Financing guarantee risk dynamic monitoring and early warning method and system based on data fusion

Abstract

The present invention provides a method and system for dynamic monitoring and early warning of financing guarantee risks based on data fusion, which relates to the technical field of financing guarantee risk early warning, including: building a business data transfer platform; monitoring batch business data sets associated with financing guarantee risk quantification from a first service end, and inputting them into the business data transfer platform; identifying the batch business data sets, determining a first type of batch business data set and a second type of batch business data set; performing feature extraction to obtain first type risk features and second type risk features; transmitting to a second service end, and using an embedded financing guarantee risk assessment model to output a financing guarantee risk index; generating a first early warning signal when it is greater than a preset financing guarantee risk index. The present invention solves the technical problems that data in traditional financing guarantee business is difficult to integrate and analyze, and that traditional monitoring and early warning methods are unable to monitor risk changes in real time and issue early warnings, resulting in insufficient timeliness and accuracy of early warnings.

Description

Technical Field

[0001] The present invention relates to the technical field of financing guarantee risk early warning, and in particular to a financing guarantee risk dynamic monitoring and early warning method and system based on data fusion. Background Art

[0002] Financing guarantees are guarantee institutions established to support small and medium-sized enterprises in obtaining financing. By providing guarantees, they help enterprises overcome financing difficulties. Therefore, risk monitoring and early warning for financing guarantees are particularly important, but existing technologies still have some problems. First, traditional financing guarantee risk assessment methods are often based on limited data and empirical judgments, making it difficult to comprehensively and objectively assess the risk status of enterprises, and prone to misjudgments and omissions. Second, traditional monitoring and early warning methods are generally static, unable to monitor risk changes in real time and issue timely warnings, resulting in insufficient timeliness and accuracy of warnings. Third, in traditional financing guarantee business, data from various links is often scattered across different systems and departments, resulting in information silos, making data integration and analysis difficult, and affecting business efficiency. Summary of the Invention

[0003] This application provides a dynamic monitoring and early warning method for financing guarantee risks based on data fusion, aiming to solve the problem of information islands in traditional financing guarantee business, which makes data difficult to integrate and analyze, and traditional monitoring and early warning methods are usually static and cannot monitor risk changes in real time and issue timely warnings, resulting in technical problems such as insufficient timeliness and accuracy of warnings.

[0004] In view of the above problems, this application provides a method and system for dynamic monitoring and early warning of financing guarantee risks based on data fusion.

[0005] The first aspect disclosed in the present application provides a dynamic monitoring and early warning method for financing guarantee risk based on data fusion, the method comprising: establishing a business data transfer platform, wherein the business data transfer platform is a transfer platform for transmitting business data between a first server end and a second server end; monitoring batch business data sets associated with the quantification of financing guarantee risk from the first server end, and inputting the batch business data sets into the business data transfer platform; the business data transfer platform identifying the batch business data sets to determine a first type of batch business data set and a second type of batch business data set, wherein the business data transfer platform includes a pre-trained term-failure network model, and the term-failure network model is used to perform data failure analysis on the batch business data sets; performing feature extraction on the first type of batch business data set and the second type of batch business data set respectively to obtain a first type of risk feature and a second type of risk feature associated with the quantification of financing guarantee risk; the business data transfer platform transmitting the first type of risk feature and the second type of risk feature to the second server end, and the second server end outputting a financing guarantee risk index using an embedded financing guarantee risk assessment model; and generating a first early warning signal when the financing guarantee risk index is greater than a preset financing guarantee risk index.

[0006] The second aspect disclosed in the present application provides a dynamic monitoring and early warning system for financing guarantee risks based on data fusion, and the system is used for the above-mentioned dynamic monitoring and early warning method for financing guarantee risks based on data fusion, and the system includes: a transfer platform construction module, the transfer platform construction module is used to build a business data transfer platform, wherein the business data transfer platform is a transfer platform for transmitting business data between a first service end and a second service end; a business data input module, the business data input module is used to monitor batch business data sets associated with the quantitative correlation of financing guarantee risks from the first service end, and input the batch business data sets into the business data transfer platform; a business data identification module, the business data identification module is used for the business data transfer platform to identify the batch business data sets, determine the first type of batch business data sets and the second type of batch business data sets, and In the embodiment, the business data transfer platform includes a pre-trained term-failure network model, which is used to perform data failure analysis on the batch business data set; a feature extraction module, which is used to perform feature extraction on the first type of batch business data set and the second type of batch business data set respectively, and obtain the first type of risk characteristics and the second type of risk characteristics that are quantitatively associated with the financing guarantee risk; a risk indicator output module, which is used for the business data transfer platform to transmit the first type of risk characteristics and the second type of risk characteristics to the second server, and the second server uses the embedded financing guarantee risk assessment model to output the financing guarantee risk indicator; an early warning signal generation module, which is used to generate a first early warning signal when the financing guarantee risk indicator is greater than the preset financing guarantee risk indicator.

[0007] According to a third aspect disclosed herein, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: establishing a business data transfer platform, wherein the business data transfer platform is a transfer platform for transmitting business data between a first server and a second server; monitoring batch business data sets associated with financing guarantee risk quantification from the first server, and inputting the batch business data sets into the business data transfer platform; identifying the batch business data sets by the business data transfer platform to determine a first type of batch business data set and a second type of batch business data set, wherein the business data transfer platform includes a pre-trained term-failure network model, and the term-failure network model is used to perform data failure analysis on the batch business data sets; performing feature extraction on the first type of batch business data set and the second type of batch business data set, respectively, to obtain a first type of risk feature and a second type of risk feature associated with financing guarantee risk quantification; the business data transfer platform transmits the first type of risk feature and the second type of risk feature to the second server, and the second server uses an embedded financing guarantee risk assessment model to output a financing guarantee risk index; and generating a first warning signal when the financing guarantee risk index is greater than a preset financing guarantee risk index.

[0008] According to a fourth aspect disclosed in the present application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the following steps: establishing a business data transfer platform, wherein the business data transfer platform is a transfer platform for transmitting business data between a first server and a second server; monitoring batch business data sets associated with financing guarantee risk quantification from the first server, and inputting the batch business data sets into the business data transfer platform; the business data transfer platform identifying the batch business data sets to determine a first type of batch business data set and a second type of batch business data set, wherein the business data transfer platform includes a pre-trained term-failure network model, and the term-failure network model is used to perform data failure analysis on the batch business data sets; performing feature extraction on the first type of batch business data set and the second type of batch business data set, respectively, to obtain a first type of risk feature and a second type of risk feature associated with financing guarantee risk quantification; the business data transfer platform transmitting the first type of risk feature and the second type of risk feature to the second server, and the second server using an embedded financing guarantee risk assessment model to output a financing guarantee risk index; and generating a first warning signal when the financing guarantee risk index is greater than a preset financing guarantee risk index.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] By utilizing the business data transfer platform, batch business data sets from different service ends can be integrated together, and data failure analysis can be performed using a pre-trained term-failure network model, thereby achieving comprehensive fusion and analysis of multi-source data and improving the accuracy and comprehensiveness of monitoring results. By extracting features from the first and second batch business data sets, risk features associated with the quantitative risk of financing guarantee risks are obtained, and risk assessment is performed in combination with the embedded financing guarantee risk assessment model, which can fully consider the impact of multiple risk factors and improve the accuracy and reliability of the assessment results. Financing guarantee risk assessment is performed using the embedded financing guarantee risk assessment model, and judgment is made based on the preset financing guarantee risk indicators. When the risk indicator exceeds the preset value, an early warning signal can be generated in a timely manner, thereby improving the timeliness and accuracy of monitoring and early warning. In summary, this data fusion-based dynamic monitoring and early warning method for financing guarantee risk improves the accuracy, comprehensiveness, and timeliness of risk monitoring and early warning by integrating multi-source data, comprehensively analyzing risk factors, and combining real-time monitoring and early warning mechanisms.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A flowchart of a method for dynamic monitoring and early warning of financing guarantee risks based on data fusion provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of a dynamic monitoring and early warning system for financing guarantee risks based on data fusion provided in an embodiment of the present application;

[0014] Figure 3 This is a diagram of the internal structure of a computer device provided in an embodiment of the present application.

[0015] Explanation of the accompanying drawings: transfer platform building module 10, business data input module 20, business data identification module 30, feature extraction module 40, risk indicator output module 50, early warning signal generation module 60. DETAILED DESCRIPTION

[0016] The embodiments of the present application provide a dynamic monitoring and early warning method for financing guarantee risks based on data fusion, thereby solving the problem of information islands existing in traditional financing guarantee business, which makes data difficult to integrate and analyze, and the technical problem that traditional monitoring and early warning methods are usually static and cannot monitor risk changes in real time and issue timely warnings, resulting in insufficient timeliness and accuracy of warnings.

[0017] After introducing the basic principles of this application, various non-limiting embodiments of this application will be specifically described below in conjunction with the accompanying drawings. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0018] like Figure 1 As shown, the embodiment of the present application provides a method for dynamic monitoring and early warning of financing guarantee risks based on data fusion, the method comprising:

[0019] Building a business data transfer platform, wherein the business data transfer platform is a transfer platform for business data transmission between the first server and the second server;

[0020] Analyze the business data transmission requirements, including the frequency of data transmission, data volume, transmission security requirements, etc. Based on the demand analysis results, design the overall architecture of the business data transfer platform, determine the interface specifications and communication protocols of the first server and the second server, and build a business data transfer platform. The business data transfer platform is an intermediate platform for business data transmission between the first server and the second server, and is used to realize data transfer, forwarding and processing. Among them, the first server is the endpoint for generating or collecting business data, and the second server is the endpoint for receiving and processing business data.

[0021] monitoring batch business data sets associated with financing guarantee risk quantification from the first server, and inputting the batch business data sets into the business data transfer platform;

[0022] The first server regularly monitors batch business data sets related to the quantification of financing guarantee risks through a monitoring system or data collection program. These data sets may include loan information, guarantee information, risk indicators, etc., and transmits these data sets to the business data transfer platform through a network connection for subsequent data processing and analysis.

[0023] The business data transfer platform identifies the batch business data set to determine a first type of batch business data set and a second type of batch business data set, wherein the business data transfer platform includes a pre-trained deadline-failure network model, and the deadline-failure network model is used to perform data failure analysis on the batch business data set;

[0024] The business data transfer platform first loads a pre-trained expiration-expiration network model. This model, trained on historical data through machine learning, is used to analyze expiration patterns in business data. A batch of business data sets are fed into the model. Based on the data set's characteristics and historical data patterns, the model performs expiration analysis on the data. Based on the analysis results, the batch business data sets are divided into the first and second categories. The first category contains high-risk data or data that has already failed, while the second category contains low-risk data or data that has not yet failed.

[0025] Performing feature extraction on the first type of batch business data set and the second type of batch business data set respectively to obtain first type risk features and second type risk features quantitatively associated with financing guarantee risks;

[0026] For the first and second types of batch business data sets, the business data transfer platform extracts features related to the quantification of financing guarantee risks through data analysis and feature engineering technology. During the feature extraction process, it is necessary to ensure that the extracted features are related to the quantification of financing guarantee risks and can reflect the risk level of the business data. Appropriate features can be selected based on domain knowledge and actual conditions, and ensure that these features can play a role in subsequent risk assessments. For example, the extracted features include but are not limited to financial indicators, risk ratings, historical defaults, collateral assessments, etc. After the features are extracted, the first and second type of risk features are obtained for subsequent data transmission and risk assessment.

[0027] The business data transfer platform transmits the first and second risk characteristics to the second server, and the second server uses the embedded financing guarantee risk assessment model to output the financing guarantee risk index;

[0028] The business data transfer platform transmits the extracted first and second risk features via the network to the second server. The second server uses an embedded financing guarantee risk assessment model to conduct a risk assessment on the received feature data. This financing guarantee risk assessment model, built based on machine learning, is used to predict and assess financing guarantee risks. Based on the processing of the feature data, it generates corresponding financing guarantee risk indicators, including risk scores, risk levels, and probabilities, which are used to indicate the extent of financing guarantee risks and provide data support for subsequent risk monitoring and early warning.

[0029] When the financing guarantee risk indicator is greater than a preset financing guarantee risk indicator, a first warning signal is generated.

[0030] Based on actual conditions, historical experience, etc., preset financing guarantee risk indicators are set as acceptable risk standards. The obtained financing guarantee risk indicators are compared with the preset financing guarantee risk indicators. When the financing guarantee risk indicator is greater than the preset financing guarantee risk indicator, a first early warning signal is generated. This early warning signal can be a notification or alarm information automatically issued by the system. The first early warning signal is notified to relevant personnel so that they can take timely action to deal with the financing guarantee risk.

[0031] Furthermore, the business data transfer platform identifies the batch business data set to determine the first type of batch business data set and the second type of batch business data set, and the method includes:

[0032] Decomposing the business data types of the batch business data set to obtain each business data type;

[0033] performing failure index analysis on each of the service data types according to the deadline-failure network model, and outputting a failure factor for each of the service data types;

[0034] The batch business data sets are classified using the failure factors of various business data types as variables to determine a first category of batch business data sets whose failure factors are greater than or equal to a preset failure factor, and a second category of batch business data sets whose failure factors are less than the preset failure factor.

[0035] Based on the structure and content of batch business data sets, they are decomposed and split into different business data types. For each decomposed data, analysis is performed based on the data format, field meaning, business rules, etc. to obtain each business data type.

[0036] The business data transfer platform loads a pre-trained term-failure network model, which is obtained through training using a training sample data set. Based on the failure characteristics of business data types in financing guarantees learned during the training process, the platform performs failure index analysis on each business data type. Specifically, the longer the time, the more impact each type of data has on the credibility, accuracy, and value of financing guarantees, and the higher the probability of failure. Based on the results of the failure index analysis, corresponding failure factors are output for each business data type. These failure factors are indicators that reflect the credibility, accuracy, and value of data in financing guarantees, and can be in the form of probability values, scores, etc.

[0037] Prepare a batch business data set to be classified, using each business data type and its corresponding failure factor as input data. Set a preset failure factor threshold to classify the batch business data set into the first and second categories. This preset value can be based on domain expert knowledge, historical data analysis, or business needs. For each data item, use the failure factor of each business data type as a variable. Classify the data set based on the preset failure factor threshold. Data with a failure factor greater than or equal to the preset failure factor is classified as the first category of batch business data sets, while data with a failure factor less than the preset failure factor is classified as the second category of batch business data sets.

[0038] Furthermore, the method further comprises:

[0039] Get the fully connected neural network layer;

[0040] Obtaining a training sample data set, wherein the training sample data set includes batch business sample data, multiple failure penalty factors, and identification information for identifying the failure factors, wherein the multiple failure penalty factors include deadline-data credibility, deadline-data accuracy, and deadline-data value;

[0041] The fully connected neural network layer is trained according to the training sample data set until the model is in a converged state, and the deadline-failure network model is output.

[0042] A fully connected neural network is a basic artificial neural network structure in which each neuron is connected to all neurons in the previous layer. Specifically, the structure of the fully connected neural network is determined, including the number of neurons in the input layer, hidden layer, and output layer, as well as the connection method between the layers. The parameters of the neural network, including weights and biases, are initialized. According to the designed structure, a fully connected neural network layer is built, and an activation function is applied to each connection between the hidden layer and the output layer to introduce nonlinear characteristics and improve the expressive power of the network. After the neural network layer is built, a loss function and an optimizer are set. The loss function is used to measure the difference between the model's predicted output and the actual label, and the optimizer is used to adjust the model parameters to minimize the loss function, ultimately obtaining an initialized fully connected neural network layer.

[0043] Batch business sample data is business data samples used to train the deadline-failure network model. These sample data can be historical data, simulated data, or synthetic data, reflecting various situations and changes in different business scenarios.

[0044] Multiple failure penalty factors are parameters used to measure the degree of data failure. These factors include term-data credibility, term-data accuracy, and term-data value. They are used to quantify the failure probability of batch business sample data in financing guarantees and serve as input features of the model. Among them, the longer the time, the more affected the credibility, accuracy, and value of each type of data in financing guarantees, and the higher the probability of failure.

[0045] The identification information of the failure factor is a label used to distinguish different failure penalty factors. In order to train the deadline-failure network model, each sample data needs to be associated with its corresponding failure penalty factor so that the model can learn the impact of each factor on data failure.

[0046] The obtained batch business sample data, multiple failure penalty factors and identification information of the failure factors are integrated to obtain a training sample data set. Such a data set can be used to train a term-failure network model to learn the failure characteristics of business data types in financing guarantees, thereby conducting failure indicator analysis.

[0047] Use the training sample dataset to train the constructed fully connected neural network layer. During the training process, adjust the model parameters through the backpropagation algorithm and optimizer to enable the model to gradually learn the characteristics and patterns of the data. Monitor the performance of the model on the training sample dataset, including changes in the loss function, improvement in accuracy and other indicators, to ensure that the model gradually converges during training. Regularly evaluate the performance of the model on the validation set, and adjust the model's hyperparameters and structure as needed.

[0048] When the model reaches convergence during training, for example, when the accuracy reaches a preset condition or the number of iterations reaches a preset number, the trained deadline-failure network model is output. This model incorporates the characteristics and patterns of business data failures learned from the training sample dataset and can be used for subsequent failure indicator analysis tasks.

[0049] Through the above steps, a fully connected neural network model can be trained on the training sample dataset until the model converges and outputs a term-expiration network model. This model can learn the failure characteristics of business data types in financing guarantees, providing a basis for subsequent failure indicator analysis.

[0050] Furthermore, the second server uses the embedded financing guarantee risk assessment model to perform risk assessment on the first and second risk characteristics, and outputs a financing guarantee risk index;

[0051] Among them, the first type of risk characteristics and the second type of risk characteristics include an optimization weight ratio, and the weight of the first type of risk characteristics is less than the weight of the second type of risk characteristics. Risk assessment is performed based on the optimization weight ratio in combination with the first type of risk characteristics and the second type of risk characteristics.

[0052] The second server activates a pre-embedded financing guarantee risk assessment model, which is a trained and optimized machine learning model. It takes the first type of risk characteristics and the second type of risk characteristics as the input of the model. The financing guarantee risk assessment model performs risk assessment based on the first type of risk characteristics and the second type of risk characteristics according to the optimal weight ratio.

[0053] Specifically, a weight ratio is set for the first and second risk features. These weight ratios reflect the importance of different features in the risk assessment, wherein the weight of the first risk feature is smaller than the weight of the second risk feature. The first and second risk features are weighted according to the set weight ratio, which can be achieved by simply multiplying each feature by the corresponding weight ratio. The weighted first and second risk features are then merged, for example, by concatenating them into a vector or matrix as a joint feature vector.

[0054] Using the embedded financing guarantee risk assessment model, we conduct a risk assessment of the combined features. This assessment process specifically considers the weighting ratios of different features. Based on the risk assessment results, we output financing guarantee risk indicators, which reflect the risk situation after assessing the combined features. This approach more accurately considers the importance of different features, thereby improving the accuracy and practicality of risk assessment.

[0055] Furthermore, the method for obtaining the optimization weight ratio includes:

[0056] Obtain m initial sub-fusion models, and assign m different weight ratios λ1:λ2 to the m initial sub-fusion models, wherein the weight λ1 of the first type of risk feature is less than the weight λ2 of the second type of risk feature;

[0057] Constructing a fusion sample set, the fusion sample set including a training sample set of the first type of risk characteristics, a training sample set of the second type of risk characteristics, and a financing guarantee risk verification sample set of the first type of risk characteristics and the second type of risk characteristics;

[0058] The m initial sub-fusion models are trained according to the fusion sample set, and an integrated fusion model is output.

[0059] Obtain m initial sub-fusion models, which are models with randomly initialized parameters, set different weight ratios λ1 and λ2, where λ1 represents the weight of the first type of risk feature, λ2 represents the weight of the second type of risk feature, and λ1<λ2, and assign different weight ratios to each initial sub-fusion model. This can be achieved by adjusting the parameters of each model to reflect different weight ratios.

[0060] Select and extract the sample data required for the first type of risk features from the available data and construct it into a training sample set. This training sample set for the first type of risk features, which contains the first type of risk features and corresponding labels, is obtained for model training. The same method is used to obtain the training sample set for the second type of risk features.

[0061] A subset of data was selected as a validation sample set for financing guarantee risk. These samples covered both the first and second risk characteristics and included labels for the associated financing guarantee risks. This validation sample set was used to evaluate the model's performance and generalization ability on new data. The training sample set for the first and second risk characteristics, as well as the financing guarantee risk validation sample set, were combined into a fusion sample set, which was used to train and validate the fusion model.

[0062] Using the fusion sample set as the training set, common machine learning training algorithms such as gradient descent and random forest are used for each initial sub-fusion model to train it using the fusion sample set. After the training is completed, the m sub-fusion models are integrated. This can be done by using simple integration methods such as voting, weighted voting, and averaging to comprehensively utilize the prediction results of all models, and the integrated fusion model is used as the final output. This integrated model integrates the prediction capabilities of all initial sub-fusion models and is used for financing guarantee risk assessment tasks.

[0063] Furthermore, the m initial sub-fusion models are trained according to the fusion sample set to output an integrated fusion model, and the method further includes:

[0064] The m initial sub-fusion models are trained according to the fusion sample set to obtain m financing guarantee risk output sample sets;

[0065] Based on the m financing guarantee risk output sample sets and the financing guarantee risk verification sample sets, an optimized sub-fusion model is obtained, wherein the probability accuracy of the optimized sub-fusion model is greater than or equal to a preset accuracy rate;

[0066] Adjust the weight ratio of the next round according to the optimized sub-fusion model, and so on, to obtain m sub-fusion models after a preset number of iteration rounds;

[0067] The m sub-fusion models are integrated to obtain an integrated fusion model, and then a weight ratio λ1:λ2 of the integrated fusion model is output.

[0068] Using the fusion sample set as input, each initial sub-fusion model is trained. Each model uses the features in the fusion sample set for training and, based on its learning algorithm and parameters, generates a corresponding set of financing guarantee risk output samples. These output sample sets reflect the risk assessment results of each sub-model on the samples in the fusion sample set. Repeat these steps for all m initial sub-fusion models to obtain m financing guarantee risk output sample sets.

[0069] Using m financing guarantee risk output sample sets, the authors compared them with the financing guarantee risk validation sample set, calculating performance metrics such as the probability accuracy of each model. A preset accuracy rate was set as the optimization target, which could be adjusted based on specific needs and practical circumstances. Sub-ensemble models with a probability accuracy rate equal to or greater than the preset accuracy rate were extracted as optimized sub-ensemble models. This optimized model exhibited better performance and generalization capabilities and could be used for subsequent financing guarantee risk assessment tasks.

[0070] Based on the performance of the optimized sub-fusion models, the weight ratios for the next round are adjusted. The weights of each model in the ensemble can be adjusted based on its performance to further improve overall performance. Using the adjusted weight ratios, the initial m sub-fusion models are trained again using the fusion sample set. This step is similar to the previous training process, but uses the adjusted weight ratios. The above steps are repeated until the preset number of iterations is reached. In each iteration, the weight ratios are adjusted based on the optimized model performance, and the sub-fusion models are retrained. After completing the preset number of iterations, the final m sub-fusion models are obtained. These models have better performance and generalization capabilities due to the weight adjustments and training optimization during the iterative process.

[0071] The m sub-fusion models are integrated by adopting simple voting, weighted voting, averaging and other integration methods. According to the performance of the integrated model, the weight ratio λ1:λ2 of the integrated fusion model is output, where λ1 represents the weight of the first type of risk feature, λ2 represents the weight of the second type of risk feature, and λ1<λ2.

[0072] Furthermore, the batch business data set is input into the business data transfer platform, and the method further includes:

[0073] Obtaining the unit byte quantity used for data transmission of each service data type in the batch service data set;

[0074] Performing real-time byte volume analysis based on the unit byte volume of each business data type, and setting a batch transmission instruction if the current real-time byte volume is greater than a preset byte volume, wherein the preset byte volume is obtained by configuring parameters of the business data transfer platform;

[0075] The batch business data sets are input into the business data transfer platform in batches according to the batch transmission instruction.

[0076] Identify each business data type in the batch business data set, and for each business data type, determine its unit byte quantity during the data transmission process. The unit byte quantity represents the number of bytes occupied by each data unit. For example, for text data, the unit byte quantity can be the number of bytes occupied by each character; for image data, it can be the number of bytes occupied by each pixel.

[0077] By monitoring data transmission traffic in real time, the current amount of business data being transmitted is monitored. Based on the unit byte size of each business data type, the total number of bytes transmitted, i.e., the real-time byte size, is calculated in real time. A preset byte size is obtained from the configuration parameters of the business data transfer platform. The preset byte size is a pre-set threshold indicating the maximum number of bytes allowed for transmission. When the real-time byte size exceeds the preset byte size, batch transmission is required. Once the real-time byte size exceeds the preset byte size, i.e., exceeds the set threshold, a batch transmission instruction is triggered, instructing the platform to process and transmit the data in batches.

[0078] According to the batch transmission instructions, the batch business data set is batch processed, and the data set is split into multiple batches according to the preset batch size. The batch-processed business data set is input into the business data transfer platform batch by batch until all batches of data are transmitted.

[0079] In summary, the data fusion-based dynamic monitoring and early warning method for financing guarantee risks provided by the embodiments of the present application has the following technical effects:

[0080] 1. The business data transfer platform can integrate business data sets from different sources and use pre-trained deadline-expiration network models to perform data failure analysis, thereby achieving comprehensive integration and analysis of multi-source data and improving the accuracy and comprehensiveness of monitoring results;

[0081] 2. By extracting features from the first and second batch business data sets, we obtain risk features that are quantitatively associated with financing guarantee risk. This, combined with the embedded financing guarantee risk assessment model, allows us to fully consider the impact of multiple risk factors and improve the accuracy and reliability of the assessment results.

[0082] 3. Use the embedded financing guarantee risk assessment model to conduct financing guarantee risk assessment, and make judgments based on the preset financing guarantee risk indicators. When the risk indicator exceeds the preset value, it can generate early warning signals in a timely manner, thereby improving the timeliness and accuracy of monitoring and early warning.

[0083] In summary, this data fusion-based dynamic monitoring and early warning method for financing guarantee risks improves the accuracy, comprehensiveness and timeliness of risk monitoring and early warning by integrating multi-source data, comprehensively analyzing risk factors, and combining real-time monitoring and early warning mechanisms.

[0084] Based on the same inventive concept as the method for dynamic monitoring and early warning of financing guarantee risks based on data fusion in the aforementioned embodiment, Figure 2 As shown, the present application provides a dynamic monitoring and early warning system for financing guarantee risks based on data fusion, the system comprising:

[0085] A transfer platform building module 10, which is used to build a business data transfer platform, wherein the business data transfer platform is a transfer platform for business data transmission between the first server and the second server;

[0086] A business data input module 20, configured to monitor batch business data sets associated with financing guarantee risk quantification from the first server and input the batch business data sets into the business data transfer platform;

[0087] A business data identification module 30 is configured for the business data transfer platform to identify the batch business data set and determine a first type of batch business data set and a second type of batch business data set. The business data transfer platform includes a pre-trained deadline-failure network model, and the deadline-failure network model is configured to perform data failure analysis on the batch business data set.

[0088] A feature extraction module 40 is configured to extract features from the first batch business data set and the second batch business data set, respectively, to obtain first and second risk features that are quantitatively associated with financing guarantee risks;

[0089] a risk indicator output module 50, which is used by the business data transfer platform to transmit the first and second risk characteristics to the second server, and the second server outputs the financing guarantee risk indicator using the embedded financing guarantee risk assessment model;

[0090] The early warning signal generating module 60 is configured to generate a first early warning signal when the financing guarantee risk indicator is greater than a preset financing guarantee risk indicator.

[0091] Furthermore, the system further includes a batch business data set identification module to perform the following operation steps:

[0092] Decomposing the business data types of the batch business data set to obtain each business data type;

[0093] performing failure index analysis on each of the service data types according to the deadline-failure network model, and outputting a failure factor for each of the service data types;

[0094] The batch business data sets are classified using the failure factors of various business data types as variables to determine a first category of batch business data sets whose failure factors are greater than or equal to a preset failure factor, and a second category of batch business data sets whose failure factors are less than the preset failure factor.

[0095] Furthermore, the system further includes a network model output module to perform the following steps:

[0096] Get the fully connected neural network layer;

[0097] Obtaining a training sample data set, wherein the training sample data set includes batch business sample data, multiple failure penalty factors, and identification information for identifying the failure factors, wherein the multiple failure penalty factors include deadline-data credibility, deadline-data accuracy, and deadline-data value;

[0098] The fully connected neural network layer is trained according to the training sample data set until the model is in a converged state, and the deadline-failure network model is output.

[0099] Furthermore, the second server uses the embedded financing guarantee risk assessment model to perform risk assessment on the first and second risk characteristics, and outputs a financing guarantee risk index;

[0100] Among them, the first type of risk characteristics and the second type of risk characteristics include an optimization weight ratio, and the weight of the first type of risk characteristics is less than the weight of the second type of risk characteristics. Risk assessment is performed based on the optimization weight ratio in combination with the first type of risk characteristics and the second type of risk characteristics.

[0101] Furthermore, the system further includes an integrated fusion model output module to perform the following operation steps:

[0102] Obtain m initial sub-fusion models, and assign m different weight ratios λ1:λ2 to the m initial sub-fusion models, wherein the weight λ1 of the first type of risk feature is less than the weight λ2 of the second type of risk feature;

[0103] Constructing a fusion sample set, the fusion sample set including a training sample set of the first type of risk characteristics, a training sample set of the second type of risk characteristics, and a financing guarantee risk verification sample set of the first type of risk characteristics and the second type of risk characteristics;

[0104] The m initial sub-fusion models are trained according to the fusion sample set, and an integrated fusion model is output.

[0105] Furthermore, the system further includes a sub-fusion model integration module to perform the following operation steps:

[0106] The m initial sub-fusion models are trained according to the fusion sample set to obtain m financing guarantee risk output sample sets;

[0107] Based on the m financing guarantee risk output sample sets and the financing guarantee risk verification sample sets, an optimized sub-fusion model is obtained, wherein the probability accuracy of the optimized sub-fusion model is greater than or equal to a preset accuracy rate;

[0108] Adjust the weight ratio of the next round according to the optimized sub-fusion model, and so on, to obtain m sub-fusion models after a preset number of iteration rounds;

[0109] The m sub-fusion models are integrated to obtain an integrated fusion model, and then a weight ratio λ1:λ2 of the integrated fusion model is output.

[0110] Furthermore, the system further includes a batch business data set input module to perform the following operation steps:

[0111] Obtaining the unit byte quantity used for data transmission of each service data type in the batch service data set;

[0112] Performing real-time byte volume analysis based on the unit byte volume of each business data type, and setting a batch transmission instruction if the current real-time byte volume is greater than a preset byte volume, wherein the preset byte volume is obtained by configuring parameters of the business data transfer platform;

[0113] The batch business data sets are input into the business data transfer platform in batches according to the batch transmission instruction.

[0114] Through the detailed description of the dynamic monitoring and early warning method for financing guarantee risks based on data fusion in the foregoing specification, those skilled in the art can clearly understand the dynamic monitoring and early warning system for financing guarantee risks based on data fusion in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method part.

[0115] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 3 As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store news data and data such as time decay factors. The network interface of the computer device is used to communicate with an external terminal via a network connection. The computer program is executed by the processor to implement a dynamic monitoring and early warning method for financing guarantee risks based on data fusion.

[0116] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0117] In one embodiment, a computer device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented: establishing a business data transfer platform, wherein the business data transfer platform is a transfer platform for transmitting business data between a first server and a second server; monitoring batch business data sets associated with financing guarantee risk quantification from the first server, and inputting the batch business data sets into the business data transfer platform; the business data transfer platform identifying the batch business data sets to determine a first type of batch business data set and a second type of batch business data set, wherein the business data transfer platform includes a pre-trained term-failure network model, and the term-failure network model is used to perform data failure analysis on the batch business data sets; performing feature extraction on the first type of batch business data set and the second type of batch business data set, respectively, to obtain a first type of risk feature and a second type of risk feature associated with financing guarantee risk quantification; the business data transfer platform transmitting the first type of risk feature and the second type of risk feature to the second server, and the second server outputting a financing guarantee risk index using an embedded financing guarantee risk assessment model; and generating a first warning signal when the financing guarantee risk index is greater than a preset financing guarantee risk index.

[0118] In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which, when executed by a processor, implements the following steps: establishing a business data transfer platform, wherein the business data transfer platform is a transfer platform for transmitting business data between a first server and a second server; monitoring batch business data sets associated with financing guarantee risk quantification from the first server, and inputting the batch business data sets into the business data transfer platform; the business data transfer platform identifying the batch business data sets to determine a first type of batch business data set and a second type of batch business data set, wherein the business data transfer platform includes a pre-trained term-failure network model, and the term-failure network model is used to perform data failure analysis on the batch business data sets; performing feature extraction on the first type of batch business data set and the second type of batch business data set, respectively, to obtain first type risk features and second type risk features associated with financing guarantee risk quantification; the business data transfer platform transmitting the first type risk features and the second type risk features to the second server, and the second server outputting a financing guarantee risk index using an embedded financing guarantee risk assessment model; and generating a first warning signal when the financing guarantee risk index is greater than a preset financing guarantee risk index.

[0119] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0120] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A dynamic monitoring and early warning method for financing guarantee risks based on data fusion is characterized by: The method comprises: Building a business data transfer platform, wherein the business data transfer platform is a transfer platform for business data transmission between the first server and the second server; monitoring, from the first server, batch business data sets associated with the quantification of financing guarantee risks, and inputting the batch business data sets into the business data transfer platform; The business data transfer platform identifies the batch business data set to determine a first type of batch business data set and a second type of batch business data set, wherein the business data transfer platform includes a pre-trained deadline-failure network model, and the deadline-failure network model is used to perform data failure analysis on the batch business data set; Performing feature extraction on the first type of batch business data set and the second type of batch business data set respectively to obtain first type risk features and second type risk features quantitatively associated with financing guarantee risks; The business data transfer platform transmits the first and second risk characteristics to the second server, and the second server uses the embedded financing guarantee risk assessment model to output the financing guarantee risk index; When the financing guarantee risk indicator is greater than the preset financing guarantee risk indicator, a first warning signal is generated; The business data transfer platform identifies the batch business data set to determine a first type of batch business data set and a second type of batch business data set, the method comprising: Decomposing the business data types of the batch business data set to obtain each business data type; performing failure index analysis on each of the service data types according to the deadline-failure network model, and outputting a failure factor for each of the service data types; Classifying the batch service data sets using the failure factors of the various service data types as variables, and determining a first category of batch service data sets having failure factors greater than or equal to a preset failure factor, and a second category of batch service data sets having failure factors less than the preset failure factor; The method further comprises: Get the fully connected neural network layer; Obtaining a training sample data set, wherein the training sample data set includes batch business sample data, multiple failure penalty factors, and identification information for identifying the failure factors, wherein the multiple failure penalty factors include deadline-data credibility, deadline-data accuracy, and deadline-data value; The fully connected neural network layer is trained according to the training sample data set until the model is in a converged state, and the deadline-failure network model is output.

2. The method according to claim 1, wherein The second server uses the embedded financing guarantee risk assessment model to perform risk assessment on the first and second risk characteristics, and outputs a financing guarantee risk indicator; wherein the first risk feature and the second risk feature comprise an optimization weight ratio, and the weight of the first risk feature is less than the weight of the second risk feature, and risk assessment is performed by combining the first risk feature and the second risk feature according to the optimization weight ratio; The method for obtaining the optimization weight ratio includes: Obtain m initial sub-fusion models, and assign m different weight ratios λ1:λ2 to the m initial sub-fusion models, wherein the weight λ1 of the first type of risk feature is less than the weight λ2 of the second type of risk feature, and the initial sub-fusion models are used to reflect the different weight ratios; Constructing a fusion sample set, the fusion sample set including a training sample set of the first type of risk characteristics, a training sample set of the second type of risk characteristics, and a financing guarantee risk verification sample set of the first type of risk characteristics and the second type of risk characteristics; The m initial sub-fusion models are trained according to the fusion sample set, and an integrated fusion model is output. The integrated model integrates the prediction capabilities of all the initial sub-fusion models and is used for financing guarantee risk assessment tasks.

3. The method according to claim 2, wherein The m initial sub-fusion models are trained according to the fusion sample set to output an integrated fusion model, and the method further includes: Training the m initial sub-fusion models according to the fusion sample set to obtain m financing guarantee risk output sample sets; Based on the m financing guarantee risk output sample sets and the financing guarantee risk verification sample sets, an optimized sub-fusion model is obtained, wherein the probability accuracy of the optimized sub-fusion model is greater than or equal to a preset accuracy rate; Adjust the weight ratio of the next round according to the optimized sub-fusion model to obtain m sub-fusion models after a preset number of iterations; The m sub-fusion models are integrated to obtain an integrated fusion model, and then a weight ratio λ1:λ2 of the integrated fusion model is output.

4. The method according to claim 1, wherein Inputting the batch business data set into the business data transfer platform, the method further includes: Obtaining the unit byte quantity used for data transmission of each service data type in the batch service data set; Performing real-time byte volume analysis based on the unit byte volume of each business data type, and setting a batch transmission instruction if the current real-time byte volume is greater than a preset byte volume, wherein the preset byte volume is obtained by configuring parameters of the business data transfer platform; The batch business data sets are input into the business data transfer platform in batches according to the batch transmission instruction.

5. The dynamic monitoring and early warning system for financing guarantee risks based on data fusion is characterized by: A system for implementing the method for dynamic monitoring and early warning of financing guarantee risks based on data fusion according to any one of claims 1 to 4, comprising: A transfer platform construction module, wherein the transfer platform construction module is used to build a business data transfer platform, wherein the business data transfer platform is a transfer platform for business data transmission between the first server and the second server; a business data input module, configured to monitor batch business data sets associated with financing guarantee risk quantification from the first server and input the batch business data sets into the business data transfer platform; a business data identification module, the business data identification module being used by the business data transfer platform to identify the batch business data set and determine a first type of batch business data set and a second type of batch business data set, wherein the business data transfer platform includes a pre-trained deadline-failure network model, and the deadline-failure network model is used to perform data failure analysis on the batch business data set; a feature extraction module, the feature extraction module being used to extract features from the first type of batch business data set and the second type of batch business data set respectively, to obtain first type risk features and second type risk features quantitatively associated with financing guarantee risks; a risk indicator output module, wherein the risk indicator output module is used by the business data transfer platform to transmit the first and second risk characteristics to the second server, and the second server uses the embedded financing guarantee risk assessment model to output the financing guarantee risk indicator; an early warning signal generating module, configured to generate a first early warning signal when the financing guarantee risk indicator is greater than a preset financing guarantee risk indicator; The system also includes a batch business data set identification module to perform the following operation steps: Decomposing the business data types of the batch business data set to obtain each business data type; performing failure index analysis on each of the service data types according to the deadline-failure network model, and outputting a failure factor for each of the service data types; Classifying the batch service data sets using the failure factors of the various service data types as variables, and determining a first category of batch service data sets having failure factors greater than or equal to a preset failure factor, and a second category of batch service data sets having failure factors less than the preset failure factor; Furthermore, the system further includes a network model output module to perform the following steps: Get the fully connected neural network layer; Obtaining a training sample data set, wherein the training sample data set includes batch business sample data, multiple failure penalty factors, and identification information for identifying the failure factors, wherein the multiple failure penalty factors include deadline-data credibility, deadline-data accuracy, and deadline-data value; The fully connected neural network layer is trained according to the training sample data set until the model is in a converged state, and the deadline-failure network model is output.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method for dynamic monitoring and early warning of financing guarantee risks based on data fusion according to any one of claims 1 to 4 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for dynamic monitoring and early warning of financing guarantee risks based on data fusion according to any one of claims 1 to 4 are implemented.

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