Early warning system and method based on artificial intelligence

Abstract

The invention relates to the technical field of artificial intelligence and risk early warning, and discloses an early warning system and method based on artificial intelligence. The system mainly comprises a data acquisition module, a data processing module, an early warning generation module and an online learning module. The core of the method is as follows: continuously collecting multi-source heterogeneous data and carrying out standardized preprocessing; extracting features by using a deep learning model and calculating risk assessment indexes; generating multi-level early warning according to the comparison of the index and the dynamic threshold value; and finally, on the basis of early warning feedback data, model parameters are updated online through an incremental learning technology so as to optimize performance. According to the invention, the continuous self-evolution of the early warning model and the interpretability of the decision-making process are innovatively realized, the high expandability of the system is ensured based on the micro-service architecture, and the accuracy, adaptability and practicability of early warning are effectively improved.

Description

Technical Field

[0001] This invention relates to the fields of artificial intelligence and risk warning technology, and more specifically, to an artificial intelligence-based early warning system and method. Background Technology

[0002] With the popularization of information technology and the Internet of Things, the monitoring data generated by various systems is experiencing explosive growth. Data-driven intelligent early warning technology has become a core development direction in risk management and security monitoring. This field is evolving from traditional simple rule-based judgments based on fixed thresholds to utilizing machine learning models to process complex, high-dimensional data. In particular, the introduction of deep learning technology enables early warning systems to automatically learn more complex anomaly patterns from massive amounts of historical data, enhancing the potential of early warning and driving the field towards greater intelligence and adaptability.

[0003] However, existing intelligent early warning technologies still have significant shortcomings. First, many systems rely on statically trained offline models, which struggle to adapt to dynamic environments where data distribution changes over time, leading to gradual performance degradation. Second, existing solutions often focus on generating warnings but lack effective explanations of the basis for warning decisions, making it difficult for users to understand and trust the model's output, thus affecting the timely handling of warnings. Furthermore, traditional system architectures are typically tightly coupled, making it difficult to cope with rapid increases in data volume and changes in business needs, posing challenges in terms of scalability and maintainability.

[0004] Therefore, this paper proposes an artificial intelligence-based early warning system and method to address the above-mentioned problems, aiming to overcome the aforementioned shortcomings and mainly solve the following three key issues: First, how to enable the early warning model to continuously learn new knowledge, adapt to the dynamically changing environment, and avoid performance degradation; Second, how to improve the transparency of the early warning process, make the model's decision-making basis understandable, and enhance the credibility of the system; Third, how to build a flexible and scalable early warning system architecture to support efficient data processing and modular updates. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an early warning system and method based on artificial intelligence to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an early warning method based on artificial intelligence, the method comprising the following steps: S1. Data acquisition steps: Continuously acquire monitoring data from at least 3 heterogeneous data sources, and perform real-time preprocessing on the monitoring data, including data cleaning, format standardization and noise filtering, to generate a standardized data stream; S2. Data processing and risk assessment steps: Use a pre-trained deep learning model to perform multi-source feature extraction and fusion analysis on the standardized data stream, and calculate one or more risk assessment indicators through an anomaly detection algorithm integrated in the deep learning model. S3. Early warning generation step: Compare the risk assessment indicators with a dynamically adjustable early warning threshold, and automatically generate multi-level early warning signals corresponding to different risk levels based on the comparison results; S4. Online learning and model update steps: Collect feedback data triggered by the warning signal, and based on the feedback data, use incremental learning technology to update the parameters of the deep learning model online to optimize the accuracy of subsequent risk assessment.

[0007] Preferably, step S1 further includes a data quality control sub-step: performing real-time integrity verification and outlier removal based on statistical distribution on the collected monitoring data to ensure that the quality of the input data meets preset standards.

[0008] Preferably, in step S2, the deep learning model is a graph neural network model, which is configured to process multi-source data with non-linear correlations and to use an attention mechanism to weightedly fuse the feature contributions of different data sources.

[0009] Preferably, step S3 further includes a warning priority management sub-step: dynamically adjusting the output frequency, push target, or display format of the warning signal based on the severity and urgency reflected by the risk assessment indicators.

[0010] Preferably, in step S4, the incremental learning technique employs online gradient descent and combines it with a sliding window mechanism with a time span of T to select historical data for model updates, wherein the value of T ranges from 7 days to 30 days.

[0011] Preferably, the method further includes a user interaction step parallel to step S3: providing a visual interface to receive user confirmation or correction instructions for the warning signal, and using the instructions as part of the feedback data for model updates in step S4.

[0012] Preferably, in the user interaction step, the visualization interface is also used to display the historical trend of the risk assessment indicators and the early warning records to the user, and to allow authorized users to manually adjust the dynamically adjustable early warning threshold.

[0013] Preferably, after step S2, a model interpretation step is also included: generating and outputting a report analyzing the key features that lead to the anomaly detection results and their contribution.

[0014] An artificial intelligence-based early warning system for the above method, the system comprising: The data acquisition module is used to execute step S1 to generate a standardized data stream; The data processing module is communicatively connected to the data acquisition module and is used to execute step S2 to calculate the risk assessment indicators. The early warning generation module is communicatively connected to the data processing module and is used to execute step S3 to generate multi-level early warning signals; The online learning module interacts with the data processing module and the early warning generation module respectively to perform step S4 to update the deep learning model.

[0015] Preferably, the system is deployed using a distributed microservice architecture, wherein the data acquisition module, data processing module, early warning generation module, and online learning module can be elastically scaled and run in parallel on multiple computing nodes as independent services.

[0016] The technical effects and advantages of this invention are as follows: Compared to existing technologies, this invention introduces an online model update mechanism based on incremental learning, enabling the core early warning model to continuously optimize using real-time feedback data and new samples. This mechanism employs an online gradient descent algorithm combining sliding window and L2 regularization, effectively constraining parameter changes while absorbing new knowledge and preventing the forgetting of historical normal patterns. This effectively solves the problem of performance degradation of static models in non-stationary data flow environments, improving the long-term accuracy and environmental adaptability of the early warning system.

[0017] Compared to existing technologies, this invention integrates a model interpretation module based on an integral gradient algorithm to provide quantitative feature contribution analysis for each early warning decision. This algorithm calculates the gradient integral of the input feature along the path from the baseline to the actual value, precisely quantifying the impact of each data feature on the final risk assessment indicator. This innovation transforms the model's "black box" decision-making into an interpretable feature importance report, greatly enhancing the transparency and credibility of early warning results and helping users make quick and accurate decisions.

[0018] Compared to existing technologies, this invention employs a distributed microservice architecture for system implementation, decoupling core functions such as data collection, processing, early warning generation, and online learning into independent, elastically scalable services. These services communicate through message brokers and well-defined application programming interfaces (APIs), achieving loose coupling between modules. This design allows the system to dynamically expand computing resources based on load, improving overall processing efficiency and reliability. Simultaneously, it facilitates independent upgrades and maintenance of individual services, enhancing the system's flexibility and scalability. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the overall structure of the present invention.

[0020] Figure 2 This is a schematic diagram of the overall framework structure of the present invention. Detailed Implementation

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

[0022] Example 1 As attached Figure 1 and Figure 2 The illustrated method is an artificial intelligence-based early warning method, which includes the following steps: S1. Data Acquisition Steps: Monitoring data is concurrently acquired from at least three heterogeneous data sources via a data acquisition agent configured with a specific data interface protocol. These heterogeneous data sources include relational databases, application programming interface (API) data streams, and IoT sensor networks. The acquired monitoring data streams undergo real-time preprocessing. This preprocessing first applies regular expression-based pattern matching technology to parse and extract fields from unstructured text data, transforming it into structured data. Then, a Z-Score normalization algorithm based on a sliding window mechanism is used to normalize the numerical data, eliminating the influence of dimensions. Finally, a Kalman filter-based noise filtering unit smooths and filters instantaneous impulse noise and random fluctuations in the data stream, generating a standardized data stream that conforms to a preset data pattern.

[0023] S2. Data Processing and Risk Assessment Steps: The standardized data stream is input into a pre-trained deep learning model for feature extraction and fusion analysis. This deep learning model adopts an encoder-decoder architecture. The encoder part consists of multiple layers of gated recurrent units stacked together to capture temporal dependencies in the data. The decoder part integrates a self-attention mechanism to calculate the correlation weights between feature vectors from different time steps and different data sources, achieving weighted fusion of features. The fused feature tensor is fed into an anomaly detector composed of fully connected layers. This detector calculates the reconstruction error between the input features and the latent space representation learned by the model on normal historical data, and outputs a quantitative risk assessment index. The value of this index is positively correlated with the degree of anomaly.

[0024] S3. Warning Generation Step: Set a dynamically adjustable warning threshold. The initial value of the threshold is determined by percentile statistics of the risk assessment indicators within the historical normal data period. Compare the risk assessment indicators calculated in real time in step S2 with the threshold. When the indicator value continuously exceeds the threshold for a preset number of consecutive time windows (e.g., 3), it is determined to be a valid warning event. The warning generation engine matches the corresponding risk level from the predefined rule base according to the magnitude and duration of the indicator value exceeding the threshold, and generates a multi-level warning signal containing the risk level, event timestamp, and main trigger feature identifier.

[0025] S4. Online Learning and Model Update Steps: Establish a feedback data collection loop to continuously collect user-marked responses to warning signals and newly generated normal data samples during system operation; use an exponentially weighted sliding window to sample historical data based on importance, prioritizing recent data; utilize the newly collected sample set to incrementally learn the deep learning model from step S2 using an online gradient descent algorithm. While minimizing the reconstruction loss of the current batch of data, introduce an L2 regularization term into the loss function to constrain the model parameter update magnitude, preventing the model from forgetting previously learned normal patterns as it adapts to new data, thereby achieving incremental optimization of model parameters.

[0026] In step S1, the data quality control sub-step is implemented as follows: After the data flows into the preprocessing pipeline, an integrity verification process is first started. This process checks whether the required fields of each data packet exist and whether the data format conforms to the agreed specifications, based on a predefined data schema. Data packets with missing key fields or incorrect formats are marked and routed to a dead-letter queue for subsequent processing. At the same time, a process control module based on dynamic statistics runs in parallel. This module calculates the moving average and standard deviation of a certain numerical feature of the most recent N data points in real time, and sets out the boundary of outliers using the box plot method. For data points that exceed the boundary, if they are judged as outliers within M consecutive time points, the elimination procedure is started, and the data is filled in with linear interpolation within the time window to ensure the continuity of the data flow input to the downstream processing module in terms of time sequence.

[0027] In step S2, the specific construction and operation process of the graph neural network model is as follows: First, the multi-source monitoring data is abstracted into graph structure data, where each data source is regarded as a node in the graph, and the known business logic or physical relationship between data sources is modeled as edges in the graph; each node uses its corresponding time series segment of standardized data stream as an initial feature vector; then, the graph neural network propagates information through multi-layer graph convolution operations, and each layer of graph convolution updates the feature representation of the target node by aggregating the feature information of the target node and its first-order neighbor nodes; on this basis, a graph attention network mechanism is introduced, which calculates the attention coefficient between node pairs through a trainable small neural network, thereby realizing differentiated information aggregation, enabling the model to pay more attention to the data source relationship that is highly related to anomaly detection; after multiple rounds of graph convolution and attention-weighted aggregation, the embedded representation of each node rich in contextual information is finally obtained. These representations are concatenated and fed into a multilayer perceptron for classification or regression to output the risk assessment index.

[0028] In step S3, the detailed technical implementation of the early warning priority management sub-step is as follows: The system maintains a priority strategy matrix, which uses the numerical range of risk assessment indicators and the instantaneous rate of increase of the indicators as two main input dimensions; when an early warning event is triggered, the system immediately calculates the average rate of change of the indicator corresponding to the event within a specific time window in the past, and inputs this rate of change along with the current indicator value into the strategy matrix for querying, mapping to obtain a comprehensive priority score; based on the preset range of this score, the system automatically decides the early warning signal release strategy: for low-priority early warnings, in-system messages or log recording are used; for medium-priority early warnings, email notifications are added; for high-priority early warnings, SMS or instant messaging message pushes are triggered, and the notification frequency is gradually increased according to preset rules until a confirmation response is received from the handling personnel.

[0029] In step S4, the specific execution process of the online gradient descent method combined with a sliding window is as follows: The system maintains a fixed-size data buffer in memory, the capacity of which corresponds to a time span T (e.g., 15 days). This buffer manages the latest standardized data stream and its corresponding feedback labels in a first-in-first-out manner. When a model update is required, a small batch of data samples is randomly sampled from this buffer. The gradient of the loss function of this batch of data under the current model parameters is calculated. A stochastic gradient descent optimizer with momentum is used to fine-tune the model's weight parameters based on the calculated gradient and the momentum direction of the previous update. The learning rate is set using an adaptive learning rate algorithm, dynamically adjusted based on the first and second moments of the parameter gradient to ensure the convergence stability of the model in a non-stationary data stream environment. After each update, the model's performance on the retained validation set is evaluated. If the performance degradation exceeds a threshold, a model version rollback mechanism is triggered.

[0030] The specific technical implementation of the user interaction steps includes: the system provides a graphical user interface based on network technology, which maintains a long connection with the backend early warning generation module through the WebSocket protocol to realize the real-time push of early warning signals; the interface embeds a visualization component to display the historical trend of risk assessment indicators in the form of a time series curve, and overlays it with the early warning event line; at the same time, it provides an early warning log list to display detailed information of each early warning; users can mark early warnings as "confirmed", "false alarm" or "missed" through button components on the interface; the backend service receives these marking information through the application programming interface, structures them into timestamped feedback events, and stores them in the feedback database, which serves as an important source of supervision signals when the online learning module performs incremental learning.

[0031] The technical implementation of the threshold adjustment function in the visualization interface is as follows: On the visualization interface of users with management privileges, an interactive threshold configuration panel is provided. In addition to displaying the current dynamic threshold curve, this panel also allows users to temporarily or permanently adjust the warning thresholds of the global or specific data sources by dragging graphical interface elements (such as sliders) or directly inputting values. After the user submits an adjustment request, the request is sent to the backend server. The server first verifies the user's permissions and the rationality of the new threshold (e.g., whether it is within the system's allowed security range). After the verification is successful, the new threshold parameters are updated to the system configuration center. The configuration center notifies all instances in the warning generation module to load the new threshold configuration in real time through a publish-subscribe pattern, ensuring that subsequent warning judgments take effect immediately.

[0032] The specific technical process of the model interpretation step is as follows: After the deep learning model completes the risk assessment of a batch of data, the integrated gradient attribution algorithm is invoked. This algorithm estimates the contribution of each input feature to the final risk assessment index by calculating the path integral of the model output score as the input feature gradually changes from the baseline value (such as the zero vector) to the actual value. The system sorts the calculated feature contributions and selects the top K features with the highest contributions. Then, these features, their contribution values, and their meaning descriptions in the original data are encapsulated together to generate a structured interpretation report. This report can be provided to external systems through an application programming interface or presented to users in the form of highlights, weight graphs, etc., on the system's internal visualization interface to explain the main data basis for this early warning decision.

[0033] An AI-based early warning system for the above method, wherein the specific technical composition and collaborative relationship of each module in the system are as follows: The data acquisition module consists of a group of collector instances deployed near the data source. Each collector performs a specific acquisition task according to its configured data source type (such as a database connector, an application programming interface to pull clients, or a message queue consumer), and sends the preprocessed data to the data processing module through an internal message bus. The data processing module runs as a microservice with a built-in model inference engine, responsible for loading and running deep learning models. It receives data from the data acquisition module through remote procedure calls or message queues, and after completing the calculation, publishes the risk assessment indicators to another topic message queue. The early warning generation module subscribes to this message queue. Its core is a rule engine, responsible for parsing and executing early warning rules, and calling the notification service interface to send early warning signals. The online learning module runs periodically. It retrieves new feedback data and samples from persistent storage, starts model training tasks, and automatically deploys the new model version to the data processing module after review.

[0034] The specific deployment and communication mechanism of the distributed microservice architecture are as follows: Each core module of the system (data acquisition, data processing, early warning generation, and online learning) is encapsulated as an independent, horizontally scalable microservice. Each microservice is packaged in an independent container image and deployed, managed, and discovered through container orchestration tools. Communication between microservices is conducted entirely through a highly available central message broker or service mesh, using asynchronous message passing or synchronous application programming interface calls. The data persistence layer adopts a database sharding strategy, storing data from different modules in different types of databases (such as time-series databases and relational databases) according to their access characteristics. All service instances are stateless, and their configuration information is uniformly read from an external configuration server, thereby achieving elastic scaling, fault isolation, and high availability of modules.

[0035] The complete implementation process of this invention is as follows: Phase 1: Data Acquisition and Preprocessing The system operates through data acquisition agents deployed on different data source nodes. These agents concurrently acquire data using the corresponding interface protocol, based on their configured data source type.

[0036] For example, for relational databases, the acquisition agent periodically executes pre-defined structured query language queries to extract incremental data; for application programming interface (API) data streams, the agent acts as a client, requesting subscriptions or pulling data via Hypertext Transfer Protocol (HTTP); for IoT sensor networks, the agent receives time-series data streams reported by sensors via IoT protocols such as message queue telemetry. All collected raw data is uniformly encapsulated into message bodies and sent in real time to the central message bus for further processing.

[0037] After data flows into the preprocessing pipeline, it undergoes rigorous data quality control. An integrity verification subprocess is activated, validating each data packet according to a predefined data schema, checking the existence of required fields, the correctness of data types, and the reasonableness of numerical ranges. For packets that fail verification, the system marks them and routes them to a dedicated dead-letter queue for isolation, awaiting manual intervention or script repair. Simultaneously, logs are logged for auditing purposes, ensuring that incomplete or incorrectly formatted data does not enter the core analysis engine. Running in parallel is a real-time outlier detection and processing module based on statistical process control.

[0038] This module maintains a dynamic reference window for continuous numerical features and calculates the moving average (μ) and moving standard deviation (σ) of the data within the window. For a newly arrived data point xi, if it satisfies |xi-μ|>kσ (where k is a configurable multiple, usually 2 or 3), it is initially identified as an instantaneous outlier.

[0039] To distinguish between genuine anomalies and reasonable fluctuations, the system introduces a persistent judgment mechanism: only when a feature point is judged as an anomaly for M consecutive sampling periods is it finally confirmed as an outlier to be removed. For the removed data points, the system uses linear interpolation of its adjacent valid data points to fill in the gaps, in order to maintain the continuity of the data sequence to the greatest extent possible.

[0040] After quality control is completed, the data enters the standardization stage. For unstructured or semi-structured text data, pre-written regular expression rules are used for pattern matching to extract key information and transform it into structured key-value pairs.

[0041] For all numerical data, the Z-Score normalization method is used, and its formula is: Where x represents the original data value, μ_history represents the long-term average value calculated based on historical data, and σ_history represents the standard deviation of historical data.

[0042] This step aims to eliminate the impact of differences in units and numerical ranges between different data sources, ensuring all features are on the same scale for efficient processing by subsequent models. Finally, the data stream passes through a smoothing filter based on the Kalman filter algorithm. This filter, through a prediction-correction loop, effectively suppresses random noise introduced during data acquisition and transmission, generating a clean, consistent, and standardized data stream, which is then published to the designated message topic.

[0043] Phase Two: Data Processing and Dynamic Risk Assessment The data processing module, as the intelligent core of the system, subscribes to standardized data streams from the preprocessing stage. Its core task is to use a built-in deep learning model to perform deep feature extraction and fusion analysis on the data, and calculate real-time risk assessment indicators. In this embodiment, a hybrid model structure combining gated recurrent units and graph attention networks is preferably adopted to simultaneously capture temporal dependencies and inter-feature correlations.

[0044] The model first addresses the time series dimension. Standardized multivariate time series data is fed into a multi-layer gated recurrent unit (GRU) network. The hidden state update mechanism of the GRU enables it to effectively learn long-term and short-term time dependency patterns in the data; its computation process can be briefly represented as follows: in, The input feature vector represents the current time step. This represents the hidden state of the previous time step. This represents the hidden state after the current time step update. After multi-layer gated recurrent unit encoding, the data at each time step is transformed into a feature vector rich in temporal context information.

[0045] Next, the system incorporates the relationships between multi-source data into the analysis. Different data sources are abstracted as nodes in a graph structure, and known business logic or physical connections are defined as edges, thus constructing a dynamic relationship graph. The initial feature vector of each node is its feature representation encoded by a gated recurrent unit at the corresponding time step. Then, the graph attention network begins to work, its core being the calculation of the association strength between nodes through an attention mechanism. For the target node i and its neighboring nodes j, their attention coefficients... The calculation method is as follows: Where W is a shared linear transformation weight matrix, and These are the feature vectors of nodes i and j, respectively. It is a single-layer feedforward neural network.

[0046] The attention coefficients are normalized using the Softmax function to obtain the weights. : This represents the original attention coefficient between node i and node j.

[0047] This represents the attention weight of node j to node i after normalization.

[0048] Let i represent the set of neighboring nodes of node i.

[0049] k represents the index of the neighboring node.

[0050] Finally, the new feature representation h'_i of node i is obtained by weighted aggregation of the features of all neighboring nodes: This represents the updated feature representation of node i.

[0051] σ represents a non-linear activation function (such as Sigmoid or ReLU).

[0052] This represents the attention weight of node j to node i after normalization.

[0053] W represents a trainable linear transformation weight matrix.

[0054] This represents the original feature representation of neighbor node j.

[0055] Let i represent the set of neighboring nodes of node i.

[0056] This process enables the model to selectively integrate information from different sources, strengthening the influence of features highly correlated with anomaly detection.

[0057] After feature extraction and fusion, the fused high-level feature tensor is fed into an anomaly detector. This detector employs an encoder-decoder architecture, where the encoder maps the input features to a low-dimensional latent space, and the decoder attempts to reconstruct the original input from this latent space. The model is pre-trained on a large amount of historical normal data, ensuring its latent space effectively represents "normal" operating patterns. In the real-time alert phase, the anomaly detector quantifies the degree of anomaly by calculating the reconstruction error of the input data x, i.e., the risk assessment metric R.

[0058] The calculation formula is as follows: R represents the calculated risk assessment indicator value (reconstruction error).

[0059] n represents the total number of dimensions of the input feature vector.

[0060] i represents the dimension index of the feature vector.

[0061] This represents the true value of the input standardized data in the i-th dimension.

[0062] This represents the reconstructed value output by the decoder of the deep learning model in the i-th dimension after reconstructing the input data x.

[0063] A higher R-value indicates a greater deviation between the current data pattern and the normal baseline learned by the system, and a higher potential risk. This metric is calculated and output in real time.

[0064] Phase 3: Multi-level Early Warning Generation and Adaptive Threshold Management The early warning generation module continuously receives risk assessment indicators (R) from the data processing module. To translate continuous R values ​​into specific early warning actions, the system employs a dynamic threshold comparison mechanism. The initial threshold is not set as a fixed value, but rather determined based on the statistical distribution of R values ​​over historical normal data periods (such as the 95th or 99th percentile). More importantly, this threshold has dynamic adjustment capabilities, allowing it to drift slowly based on the overall system operating status and user feedback to adapt to long-term, gradual changes in data distribution.

[0065] When the real-time calculated R value exceeds the current dynamic threshold, the early warning engine does not immediately issue an alarm. Instead, it introduces a short, continuous observation window (e.g., three consecutive sampling periods). Only when the R value continuously exceeds the limit within the observation window is it considered a valid early warning event. This effectively avoids false alarms caused by transient noise. For confirmed early warning events, the system queries a predefined priority strategy matrix based on two dimensions: the magnitude of the R value exceeding the threshold (ΔR = R - Threshold) and the rate of increase of the indicator (dR / dt), and automatically determines the early warning level (e.g., low, medium, high). Different levels correspond to different handling strategies: low-level early warnings may only be logged; medium-level warnings will trigger email notifications to relevant responsible persons; high-level warnings will immediately initiate emergency alerts through multiple channels such as SMS, telephone, or instant messaging tools, and may automatically notify higher-level managers according to preset escalation rules if timely confirmation is not received.

[0066] Phase 4: Model Interpretability and Decision Support To enhance user trust and operability in early warning decisions, the system initiates a model interpretation process upon generating an early warning. This process employs an integral gradient algorithm to quantitatively analyze the contribution of each input feature to the final risk assessment index R. The core idea of ​​integral gradient is to calculate the gradient integral of the model's predicted output F (i.e., R) with respect to a feature as the input feature changes from a baseline value x' (such as a zero vector or feature mean) along a path (usually a straight line) to the actual value x.

[0067] For the i-th feature, the formula for calculating its contribution φ_i is: Representing input features Contribution value to this early warning decision x represents the actual input feature vector.

[0068] This represents the selected baseline input feature vector (typically representing a "no-information" state, such as zero or the average value).

[0069] i represents the dimension index of the feature vector.

[0070] This represents the feature value of the actual input in the i-th dimension.

[0071] This represents the eigenvalue of the baseline input in the i-th dimension.

[0072] This represents the interpolation coefficients, which vary in the interval [0,1].

[0073] F represents the trained deep learning model, whose output is a risk assessment metric.

[0074] This indicates that the model output F is relative to the input features. The partial derivatives of .

[0075] Indicates the relationship from the baseline Integrate the gradient along the straight path to the actual input x.

[0076] The system summarizes the top K features with the highest contribution and their contribution values ​​to generate a concise explanatory report. This report clearly indicates which key data anomalies triggered the alert, providing users with a clear basis for decision-making.

[0077] Phase 5: Online Learning and Iterative Model Optimization The system's long-term effectiveness depends on its online learning capability. The online learning module constructs a closed-loop feedback system. It continuously collects two types of important data: first, user feedback on warning signals through the interactive interface (such as confirming validity, marking as false alarms, or pointing out missed alarms); and second, new data samples that have been verified as normal and are constantly generated during the normal operation of the system.

[0078] The model update process is performed periodically (e.g., every 24 hours or when a certain amount of new data accumulates). During an update, the system loads a recent set of new samples, B_t (defined by the sliding window size T, e.g., T=15 days), from persistent storage. Incremental learning is performed using an online gradient descent algorithm based on mini-batches. Its parameter update rule not only aims to minimize the loss function of the current batch of data (e.g., mean squared error reconstruction loss), but also introduces an L2 regularization term to constrain the magnitude of parameter changes, preventing new knowledge from overwriting old knowledge (i.e., catastrophic forgetting).

[0079] The parameter update formula is: This represents the parameter vector of the model at update period t.

[0080] This represents the new parameter vector of the model in the next update cycle t+1. This represents the learning rate, which controls the step size for each parameter update.

[0081] The loss function J represents the loss function with respect to the model parameters. In the current small batch data The gradient on.

[0082] This represents the coefficient of the L2 regularization term, used to control the strength of regularization.

[0083] Represents the model parameter vector The square of the L2 norm, i.e., the sum of the squares of the parameters.

[0084] This update method ensures that the model can smoothly adapt to changes in the environment while maintaining its memory of historical normal patterns.

[0085] Phase 6: System Deployment and Elastic Scaling At the architectural level, this system adopts a distributed microservice architecture to ensure high availability and scalability. Core functions such as data collection, data processing, alert generation, online learning, and user interaction are broken down into independent microservices. Each microservice is packaged in a container and deployed and managed using container orchestration tools. Services communicate asynchronously through highly available message middleware (such as Kafka and RocketMQ) or synchronously through well-defined RESTful APIs. This loosely coupled design allows each service to scale horizontally (shrink or expand) independently according to its load. For example, during periods of high data influx, the number of instances of the data processing module can be dynamically increased to distribute the load. All configuration information is centrally managed, and the service instances themselves are stateless, further enhancing the system's resilience, fault tolerance, and ease of maintenance.

[0086] In summary, the implementation process of this invention is a complete closed loop from data to insight, and then to action and optimization. Through rigorous data processing, advanced artificial intelligence models, flexible multi-level early warning mechanisms, transparent decision interpretation, and continuous online learning, it collectively constructs an adaptive, interpretable, and highly available intelligent early warning system capable of effectively addressing risk management challenges in dynamically changing environments.

[0087] Finally, the following points should be noted: First, in the description of this application, it should be noted that, unless otherwise specified and limited, the terms "installation", "connection", and "linkage" should be interpreted broadly, and can be mechanical or electrical connections, or internal connections between two components, or direct connections. "Up", "down", "left", "right", etc. are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may change. Secondly: The accompanying drawings of the embodiments disclosed in this invention only involve the structures involved in the embodiments disclosed in this invention. Other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of this invention can be combined with each other. In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An artificial intelligence-based early warning method, characterized in that, The method comprises the following steps: S1, a data collection step: continuously collecting monitoring data from at least three heterogeneous data sources, and performing real-time preprocessing on the monitoring data, including data cleaning, format standardization and noise filtering, to generate a standardized data stream; S2, a data processing and risk assessment step: using a pre-trained deep learning model to perform multi-source feature extraction and fusion analysis on the standardized data stream, and calculating one or more risk assessment indicators through an anomaly detection algorithm integrated in the deep learning model; S3, a warning generation step: comparing the risk assessment indicators with a dynamically adjustable warning threshold, and automatically generating multi-level warning signals corresponding to different risk levels according to the comparison results; S4, an online learning and model updating step: collecting feedback data triggered by the warning signals, and based on the feedback data, using incremental learning techniques to update the parameters of the deep learning model online to optimize the accuracy of subsequent risk assessment.

2. The early warning method based on artificial intelligence according to claim 1, characterized in that, In the S1 step, a data quality control sub-step is also included: performing real-time integrity verification and statistical distribution-based outlier rejection on the collected monitoring data to ensure that the quality of the input data meets the preset standards. 3.The early warning method based on artificial intelligence of claim 1, wherein, In the S2 step, the deep learning model is a graph neural network model configured to process multi-source data with nonlinear correlation relationships and to use an attention mechanism to weight and fuse the feature contributions of different data sources. 4.The early warning method based on artificial intelligence of claim 1, wherein, In the S3 step, a warning priority management sub-step is also included: dynamically adjusting the output frequency, push target or display form of the warning signals according to the severity and urgency reflected by the risk assessment indicators.

5. The early warning method based on artificial intelligence according to claim 1, characterized in that, In the S4 step, the incremental learning technique uses an online gradient descent method and combines a sliding window mechanism with a time span of T to select historical data for model updating, where T ranges from 7 days to 30 days.

6. The early warning method based on artificial intelligence according to claim 1, characterized in that, The method also includes a user interaction step parallel to the S3 step: providing a visualization interface to receive user confirmation or correction instructions for the warning signals, and using the instructions as part of the feedback data for model updating in the S4 step.

7. The early warning method based on artificial intelligence according to claim 6, characterized in that, In the user interaction step, the visualization interface is also used to show the user the historical trend of the risk assessment indicators and the warning records, and allows authorized users to manually adjust the dynamically adjustable warning threshold. 8.The early warning method based on artificial intelligence of claim 1, wherein, After the S2 step, a model explanation step is also included: generating and outputting a key feature and contribution analysis report that leads to an abnormal detection result.

9. An artificial intelligence-based early warning system for implementing the method of any one of claims 1 to 8, characterized in that, The system comprises: a data collection module for performing the S1 step to generate a standardized data stream; a data processing module in communication with the data collection module for performing the S2 step to calculate risk assessment indicators; a warning generation module in communication with the data processing module for performing the S3 step to generate multi-level warning signals; an online learning module interacting with the data processing module and the warning generation module, respectively, for performing the S4 step to update the deep learning model.

10. The early warning system based on artificial intelligence according to claim 9, characterized in that, The system adopts a distributed micro-service architecture deployment, wherein the data collection module, the data processing module, the early warning generation module and the online learning module can be independently served and run in parallel on multiple computing nodes.

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