An event prediction and early warning method based on Bayesian deep learning

Abstract

The present invention discloses an event prediction and early warning method based on Bayesian deep learning, which mainly includes two parts: one is the construction of a Bayesian deep learning rule model based on event characteristics, and the other is the design of a prediction and early warning model based on the construction of the Bayesian deep learning rule model. By integrating Bayesian statistical methods with deep learning models, the present invention can fully utilize the advantages of both. It can combine prior knowledge and observation data to express the uncertainty of prediction results in the form of probability distribution, efficiently model and predict complex and nonlinear time series data, and achieve early warning of potential events, so as to improve the accuracy and reliability of event prediction and early warning.

Description

Technical Field

[0001] The present invention belongs to the field of artificial intelligence and big data analysis technology, and specifically relates to an event prediction and early warning method based on Bayesian deep learning. Background Art

[0002] With the rapid development of information technology, the amount of data on various events has exploded, and how to accurately predict possible future events has become a hot topic of current research. Traditional event prediction methods are mostly based on statistical models or simple machine learning algorithms, which are difficult to effectively process high-dimensional, nonlinear, and non-stationary time series data. In addition, traditional deep learning models often ignore the uncertainty of prediction results when dealing with complex event predictions, resulting in inaccurate predictions in certain extreme or data-sparse situations. Existing technologies mostly rely on a single probabilistic prediction value, which cannot fully reflect the uncertainty of the prediction results, thus affecting the reliability and accuracy of the early warning system. In the field of event prediction, how to effectively integrate uncertainty modeling with the powerful feature extraction capabilities of deep learning remains an urgent problem to be solved.

[0003] Existing technical solutions combine the robustness of traditional statistical models with the predictive capabilities of machine learning algorithms. For example, traditional time series analysis methods such as autoregressive moving average models or seasonal decomposition models can be used to capture long-term trends and seasonal patterns in the data, and then combined with machine learning algorithms such as support vector machines and random forests to handle nonlinear relationships and complex features. The advantages of this type of solution are high computational efficiency, the ability to quickly process large-scale data sets, good robustness, fewer assumptions about the distribution of data, and the ability to resist interference from outliers and noise to a certain extent. In addition, after combining with machine learning algorithms, it can handle more complex feature relationships and pattern recognition problems, but its limitations are manifested in limited prediction accuracy and generalization capabilities, as well as greater difficulty in model fusion. In addition, although event prediction systems based on single deep learning models (such as recurrent neural networks, long short-term memory networks, etc.) can improve prediction accuracy to a certain extent, they often ignore the uncertainty in the data, resulting in a decrease in prediction performance in abnormal or extreme situations.

[0004] To overcome this shortcoming, BDL (Bayesian Deep Learning) came into being. It introduces probability distributions to represent model weights, thereby providing uncertainty information during prediction. Currently, some studies have attempted to apply Bayesian deep learning to the field of prediction and early warning, but most of them focus on specific scenarios or single event types and lack systematic solutions. The most similar implementation solution is to use traditional deep neural networks (DNNs) for event prediction. This type of model optimizes network parameters through large-scale data training to improve prediction accuracy. However, DNN models have limited ability to handle uncertainty and cannot effectively deal with hidden variables and noise in the data, which limits their application in complex social governance scenarios.

[0005] In addition to Bayesian deep learning, other Bayesian methods such as Gaussian processes can also be used for event prediction. Gaussian processes are a powerful non-parametric Bayesian method that can model the distribution of functions rather than directly modeling parameters. This method is very effective in dealing with uncertainty and is suitable for scenarios where accurate estimates of prediction confidence intervals are required. The advantage of this solution is that Gaussian processes can naturally handle uncertainty in predictions and can adapt to different data characteristics by selecting appropriate kernel functions. It has strong flexibility and nonlinear modeling capabilities, making it suitable for the prediction of complex time series data. The limitation is that the computational complexity of the Gaussian process increases significantly with the amount of data, and it may not be suitable for large-scale datasets. In addition, the Gaussian process has poor interpretability, and the choice of kernel function has a significant impact on model performance. However, selecting an appropriate kernel function usually requires a certain amount of prior knowledge and experimental adjustments.

[0006] In summary, existing event prediction technologies have the following main shortcomings:

[0007] 1. Overly optimistic predictions. Existing deep learning models often lack effective uncertainty quantification mechanisms when handling complex prediction tasks. These models are unable to effectively quantify and communicate the uncertainty of predictions. This "black box" nature leads to significant bias in predictions when data is scarce or noisy, and can even mislead decision-making. In particular, in prediction tasks, models often only provide a definitive prediction without providing information about the underlying uncertainty. This uncertainty can arise from data noise, model limitations, or unknown changes in the data distribution. The lack of uncertainty modeling can lead to increased decision-making risk, reduced trust, and difficulty adapting to change.

[0008] 2. Poor robustness. Faced with a dynamically changing social governance environment, traditional models struggle to adapt quickly to emerging event types, paradigm shifts, and dynamic changes in data distribution. This limitation stems in part from the model's lack of robustness. When data distribution changes, the model may be unable to perceive and adjust promptly due to a lack of uncertainty information to guide its adaptive learning.

[0009] 3. Lack of expert knowledge integration. Most current systems overly rely on data-driven approaches, ignoring the important role of expert knowledge in improving the accuracy and comprehensiveness of forecasts and early warnings. Expert experience often contains a deep understanding and insight into specific fields, which can compensate for the limitations and incompleteness of the data itself, thus affecting the comprehensiveness and accuracy of forecasts.

[0010] 4. High data processing complexity. Faced with complex and ever-changing governance scenarios, existing technologies struggle to effectively process large-scale, multi-dimensional data. As data volumes continue to increase, existing systems may struggle to scale effectively to meet higher processing demands. Furthermore, large-scale datasets require powerful computing resources to support model training and inference, which increases system hardware costs and energy consumption. Summary of the Invention

[0011] In order to solve the problems of insufficient uncertainty modeling, poor generalization ability, and insufficient real-time performance of traditional deep learning methods in the field of event prediction and early warning, the present invention provides an event prediction and early warning method based on Bayesian deep learning. By constructing an efficient Bayesian deep learning model, it can achieve accurate prediction of complex and nonlinear time series data, and effectively evaluate the uncertainty of the prediction results, providing a scientific basis for early warning of potential events.

[0012] An event prediction and early warning method based on Bayesian deep learning includes the following steps:

[0013] (1) Collect massive multi-source heterogeneous data under target application scenarios (such as public safety, financial risk, medical health, natural disasters, traffic management, etc.), pre-process these data and divide them into training sets, validation sets and test sets;

[0014] (2) Build a rule model based on event features using user digital profiles, which are user feature sets constructed based on multi-dimensional information including user behavior data, social data, and consumption data;

[0015] (3) Building a prediction and early warning model based on Bayesian deep learning based on the above rule model;

[0016] (4) Use the training set data to train the prediction and early warning model, and use the validation set data to tune the parameters during the model training process;

[0017] (5) Input the test set data or real-time data into the trained prediction and warning model to predict events, and issue warnings based on the prediction results output by the model.

[0018] Furthermore, each set of data in step (1) contains multiple feature variables and a target variable, wherein the feature variables can be numerical (such as price, temperature, pressure, etc.), categorical (such as product type, geographical location, etc.) or time series (such as historical transaction records, sensor time series data, etc.), and the target variable is the state of the predicted event (such as stock price fluctuation, equipment failure, disease onset, etc.) as a label.

[0019] In practical applications, data often comes from multiple channels and has different formats and structures. In order to make full use of the information in these data and improve the accuracy of prediction, we need to use multi-source heterogeneous data processing technology to integrate these data. The data preprocessing in step (1) includes four parts: data cleaning, data integration (multi-source data fusion), data enhancement (increasing data diversity) and feature engineering. The data cleaning part includes denoising, filling in missing values, and correcting or removing outliers; the feature engineering part includes feature selection, feature extraction and feature transformation. Feature selection uses methods including correlation analysis, PCA (principal component analysis), and mutual information (MI) to screen out features that have an important impact on the target variable. Feature extraction uses deep learning methods including CNN (convolutional neural network) and RNN (recurrent neural network) to automatically extract high-level features from the original data. Feature transformation improves data distribution through methods including standardization, normalization, one-hot encoding, and label encoding. Through these steps, we can convert data from different channels into data in a unified format and extract useful features for subsequent prediction tasks to ensure the quality and availability of the data.

[0020] Several types of governance events with high probability of occurrence and poor social impact need to be paid special attention, such as repeated visit events, death events, economic events, etc. Therefore, the present invention constructs a rule model based on event characteristics. The specific implementation method of step (2) is as follows: first, analyze the user digital portrait information in the target application scenario, obtain various information elements and behavior elements involved in the event, to support the construction of the rule model, the information elements include time information, location information, trajectory information, character information and event information; each type of governance scenario needs to perform ontology analysis on the event, and extract as many information elements and behavior elements of the virtual and real space and even the thinking space unique to the event as possible. On the basis of analyzing multiple similar events, summarize the common characteristics and co-occurring behaviors of the event, and construct a feature library of information elements and behavior elements unique to the event. By integrating the feature library of information elements and behavior elements of various events, a rule model based on event characteristics is obtained. In the intelligent event prediction and early warning system, the present invention uses user digital portraits to better understand users' needs and behavior patterns and predict the types of events they may participate in. The construction of user digital portraits requires comprehensive consideration of multiple factors, such as the user's age, gender, occupation, interests and hobbies, etc., and customized design based on specific application scenarios.

[0021] Furthermore, the prediction and warning model based on Bayesian deep learning combines the advantages of Bayesian algorithms and deep neural networks, models and infers the uncertainty in complex problems, and uses probability distributions to represent weights rather than single values, thereby providing uncertainty information about these predictions when making predictions. That is, under the premise of known prior probabilities and conditional probability densities, for the uncertainty problem of various event risks, the conditional probability density function is inferred through statistical learning of samples and converted into posterior probability using the Bayesian algorithm criterion; the prediction and warning model uses Bayesian neural networks to analyze historical data and real-time monitoring data, process the uncertainty in the data, estimate the probability of future events, give the confidence level of the prediction results, and provide corresponding warning information. This model not only improves the accuracy and reliability of the warning system, but also obtains robust prediction results when data is insufficient. This is the key mechanism for Bayesian deep learning to establish a prediction and warning system.

[0022] Bayesian deep learning is a method that introduces Bayesian probability theory on the basis of traditional deep learning. It allows model parameters to have uncertainty and estimates the posterior distribution of these parameters through Bayesian reasoning. This method can not only improve the prediction accuracy and generalization ability of the model, but also provide confidence intervals and uncertainty estimates of the prediction results; in intelligent event prediction and early warning systems, Bayesian deep learning can help us better understand and deal with uncertainty factors in prediction results. In addition, the Bayesian deep learning model has a powerful automatic feature extraction capability and can automatically learn useful feature representations from raw data; in intelligent event prediction and early warning systems, the present invention uses this feature of the deep learning model to reduce the burden of manual feature engineering and improve the robustness and generalization ability of features. By stacking multiple nonlinear transformation layers (such as convolutional layers, pooling layers, fully connected layers, etc.), the deep learning model can gradually abstract high-level feature representations of the data and use them for subsequent prediction tasks.

[0023] In Bayesian deep learning, posterior probabilities are not typically calculated directly because they typically involve high-dimensional integrals and are difficult to analyze. The present invention uses variational inference or Markov Chain Monte Carlo methods to approximate the posterior probability, preferably variational inference, which uses a variational autoencoder to capture the underlying representation of the data by introducing latent variables and uses variational inference to estimate the posterior distribution of the latent variables.

[0024] Deep learning is the key to the technological innovation and performance improvement of the system of the present invention. In view of the excellent capabilities of deep learning in feature extraction, pattern recognition and complex system modeling, the specific implementation method of step (3) is as follows: first, it involves an in-depth understanding of the event-related field to determine the nodes (i.e., variables) that should be included in the Bayesian neural network. These nodes represent various factors or states that affect the occurrence of the event; then, based on domain knowledge and statistical correlation analysis, further construct edges (conditional dependencies) between nodes to clarify the interactions and influence paths between various factors. On this basis, historical data or expert knowledge are used to specify conditional probability distributions for each node. These probability distributions describe the law of node status changes with the status of its parent node; deep learning architectures including CNN, RNN, and LSTM (long short-term memory network) are integrated into the Bayesian neural network to perform preliminary processing on the original data. By stacking multiple nonlinear transformation layers (such as convolutional layers, pooling layers, fully connected layers, etc.) and feature learning mechanisms, high-level abstract feature representations in the data are automatically extracted. These feature representations are then used as node inputs, providing a richer information source for the network model. This integrated approach not only enhances the system's ability to understand complex data structures, but also significantly improves the accuracy of complex pattern recognition and prediction, thereby significantly enhancing predictive performance. Through this series of construction steps, the present invention forms a clearly structured and logically rigorous Bayesian network model, providing a solid theoretical foundation and model framework for subsequent predictive analysis.

[0025] Real-time updating and optimization are important guarantees for ensuring the system's continued effective operation and adaptability to environmental changes. In the face of the ever-changing external environment and data distribution characteristics, the aforementioned step (4) requires defining the prior distribution of model parameters, designing the loss function, and using the maximum expectation algorithm to update and optimize the parameters of the Bayesian neural network in real time during the training of the prediction and early warning model. Through continuous iterative calculation and parameter adjustment, the model can gradually approach the real data distribution and event patterns, and always maintain high prediction performance and robustness. In addition, in order to prevent overfitting and improve the generalization ability of the model, regularization, random inactivation (dropout), early stopping strategy, and model evaluation and selection mechanism are also set during the training process. By comparing and evaluating the performance of different model parameters, optimization suggestions and decision support are provided to developers, and the validation set is used to realize the automated model tuning process, which can automatically adjust the model parameters and structure settings according to real-time feedback to achieve the best prediction effect. This flexible update and optimization strategy enables the system to quickly adapt to new environments and new challenges, and maintain the advanced nature and competitiveness of its prediction capabilities.

[0026] Furthermore, the specific implementation method of step (5) is as follows: first, the data (test set data or real-time data) is processed and features are extracted, and then the features are input into the trained prediction and warning model to predict events, and then an adaptive warning mechanism with uncertainty quantification is used to issue a warning based on the prediction results output by the model. The mechanism dynamically adjusts the warning threshold by analyzing the uncertainty distribution of the prediction results, and sets multiple warning levels (such as low, medium, and high), and sets corresponding threshold ranges for each level. These threshold ranges are set according to historical data, business needs, and risk tolerance. When the confidence or probability distribution of the prediction results falls into a certain threshold range, a warning of the corresponding level is triggered. The warning information includes multiple dimensions including event type, occurrence time, impact range, and severity, and is notified to relevant personnel through various methods including SMS, email, and APP push. This mechanism can improve the flexibility and accuracy of the warning.

[0027] In view of the computational challenges brought by large-scale data sets and high-dimensional feature spaces, in step (5), when using the trained prediction and warning model to predict events on real-time data, parallel computing technology is used to accelerate the model's reasoning process, and the computing tasks are distributed to multiple computing nodes for parallel execution through a distributed computing framework. At the same time, model compression technology is used to reduce the number of model parameters, and pruning technology is used to remove unimportant connections or neurons in the model to simplify the model structure and reduce the model size. This can significantly reduce the model's computational complexity and reasoning time, reduce computing time and improve computing efficiency.

[0028] The target application scenarios of the method of the present invention include (but are not limited to):

[0029] Public safety: Used to predict and warn of public safety incidents such as attacks, helping relevant departments take timely measures to protect the safety of life and property.

[0030] Financial risk: used to assess financial risks such as credit risk and market risk of enterprises and individuals, helping financial institutions and investors make more informed decisions.

[0031] Healthcare: Used to predict and warn of medical and health events such as disease outbreaks and worsening of patients’ conditions, helping medical institutions and patients take timely measures to prevent and treat diseases.

[0032] Natural disasters: used to predict and warn of natural disasters such as earthquakes, floods, and typhoons, helping relevant departments and the public to prepare for disaster prevention and mitigation in advance.

[0033] Traffic management: used to predict and warn of traffic congestion, traffic accidents and other traffic incidents, helping traffic management departments optimize traffic flow and reduce the occurrence of traffic accidents.

[0034] Based on the above technical solution, the present invention can solve the following technical problems:

[0035] 1. Uncertainty Modeling: Bayesian deep learning uses probability distributions to model parameter uncertainty, enabling a more comprehensive characterization of the uncertainty of prediction results and providing decision makers with more robust and reliable prediction intervals. This paper utilizes Bayesian theory to model the probability distribution of model parameters, thereby quantifying the uncertainty of prediction results. This uncertainty information serves as an important reference for decision-making, improving the reliability and robustness of decisions.

[0036] 2. Improved generalization: This invention introduces prior knowledge through a Bayesian approach, limiting model complexity and preventing overfitting. Furthermore, the Bayesian deep learning model automatically learns from the uncertainty in the data, thereby better adapting to changes in data distribution and improving the model's generalization. The Bayesian deep learning framework utilizes uncertainty information as a "feedback" mechanism to dynamically adjust model parameters and structure, thereby enhancing the model's robustness and adaptability in complex and changing environments, ensuring the long-term effectiveness and accuracy of the prediction and early warning system.

[0037] 3. Integrating Expert Knowledge: Bayesian deep learning technology can flexibly integrate expert prior knowledge with data-driven learning. This invention optimizes the model training process by constructing a prior distribution that incorporates expert knowledge. This ensures that predictions are both data-based and intelligent, improving the comprehensiveness and accuracy of predictions.

[0038] 4. Enhanced Data Processing Capabilities: By incorporating efficient data processing technologies such as probabilistic graphical models, Bayesian deep learning can optimize the allocation of computing resources, improve data processing efficiency, and reduce hardware costs and energy consumption for system operation while ensuring prediction accuracy. This allows for better adaptation to large-scale, multi-dimensional data processing requirements. This invention optimizes the computational efficiency and memory usage of deep learning models, combining efficient parallel computing and distributed processing technologies to reduce processing latency for large-scale datasets. Furthermore, by incorporating incremental learning and online learning mechanisms, this invention enables real-time model updates to adapt to data changes, further improving the system's real-time performance.

[0039] Therefore, the innovation and beneficial technical effects of the present invention are mainly reflected in the following aspects:

[0040] 1. High prediction accuracy and uncertainty quantification.

[0041] This paper significantly improves the accuracy of prediction tasks by introducing a Bayesian deep learning model. The Bayesian framework allows the incorporation of prior knowledge into the model training process and reflects the uncertainty in the data through posterior distribution updates. This not only improves the accuracy of predictions but also effectively quantifies the uncertainty of the prediction results. This uncertainty quantification ability is crucial for decision-making, especially in high-risk or high-cost application scenarios, and can help decision-makers better understand the reliability range of prediction results.

[0042] 2. Strong generalization ability.

[0043] Faced with complex and ever-changing data environments, the present invention builds a highly flexible model architecture by integrating multiple deep learning modules (such as convolutional neural networks, recurrent neural networks, and attention mechanisms). This multi-module fusion strategy enables the model to more effectively process different types of input data (such as time series, images, and text) and extract richer and deeper feature representations from them. As a result, the present model can maintain good performance even on unseen datasets, that is, it has strong generalization capabilities.

[0044] 3. Real-time warning.

[0045] To address applications requiring high real-time performance, this invention has implemented in-depth optimizations in algorithm design and system implementation. By employing efficient acceleration strategies (such as GPU parallel computing and algorithm pruning) and meticulously controlling the prediction process, the system ensures that it can complete complex data processing and prediction tasks in a remarkably short time. Furthermore, by integrating real-time data stream processing technology, this invention enables instant response to input data and triggers an early warning mechanism immediately when preset conditions are met.

[0046] 4. Adaptive warning threshold.

[0047] Traditional early warning systems often rely on fixed threshold settings, which can become ineffective in practice due to changes in data distribution. This paper innovatively proposes an adaptive early warning threshold adjustment method based on the uncertainty of prediction results. By analyzing the uncertainty distribution of prediction results, this method dynamically adjusts the early warning threshold to ensure that the accuracy and effectiveness of the warning remain unchanged when the data changes. This adaptive mechanism greatly improves the flexibility and robustness of the early warning system. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a schematic diagram of the event prediction and warning process based on Bayesian deep learning in the present invention.

[0049] Figure 2 Schematic diagram of the construction process of the rule model in the present invention.

[0050] Figure 3 Schematic diagram of the risk assessment process for various events in the present invention. DETAILED DESCRIPTION

[0051] In order to describe the present invention more specifically, the technical solution of the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0052] In today's highly developed information age, traditional event prediction and early warning methods still face huge challenges. To address these challenges, this paper proposes an intelligent event prediction and early warning method based on Bayesian deep learning. It combines the powerful automatic feature extraction capabilities of deep learning with the advantages of Bayesian methods in handling uncertainty and model selection, aiming to achieve accurate prediction and early warning of complex and changeable events.

[0053] This implementation focuses on several types of governance events with high probability of occurrence and poor social impact, such as repeated visits, deaths, and economic events. It uses Bayesian deep learning for prediction and early warning analysis. The specific implementation includes data preparation, data preprocessing, model building, feature fusion and extraction, prediction and early warning mechanisms, optimization algorithms, and acceleration strategies. Details are as follows:

[0054] (1) Data preparation.

[0055] Data collection and preprocessing are the primary links in the entire intelligent event prediction and early warning system, and its core function is to ensure the data quality of the subsequent analysis process. The implementation of this part widely integrates various sensor technologies and RFID (radio frequency identification) devices to achieve real-time capture of multi-dimensional information such as environmental parameters, physical conditions, and behavioral patterns. The collected raw data often contains noise, redundancy, outliers, and inconsistent formats. Therefore, a series of precise data cleaning steps are required, such as denoising, missing value filling, outlier detection and removal, data standardization or normalization, etc., to deeply purify and organize the data. In addition, data formatting is also a key link. It converts the cleaned data into a unified format and structure to facilitate direct access and efficient processing of subsequent modules, thereby providing accurate and reliable data support for subsequent analysis. The specific implementation process is as follows:

[0056] 1.1 Data Collection

[0057] Collect relevant datasets from various sources (including databases, sensors, and web crawlers), including but not limited to financial transaction records, social media activity, enterprise business systems, IoT sensor data, medical health monitoring, and factory safety alerts. These datasets are often multi-source, heterogeneous, high-dimensional, nonlinear, and highly time-series, often containing noise and missing values. To build effective predictive models, a comprehensive understanding and preprocessing of this data is necessary.

[0058] 1.2 Dataset Structure

[0059] A dataset typically contains multiple feature variables and one or more target variables. Feature variables are typically numerical (such as price, temperature, pressure), categorical (such as product type, geographic location), or time series (such as historical transaction records, time series sensor data). The target variable is the event or state we want to predict, such as a stock price fluctuation, equipment failure, or disease onset.

[0060] (2) Data preprocessing.

[0061] 2.1 Data Cleansing

[0062] Data cleaning is the first step in data preprocessing, aiming to remove noise and address outliers and missing values. Missing values ​​can be addressed using methods such as median filling (e.g., mean filling, median filling, mode filling), linear interpolation (e.g., linear interpolation and polynomial interpolation), or by deleting rows or columns containing missing values. Outliers require careful consideration and handling based on the specific business scenario and data distribution characteristics.

[0063] 2.2 Data Partitioning

[0064] After data integration (multi-source data fusion) and data enhancement (increasing data diversity), the dataset is divided into training, validation, and test sets in a ratio of 70%, 15%, and 15%. This division is intended to ensure that the model can effectively learn, evaluate, and generalize. Specifically, the training set (70%) is used to train the model, that is, to allow the model to learn the patterns and features in the data; the validation set (15%) is used for tuning during the model training process, such as adjusting hyperparameters to find the best model configuration while avoiding overfitting the model on the training set; the test set (15%) is completely independent of the training process and is used to finally evaluate the performance of the model to ensure that the model performs well on unseen data.

[0065] 2.3 Feature Engineering

[0066] Feature engineering bridges the data layer and the model layer, extracting valuable features from preprocessed data. It's a key step in improving model performance and involves feature selection, extraction, and transformation. In the feature selection phase, methods such as correlation analysis, principal component analysis, and mutual information analysis can be used to identify features that significantly influence the target variable. In the feature extraction phase, deep learning modules (such as CNNs and RNNs) can be used to automatically extract high-level features from raw data. In the feature transformation phase, methods such as standardization, normalization, and encoding (such as one-hot encoding and label encoding) can be used to improve data distribution and model training. Feature extraction is a complex process that needs to be designed based on the specific application scenario and prediction objectives. For structured data, statistical methods and machine learning algorithms can be used to extract numerical and categorical features. For unstructured data such as text, images, and audio, natural language processing, computer vision, and audio processing techniques are required for feature extraction. Furthermore, building a user digital profile requires comprehensive analysis and feature construction based on multiple dimensions of user data, including behavioral, social, and consumer data.

[0067] (3) Model construction, such as Figure 1 As shown:

[0068] 3.1 Rule Model Construction

[0069] The Bayesian deep learning rule model based on event characteristics parses the event ontology, analyzes the user digital portrait information in the governance scenario, extracts the event-specific information elements and behavioral elements, summarizes the same type of events, and constructs an event feature library to support event prediction and risk identification.

[0070] This implementation method takes the grassroots governance scenario as an example. First, the user digital portrait information in the governance scenario constructed in the previous step is analyzed to obtain various information elements and behavioral elements involved in the event to support the construction of the event feature model, such as Figure 2 As shown in the figure, information elements specifically include time, location, trajectory, person, and event information; behavioral elements include fighting, travel, communication, and sojourn. Each type of governance scenario can be analyzed through ontology analysis of the event, extracting as many information and behavioral elements as possible from the virtual and real spaces, and even the thought space, that are unique to that type of event. By analyzing multiple similar events, the common characteristics and co-occurring behaviors of these events can be summarized, and a feature library of information and behavioral elements unique to this type of event can be constructed to support risk identification in digital governance scenarios.

[0071] 3.2 Construction of prediction and early warning model

[0072] The model layer is the core of the system, responsible for building and optimizing the prediction model based on Bayesian deep learning. The prediction and warning model based on Bayesian deep learning in this invention combines the automatic feature learning capability of deep learning with the probabilistic reasoning capability of the Bayesian method. The specific construction process includes four steps:

[0073] ① Build a model: Based on the evaluation indicator system, understand the correlation between indicators at each level and build a Bayesian deep learning structural model suitable for event prediction. First, it is necessary to define an appropriate network structure to reflect the complex relationship between evaluation indicators. This usually involves a directed graph, where nodes represent evaluation indicators (or features) and edges represent the dependencies between these indicators. For Bayesian deep learning, this may be a Bayesian network (also called a belief network) or the more complex deep Bayesian network (DBN), which combines the nonlinear modeling capabilities of deep learning with the probabilistic reasoning capabilities of Bayesian networks.

[0074] The definition of the model can be based on the multiplication principle of probability, that is, for any set of events A 1, A 2,..., A n ,have( A 1, A 2,..., A n )= P ( A 1) P ( A 2∣ A 1) P ( A 3∣ A 1, A 2)... P ( A n ∣ A 1, A 2,......, A n−1 ); in Bayesian networks, this translates to the product of node probabilities, where the probability of each node depends on the state of its parents.

[0075] ② Determine the prior probability; the prior probability is an estimate of the probability of an event before any data is observed. Combine historical sample data with expert opinions to determine the prior probability of the network node, that is, the initial evidence of the risk probability. X , its prior probability P ( X ) are estimates based on available information.

[0076] ③ Parameter Learning and Conditional Probability Distribution Inference: In this step, the goal is to use parameter learning algorithms to infer the conditional probability distribution of non-root nodes in the network. Due to the dynamic and uncertain nature of events, sample data often contain some unobservable hidden variables (such as missing values). Therefore, this implementation uses the EM (Expectation Maximization) algorithm, an iterative convergence algorithm for samples with missing values, for parameter learning. Through multiple iterations, the model parameters are continuously converged towards the maximum likelihood estimate, ultimately obtaining the conditional probability distribution.

[0077] Expectation step in the EM algorithm: Calculate the posterior distribution of the latent variable P ( Z ∣ X , i t ),in Z is a hidden variable, X is the observation data, i t is the current parameter estimate.

[0078] Maximization step: Update parameters i t+1 To maximize .

[0079] ④ Calculation of posterior probability: Based on the Bayesian algorithm principle, the prior probability P ( i ) and conditional probability P ( D | i ) is converted into posterior probability P ( i | D ), which is the risk probability of the target event occurring in the model and its uncertainty. In Bayesian networks, this usually means combining the prior probability and the conditional probabilities learned by the model to calculate the posterior probability of a specific event or state.

[0080] Bayes' theorem is the core of Bayesian inference, which describes how to B Update the parameters in the case of A Beliefs:

[0081]

[0082] in: P ( i | D ) is the posterior probability that the observed data D Post Parameters i The probability distribution of P ( D | i ) is the likelihood function expressed in terms of given parameters i The data was observed under D The probability of P ( i ) is the prior probability that the parameter i beliefs or assumptions, P ( D ) is the marginal probability also known as evidence, and is a normalizing constant that ensures that the sum of the posterior probability distribution is 1.

[0083] 3.3 Bayesian Network

[0084] Bayesian neural network (BNN) is a model that applies Bayesian theory to neural networks. It no longer regards network parameters as definite values, but as probability distributions. In BNN, each network parameter (such as weight and bias) is assigned a prior distribution and updated to a posterior distribution through Bayesian inference. This probabilistic modeling method enables BNN to quantify the uncertainty of the prediction results and show stronger robustness when data is limited or noisy. According to the problem domain, the random variables (nodes) in the model are determined; at the same time, based on the causal relationship between the variables, a directed acyclic graph (DAG) is constructed to represent the dependency relationship between the variables. The inference process of Bayesian neural network BNN usually involves complex integral operations and is difficult to solve directly. Therefore, in this embodiment, an approximate inference method, variational Bayesian inference (VI), is used to solve the problem.

[0085] Variational inference is done by defining a variational distribution that is easy to sample q φ ( i ) to approximate the posterior distribution P ( i | D ), and optimize the variational parameters f To minimize the difference between the two distributions (commonly measured by KL divergence), the objective function ELBO is:

[0086]

[0087] ELBO (Evidence Lower Bound) is the lower bound of evidence. By maximizing ELBO, we can indirectly minimize the KL divergence between the true posterior and the variational distribution. The first term is the expected log-likelihood, which encourages the variational distribution to produce high-likelihood data; the second term is the KL divergence between the variational distribution and the prior, which encourages the variational distribution to be close to the prior.

[0088] Of course, Markov Chain Monte Carlo (MCMC) methods can also be used for inference. In Bayesian deep learning, Monte Carlo integration is often used to estimate expected values ​​or integrals that are difficult to calculate directly. For example, when predicting new data points, samples can be drawn from the posterior distribution and used to approximate the expected value of the predicted distribution.

[0089]

[0090] Where: f ( i ) is the parameter we want to estimate i functions of (such as predictive distributions), N is the sample size, i (i) is a sample drawn from the posterior distribution, by increasing the number of samples N , the accuracy of the estimation can be improved.

[0091] A variational autoencoder (VAE) is a generative model that combines the data compression capabilities of an autoencoder with the uncertainty modeling capabilities of Bayesian inference. VAEs capture the latent representation of the data by introducing latent variables and use variational inference to estimate the posterior distribution of these latent variables. In event prediction and early warning tasks, VAEs can be used to learn the latent structure of the data and generate predictions with uncertainty. The objective function of a VAE typically consists of a reconstruction loss and a KL divergence, which measure the accuracy of the data reconstruction and the rationality of the latent variable distribution, respectively. By optimizing this objective function, VAEs can learn the latent representation of the data and uncertainty information.

[0092] 3.4 Deep Learning

[0093] The design of neural network architecture is the cornerstone of deep learning applications, directly determining the complexity and depth of the model's ability to capture data features. First, select or design an appropriate neural network architecture based on the specific requirements of the task (such as classification, regression, or sequence prediction). For classification tasks, projects tend to use convolutional neural networks to capture spatial features or recurrent neural networks and their variants, such as LSTM, to process sequence data. For regression tasks, a simpler fully connected network (FCN) structure may be chosen. During design, considerations also need to be made regarding the dimensionality of the input layer to match the data characteristics, the number of hidden layers and the number of neurons in each layer to control the model's capacity and complexity, and the configuration of the output layer to output the prediction results.

[0094] In order to make full use of the rich information of multi-source heterogeneous data, this embodiment adopts a variety of deep learning modules for feature fusion and extraction. Among them, convolutional neural networks are good at processing images and grid data, and can automatically extract local features and spatial hierarchical structures; recurrent neural networks and their variants (such as LSTM, GRU) are good at processing time series data and can capture the time dependency and long-term memory effect in the data.

[0095] Parameter initialization is a critical step before neural network training, as it determines the starting point for model learning. Proper parameter initialization helps the model quickly converge to the optimal solution and avoid problems such as local optimality or gradient vanishing / exploding. Common parameter initialization methods include random initialization (such as uniform or normal distribution initialization), zero initialization (although generally not recommended as it may cause all neurons to have the same output at the beginning of training), and pretrained initialization (using model parameters pretrained on large-scale datasets as initialization values). When choosing an initialization method, it is necessary to consider the distribution characteristics of the data, the structure of the model, and the nature of the training algorithm.

[0096] (4) Model training.

[0097] In event prediction and early warning technology based on Bayesian deep learning, the model training stage is crucial, as it combines the advantages of Bayesian networks and deep learning.

[0098] 4.1 Bayesian Network

[0099] Parameter learning aims to use training data to accurately estimate the conditional probability distribution parameters of each variable in a Bayesian network. This process often uses the expectation-maximization (EM) algorithm, which iteratively executes expectation and maximization steps to gradually approach the true parameter values. In the expectation step, the algorithm calculates the expected value of the hidden variable based on the current parameter estimate; in the maximization step, these expected values ​​are used to update the parameters to maximize the likelihood function of the observed data. This process ensures the accuracy and effectiveness of the parameter estimates.

[0100] When the Bayesian network structure is unknown, structural learning becomes a necessary step. Classic methods such as the K2 algorithm and Tian's algorithm are used to automatically discover dependencies between variables in the data and construct a network structure that best matches the data characteristics. These methods use scoring search strategies or leverage the statistical properties of the data to guide structural learning, reducing the search space and improving learning efficiency. The result of structural learning is a Bayesian network model that is both consistent with the data and well interpretable, providing strong support for event prediction.

[0101] 4.2 Deep Learning

[0102] In deep learning modules, forward propagation is the foundation of model training. Training data is processed layer by layer through the neural network. Each layer of neurons undergoes linear transformations and nonlinear activations based on the output of the previous layer and its own weights and biases, ultimately calculating the predicted value of the output layer. This process extracts and transforms complex features, providing the foundation for subsequent loss calculations.

[0103] The loss function is used to quantify the difference between the model's predicted value and the true value. In the event prediction and warning task, an appropriate loss function (such as mean square error, cross entropy loss, etc.) is selected to evaluate the model performance. By substituting the predicted value and the true value into the loss function for calculation, a specific numerical value is obtained to reflect the model's prediction accuracy.

[0104] To optimize model parameters, the backpropagation algorithm is employed. This algorithm, based on optimization methods such as the chain rule and gradient descent, guides parameter updates by calculating the gradient of the loss function with respect to the model parameters. During backpropagation, gradient information is propagated forward layer by layer. The gradient of each layer's parameters is calculated and used to update the parameters at that layer. Through multiple iterations of forward and backward propagation, the model parameters are gradually optimized, improving predictive performance and enabling more accurate event prediction and early warning.

[0105] (5) Forecasting and early warning mechanism.

[0106] 5.1 Prediction Model Output

[0107] Based on the output of the Bayesian deep learning model, we can obtain the prediction results and their uncertainty information. The prediction results are usually expressed in the form of probability distributions, such as Gaussian distribution and Bernoulli distribution. The uncertainty information can be quantified through indicators such as the variance and confidence interval of the prediction distribution.

[0108] 5.2 Multi-threshold early warning system

[0109] To build an efficient and accurate early warning system, this implementation utilizes a multi-threshold strategy. Traditional early warning systems often rely on a single, fixed threshold to determine whether to trigger an alert. This approach can be inflexible and inflexible in complex and changing data environments. Therefore, this implementation automatically adjusts the alert threshold based on the uncertainty of the prediction results.

[0110] Specifically, multiple warning levels (such as low, medium, and high) can be set, and corresponding threshold ranges can be set for each level. These threshold ranges can be set based on historical data, business needs, and risk tolerance. When the confidence or probability distribution of the prediction results falls within a certain threshold range, the system triggers a warning of the corresponding level. In addition, the uncertainty of the prediction results can be used to dynamically adjust the threshold. For example, when the uncertainty of the prediction results is high, the threshold range can be appropriately relaxed to avoid false alarms caused by data noise or model uncertainty. Conversely, when the uncertainty of the prediction results is low, the threshold range can be tightened to improve the accuracy and timeliness of the warning.

[0111] (6) Model evaluation and optimization.

[0112] 6.1 Evaluation and Advanced Optimization Algorithms

[0113] like Figure 3 As shown in the figure, after model training is complete, the model needs to be evaluated to verify its predictive performance. Evaluation metrics typically include accuracy, recall, F1 score, and area under the ROC (Receiver Operating Characteristic) curve (AUC). By comparing the performance of different models on the validation set, the optimal model can be selected. In addition, the model needs to be further optimized, such as adjusting model parameters, improving model structure, and adding data augmentation strategies to improve the model's predictive accuracy and generalization ability. These algorithms optimize the model's parameter update process by adaptively adjusting the learning rate, thereby accelerating training while maintaining model stability.

[0114] 6.2 Parallel Computing Technology

[0115] To address the computational challenges posed by large-scale datasets and high-dimensional feature spaces, this implementation utilizes parallel computing technology to accelerate model training and inference. By distributing computational tasks across multiple computing nodes through distributed computing frameworks (such as TensorFlow and PyTorch), parallel execution can significantly reduce computational time and improve computational efficiency. Furthermore, high-performance computing devices such as GPUs (graphics processing units) can be utilized to accelerate the computational process of deep learning models. GPUs have powerful parallel computing capabilities and high-speed memory bandwidth, enabling them to quickly complete large-scale matrix operations and computational tasks such as neural network forward and backward propagation.

[0116] 6.3 Model Compression and Pruning

[0117] To further reduce the computational complexity and memory usage of the model, this implementation adopts model compression and pruning techniques. Model compression reduces the model size by reducing the number of model parameters or reducing the precision of the parameters, while pruning simplifies the model structure by removing unimportant connections or neurons in the model. These techniques can significantly reduce the computational complexity and inference time of the model while maintaining model performance.

[0118] (7) Model deployment and early warning.

[0119] After evaluation and optimization, the model can be deployed in a production environment for real-time prediction and early warning. During deployment, factors such as system stability, scalability, and security must be considered. An early warning mechanism must also be established. Alerts are generated based on the model's predictions and pre-set warning thresholds or rules, and notified to relevant personnel via SMS, email, app push notifications, and other methods. These alerts should include multiple dimensions, such as the event type, time of occurrence, scope of impact, and severity, enabling relevant personnel to take timely measures to address potential risks. During model operation, real-time monitoring is performed, and necessary adjustments and optimizations are made based on feedback data.

[0120] The above description of the embodiments is intended to facilitate understanding and application of the present invention by those skilled in the art. It is apparent that those skilled in the art can readily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without requiring creative effort. Therefore, the present invention is not limited to the above embodiments. Any improvements or modifications made by those skilled in the art based on the disclosure of the present invention should fall within the scope of protection of the present invention.

Claims

1. An event prediction and early warning method based on Bayesian deep learning, characterized in that: The steps include: (1) Collect massive multi-source heterogeneous data in the target application scenario, pre-process the data and divide it into training set, validation set and test set; each set of data contains multiple feature variables and a target variable, the feature variables are numerical, categorical or time series, and the target variable is the state of the predicted event, which serves as a label; Data preprocessing includes four parts: data cleaning, data integration, data enhancement, and feature engineering. Data cleaning includes denoising, filling in missing values, and correcting or removing outliers. Feature engineering includes feature selection, feature extraction, and feature transformation. Feature selection uses methods such as correlation analysis, PCA, and mutual information to screen out features that have a significant impact on the target variable. Feature extraction uses deep learning methods such as CNN and RNN to automatically extract high-level features from raw data. Feature transformation improves data distribution through methods such as standardization, normalization, one-hot encoding, and label encoding. (2) Use user digital portraits to construct a rule model based on event characteristics. The user digital portrait is a set of user characteristics constructed based on multi-dimensional information including behavioral data, social data, and consumption data. The specific implementation method is as follows: first, analyze the user digital portrait information in the target application scenario, and obtain various information elements and behavioral elements involved in the event to support the construction of the rule model. The information elements include time information, location information, trajectory information, character information, and event information; each type of governance scenario requires ontology analysis of the event to extract the information elements and behavioral elements of the virtual and real space and even the thinking space that are unique to this type of event. On the basis of analyzing multiple similar events, summarize the common characteristics and co-occurring behaviors of this type of event, and construct a feature library of information elements and behavioral elements that are unique to this type of event. By integrating the feature library of information elements and behavioral elements of various types of events, a rule model based on event characteristics is obtained; (3) On the basis of the above rule model, a prediction and early warning model based on Bayesian deep learning is constructed. The prediction and early warning model based on Bayesian deep learning combines the advantages of Bayesian algorithm and deep neural network, models and infers the uncertainty in complex problems, and uses probability distribution to represent weights instead of single values, so as to provide uncertainty information about these predictions when making predictions. That is, under the premise of known prior probability and conditional probability density, for the uncertainty problem of various event risks, the conditional probability density function is inferred through statistical learning of samples, and converted into posterior probability using Bayesian algorithm criteria; the prediction and early warning model adopts Bayesian neural network, analyzes historical data and real-time monitoring data, processes uncertainty in data, estimates the probability of occurrence of future events, and gives the confidence level of the prediction results; (4) Use the training set data to train the prediction and early warning model, and use the validation set data to tune the parameters during the model training process; in the process of training the prediction and early warning model, it is necessary to define the prior distribution of the model parameters, design the loss function, and use the maximum expectation algorithm to update and optimize the parameters of the Bayesian neural network in real time. Through continuous iterative calculation and parameter adjustment, the model can gradually approach the real data distribution and event patterns. In addition, regularization, random inactivation, early stopping strategy and model evaluation and selection mechanism are also set in the training process. By comparing and evaluating the performance of different model parameters, optimization suggestions and decision support are provided to developers, and the validation set is used to realize the automated model tuning process, which can automatically adjust the model parameters and structure settings according to real-time feedback to achieve the best prediction effect. (5) Input the test set data or real-time data into the trained prediction and warning model to predict events, and issue warnings based on the prediction results output by the model. The specific implementation method is: first, process the data and extract features, then input the features into the trained prediction and warning model to predict events, and then use the uncertainty quantification adaptive warning mechanism to issue warnings based on the prediction results output by the model. This mechanism dynamically adjusts the warning threshold by analyzing the uncertainty distribution of the prediction results, and sets multiple warning levels, and sets corresponding threshold ranges for each level. These threshold ranges are set according to historical data, business needs and risk tolerance. When the confidence or probability distribution of the prediction results falls into a certain threshold range, the corresponding level of warning is triggered. The warning information includes multiple dimensions such as event type, occurrence time, impact range, and severity, and is notified to relevant personnel through various methods such as SMS, email, and APP push; When using a trained prediction and warning model to predict events based on real-time data, parallel computing technology is used to accelerate the model's reasoning process. Computational tasks are distributed to multiple computing nodes for parallel execution through a distributed computing framework. At the same time, model compression technology is used to reduce the number of model parameters. Pruning technology is used to remove unimportant connections or neurons in the model to simplify the model structure and reduce the model size.

2. The event prediction and early warning method based on Bayesian deep learning according to claim 1, characterized in that: Variational inference methods or Markov chain Monte Carlo methods are used to approximate the posterior probability.

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