A machine learning based consumer data report generation system

Abstract

This invention belongs to the field of data processing technology, specifically relating to a consumer data report generation system based on machine learning. This system aims to solve technical challenges such as insufficient depth in feature mining of large-scale heterogeneous consumer data and homogenization of report generation. The system includes: a multi-source heterogeneous data adaptive ingestion module, a nonlinear feature automated calculation engine, a heterogeneous behavioral semantic association graph construction unit, an incremental machine learning inference center, and a multi-dimensional logical adaptive report generation platform. Through standardized mapping, nonlinear feature extraction, and semantic graph modeling, combined with incremental model inference, the system can automatically match narrative logic according to needs and generate structured, visual reports. This invention achieves deep perception and dynamic real-time processing of massive amounts of consumer data, improving report production efficiency and business insight, and providing an efficient and adaptive solution for consumer decision-making.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, specifically relating to a consumer data report generation system based on machine learning. Background Technology

[0002] With the global digital transformation and the continued expansion of e-commerce, consumer data has become a key strategic resource driving business decisions, market forecasting, and user profiling. In the field of big data analytics and information processing, the refined mining of massive transaction data is not only a core means for enterprises to optimize resource allocation, but also an important support for fintech to improve service quality. It encompasses multiple key technical aspects such as data collection, storage, cleaning, statistical analysis, and result visualization.

[0003] Among them, consumer data analysis and report generation technology based on machine learning, as a cutting-edge direction of business intelligence, uses various learning algorithms to model the characteristics of massive amounts of consumer behavior. This technology aims to adaptively identify consumption trends, discover abnormal transactions, and predict potential market demand through models, transforming chaotic raw consumption records into professional data reports with logical, forward-looking, and decision-guiding significance.

[0004] Existing technologies exhibit significant limitations when dealing with the ever-increasing and highly heterogeneous consumer data. Most solutions still rely excessively on rigid statistical templates and predefined association rules, lacking the ability to capture deep nonlinear relationships between multidimensional features. This results in highly homogenized reports that fail to reflect subtle market fluctuations. Furthermore, existing data processing workflows suffer from bottlenecks in feature engineering automation and model inference response speed when faced with real-time influxes of high-frequency consumer data, often leading to significant time lags in report generation and failing to meet the immediacy requirements of dynamic decision-making. In addition, existing systems are still insufficient in cross-scenario semantic mapping and report text logic construction, making it difficult to automatically adjust the report's focus and narrative logic according to different analytical needs, thus limiting the interpretability and practical application value of the output. Therefore, how to build a consumer data reporting system that can balance processing efficiency, analytical depth, and adaptive logical generation has become a pressing technical challenge in the field. Summary of the Invention

[0005] The purpose of this invention is to provide a consumer data report generation system based on machine learning to solve the technical problems of insufficient feature mining depth, limited real-time processing performance, and serious homogenization of report generation logic in the processing of large-scale heterogeneous consumer data.

[0006] The technical solution of the present invention is to provide a consumer data report generation system based on machine learning. The system includes: a multi-source heterogeneous data adaptive ingestion module, a nonlinear feature automated calculation engine, a heterogeneous behavior semantic association graph construction unit, an incremental machine learning inference center, and a multi-dimensional logical adaptive report generation platform.

[0007] The multi-source heterogeneous data adaptive ingestion module is used to perform unified and standardized mapping of streaming consumption data and existing batch processing data accessing the system through a pre-defined protocol adapter cluster. The module integrates a self-dynamic data cleaning operator, which can perform multi-order interpolation compensation for missing values ​​in the data based on predefined quality assessment indicators, and perform robust filtering of outliers based on local outlier factors. The multi-source heterogeneous data adaptive ingestion module utilizes a distributed message queue to achieve high-speed data buffering, ensuring data integrity and temporal consistency in high-concurrency access scenarios.

[0008] A nonlinear feature automated calculation engine, connected to a multi-source heterogeneous data adaptive ingestion module, is used to extract deep feature representations from raw consumption records. The engine employs an autoencoder-based dimensionality reduction mapping mechanism to project high-dimensional, sparse consumption feature vectors onto a low-dimensional, dense manifold space. Through high-order tensor decomposition, the engine extracts multi-dimensional latent features encompassing consumption frequency, consumption time period, geographical location preference, and category association, and dynamically filters the feature subset using the mutual information gain criterion to eliminate redundant and interfering dimensions.

[0009] The Heterogeneous Behavioral Semantic Association Graph Construction Unit is used to construct a multimodal association graph with consumer entities, product entities, payment terminals, and geographical nodes as core vertices based on extracted nonlinear features. The unit utilizes entity alignment algorithms to eliminate semantic ambiguity in data from different channels and quantifies the association strength between nodes based on graph convolutional networks. By calculating the structural hole effect and community clustering coefficient of nodes, the Heterogeneous Behavioral Semantic Association Graph Construction Unit mines cross-domain consumer preference transfer paths, thereby providing structured semantic support for subsequent model inference.

[0010] The Incremental Machine Learning Inference Center is used for real-time trend prediction and anomaly identification of processed data. The center employs a learning framework with asynchronous parameter updates, supporting dynamic fine-tuning of model weights by receiving new samples without service interruption. The center incorporates a combined model integrating a Long Short-Term Memory network and an attention mechanism to capture long-range dependencies in consumer behavior over time. Through knowledge distillation technology, the center transfers the complex decision-making logic of deep learning models to a lightweight inference engine to meet millisecond-level real-time response requirements.

[0011] This multi-dimensional, logically adaptive report generation platform automatically constructs structured consumer reports based on preset analysis dimensions and user-side requirements. The platform includes a semantic topology layout operator, which automatically matches the optimal narrative logic chain based on the quantitative results output by the inference center. Through natural language generation technology, the platform transforms dry statistical indicators into textual descriptions that conform to professional contexts, and utilizes a dynamic rendering engine to generate visual reports including multi-dimensional trend charts, heatmaps, and correlation matrices.

[0012] The multi-source heterogeneous data adaptive ingestion module employs a sliding window-based real-time sampling strategy when processing streaming data. In this embodiment, to ensure the capture accuracy of high-frequency trading data, the step size of the sliding window can be set to 100 milliseconds. Of course, in other embodiments, this step size can also be adjusted to 50 milliseconds or 200 milliseconds depending on the frequency of the data stream; this invention does not impose any limitations on this, to ensure the capture accuracy of high-frequency trading data. The module also executes a multi-replica redundant storage protocol, maintaining three data backups on different physical storage nodes to improve the system's fault tolerance in the event of hardware failure.

[0013] During feature extraction, the nonlinear feature automated calculation engine uses a nonlinear activation function to constrain the hidden layer, and the slope parameter of the activation function is adaptively adjusted according to the distribution density of the input data. The calculation engine also includes an adaptive feature importance evaluator, which is based on the feature importance score of the random forest algorithm. It assigns a weight value between 0 and 1 to each original feature, and only features with a weight value greater than 0.15 are allowed to enter the subsequent calculation stage.

[0014] The heterogeneous behavioral semantic association graph construction unit employs an improved weighted sampling algorithm when establishing node connections. This algorithm not only considers the transaction amount but also introduces a logarithmic decay factor for interaction frequency, thus more realistically reflecting the stickiness of consumer behavior. The unit uses an in-memory graph database to store graph data, supporting second-level query response times for hundreds of millions of nodes.

[0015] The incremental machine learning inference center incorporates an early stopping mechanism during model training to prevent overfitting. When the validation set loss function decreases by less than 0.001 over 10 consecutive training epochs, the system automatically stops parameter updates and saves a snapshot of the current model. The inference center is also equipped with a dynamic load balancer, which distributes inference tasks to the optimal processor cores based on current computing resource utilization.

[0016] The multi-dimensional logical adaptive report generation platform employs a strategy gradient-based logical routing algorithm when constructing report text. This algorithm selects the template combination with the highest logical consistency from 500 pre-set semantic templates based on the report's subject category, such as market research, risk warning, or user profiling. Before outputting visualization results, the platform performs data consistency checks to ensure that the numerical descriptions in the report perfectly match the chart displays.

[0017] The system also includes a global metadata management unit, which is used to version control all data definitions, transformation logic, and model parameters throughout the entire data processing lifecycle. This unit resolves the issue of data semantic conflicts in cross-module collaboration by maintaining a globally unified data dictionary.

[0018] The multi-source heterogeneous data adaptive ingestion module includes a self-healing gateway. When the external interface response delay is detected to be more than 2 seconds, the gateway will automatically switch to the backup link and record the fault log for subsequent analysis.

[0019] When processing image-based consumer data, such as photos of electronic invoices, the nonlinear feature automated calculation engine integrates a deep convolutional neural network for optical character recognition and converts the recognition results into structured key-value pairs stored in the feature vector space.

[0020] The heterogeneous behavioral semantic association graph construction unit introduces a time-aware mechanism, giving edges in the graph a time-sensitive attribute. When the interaction between two nodes occurred 365 days ago, the weight of that edge will decay to 20% of its original value, thus enabling the graph to more sensitively reflect current consumption trends.

[0021] The incremental machine learning inference center supports multi-task parallel learning and can simultaneously perform consumption prediction, fraud detection, and user classification tasks. The tasks complement each other through a shared representation layer, which improves the overall learning efficiency.

[0022] The multi-dimensional logic adaptive report generation platform has a function for filtering permission-sensitive content. Before generating the report, the platform will perform anonymization processing on sensitive data in the report according to the permission level of the current requesting user. For example, it will mask mobile phone numbers that involve personal privacy and use range-based blurring for precise amounts involving trade secrets.

[0023] The system's underlying operating environment is based on a containerized deployment architecture, supporting horizontal scaling according to system load. When the system memory utilization reaches 85% or higher, the container scaling logic is automatically triggered, and the new compute node is deployed within 30 seconds.

[0024] The nonlinear feature automated calculation engine accelerates large-scale tensor operations by leveraging the parallel computing capabilities of graphics processing units, resulting in a performance improvement of more than 15 times compared to traditional central processing units.

[0025] The heterogeneous behavioral semantic association graph construction unit adopts differential privacy technology, adding Laplace noise of a specific intensity during the graph structure publishing process to ensure that while outputting macro trend reports, it is impossible to infer the consumption behavior of specific individuals.

[0026] The incremental machine learning inference center integrates a model performance monitor that can monitor the accuracy of inference results in real time. When the accuracy falls below the warning threshold of 80%, the monitor automatically triggers a full retraining mechanism, calling up all historical data to perform deep calibration of the model.

[0027] The multi-dimensional logical adaptive report generation platform supports interactive report queries. Users can drill down into specific parts of the report using natural language commands, and the platform will generate new sub-analysis reports in real time by calling the inference center based on the context of the user's questions.

[0028] The multi-source heterogeneous data adaptive ingestion module also includes a data pre-normalization subunit, which scales the raw consumption data of different dimensions to a standard range of 0 to 1, eliminating the adverse effects of differences in index magnitude on the model convergence speed.

[0029] In the feature combination stage, the nonlinear feature automated calculation engine adopts a second-order feature interaction pooling technique, which can capture the complex synergistic effects between features, such as abnormal consumption surges under a specific time period and a specific geographical location.

[0030] The heterogeneous behavioral semantic association graph construction unit supports multi-level cascading queries, which can trace all related backgrounds up to 5 levels above a consumption event, thereby realizing full-link attribution analysis.

[0031] The incremental machine learning inference center outputs a confidence score along with the prediction results. When the score is below 0.7, the system automatically marks the prediction result as suspicious and recommends that it be reviewed by human experts for secondary review.

[0032] The multi-dimensional logical adaptive report generation platform incorporates style transfer technology during language rendering, which can automatically adjust the language style according to the professional habits of the report audience. For example, it can use more terminology in reports for technical managers and more action-oriented suggestions in reports for operations personnel.

[0033] The system's security layer employs a multi-factor authentication mechanism based on behavioral biometrics to rigorously audit all administrator operations that access the system backend.

[0034] The nonlinear feature automated calculation engine includes an online feature learner that can learn from the actual business results after the report output based on feedback loops, and continuously optimize the feature weight configuration.

[0035] The heterogeneous behavioral semantic association graph construction unit enhances the system's analytical capabilities during the cold start phase by building a dynamic knowledge base that combines consumption rules summarized by industry experts with machine learning models.

[0036] The incremental machine learning inference center adopts a distributed parameter server architecture, which splits and stores large model parameters on multiple nodes, solving the problem of insufficient single-node memory.

[0037] The multi-dimensional logical adaptive report generation platform supports multi-language output and can automatically translate Chinese reports into the target language based on geographical region settings, while maintaining the accuracy of the original semantics.

[0038] The multi-source heterogeneous data adaptive ingestion module integrates a heartbeat detection mechanism, sending a probe packet to the data source every 5 seconds to monitor the online status of the data source and the bandwidth fluctuation of the transmission link in real time.

[0039] When performing data dimensionality reduction, the nonlinear feature automated calculation engine uses the isometric mapping algorithm in manifold learning, which can preserve the geometric topology in the original high-dimensional data space to the greatest extent.

[0040] In the calculation of node importance, the heterogeneous behavioral semantic association graph construction unit introduces an improved algorithm based on feature vector centrality, which prioritizes the presentation of high-influence core consumer nodes in prominent positions in the report.

[0041] The incremental machine learning inference center uses a stochastic gradient descent algorithm with momentum during the model optimization phase, which effectively accelerates the convergence process of complex loss function surfaces.

[0042] The multi-dimensional logical adaptive report generation platform includes an adaptive typesetting unit that can automatically adjust the size ratio of charts and the line spacing of text according to the display size of the report on different terminals, ensuring a good reading experience on both mobile and desktop devices.

[0043] The system's data exchange layer adopts a highly efficient binary serialization protocol, which reduces the time consumption of data serialization and deserialization by 60% compared to traditional text protocols.

[0044] The nonlinear feature automated calculation engine supports joint probability estimation of missing features and realizes intelligent supplementation of missing data by constructing a latent variable model.

[0045] When detecting abnormal subgraphs, the heterogeneous behavioral semantic association graph construction unit adopts a judgment criterion based on the minimum description length, which can identify highly organized risky behaviors such as consumption cash-out.

[0046] The incremental machine learning inference center supports rapid rollback of model versions. If a newly released model performs abnormally in the production environment, the administrator can switch the system back to the previous stable model version within 10 seconds via the console.

[0047] The multi-dimensional logic adaptive report generation platform integrates a voice broadcasting plugin, which uses high-quality speech synthesis technology to achieve voice output of key report summaries.

[0048] The system is configured with a dynamic caching management strategy, which keeps frequently queried hot data and report templates in high-speed memory, and automatically migrates less frequently accessed cold data to low-cost persistent object storage.

[0049] Compared with the prior art, the advantages and positive effects of the present invention are as follows: This invention achieves deep perception and efficient processing of massive and complex consumer data by constructing a multi-source heterogeneous data adaptive ingestion module and a nonlinear feature automated calculation engine. By utilizing high-order tensor decomposition and autoencoder technology, the system can accurately capture deep, nonlinear consumer trends from seemingly chaotic transaction records, effectively overcoming the limitations of traditional statistical methods in mining complex market patterns, and making the feature extraction dimensions more comprehensive and representative.

[0050] This invention introduces a heterogeneous behavioral semantic association graph construction unit, fundamentally changing the status quo of isolated analysis of consumer data. By establishing an association network covering multiple dimensions such as subjects, entities, and geographical locations, the system can reveal the deep semantic logic and cross-domain transfer paths behind consumer behavior. This structured analysis method based on graph neural networks significantly improves the system's explanatory power for complex market fluctuations, making the generated reports not only data-supported but also logically deep and business-insightful.

[0051] This invention employs an incremental machine learning inference center, which greatly improves the system's timeliness and adaptability. Through asynchronous parameter updates and an online learning framework, the system can absorb the latest consumer data feedback in real time, enabling the model to self-iterate and calibrate. This not only solves the problem of lag in existing technologies when facing sudden market changes, but also ensures low-latency inference performance in high-concurrency environments through technologies such as knowledge distillation, meeting the real-time requirements of enterprise-level applications for dynamic decision-making.

[0052] This invention solves the problems of severe report homogenization and poor readability through a multi-dimensional logical adaptive report generation platform. Based on logical routing algorithms and semantic topology layout, the system can automatically match the optimal narrative logic and display style for different application scenarios and user permissions. This adaptive generation mechanism not only improves the efficiency of report production, but also transforms complex data analysis results into professional documents that are easy to understand and have high decision-making reference value through natural language generation and dynamic rendering technology, greatly enhancing the actual transformation capability of data assets.

[0053] This invention constructs a closed-loop system architecture with self-healing and high fault tolerance capabilities; from the underlying containerized deployment to the upper-layer model monitoring and rollback mechanism, it ensures the high stability and security of the system in a large-scale commercial environment; the application of differential privacy technology achieves a balance between data utilization and privacy protection, providing reliable technical support for the analysis of financial-grade sensitive consumer data, and has extremely high social benefits and economic value. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of the overall technical solution architecture proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the incremental machine learning inference center in this invention; Figure 3 This is a logical flowchart of the adaptive ingestion and standardized mapping of multi-source heterogeneous data in this invention. Figure 4 This is a schematic diagram of the multi-level interaction relationships and data flow of the heterogeneous behavioral semantic association graph in this invention; Figure 5 This is a logical flow framework diagram of the multi-dimensional logical adaptive report generation in this invention. Detailed Implementation

[0055] Example 1 Please refer to the attached document. Figure 1 This embodiment discloses a consumer data report generation system based on machine learning. Built on a distributed computing architecture, the system achieves fully automated processing from raw consumer data acquisition to final professional report output through highly modular component collaboration. The system's underlying operating environment adopts a containerized deployment architecture, which supports flexible horizontal scaling based on real-time computing load. When the system's CPU utilization or memory usage consistently exceeds 85%, the system's internal resource scheduler automatically triggers container scaling logic, ensuring the deployment of new computing nodes and load balancing configuration are completed within 30 seconds.

[0056] The system first employs a multi-source heterogeneous data adaptive ingestion module to perform the data entry function. (Combined with attached...) Figure 3 As shown, this module uses a pre-defined protocol adapter cluster to perform unified and standardized mapping on streaming consumption data and existing batch processing data accessing the system. The protocol adapter cluster supports multiple mainstream communication protocols, including Hypertext Transfer Protocol, Message Queuing Telemetry Transfer Protocol, and Database Connection Protocol. During the data ingestion process, the multi-source heterogeneous data adaptive ingestion module includes a data pre-normalization subunit. This subunit is responsible for linearly scaling the raw consumption data of different dimensions, such as transaction amount, transaction duration, and points, to a standard range of 0 to 1, thereby eliminating the adverse effects of differences in indicator magnitude on the subsequent model convergence speed.

[0057] The multi-source heterogeneous data adaptive ingestion module integrates a self-dynamic data cleaning operator. This operator performs multi-order interpolation compensation for missing values ​​in the data stream based on predefined quality assessment metrics. For time-series consumer data, the operator employs a cubic spline interpolation algorithm to ensure feature continuity. Simultaneously, for outliers in the data, the module performs robust filtering based on local outlier factors. This filtering mechanism identifies and removes noisy data caused by system failures or data entry errors by calculating the density ratio between a specific data point and its neighbors. To handle high-concurrency access scenarios, the module utilizes a distributed message queue for high-speed data buffering. The message queue employs a partitioned storage strategy to ensure data integrity and temporal consistency during concurrent writes.

[0058] In terms of storage security, the multi-source heterogeneous data adaptive ingestion module implements a multi-replica redundant storage protocol. The system maintains three completely identical data backups on different physical storage nodes. When any one of these storage nodes experiences a hardware failure, the system can automatically switch to the backup node, thereby improving overall fault tolerance. Furthermore, this module includes a self-healing gateway. When the gateway detects a response delay of more than 2 seconds from an external interface, it automatically switches to a preset backup link and simultaneously records fault logs and link status parameters, providing decision-making support for subsequent system maintenance. A heartbeat detection mechanism sends probe packets to the data source every 5 seconds to monitor the online status of the data source and bandwidth fluctuations of the transmission link in real time.

[0059] Connected to the multi-source heterogeneous data adaptive ingestion module is a nonlinear feature automatic calculation engine. This engine's task is to leverage the parallel computing power of the graphics processing unit (GPU) to extract deep feature representations from standardized consumption records. When processing image-based consumption data, such as photos of electronic invoices or screenshots of product barcodes, the engine integrates a deep convolutional neural network for optical character recognition and converts the recognition results into structured key-value pairs stored in the feature vector space.

[0060] The nonlinear feature automated calculation engine employs a dimensionality reduction mapping mechanism based on autoencoders. This mechanism projects high-dimensional, sparse consumer feature vectors onto a low-dimensional, dense manifold space through an encoder layer. During data dimensionality reduction, the engine uses the isometric mapping algorithm from manifold learning, which maximizes the preservation of the geometric topology of the original high-dimensional data space. To capture more complex consumption patterns, the nonlinear feature automated calculation engine uses high-order tensor decomposition to extract multi-dimensional latent features encompassing consumption frequency, consumption time period, geographical location preference, and category association. Simultaneously, in the feature combination stage, the engine employs second-order feature interaction pooling, which can capture complex synergistic effects between features, such as anomalous consumption surges that may occur under specific time periods and geographical location combinations.

[0061] The nonlinear feature automated calculation engine constrains the hidden layer using a nonlinear activation function during feature extraction. The slope parameter of this activation function is not fixed but adaptively adjusted based on the distribution density of the input data. The engine also includes an adaptive feature importance evaluator. This evaluator uses a feature importance scoring mechanism based on the random forest algorithm, assigning a weight value between 0 and 1 to each original feature. The system sets a strict screening threshold; only features with a weight value exceeding 0.15 are allowed to enter subsequent calculation stages, thus eliminating a large number of redundant interference dimensions. For missing features, the engine supports joint probability estimation, intelligently imputing missing data by constructing a latent variable model. Furthermore, the engine includes an online feature learner that, based on a feedback loop, performs reverse learning on the actual business results after report output, continuously and dynamically optimizing the feature weight configuration.

[0062] Next, the heterogeneous behavioral semantic association graph construction unit, based on the extracted nonlinear features, constructs a multimodal association graph with consumer subjects, product entities, payment terminals, and geographical nodes as core vertices. Please refer to the appendix. Figure 4 This unit utilizes entity alignment algorithms to eliminate semantic ambiguity in data from different channels, ensuring the uniqueness of the same consumer's identity across different dimensions. It also uses graph convolutional networks to quantitatively model the strength of associations between nodes. An improved weighted sampling algorithm is employed when establishing node connections. This algorithm considers not only the transaction amount but also introduces a logarithmic decay factor for interaction frequency, thus more realistically reflecting the stickiness of consumer behavior.

[0063] The heterogeneous behavioral semantic association graph construction unit introduces a time-aware mechanism, giving edges in the graph a distinct time-sensitivity attribute. When an interaction between two nodes occurred 365 days ago, the weight of that edge automatically decays to 20% of its original value, allowing the graph to more sensitively reflect current consumption trends and behavioral changes. For efficient querying, this unit uses an in-memory graph database to store graph data, supporting second-level query responses for hundreds of millions of nodes. Furthermore, this unit supports multi-level cascading queries, enabling the tracing of all related contexts up to five levels above a consumption event, thus achieving end-to-end attribution analysis.

[0064] In terms of analytical depth, the heterogeneous behavioral semantic association graph construction unit mines cross-domain consumption preference transfer paths by calculating the structural hole effect and community clustering coefficient of nodes. When detecting abnormal subgraphs, a judgment criterion based on minimum description length is adopted, which can identify highly organized risky behaviors such as cash-out consumption or fraudulent transactions. In the calculation of node importance, an improved algorithm based on eigenvector centrality is introduced, prioritizing the presentation of core consumption nodes with high influence. To protect user privacy, this unit employs differential privacy technology, adding Laplace noise of a specific intensity during the graph structure publication process, ensuring that while outputting macro-trend reports, external attackers cannot reverse-engineer the specific consumption behavior of individual users. Furthermore, this unit also builds a dynamic knowledge base, combining consumption rules summarized by industry experts with machine learning models, effectively improving the system's cold-start analysis capabilities when facing new domains.

[0065] The core decision-making component of the system is the incremental machine learning inference center. (Combined with the attached...) Figure 2 As shown, this center is used for real-time trend prediction and anomaly identification of processed map data and feature vectors. The inference center adopts a learning framework with asynchronous parameter updates, which supports real-time reception of new samples for dynamic fine-tuning of model weights without service interruption. Internally, the center employs a distributed parameter server architecture, splitting and storing the massive model parameters across multiple computing nodes, effectively solving the technical bottleneck of insufficient GPU memory on a single node.

[0066] The incremental machine learning inference center incorporates a combined model that integrates a Long Short-Term Memory (LSTM) network and an attention mechanism. The LSM network captures the long-term dependencies of consumer behavior over time, while the attention mechanism focuses on the key time points that contribute most to the prediction results. During model optimization, the system uses a stochastic gradient descent algorithm with momentum, effectively accelerating the convergence of the complex loss function surface. To prevent overfitting during training, the inference center introduces an early stopping mechanism. When the loss function on the validation set decreases by less than 0.001 over 10 consecutive training epochs, the system automatically stops parameter updates and saves a snapshot of the current optimal model.

[0067] The incremental machine learning inference center utilizes knowledge distillation technology to migrate the complex decision-making logic of deep learning models to a lightweight inference engine, meeting millisecond-level real-time response requirements. The inference center is also equipped with a dynamic load balancer, which distributes inference tasks to the optimal processor cores in real time based on current computing resource utilization. The center supports multi-task parallel learning, capable of simultaneously executing consumption prediction, fraud detection, and user classification tasks, with each task achieving feature complementarity through a shared representation layer.

[0068] On the output side, the inference center integrates a model performance monitor, enabling real-time monitoring of inference result accuracy. When the accuracy falls below the 80% warning threshold, the monitor automatically triggers a full retraining mechanism, calling upon all historical data to perform deep model calibration. Furthermore, the center supports rapid model version rollback; if a newly released model performs abnormally in the production environment, administrators can switch the system back to the previous stable model version within 10 seconds via the console. When outputting prediction results, the system simultaneously includes a confidence score. When the score is below 0.7, the system automatically marks the prediction result as suspicious and recommends secondary review by human experts.

[0069] Finally, the multi-dimensional logical adaptive report generation platform is responsible for automatically constructing a structured consumption report based on preset analysis dimensions and user-side requirements. (Combined with appendix...) Figure 5 As shown, the platform includes a semantic topology layout operator, which can automatically match the optimal narrative logic chain based on the quantification results output by the inference center. When constructing the report text, the platform employs a policy gradient-based logical routing algorithm. This algorithm selects the template combination with the highest logical consistency from 500 pre-set semantic templates, based on the report's topic category, such as market research, risk warning, or user profiling.

[0070] This multi-dimensional, logically adaptive report generation platform uses natural language generation technology to transform dry statistical indicators into textual descriptions that conform to professional contexts. During language rendering, style transfer technology is incorporated, automatically adjusting the language style based on the professional habits of the report's audience. For example, reports for technical managers will use more algorithmic terminology, while reports for business operations personnel will offer more action-oriented suggestions. The platform also utilizes a dynamic rendering engine to generate visual reports including multi-dimensional trend charts, heatmaps, and correlation matrices. Before outputting the visualizations, the platform performs rigorous data consistency checks to ensure that the numerical descriptions in the report perfectly match the content displayed in the charts.

[0071] To ensure information security, the multi-dimensional, logic-adaptive report generation platform features access-sensitive content filtering. Before generating a report, the platform de-identifies sensitive data based on the requesting user's permission level. For example, it masks phone numbers containing personal information and uses range-based fuzzy processing for precise monetary amounts involving trade secrets. Furthermore, the platform supports interactive report queries. Users can use natural language commands to perform in-depth drill-down analysis on specific sections of the report, and the platform, based on the user's question context, calls the inference center in real time to generate new sub-reports.

[0072] The multi-dimensional, logically adaptive report generation platform also includes an adaptive typesetting unit. This unit automatically adjusts the size of charts and the line spacing of text based on the report's display size on different devices, ensuring a good reading experience on both mobile and desktop. Furthermore, the platform integrates a voice broadcast plugin, utilizing high-quality speech synthesis technology to provide narrated output of key report summaries. The platform supports multilingual output and can automatically translate Chinese reports into the target language based on geographic region settings.

[0073] The system also features a global metadata management unit. This unit manages the versioning of all data definitions, transformation logic, and model parameters throughout the data processing lifecycle. By maintaining a globally unified data dictionary, this unit resolves data semantic conflicts in cross-module collaboration. The system's data exchange layer employs a highly efficient binary serialization protocol. Compared to traditional text protocols, data serialization and deserialization time is reduced by 60%.

[0074] In addition, the system is configured with a dynamic caching management strategy. This strategy keeps frequently queried hot data and report templates residing in high-speed memory, while automatically migrating less frequently accessed cold data to low-cost persistent object storage. The system's security layer employs a multi-factor authentication mechanism based on behavioral biometrics, rigorously auditing all administrator operations accessing the system backend to ensure the security and compliance of system operation.

[0075] At the algorithm level, the incremental machine learning inference center in this embodiment adopts a structured loss function defined as follows to balance prediction accuracy and model complexity when performing loss function calculation:

[0076] In the above formula, This represents the total loss value. The first term on the right-hand side of the equation is the mean squared error term, where... Represents the total number of samples. Representing the The actual consumption observations of a sample, while This represents the predicted value output by the model. The mean squared error term measures the deviation between the model's prediction and the actual observed data; minimizing this term can improve prediction accuracy. The second term on the right-hand side of the equation is the regularization term, used to prevent the model from overfitting. This is a preset regularization coefficient used to balance the relationship between prediction accuracy and model sparsity. Represents the total number of model parameters. Representing the The model has several weight parameters. By introducing the sum of the absolute values ​​of these weight parameters, the loss function can guide the model towards sparsity, thereby eliminating feature connections that contribute little to the prediction results and improving inference efficiency.

[0077] To further quantify the influence transfer between nodes in the heterogeneous behavior semantic association graph, the heterogeneous behavior semantic association graph construction unit adopts the following defined node state update rules when performing graph convolution operations:

[0078] In the above formula, Representing the The node feature matrix after adding one convolutional layer, and Then it represents the first The input feature matrix of the layer. (Symbol) This represents an adjacency matrix containing self-connections, used to describe the topological connections between nodes in a graph. (Symbol) This represents the corresponding degree matrix, and its function is to normalize the adjacency matrix to prevent gradient explosion or vanishing problems when stacking convolutional layers in depth. Representing the The learnable weight matrix of a layer is responsible for performing nonlinear transformations and dimension mappings on the input node features. (Symbols) Representing nonlinear activation functions, typically linear rectified functions or hyperbolic tangent functions are used to enhance the nonlinear expressive power of the model. Through this multi-layer convolution operation, the system can deeply fuse the local behavioral features of consumers with their structural features in the global graph.

[0079] In actual operation, this system first continuously accesses transaction settlement data, mobile payment records, and offline POS terminal data from various provinces through a multi-source heterogeneous data adaptive ingestion module. The data enters the self-healing gateway via a protocol adapter cluster, where the gateway monitors the data flow in real time. If the packet loss rate of a data link exceeds 0.05%, the self-healing gateway immediately initiates link redirection logic, scheduling subsequent data to a high-performance backup channel. The ingested raw data undergoes dimensional unification in the data pre-normalization subunit before entering the self-dynamic data cleaning operator. Here, for missing transaction geographic location information, the operator calls a mean interpolation algorithm based on historical behavioral trajectories to complete the data.

[0080] The cleaned data is pushed to a nonlinear feature automated calculation engine. The engine calls upon a graphics processing unit cluster to first execute an optical character recognition task based on a deep convolutional neural network, parsing the merchant name, product details, and tax amount from scanned paper invoices. Subsequently, the engine uses an autoencoder to compress the original sparse feature space, containing 2000 dimensions, into a dense manifold space of 128 dimensions. During this process, an adaptive feature importance evaluator scores each dimension. For example, it finds that the weight of the consumption time feature is 0.85, while the weight of the payment terminal's system version number feature is only 0.02. Based on a screening threshold of 0.15, the payment terminal's system version number feature is automatically removed and no longer participates in subsequent calculations.

[0081] The processed feature vectors are input into the heterogeneous behavioral semantic association graph construction unit. This unit constructs a dynamic graph with 50 million nodes and 200 million edges in a memory-level graph database. Each node in the graph represents an independent consumer entity or geographical location. When the system detects that two originally unrelated consumer accounts frequently generate high-value transactions at the same geographical node within a short period of time, the weighted sampling algorithm increases the weight of the edge between these two nodes. Simultaneously, a time-aware mechanism scans the entire graph in real time, performing a 20% weight decay on outdated association edges that have not interacted for more than 365 days to maintain the timeliness of the graph.

[0082] The incremental machine learning inference center extracts subgraph structural features from the graph in real time and inputs them into the Long Short-Term Memory network in combination with time-series features. During computation, the attention mechanism allocates 60% of the weights to the consumption fluctuation features of the most recent 3 days, and the remaining 40% to the trend features of the past 30 days. When the inference center predicts a surge in a certain product category within the next 24 hours, if the confidence score reaches 0.92, the result is directly sent to the report generation platform. If the prediction accuracy monitor detects that the current hit rate has dropped to 78% due to seasonal fluctuations, a full retraining mechanism is immediately triggered, using seasonal consumption data from the past 3 years to strengthen and calibrate the model.

[0083] Finally, the multi-dimensional logical adaptive report generation platform receives the prediction vector from the inference center. The logical routing algorithm identifies the current request as a market research-related need, and thus retrieves the corresponding professional description module from the semantic template library. Natural language generation technology converts the predicted value into a Chinese text description. The layout unit adjusts the scaling of the visual heatmap to 1.2x and sets the line spacing to 1.5x based on the 12.9-inch tablet terminal currently being used by the administrator. The entire process, from data access to report presentation, takes less than 2 seconds.

[0084] Example 2 Building upon Example 1, this example focuses on describing the system's in-depth application in complex financial fraud scenarios. In this specific implementation scenario, the multi-source heterogeneous data adaptive acquisition module enhances the ability to capture mobile sensor data, including non-traditional consumer data such as device gravity sensing, touch pressure, and swipe trajectories.

[0085] When processing these high-frequency sensor data, the nonlinear feature automated calculation engine introduces wavelet transform operators to extract microscopic behavioral features in the time-frequency domain. In this scenario, the adaptive feature importance evaluator assigns extremely high weights to abnormal scrolling speeds and irregular pressure values, as these features are typically highly correlated with illegal operations by automated scripts or group control devices.

[0086] The heterogeneous behavioral semantic association graph construction unit constructs a dedicated subgraph for fraud detection. By calculating the structural hole effect of nodes, this unit can identify key transit nodes connecting multiple consumer clusters. If a node has frequent small-amount financial transactions with a large number of unrelated consumers within a short period of time, and the time intervals between these transactions exhibit a high degree of periodicity, the graph construction unit will mark it as a potential money laundering node using a criterion based on minimum description length.

[0087] In this embodiment, the incremental machine learning inference center employs a multi-task parallel learning strategy. In addition to the regular consumption prediction task, the inference center simultaneously runs a fraud simulation task based on an adversarial generative network. By generating fake fraud samples, the identification model is trained, enabling it to have stronger generalization capabilities. The role of knowledge distillation here is to compress the complex adversarial model into a real-time interception operator with millisecond-level response, deployed at the forefront of the transaction.

[0088] The multi-dimensional, logic-adaptive report generation platform automatically switches to a risk warning style when dealing with such risk reports. The report prominently highlights abnormal connection paths in the correlation matrix and uses a dynamic rendering engine to highlight high-risk areas in red. To meet audit requirements, the global metadata management unit performs full documentation of the generation logic, the model version used, and the data source for each risk report.

[0089] To protect the privacy of those under investigation, the system employs differential privacy-preserving noise enhancement by its heterogeneous behavioral semantic association graph construction unit when generating external reports for regulatory authorities. While maintaining the accuracy of gang detection, the system applies a Laplace distribution-based randomization shift to the individual's specific location information and precise spending amounts, ensuring the report serves as an early warning system while remaining legally compliant.

[0090] Example 3 Building upon Examples 1 and 2, this example demonstrates the application of this system in the precise modeling of user profiles for large retail chain enterprises. In this scenario, the multi-source heterogeneous data adaptive ingestion module integrates access to electronic fence data from offline stores. When an anonymous user enters the store area, the self-healing gateway ensures stable transmission of location pulse data.

[0091] The nonlinear feature automated calculation engine performs high-order tensor decomposition to meet user profiling needs. The engine constructs a fourth-order tensor from the user's purchase history, preferred brands, store visit frequency, and dwell time. Through tensor decomposition, it extracts the user's consumer lifecycle features. An adaptive feature importance evaluator found that the correlation weight between user dwell time in a specific product category and the final conversion rate is as high as 0.92; therefore, this feature was selected as a core indicator for building user profiles.

[0092] In this scenario, the heterogeneous behavioral semantic association graph construction unit focuses on mining the strength of associations between product entities. If two seemingly unrelated product categories have a high community clustering coefficient in the graph, it indicates that they have significant complementary consumption characteristics. Through multi-level cascading queries, the system can trace back to the time points and geographical environments in which such complementary consumption typically occurs.

[0093] The incremental machine learning inference center generates a dynamically evolving prediction vector for each user profile. Because user preferences change in real time with seasons and promotional activities, the asynchronous parameter update framework of the inference center ensures the timeliness of the profiles. When the model performance monitor detects a decline in the recommendation conversion rate of a certain user profile, it triggers a local retraining mechanism, calibrating parameters only for data from that specific customer group, thus saving computational resources.

[0094] The multi-dimensional, logic-adaptive report generation platform generates reports in different styles for store managers and headquarters decision-makers. Reports for store managers focus on shelf layout optimization suggestions and have a more action-oriented language style; reports for headquarters decision-makers focus on regional consumption trends and competitive landscape analysis, using numerous correlation matrices and multi-dimensional trend charts. Style transfer technology ensures that the same set of data analysis results can deliver maximum decision-making value to different audiences.

[0095] The underlying operating environment of the entire system leverages the horizontal scalability of a containerized architecture to smoothly support the surge in data processing requests during major promotional events such as Singles' Day. When the system detected that the traffic at the data exchange layer reached 10GB per second, the automatic scaling logic launched 50 new data ingestion operator images within 15 seconds. Simultaneously, the dynamic caching management strategy kept the association rules for best-selling products residing in high-speed memory, keeping the response time for interactive report queries within 500 milliseconds.

[0096] Example 4 This embodiment further illustrates the system's self-healing and high fault tolerance mechanisms under extreme hardware environments. In a simulated physical data center power outage experiment, the system demonstrated excellent stability. Due to the implementation of a multi-replica redundant storage protocol, the multi-source heterogeneous data adaptive ingestion module detected the anomaly within one second of the primary storage node's failure via a heartbeat detection mechanism.

[0097] The system immediately activated its self-healing gateway, automatically switching the real-time streaming consumption data to a remote backup center. Thanks to the use of a highly efficient binary serialization protocol, cross-regional data transmission latency was compressed to within 50 milliseconds, ensuring no data loss or out-of-order processing. Simultaneously, the incremental machine learning inference center utilized a model snapshot rollback mechanism to quickly restore the operating environment to its last stable state before the failure occurred.

[0098] When computing resources are limited, the inference center uses a dynamic load balancer to prioritize the generation of high-priority risk warning reports, while temporarily suspending low-priority routine statistical reporting tasks. This degradation strategy ensures the continuity of core business operations. Once the physical environment returns to normal, the containerized deployment architecture automatically reclaims redundant standby nodes, restoring system resource usage to normal levels.

[0099] After the fault is recovered, the global metadata management unit automatically generates a full-link fault backtracking report. This report details the data gap interpolation compensation process during the fault, as well as the communication retry logs between modules. Through the dynamic rendering engine of the multi-dimensional logical adaptive report generation platform, operations personnel can intuitively view the subtle disturbance curves caused by the fault on the model prediction accuracy. The system has a failover mechanism, automatically switching to a backup node when the primary node fails.

[0100] During fault recovery, the nonlinear feature automated calculation engine utilizes an online feature learner to intelligently supplement lost data segments based on historical probability distributions. This supplementation mechanism, based on joint probability estimation, ensures that the generated consumption report maintains a consistency of over 0.95 with the true trend even in extreme cases of data loss, fully demonstrating the robustness and reliability of the proposed solution in complex industrial environments.

[0101] In summary, this invention constructs a closed-loop system with adaptive feature mining, structured semantic understanding, real-time incremental inference, and intelligent logical routing through deep collaboration of multiple modules. This system not only solves the performance challenges of processing massive heterogeneous consumer data but also achieves efficient transformation of data insights into business decisions through innovative graph modeling and natural language rendering technologies. Its containerized architecture and self-healing mechanism provide a solid guarantee of stability for large-scale commercial use, demonstrating significant technological advancements and broad application prospects.

Claims

1. A consumer data report generation system based on machine learning, characterized in that, include: The multi-source heterogeneous data adaptive ingestion module is used to perform unified and standardized mapping of streaming consumption data and existing batch processing data in the access system through a protocol adapter cluster, and to perform dynamic data cleaning and outlier filtering. The nonlinear feature automatic calculation engine is connected to the multi-source heterogeneous data adaptive ingestion module and is used to extract deep nonlinear features from the standardized consumption records and to reduce the dimensionality and remove redundancy from the high-dimensional sparse feature vector. The heterogeneous behavioral semantic association graph construction unit is used to construct a multimodal association graph with consumer subjects, commodity entities, payment terminals and geographical nodes as core vertices based on the nonlinear features, and to quantify the semantic association strength between nodes through graph neural networks. An incremental machine learning inference center is used to perform real-time trend prediction and anomaly identification based on the correlation graph and feature vectors, and supports online dynamic fine-tuning of model parameters; This multi-dimensional logic-adaptive report generation platform automatically matches narrative logic with user requirements and inference results to generate structured consumer data reports that include natural language descriptions and visual charts.

2. The machine learning based consumption data report generation system of claim 1, wherein, The multi-source heterogeneous data adaptive ingestion module includes: Protocol adapter subunit, used to be compatible with multiple data access protocols to achieve unified access to heterogeneous data sources; The data prenormalization subunit is used to scale raw consumption data of different dimensions to a uniform numerical range. The self-dynamic data cleaning subunit is used to interpolate and compensate for missing values ​​and perform robust filtering of outliers; The self-healing gateway subunit is used to automatically switch to the backup data channel when an anomaly is detected in the primary link.

3. The consumer data report generation system based on machine learning according to claim 1, characterized in that, The automated nonlinear feature calculation engine includes: An autoencoder dimensionality reduction unit is used to project high-dimensional consumption features onto a low-dimensional dense manifold space; Higher-order tensor decomposition units are used to extract implicit features covering consumption frequency, time period, geographical location, and category correlation. An adaptive feature filtering unit is used to dynamically remove redundant dimensions based on feature importance scores; The image semantic parsing unit is used to perform optical character recognition on electronic invoice image data and convert it into structured features.

4. The consumer data report generation system based on machine learning according to claim 1, characterized in that, The heterogeneous behavior semantic association graph construction unit includes: Entity alignment sub-units are used to eliminate identity ambiguity in cross-channel data to ensure node uniqueness; The time-aware edge weight calculation subunit is used to dynamically decay the strength of historical associations based on the timeliness of interactions; Graph convolutional embedding subunits are used to fuse local behavior and global structural features through multi-layer graph neural networks; Anomaly subgraph detection subunit is used to identify potential risky consumer groups based on structured criteria.

5. The consumer data report generation system based on machine learning according to claim 1, characterized in that, The incremental machine learning inference center includes: Combined prediction model units are used to combine long short-term memory networks and attention mechanisms to capture temporal dependencies; The knowledge distillation and compression unit is used to transfer the decision logic of complex models to a lightweight inference engine; A multi-task learning scheduling unit is used to execute consumption prediction, fraud detection, and user classification tasks in parallel; The model performance monitoring and rollback unit is used to trigger retraining or version rollback when the accuracy falls below a threshold.

6. The consumer data report generation system based on machine learning according to claim 1, characterized in that, The multi-dimensional logical adaptive report generation platform includes: The logical routing decision unit is used to select the optimal narrative logic from a pre-built template library based on the report topic category; The Natural Language Generation Unit is used to convert statistical indicators into text descriptions that conform to professional contexts. The style transfer rendering unit is used to adjust the language style and suggestion orientation based on the audience role. The dynamic visualization engine unit is used to generate multi-dimensional trend charts, heat maps, and correlation matrices.

7. The consumer data report generation system based on machine learning according to claim 6, characterized in that, The multi-dimensional logical adaptive report generation platform also includes: The permission-sensitive de-identification unit is used to mask or obfuscate sensitive information according to the user's permission level. An interactive drill-down query unit is used to generate in-depth analysis sub-reports in response to natural language commands; Adaptive typesetting unit, used to dynamically adjust the chart ratio and text line spacing according to the terminal display size; The multilingual output unit is used to automatically translate report content by region and maintain semantic consistency.

8. The consumer data report generation system based on machine learning according to claim 1, characterized in that, The system also includes a global metadata management unit, which is used to manage the full lifecycle version of data definitions, transformation logic and model parameters, and resolves cross-module semantic conflicts through a unified data dictionary.

9. The consumer data report generation system based on machine learning according to claim 1, characterized in that, The heterogeneous behavior semantic association graph construction unit integrates a differential privacy protection mechanism during the graph publishing process, which prevents individual consumption behavior from being inferred in reverse by injecting Laplace noise.

10. The consumer data report generation system based on machine learning according to claim 1, characterized in that, The system adopts a containerized deployment architecture at its underlying layer, supports automatic horizontal scaling based on resource utilization, and configures dynamic caching strategies to keep hot data in high-speed memory to optimize access performance.

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