Event prediction and early warning method based on Bayesian deep learning

Abstract

The invention discloses an event prediction and early warning method based on Bayesian deep learning. The event prediction and early warning method mainly comprises the following two parts: constructing a Bayesian deep learning rule model based on event characteristics, and designing a prediction and early warning model on the basis of constructing the Bayesian deep learning rule model. According to the method, the Bayesian statistical method and the deep learning model are fused, the advantages of the Bayesian statistical method and the deep learning model can be fully utilized, the uncertainty of a prediction result can be expressed in a probability distribution form by combining priori knowledge and observation data, efficient modeling and prediction are performed on complex and nonlinear time sequence data, early warning of potential events is realized, and the prediction efficiency is improved. The accuracy and reliability of event prediction and early warning are improved.

Description

Technical Field

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

[0002] With the rapid development of information technology, various types of event data have grown explosively. How to accurately predict future possible events has become a hot topic in current research. Traditional event prediction methods are mostly based on statistical models or simple machine learning algorithms, and it is difficult to effectively process high-dimensional, non-linear, and non-stationary time series data. Moreover, when traditional deep learning models are used to handle complex event predictions, the uncertainty of prediction results is often ignored, resulting in inaccurate prediction results in some extreme or data-sparse situations. Existing technologies mostly rely on a single probability prediction value and cannot comprehensively reflect the uncertainty of 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 ability of deep learning is still an urgent problem to be solved.

[0003] Existing technical solutions combine the robustness of traditional statistical models and the prediction ability 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 the long-term trends and seasonal patterns in the data, and then machine learning algorithms such as support vector machines and random forests can be combined to handle non-linear relationships and complex features. The advantages of such solutions are high computational efficiency, the ability to quickly process large-scale data sets, good robustness, few distribution assumptions about the data, and the ability to resist the interference of outliers and noise to a certain extent. In addition, after combining machine learning algorithms, more complex feature relationships and pattern recognition problems can be handled. However, the limitations are limited prediction accuracy and generalization ability, and greater difficulty in model fusion. In addition, event prediction systems based on a single deep learning model (such as recurrent neural networks, long short-term memory networks, etc.) can improve the prediction accuracy to a certain extent, but often ignore the uncertainty in the data, resulting in a decline in prediction performance in abnormal or extreme situations.

[0004] To overcome this shortcoming, BDL (Bayesian Deep Learning) emerged. It represents model weights by introducing probability distributions, 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 a systematic solution. The most similar implementation solution is to use traditional deep neural networks (DNNs) for event prediction. Such models optimize network parameters through large-scale data training to improve prediction accuracy. However, DNN models have limited capabilities in dealing with uncertainty, cannot effectively handle latent variables and noise in the data, and limit 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 process is a powerful non-parametric Bayesian method that can model the distribution of functions rather than directly model parameters. This method is very effective in dealing with uncertainty and is suitable for scenarios that require accurate estimation of prediction confidence intervals. The advantages of this solution are that Gaussian processes can naturally handle uncertainty in prediction, can adapt to different data characteristics by choosing appropriate kernel functions, have strong flexibility, and have non-linear modeling capabilities, making them suitable for predicting complex time series data. The limitations are that as the amount of data increases, the computational complexity of Gaussian processes increases significantly, which may not be applicable to large-scale data sets; in addition, Gaussian processes have poor interpretability, and the choice of kernel function has an important impact on model performance, but choosing an appropriate kernel function usually requires certain prior knowledge and experimental adjustment.

[0006] In summary, the existing event prediction technologies mainly have the following disadvantages: 1. The prediction results are overly optimistic. When dealing with complex prediction tasks, existing deep learning models often lack an effective uncertainty quantification mechanism and cannot effectively quantify and transmit the uncertainty of prediction results. This "black box" characteristic leads to large deviations in prediction results when data is scarce or noisy, and even misleads decision-making; especially in prediction tasks, the model often can only give a definite prediction result and cannot provide the uncertainty information behind this prediction result. This uncertainty may stem from data noise, model limitations, or unknown changes in data distribution. The lack of uncertainty modeling can lead to problems such as increased decision-making risks, reduced trust, and difficulty in adapting to changes.

[0007] 2. Poor robustness. Facing the dynamically changing social governance environment, traditional models are difficult to quickly adapt to emerging event types, pattern changes, and dynamic changes in data distribution. This limitation partly stems from the lack of sufficient robustness in the models; when the data distribution changes, the models may not be able to perceive and adjust in a timely manner because of the lack of uncertainty information to guide the adaptive learning of the models.

[0008] 3. Lack of integration of expert knowledge. Most current systems overly rely on data-driven methods and neglect the important role of expert knowledge in improving the accuracy and comprehensiveness of prediction and early warning; expert experience often contains profound understanding and insights into specific fields, which can make up for the limitations and incompleteness of the data itself, affecting the comprehensiveness and accuracy of the prediction.

[0009] 4. High complexity in data processing. Facing complex and changeable governance scenarios, existing technologies are difficult to effectively process large-scale and multi-dimensional data; as the amount of data continues to increase, existing systems may be difficult to effectively expand to meet higher processing requirements. Moreover, large-scale data sets require powerful computing resources to support the training and inference of models, which increases the hardware cost and energy consumption of the system. Summary of the Invention

[0010] In order to solve the problems of insufficient uncertainty modeling, poor generalization ability, and lack of real-time performance in the field of event prediction and early warning by traditional deep learning methods, the present invention provides an event prediction and early warning method based on Bayesian deep learning. By constructing an efficient Bayesian deep learning model, accurate prediction of complex and non-linear time series data can be achieved, and the uncertainty of the prediction results can be effectively evaluated, providing a scientific basis for the early warning of potential events.

[0011] An event prediction and early warning method based on Bayesian deep learning includes the following steps: (1) Collect massive multi-source heterogeneous data in target application scenarios (such as public safety, financial risks, medical health, natural disasters, traffic management, etc.), and after preprocessing these data, divide them into a training set, a validation set, and a test set; (2) Use the user digital portrait to construct a rule model based on event characteristics, where the user digital portrait is a set of user characteristics constructed according to multi-dimensional information of the user including behavior data, social data, and consumption data; (3) Build a prediction and early warning model based on Bayesian deep learning on the basis of the above rule model; (4) Use the training set data to train the prediction and early warning model, and use the validation set data to optimize the parameters during the model training process; (5) Input the test set data or real-time data into the trained prediction and early warning model for event prediction, and issue an early warning according to the prediction results output by the model.

[0012] Further, each set of data in step (1) includes multiple feature variables and one target variable. 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.). The target variable is the state of the prediction event (such as stock price increase or decrease, equipment failure occurrence, disease onset, etc.), which serves as a label.

[0013] In practical applications, data often comes from multiple channels and has different formats and structures. To fully utilize the information in these data and improve the accuracy of prediction, we need to adopt multi-source heterogeneous data processing techniques to integrate this data. The preprocessing of data in step (1) includes four parts: data cleaning, data integration (multi-source data fusion), data augmentation (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. Among them, feature selection screens out the features that have an important impact on the target variable through methods including correlation analysis, PCA (principal component analysis), and mutual information (MI). Feature extraction automatically extracts high-level features from the original data through deep learning methods including CNN (convolutional neural network) and RNN (recurrent neural network). Feature transformation improves the data distribution through methods including standardization, normalization, one-hot encoding, and label encoding. Through these steps, we can convert the data from different channels into data with a unified format and extract useful features for subsequent prediction tasks to ensure the quality and usability of the data.

[0014] Several types of governance events with a high occurrence probability and poor social impact need to be focused on, such as repeated visit events, fatality events, economic events, etc. Therefore, the present invention constructs a rule model based on event characteristics. The specific implementation manner of step (2) is as follows: First, analyze the user digital portrait information in the target application scenario to 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, person information, and event information. For each type of governance scenario, it is necessary to perform ontology analysis on the event to extract as many information elements and behavior elements in the virtual, real, and even thinking spaces unique to this type of event as possible. Based on the analysis of 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 behavior elements unique to this type of event. By integrating the feature libraries 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 the user digital portrait to better understand the user's needs and behavior patterns and predict the possible types of events the user may participate in. The construction of the user digital portrait needs to comprehensively consider multiple factors, such as the user's age, gender, occupation, interests and hobbies, etc., and carry out customized design in combination with the specific application scenario.

[0015] Furthermore, the prediction and early warning model based on Bayesian deep learning integrates the advantages of the Bayesian algorithm and the deep neural network, models and infers the uncertainty in complex problems. By using probability distributions to represent weights instead of single values, uncertainty information about these predictions is provided simultaneously when giving predictions. That is, on the premise of knowing the prior probability and conditional probability density, for the uncertainty problem of various event risks, the conditional probability density function is inferred through the statistical learning of samples, and is transformed into the posterior probability using the Bayesian algorithm criterion. This prediction and early warning model uses a Bayesian neural network. By analyzing historical data and real-time monitoring data, it processes the uncertainty in the data, estimates the occurrence probability of future events, gives the confidence level of the prediction results, and provides corresponding early warning information. This model not only improves the accuracy and reliability of the early warning system, but also can obtain robust prediction results in the case of insufficient data. This is the key mechanism for establishing a prediction and early warning system by Bayesian deep learning.

[0016] Bayesian deep learning introduces the method of 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 inference. This method can not only improve the prediction accuracy and generalization ability of the model, but also give the confidence interval and uncertainty estimation of the prediction results. In the intelligent event prediction and early warning system, Bayesian deep learning can help us better understand and cope with the uncertain factors in the prediction results. In addition, the Bayesian deep learning model has a powerful automatic feature extraction ability and can automatically learn useful feature representations from the original data. In the intelligent event prediction and early warning system, the present invention utilizes this characteristic of the deep learning model to reduce the burden of manual feature engineering and improve the robustness and generalization ability of the features. By stacking multiple non-linear transformation layers (such as convolutional layers, pooling layers, fully connected layers, etc.), the deep learning model can gradually abstract the high-level feature representations of the data and use them for subsequent prediction tasks.

[0017] In Bayesian deep learning, the posterior probability is usually not directly calculated because this usually involves high-dimensional integration and is difficult to analyze. The present invention uses the variational inference method or the Markov chain Monte Carlo method to approximately estimate the posterior probability, preferably the variational inference method, which uses a variational autoencoder to capture the latent representation of the data by introducing latent variables and uses variational inference to estimate the posterior distribution of the latent variables.

[0018] Deep learning is the key to the technical innovation and performance improvement of the system of the present invention. In view of the excellent ability 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 the edges (conditional dependencies) between the nodes to clarify the interactions and influence paths between the factors, and on this basis, use historical data or expert knowledge to specify conditional probability distributions for each node. These probability distributions characterize the law of node state changes with the state of its parent node; integrate deep learning architectures including CNN, RNN, LSTM (long short-term memory network) into the Bayesian neural network to perform preliminary processing on the original data, and automatically extract high-level abstract feature representations in the data by stacking multiple nonlinear transformation layers (such as convolutional layers, pooling layers, fully connected layers, etc.) and feature learning mechanisms. These feature representations are then used as node inputs, providing a richer information source for the network model. Through this integration, the system not only enhances the ability to understand complex data structures, but also significantly improves the recognition accuracy and prediction accuracy of complex patterns, thereby greatly enhancing the prediction performance. Through this series of construction steps, the present invention forms a Bayesian network model with a clear structure and strict logic, which provides a solid theoretical foundation and model framework for subsequent prediction analysis.

[0019] Real-time updating and optimization are important guarantees to ensure the continuous and effective operation of the system and adapt to environmental changes. In the face of the ever-changing external environment and data distribution characteristics, in the process of training the prediction and early warning model, the step (4) needs to define the prior distribution of 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 rules, 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 in the training process. Through the performance comparison and evaluation 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 and competitive nature of its prediction capabilities.

[0020] Furthermore, the specific implementation of step (5) is as follows: First, the data (test set data or real-time data) is processed and features are extracted. Then, the features are input into the trained prediction and early warning model for event prediction. Furthermore, an adaptive early warning mechanism with uncertainty quantification is used for early warning according to the prediction results output by the model. This mechanism dynamically adjusts the early warning threshold by analyzing the uncertainty distribution of the prediction results, and sets multiple early warning levels (such as low, medium, high), and sets corresponding threshold ranges for each level. These threshold ranges are set according to historical data, business requirements, and risk tolerance. When the confidence or probability distribution of the prediction results falls into a certain threshold range, the corresponding level of early warning is triggered. The early warning information includes multiple dimensions such as event type, occurrence time, impact range, and severity, and notifies relevant personnel through multiple methods including text messages, emails, and APP push. This mechanism can improve the flexibility and accuracy of early warning.

[0021] Regarding the computational challenges brought by large-scale datasets and high-dimensional feature spaces, when using the trained prediction and early warning model to perform event prediction on real-time data in step (5), parallel computing technology is adopted to accelerate the inference process of the model. 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. By pruning technology, unimportant connections or neurons in the model are removed to simplify the model structure and reduce the model size, which can significantly reduce the computational complexity and inference time of the model, reduce the computing time, and improve the computing efficiency.

[0022] The target application scenarios of the method of the present invention include (but are not limited to): Public safety: Used to predict and early warn public safety events such as attacks, and help relevant departments take timely measures to ensure the safety of life and property.

[0023] Financial risks: Used to evaluate financial risks such as the credit risks and market risks of enterprises and individuals, and help financial institutions and investors make more informed decisions.

[0024] Medical and health: Used to predict and early warn medical and health events such as the outbreak of diseases and the deterioration of patients' conditions, and help medical institutions and patients take timely measures to prevent and treat diseases.

[0025] Natural disasters: Used to predict and early warn the occurrence of natural disasters such as earthquakes, floods, and typhoons, and help relevant departments and the public make preparations for disaster prevention and mitigation in advance.

[0026] Traffic management: Used to predict and early warn traffic events such as traffic jams and traffic accidents, and help traffic management departments optimize traffic flow and reduce the occurrence of traffic accidents.

[0027] Based on the above technical solutions, the present invention can solve the following technical problems: 1. Uncertainty Modeling; Bayesian deep learning models the uncertainty of parameters by introducing probability distributions, which can more comprehensively characterize the uncertainty of prediction results and provide a more robust and reliable prediction interval for decision-makers. The present invention uses Bayesian theory to model the probability distribution of model parameters, thereby quantifying the uncertainty of prediction results. This uncertainty information can be used as an important reference for decision-making, improving the reliability and robustness of decision-making.

[0028] 2. Improving Generalization Ability; The present invention introduces prior knowledge through Bayesian methods to limit model complexity and prevent overfitting. At the same time, Bayesian deep learning models can automatically learn the uncertainty in data, thus better adapting to changes in data distribution and improving the generalization ability of the models; the Bayesian deep learning framework can use uncertainty information as a "feedback" mechanism to dynamically adjust model parameters and structures, thereby enhancing the robustness and adaptive ability of the models in complex and changing environments and ensuring the long-term effectiveness and accuracy of the prediction and early warning system.

[0029] 3. Integrating Expert Knowledge: Bayesian deep learning technology can flexibly integrate expert prior knowledge and data-driven learning. The present invention optimizes the model training process by constructing a prior distribution containing expert knowledge, making the prediction results based on both data and wisdom, and enhancing the comprehensiveness and accuracy of prediction.

[0030] 4. Enhancing Data Processing Ability: Bayesian deep learning, by introducing efficient data processing technologies such as probabilistic graphical models, can optimize the allocation of computing resources while ensuring prediction accuracy, improve data processing efficiency, reduce the hardware costs and energy consumption of system operation, and thus better adapt to the data processing requirements of large-scale and multi-dimensional data. The present invention optimizes the computing efficiency and memory usage of deep learning models, combines efficient parallel computing and distributed processing technologies, and reduces the processing latency of large-scale data sets. In addition, the present invention introduces incremental learning and online learning mechanisms, enabling the model to be updated in real time to adapt to data changes and further improving the real-time performance of the system.

[0031] Therefore, the innovation and beneficial technical effects of the present invention are mainly reflected in the following aspects: 1. High prediction accuracy and uncertainty quantification.

[0032] By introducing a Bayesian deep learning model, the present invention significantly improves the accuracy of prediction tasks. The Bayesian framework allows the incorporation of prior knowledge during model training and reflects the uncertainty in data through posterior distribution updates, thereby not only improving the accuracy of prediction but also effectively quantifying the uncertainty of 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.

[0033] 2. Strong generalization ability.

[0034] Facing the complex and changeable data environment, the present invention constructs a highly flexible model architecture by integrating multiple deep learning modules (such as convolutional neural network, recurrent neural network, attention mechanism, etc.). This multi-module fusion strategy enables the model to more effectively process different types of input data (such as time series, images, texts, etc.) and extract richer and deeper feature representations therefrom. Therefore, the model of the present invention can also maintain good performance on unseen data sets, that is, it has strong generalization ability.

[0035] 3. Real-time warning.

[0036] In order to cope with application scenarios with high real-time requirements, the present invention has carried out in-depth optimization in algorithm design and system implementation. By adopting efficient acceleration strategies (such as GPU parallel computing, algorithm pruning, etc.) and fine control of the prediction process, it is ensured that the system can complete complex data processing and prediction tasks in an extremely short time. At the same time, combined with real-time data stream processing technology, the present invention can achieve instant response to input data and trigger the warning mechanism immediately when the preset conditions are met.

[0037] 4. Adaptive warning threshold.

[0038] Traditional warning systems often rely on fixed threshold settings, which may fail in practical applications due to changes in data distribution. The present invention innovatively proposes an adaptive warning threshold adjustment method based on the uncertainty of prediction results. This method dynamically adjusts the warning threshold by analyzing the uncertainty distribution of prediction results to ensure the accuracy and effectiveness of warning when data changes. This adaptive mechanism greatly improves the flexibility and robustness of the warning system. Brief description of the drawings

[0039] Figure 1 It is a schematic diagram of the event prediction and warning process based on Bayesian deep learning of the present invention.

[0040] Figure 2 It is a schematic diagram of the construction process of the rule model in the present invention.

[0041] Figure 3 It is a schematic diagram of the risk assessment process of various events in the present invention. Detailed implementation manners

[0042] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the drawings and specific implementation manners.

[0043] In today's highly informationized era, traditional event prediction and early warning methods still face huge challenges. To address these challenges, the present invention proposes an intelligent event prediction and early warning method based on Bayesian deep learning, which combines the powerful automatic feature extraction ability of deep learning and the advantages of Bayesian methods in dealing with uncertainty and model selection, aiming to achieve accurate prediction and early warning of complex and changeable events. This embodiment focuses on several types of governance events with high occurrence probability and poor social impact, such as repeated visit events, fatality events, economic events, etc., and uses Bayesian deep learning for prediction and early warning analysis. The specific implementation includes data preparation, data preprocessing, model construction, feature fusion and extraction, prediction and early warning mechanism, optimization algorithm and acceleration strategy, and the details are as follows: (1) Data preparation.

[0044] Data collection and preprocessing is the primary link of 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 states, and behavior patterns. The collected raw data often contains problems such as noise, redundancy, outliers, and inconsistent formats. Therefore, it is necessary to deeply purify and organize the data through a series of precise data cleaning steps, such as denoising, missing value filling, outlier detection and removal, data standardization or normalization, etc. In addition, data formatting processing is also a key link, which converts the cleaned data into a unified format and structure, facilitating the direct access and efficient processing of subsequent modules, thereby providing accurate and reliable data support for subsequent analysis work. The specific implementation process is as follows: 1.1 Data collection Collect relevant data sets from various sources (including databases, sensors, web crawlers, etc.), including but not limited to financial transaction records, social media activities, enterprise business systems, Internet of Things sensor data, medical health monitoring, factory safety early warning, etc. These data sets usually have characteristics such as multi-source heterogeneity, high-dimensionality, non-linearity, and strong time series, and often contain noise and missing values. In order to build an effective prediction model, it is necessary to comprehensively understand and preprocess these data.

[0045] 1.2 Data set structure The data set usually contains multiple feature variables and one or more target variables. The feature variables are mainly numerical (such as price, temperature, pressure, etc.), categorical (such as product type, geographical location, etc.), or time series (such as historical transaction records, time series sensor data, etc.). The target variable is the event or state that we want to predict, such as stock price increase or decrease, equipment failure occurrence, disease onset, etc.

[0046] (2)Data preprocessing.

[0047] 2.1 Data cleaning Data cleaning is the first step of data preprocessing, aiming to remove noise, handle outliers and missing values in the data. For missing values, methods such as median filling (such as mean filling, median filling, mode filling), linear interpolation (such as linear interpolation, polynomial interpolation) or deleting rows or columns containing missing values can be used for processing; for outliers, they need to be judged and processed according to the specific business scenario and data distribution characteristics.

[0048] 2.2 Data partitioning After data integration (multi-source data fusion) and data augmentation (increasing data diversity), the dataset is partitioned into a training set, a validation set and a test set, with a ratio of 70%, 15%, 15%. This partitioning aims 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 let the model 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 and avoid overfitting of 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 can also perform well on unseen data.

[0049] 2.3 Feature engineering Feature engineering is the bridge connecting the data layer and the model layer, responsible for extracting valuable features from the preprocessed data, which is a key step in improving the model performance, including feature selection, feature extraction and feature transformation, etc. In the feature selection stage, methods such as correlation analysis, principal component analysis, mutual information, etc. can be used to screen out features that have important impacts on the target variable; in the feature extraction stage, deep learning modules (such as CNN, RNN) can be used to automatically extract high-level features from the original data; in the feature transformation stage, methods such as standardization, normalization, encoding (such as one-hot encoding, label encoding), etc. can be used to improve the data distribution and model training effect. Feature extraction is a complex process that needs to be designed according to the specific application scenario and prediction target. For structured data, statistical methods and machine learning algorithms can be used to extract numerical features and categorical features; for unstructured data such as text, images and audio, technologies such as natural language processing, computer vision and audio processing need to be used for feature extraction. In addition, in order to build a user digital portrait, it is also necessary to combine multi-dimensional information such as the user's behavior data, social data, consumption data, etc., and conduct comprehensive analysis and feature construction.

[0050] (3)Model construction, such as Figure 1 shown: 3.1 Rule model building The Bayesian deep learning rule model based on event features analyzes the user digital portrait information in the governance scenario through event ontology parsing, extracts the information elements and behavior elements unique to the event, and summarizes the same type of events to construct an event feature library to support event prediction risk identification.

[0051] Taking the grass-roots governance scenario as an example, this implementation method first analyzes the user digital portrait information in the governance scenario constructed in the previous step, obtains various information elements and behavior elements involved in the event, and supports the construction of the event feature model, such as Figure 2 As shown, the information elements specifically include time information, location information, trajectory information, personnel information, event information, etc.; the behavior elements include fighting, traveling, communicating, staying, etc. Each type of governance scenario can parse the event ontology to extract as many information elements and behavior elements in the virtual and real spaces or even the thinking space unique to this type of event as possible. Based on the analysis of 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 behavior elements unique to this type of event to support the risk identification in the digital governance scenario.

[0052] 3.2 Prediction and early warning model construction The model layer is the core of the system, responsible for constructing and optimizing the prediction model based on Bayesian deep learning. In this invention, the prediction and early warning model based on Bayesian deep learning combines the automatic feature learning ability of deep learning and the probabilistic reasoning ability of Bayesian methods. The specific construction process includes four steps: ① Construct the model; based on the evaluation index system, understand the correlation relationship between the indicators at each level, and construct a Bayesian deep learning structure model suitable for event prediction. First, a suitable network structure needs to be defined to reflect the complex relationship between the evaluation indicators, which usually involves a directed graph, where the nodes represent the evaluation indicators (or features), and the edges represent the dependence relationship between these indicators. For Bayesian deep learning, this may be a Bayesian network (also called a belief network) or a more complex deep Bayesian network (DBN), which combines the nonlinear modeling ability of deep learning and the probabilistic reasoning ability of Bayesian networks.

[0053] The definition of the model can be based on the multiplication principle of probability, that is, for any event set A 1, A 2,..., A n , there is ( 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 a Bayesian network, this translates to the product of node probabilities, where the probability of each node depends on the state of its parent nodes.

[0054] ② Determine the prior probability; the prior probability is an estimate of the probability of an event occurring before any data is observed. Combining historical sample data and expert opinions, determine the prior probability of the network nodes, that is, the initial evidence of the risk probability. For node X , its prior probability P ( X ) is an estimate based on the available information.

[0055] ③ Parameter learning and conditional probability distribution inference; in this step, the goal is to use a parameter learning algorithm to infer the conditional probability distribution of non-root nodes in the network. Due to the dynamics and uncertainty of event occurrence, there are often some unobservable latent variables in the sample data (for example, there are missing values). Therefore, in this embodiment, an iterative convergence algorithm for samples with missing values - the EM (Expectation-Maximization) algorithm - is used for parameter learning. Through multiple iterations, the model parameters continuously tend to the maximum likelihood estimate, and finally the conditional probability distribution is obtained.

[0056] Expectation step in the EM algorithm: Calculate the posterior distribution of the latent variable P ( Z ∣ X , θ t ), where Z is the latent variable, X is the observed data, θ t is the current parameter estimate.

[0057] Maximization step: Update the parameter θ t+1 to maximize .

[0058] ④ Posterior probability calculation; Based on the Bayesian algorithm criterion, convert the prior probability P ( θ ) and the conditional probability P ( D | θ ) into the posterior probabilityP ( θ | D ),i.e., the risk probability of the target event occurring in the model and its uncertainty. In a Bayesian network, this typically means combining the prior probability and the conditional probability learned through the model to calculate the posterior probability of a specific event or state.

[0059] Bayes' theorem is the core of Bayesian inference, which describes how to update the belief in a parameter B given the observed data A :

[0060] Where: P ( θ | D ) is the posterior probability representing the probability distribution of the parameter D after observing the data θ , P ( D | θ ) is the likelihood function representing the probability of observing the data θ given the parameter D , P ( θ ) is the prior probability representing the belief or hypothesis about the parameter θ before observing the data, P ( D ) is the marginal probability, also known as the evidence, which is a normalization constant that ensures the sum of the posterior probability distribution is 1.

[0061] 3.3 Bayesian Network Section A Bayesian neural network (BNN) is a model that applies Bayesian theory to neural networks. It no longer treats network parameters as deterministic values but as probability distributions. In a BNN, each network parameter (such as weights and biases) is assigned a prior distribution and updated to a posterior distribution through Bayesian inference. This probabilistic modeling approach enables the BNN to quantify the uncertainty of prediction results and exhibit stronger robustness in cases of limited data or noisy data. According to the problem domain, determine the random variables (nodes) in the model; at the same time, based on the causal relationships between the variables, construct a directed acyclic graph (DAG) to represent the dependency relationships between the variables. The inference process of the 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 adopted for solving.

[0062] Variational inference solves the problem by defining an easy-to-sample variational distribution q φ ( θ) approximate the posterior distribution P ( θ | D ) and optimize the variational parameters φ to minimize the difference between these two distributions (usually measured by KL divergence). The objective function ELBO is as follows:

[0063] ELBO (Evidence Lower Bound) is the lower bound of evidence. By maximizing ELBO, the KL divergence between the true posterior and the variational distribution can be indirectly minimized. The first term is the expected log-likelihood, which encourages the variational distribution to generate 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.

[0064] Of course, the Markov Chain Monte Carlo (MCMC) method 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 predictive distribution.

[0065]

[0066] In the formula: f ( θ ) is the function about the parameter θ to be estimated (such as the predictive distribution), N is the number of samples, θ (i) are the samples drawn from the posterior distribution. By increasing the number of samples N , the accuracy of the estimation can be improved.

[0067] The Variational Autoencoder (VAE) is a generative model that combines the data compression ability of the autoencoder and the uncertainty modeling ability of Bayesian inference. VAE captures the latent representation of data by introducing latent variables and uses variational inference to estimate the posterior distribution of the latent variables. In event prediction and early warning tasks, VAE can be used to learn the latent structure of data and generate predictive results with uncertainty. The objective function of VAE usually includes two parts: reconstruction loss and KL divergence, which are used to measure the accuracy of data reconstruction and the rationality of the latent variable distribution respectively. By optimizing this objective function, VAE can learn the latent representation and uncertainty information of data.

[0068] 3.4 Deep Learning Part The design of neural network structures is the cornerstone of deep learning applications, which directly determines the complexity and depth of the model's ability to capture data features. First, select or design a suitable neural network architecture according to the specific requirements of the task (such as classification tasks, regression tasks, or sequence prediction tasks). For classification tasks, convolutional neural networks are preferred in the project to capture spatial features or recurrent neural networks and their variants such as LSTM to process sequence data; for regression tasks, a relatively simple fully connected network (FCN) structure may be selected. When designing, it is also necessary to consider the dimension of the input layer to match the data features, the number of hidden layers and the number of neurons in each layer to control the capacity and complexity of the model, and the configuration of the output layer to output the prediction results.

[0069] 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-like 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 dependence and long-term memory effects in the data.

[0070] Parameter initialization is a crucial step before neural network training, which determines the starting point of model learning. Reasonable parameter initialization helps the model converge to the optimal solution quickly and avoid problems such as falling into local optima or vanishing / exploding gradients. Common parameter initialization methods include random initialization (such as uniform distribution or normal distribution initialization), zero initialization (although it is usually not recommended because it may cause all neurons to have the same output at the beginning of training), and pre-training initialization (using the model parameters pre-trained on a large-scale dataset 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.

[0071] (4)Model training.

[0072] In the event prediction and early warning technology based on Bayesian deep learning, the model training stage is crucial, which combines the advantages of Bayesian networks and deep learning.

[0073] 4.1 Bayesian network part Parameter learning aims to accurately estimate the conditional probability distribution parameters of each variable in the Bayesian network using training data. This process often uses the expectation maximization (EM) algorithm. By iteratively performing the expectation step and the maximization step, it gradually approaches the true parameter values. In the expectation step, the algorithm calculates the expected values of the hidden variables based on the current parameter estimates; 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 parameter estimation.

[0074] When the Bayesian network structure is unknown, structure learning becomes a necessary step. Classical methods such as the K2 algorithm and Tian's algorithm are used to automatically discover the dependencies between variables from data and construct a network structure that best conforms to the characteristics of the data. These methods guide structure learning through scoring search strategies or by leveraging the statistical characteristics of the data, reducing the search space and improving learning efficiency. The result of structure learning is a Bayesian network model that not only conforms to the actual data but also has good interpretability, providing strong support for event prediction.

[0075] 4.2 Deep learning part In the deep learning module, forward propagation is the basis for model training. The training data is processed layer by layer through the various layers of the neural network. Each layer of neurons performs a linear transformation and a non-linear activation based on the output of the previous layer and its own weights and biases, and finally calculates the predicted value of the output layer. This process realizes the extraction and transformation of complex features and provides the basis for subsequent loss calculation.

[0076] The loss function is used to quantify the difference between the model's predicted value and the true value. In the event prediction and early warning task, an appropriate loss function (such as mean squared 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 value is obtained to reflect the prediction accuracy of the model.

[0077] To optimize the model parameters, the backpropagation algorithm is adopted. This algorithm is based on optimization methods such as the chain rule and gradient descent, and guides the update direction of the parameters by calculating the gradient of the loss function with respect to the model parameters. During the backpropagation process, the gradient information propagates forward layer by layer. The gradient of each layer of parameters is calculated and used to update the parameters of that layer. By iterating the forward propagation and backpropagation processes multiple times, the model parameters are gradually optimized, and the prediction performance is improved, thus achieving more accurate event prediction and early warning.

[0078] (5)Prediction and early warning mechanism.

[0079] 5.1 Prediction model output Based on the output of the Bayesian deep learning model, the prediction result and its uncertainty information can be obtained. The prediction result is usually represented in the form of a probability distribution, such as a Gaussian distribution, a Bernoulli distribution, etc.; the uncertainty information can be quantified by indicators such as the variance of the prediction distribution and the confidence interval.

[0080] 5.2 Multi-threshold early warning system To construct an efficient and accurate early warning system, this embodiment adopts a multi-threshold strategy. Traditional early warning systems often rely on a single fixed threshold to determine whether to trigger an early warning, and this method may not be flexible and robust enough in a complex and changing data environment. Therefore, this embodiment automatically adjusts the early warning threshold according to the uncertainty of the prediction result.

[0081] 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 according to historical data, business requirements, and risk tolerance; when the confidence or probability distribution of the prediction result falls within a certain threshold range, the system triggers a warning of the corresponding level. In addition, the uncertainty of the prediction result can be used to dynamically adjust the threshold. For example, when the uncertainty of the prediction result 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 result is low, the threshold range can be tightened to improve the accuracy and timeliness of the warning.

[0082] (6) Model evaluation and optimization.

[0083] 6.1 Evaluation and advanced optimization algorithms As Figure 3 shown, after the model training is completed, the model needs to be evaluated to verify its prediction performance. The evaluation metrics usually include accuracy, recall rate, F1 score, area under the ROC (Receiver Operating Characteristic Curve) curve (AUC), etc. 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, adding data augmentation strategies, etc., to improve the prediction accuracy and generalization ability of the model. These algorithms optimize the parameter update process of the model by adaptively adjusting the learning rate, thereby accelerating the training speed while maintaining the stability of the model.

[0084] 6.2 Parallel computing technology To address the computational challenges brought by large-scale datasets and high-dimensional feature spaces, this embodiment uses parallel computing technology to accelerate the model training and inference processes. By distributing the computational tasks to multiple computing nodes for parallel execution through a distributed computing framework (such as TensorFlow, PyTorch, etc.), the computational time can be significantly reduced and the computational efficiency can be improved. In addition, we can also use high-performance computing devices such as GPUs (Graphics Processing Units) to accelerate the computational process of deep learning models. GPUs have powerful parallel computing capabilities and high-speed memory bandwidth, and can quickly complete large-scale matrix operations and neural network forward and backward propagation computational tasks.

[0085] 6.3 Model compression and pruning To further reduce the computational complexity and memory footprint of the model, this embodiment adopts model compression and pruning techniques. Model compression reduces the model size by reducing the number of model parameters or lowering 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 its performance.

[0086] (7) Model deployment and warning.

[0087] The evaluated and optimized model can be deployed to the actual production environment for real-time prediction and warning. During the deployment process, factors such as system stability, scalability, and security need to be considered. At the same time, a warning mechanism needs to be established to generate warning information based on the prediction results of the model and preset warning thresholds or rules, and notify relevant personnel through various methods such as text messages, emails, and APP push. The warning information should include multiple dimensions such as the type of event, occurrence time, scope of influence, and severity, so that relevant personnel can take timely measures to address potential risks. During the operation of the model, real-time monitoring is carried out, and necessary adjustments and optimizations are made to the model based on the feedback data.

[0088] The above description of the embodiments is for those of ordinary skill in the art of this technology to understand and apply the present invention. It is obvious that those skilled in the art can easily make various modifications to the above embodiments and apply the general principles described herein to other embodiments without creative labor. Therefore, the present invention is not limited to the above embodiments, and all improvements and modifications made by those skilled in the art based on the disclosure of the present invention should fall within the protection scope of the present invention.

Claims

1. An event prediction and early warning method based on Bayesian deep learning, characterized in that, It includes the following steps: (1) Collect a large amount of multi-source heterogeneous data in the target application scenario, preprocess these data, and divide them into a training set, a validation set, and a test set; (2) Use the user digital portrait to construct a rule model based on event features. The user digital portrait is a set of user characteristics constructed according to multi-dimensional information of users including behavior data, social data, and consumption data; (3) Build a prediction and early warning model based on Bayesian deep learning on the basis of the above rule model; (4) Use the training set data to train the prediction and early warning model, and use the validation set data to optimize the parameters during the model training process; (5) Input the test set data or real-time data into the trained prediction and early warning model for event prediction, and issue an early warning according to the prediction results output by the model.

2. The event prediction and early warning method based on Bayesian deep learning according to claim 1, characterized in that: In step (1), each group 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, serving as a label.

3. The event prediction and early warning method based on Bayesian deep learning according to claim 1, characterized in that: In step (1), the data preprocessing includes four parts: data cleaning, data integration, data augmentation, 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. Among them, feature selection screens out features that have an important impact on the target variable through methods including correlation analysis, PCA, and mutual information. Feature extraction automatically extracts high-level features from the original data through deep learning methods including CNN and RNN. Feature transformation improves the data distribution through methods including standardization, normalization, one-hot encoding, and label encoding.

4. The event prediction and early warning method based on Bayesian deep learning according to claim 1, characterized in that: The specific implementation method of step (2) is as follows: First, analyze the user digital portrait information in the target application scenario, and 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, person information, and event information; For each type of governance scenario, it is necessary to conduct ontology analysis on the event, extract as many information elements and behavior elements in the virtual and real space or even the thinking space unique to this type of event as possible. 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 behavior elements unique to this type of event. By integrating the feature libraries of information elements and behavior elements of various events, a rule model based on event features is obtained.

5. The event prediction and early warning method based on Bayesian deep learning according to claim 1, characterized in that: The prediction and early warning model based on Bayesian deep learning combines the advantages of Bayesian algorithms and deep neural networks, models and infers the uncertainties in complex problems, and represents weights using probability distributions instead of single values, thereby providing information about the uncertainties of these predictions when giving predictions. That is, on 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 the statistical learning of samples, and is transformed into posterior probability using Bayesian algorithm criteria; this prediction and early warning model uses a Bayesian neural network, processes the uncertainties in the data by analyzing historical data and real-time monitoring data, estimates the occurrence probability of future events, and gives the confidence level of the prediction results.

6. The event prediction and early warning method based on Bayesian deep learning according to claim 5, characterized in that: The variational inference method or Markov chain Monte Carlo method is used to approximately estimate the posterior probability, and the variational inference method is preferred. It uses a variational autoencoder to capture the latent representation of the data by introducing latent variables, and uses variational inference to estimate the posterior distribution of the latent variables.

7. The event prediction and early warning method based on Bayesian deep learning according to claim 5, characterized in that: 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 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, the edges between the nodes are further constructed to clarify the interaction and influence paths between the factors. On this basis, historical data or expert knowledge is used to assign conditional probability distributions to each node. These probability distributions describe the law of the node state changing with the state of its parent node; deep learning architectures including CNN, RNN, and LSTM are integrated into the Bayesian neural network to preliminarily process the original data, and high-level abstract feature representations in the data are automatically extracted through stacking multiple nonlinear transformation layers and feature learning mechanisms. These feature representations are then used as the input of the nodes.

8. The event prediction and early warning method based on Bayesian deep learning according to claim 5, characterized in that: During the training process of the prediction and early warning model in step (4), it is necessary to define the prior distribution of the model parameters, design the loss function, and use the expectation-maximization algorithm to update and optimize the parameters of the Bayesian neural network in real time. Through continuous iterative calculations and parameter adjustments, the model can gradually approximate the real data distribution and event law; in addition, regularization, dropout, early stopping strategies, and model evaluation and selection mechanisms are also set during the training process. Through the performance comparison and evaluation of different model parameters, optimization suggestions and decision-making support are provided for developers, and an automated model tuning process is realized using the validation set, which can automatically adjust the model parameters and structure settings according to real-time feedback to achieve the optimal prediction effect.

9. An event prediction and early warning method based on Bayesian deep learning according to claim 5, characterized in that: The specific implementation method of step (5) is as follows: First, the data is processed and features are extracted. Then, the features are input into the trained prediction and early warning model for event prediction. Furthermore, an adaptive early warning mechanism with uncertainty quantification is adopted for early warning according to the prediction results output by the model. This mechanism dynamically adjusts the early warning threshold by analyzing the uncertainty distribution of the prediction results, and sets multiple early warning levels, and sets corresponding threshold ranges for each level. These threshold ranges are set according to historical data, business requirements, and risk tolerance. When the confidence level or probability distribution of the prediction results falls into a certain threshold range, the corresponding level of early warning is triggered. The early warning information includes multiple dimensions such as event type, occurrence time, influence range, and severity, and notifies relevant personnel through multiple methods including SMS, email, and APP push.

10. A method for event prediction and early warning based on Bayesian deep learning according to claim 1, characterized in that: When using the trained prediction and early warning model to conduct event prediction on real-time data in step (5), parallel computing technology is adopted to accelerate the inference process of the model. 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 adopted 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.

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