adversarial generative networks. The system constructs a path simulator as a discriminator to identify pseudo-high-value paths that, while conforming to mathematical statistical laws, are illogical in actual business operations. Through continuous interaction between the generator and the discriminator, the system can filter out path patterns that truly have sustainable conversion potential, improving the robustness of pattern recognition.

[0092] To address hardware resource load, the optimization model in step 5 integrates a resource-aware scheduler. This scheduler monitors the CPU utilization, available memory, and network I / O latency of computing nodes in real time. When the load exceeds a first threshold, the system automatically reduces the number of computational layers in the graph neural network, performing only the most critical shallow representation learning. When the load exceeds a second threshold, the system pauses real-time computation for low-value user groups and instead issues a general optimization strategy based on historical average data, prioritizing system response speed for high-net-worth users and core business processes.

[0093] In step 6, the closed-loop iterative process incorporates adjustments for external environmental variables. The monitoring subsystem not only collects internal conversion data but also obtains external macroeconomic indicators, competitor promotional information, and sudden trending news data via API. These external variables are then incorporated as bias terms into the reinforcement learning reward function. For example, during holidays, the system automatically increases the baseline expected value of conversion behavior, eliminating spurious growth caused by seasonal factors, allowing the model to more accurately capture the pure gains from the optimization strategy.

[0094] The system also possesses robust anomaly defense capabilities. In addition to identifying fraudulent activity paths, the anomaly defense module integrates detection logic for adversarial attacks. If malicious disturbances are detected in the input data stream that deliberately induce the system to reduce resource allocation weights, the system will immediately lock the risky data source and switch to safe mode. In safe mode, all resource allocation decisions undergo rule-based secondary verification to ensure the stability of the system output.

[0095] To address the cold start problem, this embodiment also introduces knowledge graph transfer learning technology. The system pre-constructs a large-scale knowledge graph containing general product attributes, industry knowledge, and general human behavioral logic. For newly launched products or users, the system uses semantic matching to transfer relevant prior knowledge from the knowledge graph to the current value conversion graph. For example, if a new product is a health-related hardware device, the system will pre-configure relevant guidance paths for that product node based on the strong correlation logic between health management and continuous check-in in the knowledge graph, shortening the market adaptation period for the new product.

[0096] In this embodiment, attribution analysis technology is further extended to time-varying attribution. The system records the contribution curve of each node to the overall value conversion at different time points. Managers can observe through dashboards how a product gradually evolves from an initial traffic entry point to a mature profit center. This dynamic perspective helps companies develop more targeted resource support strategies at different stages of the product lifecycle.

[0097] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for optimizing product ecosystem value transformation based on big data analysis, characterized in that, Includes the following steps: The system acquires full-volume raw user interaction data within the product ecosystem through multi-source data acquisition interfaces, and cleans, denoises, and standardizes the raw data to construct a structured set of user behavior features. Based on the user behavior feature set, a multi-dimensional product ecosystem value conversion graph is constructed using graph theory principles. The interaction between each user and product node is defined as a node and a directed edge in the graph, and each directed edge is assigned a multi-dimensional attribute vector including timestamp, action attribute and value weight. The graph neural network is used to perform global representation learning on the product ecological value transformation graph, extract high-dimensional feature representations of nodes, and automatically mine high-value transformation path patterns in the global graph space through dynamic programming algorithm. A reverse churn analysis is conducted on the product ecosystem value conversion map to identify key triggering nodes for user value decline or behavior interruption, in order to construct a negative value decay path pattern. Establish a real-time optimization decision-making model that couples positive path reinforcement with negative path blocking. Based on the identified high-value conversion path patterns and negative value decay path patterns, dynamically generate resource allocation weights and intervention strategies for each product node. The resource allocation weights and intervention strategies are fed back to the execution module of the product ecosystem to adjust the user reach logic in real time, and the product ecosystem value conversion map is iterated in a closed loop based on the conversion feedback data.

2. The product ecosystem value transformation and optimization method based on big data analysis according to claim 1, characterized in that, Acquire full volume of raw user interaction data within the product ecosystem through multi-source data acquisition interfaces, including: collecting full volume of log information through the hardware terminal layer, application software layer, and cloud service layer. The raw data includes hardware device activation records, hardware parameter status change signals, functional module click frequency, page dwell time, screen swipe trajectory, service subscription status changes, order transaction records, and unstructured evaluation information. The cleaning and denoising process is implemented through an outlier detection algorithm based on statistical probability distribution, including: performing sliding window statistics on the collected interaction frequencies, and calculating the mean number of operations and the standard deviation of the number of operations for each user within a unit of time. When the value of a data point deviates from the mean by more than three times the standard deviation, the data point is identified as an invalid record with an abnormally high or low frequency. During the standardization process, data from different sources are aligned to a time base using a globally unified Coordinated World Clock. A discretization method is used to convert continuous numerical features into categorical feature labels based on preset quantiles; For missing values, imputation is performed based on the mean or mode of the user's historical behavior trajectory.

3. The product ecosystem value transformation and optimization method based on big data analysis according to claim 2, characterized in that, Utilizing graph theory principles, a multi-dimensional product ecosystem value transformation graph is constructed, including: using heterogeneous graph modeling technology to divide nodes into three categories: user nodes, product nodes, and service nodes; The directed edges represent the user's movement behavior between different nodes, and the value weights are determined by a preset value evaluation function. The value evaluation function includes two evaluation dimensions: direct contribution and guiding potential. Among them, the direct contribution is determined based on the immediate economic value or activity increment generated by the behavior; The potential for guidance is determined based on the conditional probability of core conversion behavior occurring within a preset time period after the current action is performed; When the conditional probability value is greater than the preset probability threshold, a potential score is added to the corresponding directed edge. When a user performs an uninstall or disables push notifications, a negative weight is assigned to the corresponding directed edge. Each directed edge maintains a global clock vector to record the physical time and logical step size of the user's jumps between nodes; The action attribute vector includes browsing, clicking, deep usage, sharing, and purchasing behavior types, and each behavior type corresponds to a different decay factor; The decay factor decreases exponentially over time.

4. The product ecosystem value transformation and optimization method based on big data analysis according to claim 3, characterized in that, The graph neural network is used to perform global representation learning on the product ecosystem value transformation map, including: in each layer of aggregation processing, a local neighborhood set is constructed for each node, and the correlation score between the current node and each of its neighboring nodes is calculated through an attention mechanism; The correlation score is obtained by calculating the dot product between node feature vectors and then processing it with a non-linear activation function. The relevance scores are converted into importance weights with a sum of 1 through normalization. The feature vector of each neighboring node is multiplied with its corresponding importance weight, and all the product results are weighted and summed to update the hidden state of the current node. After a preset number of feature iterations, the topological structure information and multidimensional attribute information of the local subgraph in which each node is located are integrated into the high-dimensional hidden state of each node.

5. The product ecosystem value transformation and optimization method based on big data analysis according to claim 4, characterized in that, The algorithm automatically mines high-value conversion path patterns through dynamic programming, including: defining the conversion path as an ordered set of nodes and edges, and using a forward recursion method, combined with the node embedding vector output by the graph neural network, to calculate the transition probability of all paths from the initial contact node to the final high-value conversion node hop by hop. Set path depth threshold and probability pruning threshold; The path depth threshold is used to limit the maximum step size of the search; The probability pruning threshold is used to filter out paths with a transition probability less than a preset benchmark value at each search level. Retain high-quality paths with conversion success rates higher than the preset benchmark as high-value conversion path models; The high-value conversion path model includes purchasing behavior, knowledge sharing behavior, community interaction behavior, and brand recommendation behavior; By calculating the contribution of the community interaction behavior to the overall activity of the ecosystem and multiplying it by the social value conversion factor, a virtual value weight is assigned to the social behavior.

6. The product ecosystem value transformation and optimization method based on big data analysis according to claim 5, characterized in that, Constructing a negative value decay path pattern includes: tracing back the historical behavior of users who churned, and using sliding window technology to extract the time series before the churn occurred; By comparing the behavioral differences between churned users and continuously active users, negative behavioral sequences that occurred before churn were identified, and the path formed by these negative behavioral sequences was determined as a negative value decay path. Establish churn risk warning trigger conditions based on dynamic probability thresholds; The dynamic probability threshold is automatically adjusted based on the user's current lifecycle stage; For newly registered users, the sensitivity of the churn warning is set to trigger an alert when the churn probability exceeds the first preset threshold; For mature users, when the churn probability exceeds the second preset threshold and shows a continuous upward trend, the intervention logic is activated. The first preset threshold is less than the second preset threshold.

7. The product ecosystem value transformation and optimization method based on big data analysis according to claim 6, characterized in that, Establish a real-time optimization decision model and generate resource allocation weights and intervention strategies, including: using a multi-task collaborative learning framework to simultaneously address two objectives: maximizing the trigger probability of high-value conversion paths and minimizing the activation frequency of negative-value decay paths. A coupling factor is introduced to calculate the net contribution value of the product node; The net contribution value is equal to the expected value increment of a node in the positive path minus its expected value loss in the negative path. Based on the magnitude of the net contribution value, a dynamic decision is made to either enhance guidance for the node or implement strategic blocking. When generating resource allocation weights, a nonlinear optimization algorithm is used to make the resources obtained by each product node positively correlated with its centrality index in multiple high-value paths; The centrality metric is derived by calculating the sum of the value weights of all high-value paths that pass through the nodes.

8. The product ecosystem value transformation and optimization method based on big data analysis according to claim 7, characterized in that, The resource allocation weights and intervention strategies are fed back to the execution module of the product ecosystem, including: adopting a distributed streaming processing architecture, updating the user's latest position in the product ecosystem value conversion map in real time whenever the user interacts, and reassessing the churn risk and conversion potential of the current path. Based on the evaluation results, the order of interface elements, recommended product lists, or personalized service content displayed to users can be adjusted using dynamic interface rendering technology. The logic for adjusting resource allocation weights retrieves data on the processing capacity of the cloud server in real time. When the system's computational load exceeds a first predetermined threshold, the number of computational layers in the graph neural network is automatically reduced, and shallow representation learning is performed. When the system's computational load exceeds the second predetermined threshold, real-time computation for low-value user groups is suspended, and a general optimization strategy based on historical average data is instead issued.

9. The product ecosystem value transformation and optimization method based on big data analysis according to claim 8, characterized in that, The product ecosystem value conversion map is iterated in a closed loop based on conversion feedback data, including: collecting conversion rate fluctuation values, user retention rate change ratio, and cumulative growth amount of total ecosystem value after strategy implementation. The reinforcement learning algorithm is used to automatically correct the model parameters, and the value increase after the policy is issued is used as a reward signal; When the reward signal is positive, the weight coefficients of the corresponding path and the connection strength of the graph neural network are enhanced. When the reward signal is negative, the trigger probability of the corresponding strategy is reduced; Perform multi-dimensional attribution analysis. When a high-value conversion event is observed, the total value generated by the conversion is back-allocated to each preceding node on the path in a non-linear proportion. The allocation ratio is determined based on the contribution of each node to the increase in the probability of subsequent conversion, and the time-varying attribution contribution curve of each node to the overall value conversion at different time points is recorded.

10. The product ecosystem value transformation and optimization method based on big data analysis according to claim 9, characterized in that, It also includes the following security and cold start processing steps: using differential privacy protection technology, random noise of varying intensity is added to the original data or feature vectors according to a preset privacy budget during the construction of the map and the extraction of features; The abnormal behavior defense module identifies and filters malicious traffic-boosting paths. The identification process includes analyzing the density of node connections in the graph and the repeatability characteristics of the time series; If a disturbance is detected in the data stream that deliberately induces the system to reduce the weight of resource allocation, the system will switch to safe mode to make the resource allocation decision undergo a rule-based secondary verification. For newly launched product nodes or new users, similarity-based collaborative filtering technology is used to associate new nodes with existing nodes in the attribute vector space by calculating the cosine similarity between the new nodes and existing nodes, and then associating them with the similar path patterns with the closest features in the graph. By leveraging knowledge graph transfer learning technology, prior knowledge is transferred from a knowledge graph containing common product attributes through semantic matching, and a guidance path is pre-configured for new product nodes.

Images