Dynamic modeling and self-learning optimization method in intelligent AI data insight analysis

Abstract

The invention provides a dynamic modeling and self-learning optimization method in intelligent AI data insight analysis, and the method comprises the steps: obtaining multi-source heterogeneous data, and carrying out the preprocessing of the multi-source heterogeneous data; identifying the multi-source heterogeneous data through the anomaly detection model to obtain an anomaly identification result; dynamic modeling and self-learning optimization are carried out on the anomaly detection model, the dynamic modeling is carried out by means of a branch neural architecture search, and the self-learning is realized by means of deep learning and reinforcement learning; and performing anomaly repair according to an anomaly recognition result. Through dynamic modeling, self-learning optimization and other capabilities, intelligent insight analysis of business data and operation and maintenance logs is realized, and the stability of system operation and the accuracy of the business data are guaranteed.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and more specifically, to a dynamic modeling and self-learning optimization method for intelligent AI data insight analysis. Background Technology

[0002] As enterprises deepen their digital transformation, the scale and complexity of data are increasing dramatically, posing serious challenges to traditional data analysis methods in terms of dynamism, intelligence, and automation. Current systems generally suffer from rigid data processing workflows, limited anomaly detection methods, lagging model updates, and reliance on manual intervention, making it difficult to meet enterprises' needs for real-time, accurate, and adaptive data insights. Therefore, there is an urgent need for a comprehensive AI-powered data intelligence analysis method that integrates dynamic modeling, self-learning optimization, and intelligent repair to cope with complex and ever-changing business environments, including system and data stability. Summary of the Invention

[0003] In view of this, the present invention proposes a dynamic modeling and self-learning optimization method for intelligent AI data insight analysis, in order to solve the problems existing in the prior art.

[0004] To achieve the above objectives, this invention proposes a dynamic modeling and self-learning optimization method for intelligent AI data insight analysis, comprising: Acquire multi-source heterogeneous data and preprocess the multi-source heterogeneous data; Anomaly detection models are used to identify multi-source heterogeneous data, and anomaly identification results are obtained. The anomaly detection model is dynamically modeled and self-learned for optimization. The dynamic modeling is performed through a search of subdivided neural architectures, and the self-learning is achieved through deep learning and reinforcement learning. Anomaly repair is performed based on the anomaly identification results.

[0005] Optionally, the multi-source heterogeneous data includes business data, log data, and system data transmitted from different business platforms.

[0006] Optionally, multi-source heterogeneous data can be preprocessed using an ETL engine and standardization tools, wherein the preprocessing includes missing value imputation, duplicate removal, normalization, feature encoding, and dimensionality reduction.

[0007] Optionally, the anomaly detection model is constructed using one or more combinations of clustering algorithms, classification algorithms, similarity calculations, and deep neural networks.

[0008] Optionally, the self-learning optimization process of the anomaly detection model includes: Automatically acquire feedback information, generate training samples based on the feedback information, and automatically learn from the anomaly detection model based on the number of training samples or the performance of the anomaly detection model to obtain a new model. Run the new model in isolation, compare the performance of the new model with the anomaly detection model, and replace the anomaly detection model based on the new model.

[0009] Optionally, the automated modeling process for the anomaly detection model includes: For deep neural networks in anomaly detection models, relevant constraints are constructed, including constraints on network depth, number of parameters, and operation type. Construct a hypernetwork containing candidate operations with replaceable architectures, wherein each candidate operation is assigned a corresponding architecture weight; wherein the hypernetwork is a computation graph structure, wherein the computation graph contains feature maps generated by different candidate operations as nodes and candidate operations as edges, wherein different replaceable architectures in the candidate operations have corresponding network weights. The network weights and architecture weights of the supernetwork are updated sequentially or alternately. Based on the final architecture weights, the corresponding candidate operations and corresponding network weights are selected as the final architecture and corresponding network weights.

[0010] Optionally, a self-learning optimization loop for the anomaly detection model can be implemented by deploying a simulation environment.

[0011] On the other hand, the present invention provides a dynamic modeling and self-learning optimization system for intelligent AI data insight analysis, used to perform the above-mentioned methods.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: Deep AI data insight and analysis services, based on artificial intelligence, big data analytics, and intelligent operation and maintenance technologies, aim to address the shortcomings of existing information service platforms in data anomaly analysis and operational fault analysis. Through data cleaning, pattern recognition, anomaly detection, dynamic modeling, and self-learning optimization capabilities, the system achieves intelligent insight and analysis of business data and operational logs, ensuring system stability and the accuracy of business data.

[0013] The dynamic modeling and self-learning optimization method for intelligent AI data insight analysis provided in this invention effectively overcomes the shortcomings of existing data processing systems in terms of intelligence, adaptability, and automation, demonstrating significant comprehensive technical effects. By constructing a unified data governance platform integrating ETL and standardized tools, the system achieves automated cleaning, transformation, and reconstruction of multi-source heterogeneous data, ensuring high-quality data input and laying a reliable foundation for subsequent analysis. In anomaly detection, the method breaks through the limitations of traditional single algorithms, innovatively integrating multiple technologies such as DBSCAN clustering, decision trees, vector similarity analysis, and deep neural networks to form a "multi-model, multi-strategy" detection mechanism that can dynamically adapt to different scenarios, significantly improving the recognition accuracy and real-time performance in complex environments.

[0014] Leveraging a dynamic modeling and self-learning engine based on deep learning and reinforcement learning, the system enables models to continuously evolve and dynamically adjust their structure. This allows for adaptive optimization in response to changes in business and data, effectively avoiding static model failures and significantly reducing manual maintenance costs. Furthermore, the approach goes beyond anomaly detection, constructing an AI-driven repair and self-healing mechanism covering various issues such as data source anomalies, coordinate errors, and push failures. It supports both automatic and manual data collection, ensuring business continuity and data integrity. At the operations and maintenance support level, the system incorporates natural language processing and intelligent log parsing technologies to automatically locate fault root causes and generate handling suggestions, greatly improving operational response efficiency and intelligence.

[0015] Furthermore, by combining a visual insight platform with multi-strategy simulation capabilities, the system provides users with an intuitive data presentation and decision-making environment, supporting the evaluation and optimization of business strategies in virtual scenarios, forming a data-driven closed-loop decision-making system. Overall, this invention achieves a fully intelligent closed-loop process from data perception, anomaly detection, dynamic modeling, self-healing repair to root cause localization, driving a fundamental shift in data analysis systems from "passive response" to "proactive insight," and from "rule dependence" to "learning and evolution." This provides solid technical support for enterprises to achieve system stability, efficient operation and maintenance, and scientific decision-making in complex and ever-changing digital environments. Attached Figure Description

[0016] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. In the drawings: Figure 1 This is a schematic diagram of the method flow in an embodiment of the present invention. Detailed Implementation

[0017] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0018] This embodiment proposes a dynamic modeling and self-learning optimization method for intelligent AI data insight analysis, breaking through the traditional passive security protection mechanism. It introduces AI to achieve proactive and predictive technology, enhancing intelligent security protection capabilities across the entire stack and domain. Targeted and proactive security management improvements are implemented based on the characteristics of existing application platforms. Leveraging intelligent AI technology, security issues or unstable factors are proactively identified from system indicators and business activity data, enabling proactive risk discovery and automatic risk management, thus preventing problems before they occur. This improves intelligent security protection capabilities across the entire stack and domain, covering network, applications, data, identity and permissions, and intelligent security auditing.

[0019] This invention proposes a dynamic modeling and self-learning optimization method for intelligent AI data insight analysis, such as... Figure 1 As shown, it includes the following: As business scale continues to expand and multi-source data is integrated, traditional data processing and analysis methods can no longer meet the platform's needs in anomaly detection, operational support, and intelligent decision-making. The client urgently requires a deep data insight and analysis service based on artificial intelligence technology to address the following core issues: End-to-end data governance: enabling the cleaning, transformation, standardization, and reconstruction of scattered, heterogeneous, and multi-type data to ensure high-quality data input.

[0020] Intelligent anomaly detection: Based on AI algorithms, potential data anomalies and fluctuations are detected in real time to avoid business interruption and risk amplification.

[0021] Dynamic modeling and self-learning: Constructing a continuously evolving model system that can adapt to changes in data distribution and business development needs.

[0022] Abnormal data and business recovery: It is not only necessary to detect anomalies, but also to have the ability to automatically repair and re-collect data to ensure business continuity.

[0023] Operation and maintenance fault root cause analysis: Through intelligent analysis of logs and operational data, it enables rapid location of complex faults and generation of intelligent handling suggestions.

[0024] The focus of deep AI data insight and analysis services The following aspects need to be given special attention during the construction of this project: End-to-end data governance and preprocessing: ensuring the accuracy, consistency, and usability of data to provide high-quality input for AI models.

[0025] Intelligent pattern recognition and anomaly detection: Establish a multi-algorithm fusion mechanism to achieve real-time and accurate anomaly detection.

[0026] Dynamic modeling and self-learning capabilities: Support continuous model optimization to adapt to ever-changing business and data environments.

[0027] Anomaly repair and self-healing mechanism: Provides intelligent repair and automatic data replenishment to ensure uninterrupted system operations.

[0028] Intelligent operation and maintenance and fault location: By leveraging log analysis and root cause tracing, a rapid response operation and maintenance support capability is formed.

[0029] Challenges of providing deep AI data insight and analysis services: In the actual implementation process, the construction will face the following main challenges: Data source complexity: diverse data types (structured, semi-structured, unstructured), inconsistent standards, and significant challenges in integration and governance.

[0030] The conflict between real-time performance and accuracy: In high-concurrency business environments, achieving low latency and high accuracy in anomaly detection is a technical bottleneck.

[0031] Model failure risk: Static models are difficult to adapt to dynamic changes in the business environment and data distribution, and require continuous iterative optimization.

[0032] Anomaly repair and business continuity: Simple anomaly detection is insufficient to meet business needs; efficient repair and link assurance must be implemented simultaneously.

[0033] Complex fault correlation analysis: With long fault chains and multiple systems involved, accurately identifying the root cause and generating feasible solutions is a key challenge in technology implementation.

[0034] Solutions for in-depth AI data insight and analysis services: Deep AI data insight and analysis services, based on artificial intelligence, big data analytics, and intelligent operation and maintenance technologies, aim to address the shortcomings of existing information service platforms in data anomaly analysis and operational fault analysis. Through data cleaning, pattern recognition, anomaly detection, dynamic modeling, and self-learning optimization capabilities, the system achieves intelligent insight and analysis of business data and operational logs, ensuring system stability and the accuracy of business data.

[0035] The system adopts big data processing frameworks (such as Spark and Flink) and AI modeling platforms (such as TensorFlow and PyTorch) to build end-to-end data processing and analysis capabilities, covering modules such as data cleaning, aggregation and transformation, pattern recognition, anomaly detection and dynamic modeling.

[0036] In response to the above needs and challenges, the following comprehensive solution is proposed: 1. Unified data governance and preprocessing platform Build a comprehensive governance platform covering the entire process of data collection, cleaning, transformation, and reconstruction. Through an ETL engine and standardized tools, it supports operations such as missing value imputation, duplicate removal, normalization, feature encoding, and dimensionality reduction, ensuring data consistency and high quality. This content guarantees a high-quality data foundation for subsequent dynamic modeling and learning.

[0037] The aforementioned multi-source heterogeneous data includes business data, log data, system data, and other data accessed by existing different business platforms.

[0038] The platform has data cleaning and restructuring capabilities: The system supports the aggregation and structural reconstruction of distributed and heterogeneous data sources to form a unified and standardized data structure; Supports automated error data identification and repair, including missing value filling, duplicate value removal, and outlier correction; It supports feature engineering capabilities, such as normalization, feature encoding, and dimensionality reduction, to accelerate the transformation of raw data into model input data.

[0039] Specifically, the ETL engine is a core tool for data governance, collecting multimodal data from multiple sources. This data includes records in databases, application-generated log files, and information provided by other systems via APIs. It identifies missing data and fills in the gaps using predefined methods, removes identical duplicate records, and corrects unreasonable or out-of-range "outliers," ensuring data integrity and cleanliness.

[0040] Then, the engine will transform and reconstruct the cleaned data. It will unify the data format, such as changing all dates to the "year-month-day" format, or converting all data to a unified format, such as converting all images to JPG, converting text descriptions into numerical codes that machines can better understand, and even calculating and generating new and more valuable business metrics.

[0041] It also records the source data for each finished product and the processes it went through. This allows for rapid tracing of the source should any data issue arise.

[0042] In the ETL engine, an additional standardization tool is added. This tool is used to perform the normalization, feature encoding, and dimensionality reduction operations mentioned above. In this section, the standardization tool is set up within the ETL engine. It is used as a parallel or serial tool for missing data filling and duplicate removal. As an extensible toolkit, in addition to the normalization, feature encoding, and dimensionality reduction operations mentioned above, the standardization tool can also preset other data processing methods, such as feature extraction, to perform more data processing to complete the original data processing flow mentioned above.

[0043] 2. Anomaly detection mechanism based on multi-algorithm fusion This approach introduces a multi-algorithm joint detection mechanism, incorporating DBSCAN clustering, decision trees, vector similarity analysis, and deep neural networks to form a "multi-model, multi-strategy" fusion mechanism. It supports real-time streaming and batch detection, balancing accuracy and performance, and is adaptable to large-scale, high-concurrency scenarios. This content provides the model foundation for subsequent dynamic modeling and self-learning.

[0044] This mechanism possesses intelligent pattern recognition and anomaly detection capabilities: The system utilizes clustering algorithms (DBSCAN), classification algorithms (decision trees), similarity calculation (vector analysis), and deep neural networks to achieve potential pattern mining and trend recognition. It supports real-time streaming data detection, which can quickly identify abnormal fluctuation points and issue alarms and responses; AI models enable data classification, prediction, and early warning, supporting intelligent data management and operation and maintenance decisions.

[0045] Specifically, the DBSCAN clustering algorithm is primarily used for unlabeled data scenarios. It does not rely on predefined categories but instead discovers naturally formed clusters based on the density of data point distribution. Isolated points far removed from any high-density areas are identified as anomalies. This method is particularly suitable for exploratory analysis, such as discovering rare but potentially threatening anomalous access patterns from massive user behavior logs, or identifying anomalous location points within obtained address locations or other content.

[0046] Decision tree algorithms, on the other hand, focus on scenarios with clearly defined rules and a high degree of interpretability. They transform business knowledge into clear decision logic by constructing a tree of "if-then" conditional branches. When data deviates from these predefined or learned rule paths, the system can quickly flag anomalies. This allows it to provide interpretable paths in scenarios such as multi-source data or other conflicts, or to monitor whether industrial equipment parameters exceed safety limits, ensuring both detection efficiency and providing maintenance personnel with intuitive judgment criteria.

[0047] Vector similarity analysis demonstrates unique value in processing high-dimensional data, especially unstructured information such as text and behavioral sequences. Its core principle is to transform data into points in a vector space and quantify the degree of deviation by calculating the "distance" or "angle" between new data and historical normal pattern vectors. For example, in the field of operations and maintenance, system logs can be converted into semantic vectors. By comparing the degree of deviation between new data and data under historical normal patterns, the degree of deviation can be used to determine whether the data source is abnormal or whether there is duplicate data, thereby quickly identifying new error patterns that significantly differ from previous stable states.

[0048] Deep neural networks are powerful tools for processing complex, nonlinear patterns. They can employ convolutional neural networks, recurrent neural networks, fully connected neural networks, long short-term memory artificial neural networks, and more. They can automatically learn deep feature representations from raw data, and are particularly adept at capturing dynamic dependencies in time series data or subtle anomalies in images and signals. For example, by learning the characteristics of vibration signals from normal equipment through an autoencoder model, once a new signal with a significantly increased reconstruction error appears, even if its shape is unprecedented, it can be identified as a potential early sign of failure. This capability makes them crucial in predictive maintenance and early warning of anomalies in complex business indicators. While deep neural networks are powerful tools for handling complex data, they cannot process all of the aforementioned data types. However, their data processing lacks the real-time performance and interpretability required by other methods. In specific scenarios, it is still necessary to use the tools mentioned above.

[0049] Based on the characteristics of the data, the aforementioned single approach can be used, while multi-model and multi-strategy joint applications are also possible. During the joint process, parallel and serial approaches can be used for identification, with the parallel approach emphasizing synchronous collaboration and comprehensive verification among multiple models. Within this framework, four algorithms simultaneously and independently analyze the same input data. DBSCAN outputs outlier scores from the perspective of data distribution density; the decision tree provides classification labels and confidence levels based on rule logic; vector similarity calculation returns the deviation from the baseline pattern; and the deep neural network generates anomaly probabilities based on complex pattern recognition. These outputs are not independent but converge into a fusion decision center. This center comprehensively evaluates multiple signals through weighted voting, confidence level stacking, or more advanced meta-learning models. For example, when both DBSCAN and the neural network indicate a highly anomaly for a data point, but the decision tree determines it as normal due to rule coverage limitations, the fusion center will assign higher weights to the former two based on historical performance, ultimately classifying it as an anomaly. The advantage of this parallel approach is that it can obtain multi-dimensional insights at once, improve the robustness and coverage of decisions through cross-validation, and is particularly adept at handling complex anomalies within the cognitive blind spots of a single model.

[0050] The serial approach focuses on building a progressively filtering, layer-by-layer processing pipeline. Data first flows into the fastest and lightest detection layer. The first layer is typically handled by a decision tree or rule engine, performing high-speed hard rule filtering to quickly eliminate explicit anomalies such as format errors and threshold violations. Data that passes this stage proceeds to the second layer, where DBSCAN and vector similarity perform pattern-level screening: DBSCAN quickly clusters batches of data to identify density outliers; vector similarity compares the data with a normal pattern library to identify individuals deviating from the norm. At this point, most of the normal data has been released, and only a few suspicious data points are sent to the third layer for in-depth analysis and final decision-making by a deep neural network. This serial structure acts like a precision funnel, ensuring that computational resources are concentrated to the most suspicious data, optimizing processing efficiency, and providing a traceable diagnostic path from simple rule violations to complex pattern anomalies.

[0051] In practical system design, purely parallel or serial approaches are often replaced by more flexible hybrid strategies. A typical hybrid architecture might adopt the principle of "macro-serial, micro-parallel." For example, the system as a whole maintains a serial pipeline, but in key stages—such as the pattern screening stage in the second layer—parallel mechanisms are used internally, allowing DBSCAN and vector similarity to run simultaneously to accelerate the processing speed of this layer. Alternatively, in the final adjudication layer, for edge cases that the neural network determines to be anomalous but with low confidence, decision tree rule verification and secondary vector similarity comparison are initiated in parallel for careful arbitration. This design absorbs the resource efficiency and process clarity of serial processes while incorporating the coverage breadth and robustness of parallel processing, enabling the system to dynamically adjust the depth and breadth of its analysis based on data characteristics, load conditions, and business needs.

[0052] The above provides several modeling schemes, and the architecture of the above models or more models can be selected and extended through subsequent dynamic modeling and self-learning methods.

[0053] 3. Dynamic Modeling and Self-Learning Engine Deep learning and reinforcement learning techniques are used to achieve continuous training and optimization of the model during operation.

[0054] It supports flexible parameter adjustment and dynamic structural reconstruction, ensuring that the model can automatically adapt to business needs and data distribution.

[0055] This engine possesses the following dynamic modeling and self-learning capabilities: Based on deep learning and reinforcement learning techniques, the model can continuously absorb new data for continuous training and optimization; It supports flexible configuration of model structure (depth, width, and connection method can be adjusted) for dynamic modeling to meet the needs of different business scenarios; It provides decision simulation and multi-strategy simulation functions, combined with iterative optimization algorithms, to assist business in formulating optimal strategies and decision paths.

[0056] The dynamic modeling and self-learning engine possesses three capabilities: dynamic learning, flexible structure, and policy optimization. Based on the aforementioned single or joint models, subsequent content can be selected for dynamic modeling and learning. Furthermore, other deep learning models or AI models can be added for further dynamic modeling and learning, enabling multi-model extension.

[0057] In dynamic learning, feedback data is collected and integrated with multiple heterogeneous business systems, such as manual review platforms, business databases, and monitoring and alarm systems. The system uses lightweight SDKs or APIs to embed tracking points at key nodes in these business systems, capturing subsequent events related to the prediction results. For example, when a human reviewer clicks "confirm" or "false alarm" on a "coordinate anomaly" alarm on the platform, this action and the corresponding request ID are captured and sent to the feedback center. More implicit feedback is obtained through periodic batch data processing jobs; for example, the predicted coordinates are compared daily with another authoritative geographic information database to identify missed alarms. All these feedback events are correlated with the original prediction logs within a time window through a streaming connection service, ultimately generating training samples of features, labels, and model versions with real labels. These training samples are then used to perform autonomous deep learning or reinforcement learning on the aforementioned model.

[0058] Dynamic learning relies on an intelligent scheduler, whose decision-making logic is based on a set of configurable policies. It continuously monitors two circular buffers: one for newly labeled samples and the other for a sliding window of the model's online performance metrics to be learned. Its decision-making algorithm can be formalized as a policy function, for example: if the number of new samples exceeds a preset threshold, or the accuracy of the sliding window decreases by more than a set value, or the current time is within a predefined low-load period, a learning task is triggered. The triggered learning task is not full-scale training, but rather, in an isolated training cluster, the current online model is loaded from the model repository, and elastic weight consolidation training is performed using new samples. The key to this algorithm is calculating the parameter importance matrix, which is estimated by evaluating the diagonal elements of the Fisher information matrix of the model parameters on the old training data. After training, the new model runs for a period in a "shadow mode" isolated from the online environment, receiving the same online traffic but not contributing. Its prediction results are compared with the online model. Once performance improvement is confirmed and no significant regressions are observed, the online model is gradually replaced using a blue-green deployment or canary deployment strategy.

[0059] The dynamic configuration of the resilient model architecture integrates neural architecture search, model compilation, and continuous deployment capabilities. The resilient model architecture is primarily designed for dynamic modeling of deep neural networks. The resilient model architecture receives natural language or structured form input and transforms it into constraints for the search space through a pre-trained semantic parsing model or rule engine. For example, latency constraints are translated into restrictions on network depth, number of parameters, and types of operations used, such as replacing standard convolutions with depthwise separable convolutions to reduce computational cost.

[0060] The system predefines a supernetwork containing candidate operations with multiple replaceable architectures. Each operation has a learnable architecture weight parameter on each edge. The search process is performed on a split validation set, updating both network weights and architecture weight parameters simultaneously by alternately optimizing a composite loss function. This composite loss function consists of three parts: a performance loss on the validation set, a regularization penalty for architecture complexity, and a penalty based on the estimated inference latency. After the search converges, the discrete final architecture is obtained by selecting the operation with the largest weight on each edge.

[0061] Specifically, hypernetworks transform the traditional discrete architecture search space into a continuous, differentiable hypernetwork representation through differentiable neural architecture search, thereby allowing the use of efficient gradient optimization methods to explore the optimal architecture.

[0062] The construction of hypernetworks begins with the formal definition of the search space. All possible neural network components—including candidate operations for replacing different types of layers (such as fully connected layers, convolutional layers, pooling layers, attention mechanisms, etc. of different sizes) and their potential connections—are integrated into a unified, overparameterized computational graph. In this graph, deterministic single operations in the model to be built are replaced by multiple parallel candidate operations. For example, at a feature transformation node, the system does not simply place a 3x3 convolutional layer, but simultaneously deploys a set of candidate operators, such as 3x3 convolution, 5x5 convolution, max pooling, and the identity mapping that directly passes the input. These candidate operations physically coexist within the hypernetwork, forming a pool of alternatives for architecture exploration.

[0063] To make the search process differentiable, the system introduces a learnable parameter called "architectural weight" for each candidate operation. Initially, all candidate operations are active, but their contribution to the final output is determined by the coefficients of these architectural weights, normalized using a soft-maximum function. This means that during forward propagation, the node's output is actually a weighted sum of the outputs of all candidate operations, with the weights determined by the architectural parameter.

[0064] The training process employs a two-layer optimization mechanism. On one hand, the model needs to learn the specific parameters of each operation in the supernetwork corresponding to the layer itself (such as the weights of the convolutional kernels), which are called network weights. On the other hand, the system needs to optimize the architecture weights to evaluate the effectiveness of different operation combinations. These two types of weights are updated alternately or jointly during the same training process using gradient descent. All possible sub-architectures (i.e., networks formed by selecting different combinations of candidate operations) share the same set of backbone network weights. This allows for the evaluation of thousands of potential architectures without training each one from scratch; simply adjusting the architecture weights is sufficient to estimate their performance, thereby achieving an order-of-magnitude improvement in search efficiency.

[0065] Specifically, the network weights mentioned above refer to the architecture or weights of different layers within candidate operations. Architecture weights represent the probability of selecting a candidate operation. Nodes are feature maps generated at different stages. These different candidate structures are virtually connected in parallel through the feature maps and candidate operations to generate a large model containing multiple candidate operations. The training set is used to first update the different network weights. During the update process, a candidate operation is randomly sampled between each node for training. During training, the random structures within the random candidate operations are frozen as network weights. Through continuous random sampling and weight freezing, each network weight in the large model including different candidate operations is trained. After the network weights are trained, the architecture weights are used as weighted terms in the feature maps output by each candidate operation on the validation set. A weighted sum is then performed for forward propagation of the large model to obtain the final result. Backpropagation is then performed based on the final result to adjust the architecture weights. Both network weights and architecture weights are updated using gradient descent. The network weights and architecture weights can be adjusted alternately or sequentially. The forward path with the largest architecture weight in the large model is selected as the architecture content.

[0066] Once the joint training converges, the system performs the architecture derivation step. For each edge or node in the hypernetwork, the system selects the candidate operation with the highest architecture weight and removes all other candidates. This process is equivalent to pruning from the continuously relaxed hypernetwork representation, ultimately obtaining a discrete, deterministic optimal target network architecture. This architecture inherits the excellent properties learned during hypernetwork training and can be directly used for deployment or further fine-tuning. It achieves efficient and automated model structure discovery and generation to produce architecture description files.

[0067] To accelerate the search, the system employs weight sharing and a surrogate predictor. The hypernetwork undergoes a complete pre-training on the training set at the start of the search; subsequently, evaluating any sub-architecture simply requires activating the corresponding path and performing forward propagation, without the need for separate training. For hardware metrics such as latency, the system pre-builds a lookup table or trains a lightweight regression model, which quickly predicts performance based on the architecture description.

[0068] Next, based on the exported architecture description file, corresponding pre-compiled operator modules are pulled from a model component container repository. These modules are connected through standardized interfaces and assembled into a complete computation graph. The assembled model undergoes graph optimization and compilation for the target hardware, and is finally packaged into an executable inference engine container. This container is pushed to the model repository and deployed through a model service mesh integrated with the container orchestration system, enabling automatic deployment of new models, traffic routing, and version management.

[0069] In dynamic modeling systems, decision simulation and multi-strategy simulation are the core mechanisms driving model self-evolution. This mechanism simulates the dynamic changes of real business scenarios by constructing a virtual environment, enabling the model to be trained and optimized in a secure and controllable digital sandbox, thereby adapting to constantly changing real-world conditions.

[0070] The strategy optimization function first establishes a highly realistic business simulator. This simulator is not a simple rule engine, but a dynamic environment model learned through generative models based on historical interaction data, system logs, and multi-dimensional indicators. It can accurately reflect the complex relationships and temporal evolution patterns between business elements, such as how user behavior changes with strategy adjustments, how data distribution drifts with the seasons, and how system anomalies trigger chain reactions. Simultaneously, the system needs to define clear evaluation criteria—quantifying business objectives such as "maximizing detection accuracy," "minimizing response latency," and "controlling false alarm costs" into calculable reward functions, providing clear guidance for subsequent strategy optimization.

[0071] Based on this simulated environment, the system initiates a self-learning optimization loop. In the initial phase, multiple candidate policy combinations are generated, encompassing adjustable dimensions such as model structure, detection thresholds, and action parameters. The model structure is modeled using the aforementioned dynamic modeling method. All policies are deployed in parallel within the simulated environment to process virtual business flows, and the system comprehensively records the overall performance of each policy over long-term operation. By analyzing the performance data using reinforcement learning algorithms, the system can identify the success patterns of efficient policies and generate a new generation of improved policies based on these patterns. This iterative process of "evaluation-generation-re-evaluation" continues, allowing the policies to evolve continuously in the simulated environment, gradually approaching the global optimum under dynamic conditions.

[0072] When the strategy, after thorough simulation and optimization, enters the actual deployment phase, the system employs a rigorous, incremental verification process. The new strategy is first run in parallel with real traffic in shadow mode to verify its consistency with simulation predictions without impacting business operations. Once confirmed effective, the old strategy is gradually replaced through controlled methods such as canary deployments. After deployment, the system continues to run, collecting real-time business feedback for online fine-tuning and continuously updating the simulator to maintain a high degree of synchronization with the real world. When a sudden change in data distribution or a completely new abnormal pattern is detected, the system can automatically trigger an emergency simulation and quickly generate a response plan adapted to the new scenario.

[0073] Ultimately, this entire mechanism endows the dynamic modeling system with three core capabilities: as an accelerator, it can simulate the long-term evolution of physical time in digital time, greatly compressing the model evolution cycle; as an optimizer, it finds the optimal decision boundary that surpasses human experience through automated optimization; and as a stabilizer, it fully assesses risks before strategy changes, ensuring business continuity while continuously improving the system's intelligence level. This transforms the modeling system from a tool that passively adapts to data into a cognitive intelligent agent capable of proactively planning, predicting, and optimizing future states.

[0074] 4. Abnormal Repair and Self-Healing Mechanisms It provides an automatic repair and re-collection mechanism for issues such as abnormal data sources, incorrect latitude and longitude coordinates, push failures, and timeouts. It supports both automatic and manual re-collection modes to improve data integrity and ensure uninterrupted business operations.

[0075] This mechanism enables the identification, analysis, and repair of abnormal business data. Specifically, it leverages comprehensive AI capabilities to identify, analyze, and automatically repair various types of business data anomalies, ensuring data integrity and accuracy. As an extension of anomaly identification, subsequent anomaly repair primarily includes: Data source anomalies and duplicate data anomalies: Automatically identify multi-source data conflicts and execute cleaning and merging strategies; Coordinate system / latitude and longitude anomalies: Automatically corrects deviation data based on geospatial analysis algorithms; Address anomalies: Combining natural language processing (NLP) and geographic information services (GIS) to correct non-standard or missing address data; Latitude and longitude coordinate system verification: Corrects positioning errors through cross-platform coordinate system transformation and comparative analysis; Data push failure and timeout: The system automatically captures push failure logs and executes retry and re-collection mechanisms; Failed push task re-collection: Supports scheduled automatic / manual triggering of re-collection to avoid data loss due to network or system anomalies.

[0076] 5. Intelligent log analysis and root cause localization Natural language processing and pattern recognition technologies are incorporated to intelligently analyze log data. For scenarios such as page access timeouts, database connection interruptions, network latency, and service anomalies, the system can automatically generate root cause analysis and handling suggestions, shortening maintenance response time. As an extension of anomaly identification, subsequent anomaly root cause analysis is performed. This content provides root cause analysis of abnormal logs and operational handling suggestions. Through an intelligent log analysis platform, it performs semantic parsing, pattern recognition, and root cause reasoning on abnormal logs, and combines this with AI models to generate handling suggestions, significantly improving operational efficiency.

[0077] Page response timeout root cause analysis: Analyze the performance of the front-end and back-end links, identify request bottlenecks, and provide solutions such as load balancing and cache optimization; Root cause analysis of page inaccessibility: Check network connectivity and service availability; it is recommended that the operations team restart the container or service. Platform operation service anomaly analysis: By combining logs and monitoring data, identify abnormal nodes in microservices and recommend application isolation and disaster recovery switching; Database connection anomaly analysis: Through SQL statement parsing and connection pool monitoring, it provides suggestions for optimizing connection pool parameters or scaling up database instances; Network latency anomaly analysis: Based on link tracing and network monitoring, locate latency nodes and recommend network optimization or routing switching strategies.

[0078] 6. Visual insights and intelligent decision support Build a visualization and insight platform to intuitively present anomaly distributions, data trends, and model conclusions. Support multi-strategy simulation, business simulation, and iterative optimization to form a data-driven intelligent decision support system.

[0079] Overall, the deep AI data insight and analysis service, through AI-driven intelligent data governance and operation and maintenance capabilities, can achieve closed-loop management from data source to logs, from anomaly detection to root cause analysis, providing a solid guarantee for stable system operation and business continuity.

[0080] Through the above solutions, the deep AI data insight and analysis service will comprehensively enhance the platform's capabilities in data governance, anomaly detection, operation and maintenance support, and intelligent decision-making, achieving a leapfrog improvement from "data perception" to "intelligent insight," and providing a solid guarantee for the stable operation and innovative development of customers' digital businesses.

[0081] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A dynamic modeling and self-learning optimization method for intelligent AI data insight analysis, characterized in that, include: Acquire multi-source heterogeneous data and preprocess the multi-source heterogeneous data; Anomaly detection models are used to identify multi-source heterogeneous data, and anomaly identification results are obtained. The anomaly detection model is dynamically modeled and self-learned for optimization. The dynamic modeling is performed through a search of subdivided neural architectures, and the self-learning is achieved through deep learning and reinforcement learning. Anomaly repair is performed based on the anomaly identification results.

2. The method according to claim 1, characterized in that, The multi-source heterogeneous data includes business data, log data, and system data transmitted from different business platforms.

3. The method according to claim 1, characterized in that, Multi-source heterogeneous data is preprocessed using an ETL engine and standardization tools, including missing value imputation, duplicate removal, normalization, feature encoding, and dimensionality reduction.

4. The method according to claim 1, characterized in that, The anomaly detection model is constructed using one or more of the following methods: clustering algorithm, classification algorithm, similarity calculation, and deep neural network.

5. The method according to claim 1, characterized in that, The self-learning optimization process of the anomaly detection model includes: Automatically acquire feedback information, generate training samples based on the feedback information, and automatically learn from the anomaly detection model based on the number of training samples or the performance of the anomaly detection model to obtain a new model. Run the new model in isolation, compare the performance of the new model with the anomaly detection model, and replace the anomaly detection model based on the new model.

6. The method according to claim 1, characterized in that, The automated modeling process for anomaly detection models includes: For deep neural networks in anomaly detection models, relevant constraints are constructed, including constraints on network depth, number of parameters, and operation type. Construct a hypernetwork containing candidate operations with replaceable architectures, wherein each candidate operation is assigned a corresponding architecture weight; wherein the hypernetwork is a computation graph structure, wherein the computation graph contains feature maps generated by different candidate operations as nodes and candidate operations as edges, wherein different replaceable architectures in the candidate operations have corresponding network weights. The network weights and architecture weights of the supernetwork are updated sequentially or alternately. Based on the final architecture weights, the corresponding candidate operations and corresponding network weights are selected as the final architecture and corresponding network weights.

7. The method according to claim 1, characterized in that, The anomaly detection model undergoes a self-learning optimization loop by deploying a simulation environment.

8. A dynamic modeling and self-learning optimization system for intelligent AI data insight analysis, characterized in that, Used to perform the method described in any one of claims 1-7.

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