Asset operation risk prediction and early warning method and system based on big data analysis

Abstract

The invention relates to the technical field of financial data analysis, in particular to an asset operation risk prediction and early warning method and system based on big data analysis, and the method comprises the steps: obtaining structured data, unstructured data, time series data, spatial data and enterprise graph data related to asset operation in real time based on a distributed collection engine; generating a unified risk feature vector; a risk conduction diagram network of the enterprise is constructed according to the unified risk feature vector, nodes of the risk conduction diagram network represent asset risk states, edge weights represent risk conduction intensity, and a risk thermodynamic diagram containing key risk conduction paths is output; based on the risk thermodynamic diagram, dynamically optimizing an early warning threshold through a meta-learning framework, and generating an early warning level signal; and according to the early warning level signal and the risk conduction path, matching an optimal risk disposal instruction in a preset risk strategy database, and outputting the optimal risk disposal instruction to an execution terminal. The method has the effects of improving the asset operation risk early warning precision and the response time.

Description

Technical Field

[0001] This invention relates to the technical field of financial data analysis, and in particular to a method and system for predicting and warning of asset operation risks based on big data analysis. Background Technology

[0002] In recent years, with the rapid development of big data and artificial intelligence technologies, intelligent risk prediction tools have been gradually introduced into the financial and corporate asset management fields. Traditional financial asset risk management mainly relies on human experience and static statistical models, such as historical return distribution analysis. However, modern technologies have begun to integrate multi-source data to build predictive models, such as calculating cumulative probability functions through the historical return distribution of assets, setting fixed thresholds to trigger early warnings, RFID and image recognition-based warehouse asset loss early warning systems, and market shift parameters through comparative analysis of competitor data.

[0003] However, the aforementioned existing technologies still have significant limitations. Existing asset risk warnings mostly rely on single structured data (such as financial indicators and transaction records), lacking the integration and analysis of unstructured data and implementation data. This results in insufficient coverage of asset operation risk factors, leading to low accuracy of risk warning results. Secondly, using historical return distribution to set static warning thresholds cannot adapt to sudden market fluctuations, resulting in delayed asset operation warning responses and missing the golden time for risk disposal. Therefore, there is room for improvement. Summary of the Invention

[0004] To improve the accuracy and response time of asset operation risk early warning and enhance the security of corporate assets, this application provides a method and system for asset operation risk prediction and early warning based on big data analysis.

[0005] Firstly, the above-mentioned inventive objective of this application is achieved through the following technical solution:

[0006] A method for predicting and warning of asset operation risks based on big data analysis, comprising the following steps:

[0007] Based on a distributed acquisition engine, structured data, unstructured data, time-series data, spatial data, and enterprise diagram data related to asset operation are acquired in real time. A unified risk feature vector is generated based on the structured data, unstructured data, time-series data, spatial data, and enterprise diagram data.

[0008] The enterprise's risk transmission graph network is constructed based on the unified risk feature vector. The nodes of the risk transmission graph network represent the asset risk status, the edge weights represent the risk transmission intensity, and a risk heat map containing key risk transmission paths is output.

[0009] Based on the aforementioned risk heatmap, the early warning threshold is dynamically optimized using a meta-learning framework to generate early warning level signals.

[0010] Based on the warning level signal and risk transmission path, the optimal risk handling instruction is matched in the preset risk strategy database and output to the execution terminal.

[0011] By adopting the above technical solution, in the process of risk prediction for enterprise asset operations, a distributed data acquisition engine integrates structured data (including financial indicators and transaction records), unstructured data (including public opinion texts and contract documents), time-series data (IoT sensor logs), spatial data (supply chain geographic coordinates), and graph data (enterprise equity / guarantee relationships). This multi-type heterogeneous data fusion forms a unified risk feature vector, covering all dimensions of asset operations, thereby improving the accuracy of risk factor identification. Based on this unified risk feature vector, a risk transmission graph network for the enterprise is constructed. In this network, nodes represent asset risk status, and edge weights represent risk transmission intensity. Through dynamic topology generation and edge weight formulas, the output includes relevant... This system utilizes a risk heatmap to map key risk transmission paths, quantifying the intensity of risk transmission between enterprises and improving the accuracy of locating high-risk transmission areas. It then uses the obtained risk heatmap and a meta-learning framework to dynamically optimize early warning thresholds, generating different levels of early warning signals for tiered risk management. Based on these signals and the risk transmission paths outlined in the heatmap, it matches optimal risk management instructions within a risk strategy database, enabling rapid risk response and improving asset operation risk early warning response time. Through a closed-loop architecture of data fusion, graph construction, dynamic early warning, and intelligent execution, it comprehensively addresses the three major pain points of existing technologies: data silos, rigid early warnings, and delayed responses. This improves the accuracy and response time of asset operation risk early warnings, ultimately enhancing the security of enterprise assets.

[0012] In a preferred embodiment, this application can be further configured as follows: the real-time acquisition of structured data, unstructured data, time-series data, spatial data, and enterprise diagram data related to asset operation based on a distributed acquisition engine, and the generation of a unified risk feature vector based on the structured data, unstructured data, time-series data, spatial data, and enterprise diagram data, specifically includes:

[0013] Align structured data and time-series data with time windows to eliminate time delay differences and generate numerical features of assets;

[0014] Semantic features are extracted from unstructured data, and risk labels for key terms in the semantic features are identified using CNN.

[0015] Based on spatial topology indexes, spatial data is associated to generate spatial risk density features. A unified risk feature vector is then generated based on the asset's numerical features, semantic features, and spatial risk density features.

[0016] By adopting the above technical solutions, the sliding time window technique is used to eliminate the time delay difference between structured data (such as transaction records) and IoT time-series data (such as device sensor logs), thereby reducing the time-series correlation error rate in subsequent analysis. The semantic features and risk labels of unstructured text (public opinion / contracts) are parsed to identify implicit risk signals (such as contract breach clauses and negative public opinion sentiment), supplementing the blind spots of traditional structured data. Based on spatial topology index, spatial data is correlated to generate spatial risk density features. The time-series aligned data, semantic features, spatial distribution features, and graph relationship features are integrated into a unified vector, solving the feature distortion problems caused by multi-source data format conflicts and time delay differences in traditional risk prediction. This provides highly consistent and low-noise input data for the construction of dynamic risk maps.

[0017] In a preferred embodiment, this application can be further configured as follows: The construction of a risk transmission graph network for the enterprise based on the unified risk feature vector, wherein the nodes of the risk transmission graph network represent the asset risk status, the edge weights represent the risk transmission intensity, and a risk heatmap containing key risk transmission paths is output, specifically including:

[0018] Based on the unified risk vector features, the proportion of guaranteed amount, transaction frequency, and public opinion correlation strength are obtained, and the edge weights are updated according to a preset time period: ;

[0019] When a node's risk value suddenly exceeds a threshold, a local subgraph reconstruction is triggered and high-risk propagation areas are marked.

[0020] Based on the cellular automata model, the diffusion path of risk in the subgraph is simulated, and a risk heatmap containing the identification of key transmission paths is generated.

[0021] By adopting the above technical solution, multi-source risk transmission factors are integrated in real time through industry adjustment coefficients, edge weights are updated using preset time periods to reduce the error rate of edge weight calculation and improve the identification accuracy of risk factors. High-risk sources are located in real time based on node risk mutation thresholds, and the diffusion path of risk in the subgraph is simulated using a cellular automaton model to generate a risk heatmap containing key transmission path markers. Through a four-step collaborative approach of dynamic edge weight update mechanism, local subgraph reconstruction trigger rules, cellular automaton risk diffusion simulation, and key transmission node annotation, the problems of lag in response and fuzzy quantification of transmission paths in traditional static graphs are solved, significantly improving the real-time performance and accuracy of risk transmission prediction.

[0022] In a preferred embodiment, this application can be further configured as follows: the simulation of the risk diffusion path in the subgraph based on the cellular automata model to generate a risk heatmap containing key transmission path identifiers specifically includes:

[0023] Initialize the node state, and calculate the influence of neighboring node infection based on the initialized node state and edge weights;

[0024] The infection probability of a node is calculated based on the infection influence of the neighboring nodes. Key transmission nodes are marked according to the infection probability of the node, and a risk heat map containing key transmission path identifiers is generated.

[0025] By adopting the above technical solution, the node state is initialized, and the complex risk state is abstracted into a binary infection label, which solves the computational redundancy problem of the traditional continuous risk value model. The infection influence of neighboring nodes is calculated by integrating edge weights and initialized node states. The infection probability of nodes on the risk transmission path is calculated using the infection influence of neighboring nodes. Key transmission nodes are marked according to the node infection probability. Key transmission nodes are located based on statistical significance, replacing manual experience screening. A risk heat map containing key transmission path identifiers is generated, which improves the accuracy of high-risk hub identification and breaks through the adaptability limitation of the traditional risk transmission model to complex network topology, thus improving the efficiency of risk path prediction.

[0026] In a preferred embodiment, this application can be further configured such that: the step of dynamically optimizing the warning threshold and generating a warning level signal based on the risk heatmap using a meta-learning framework specifically includes:

[0027] Acquire historical asset operation data, and generate an initial early warning threshold based on the historical asset operation data;

[0028] Real-time acquisition of market data; dynamic updating of the initial warning threshold based on the market data to obtain the updated warning threshold.

[0029] The node risk value is obtained based on the risk heat map. When the node risk value is greater than the updated warning threshold, a red warning signal is generated; otherwise, a yellow warning signal is generated.

[0030] By adopting the above technical solution, the initial warning threshold is trained using historical asset operation data, so that the warning benchmark matches the industry risk baseline. Real-time market data is integrated, and the threshold sensitivity is dynamically adjusted through an exponential decay function to dynamically update the initial warning threshold and obtain the updated warning threshold. This overcomes the rigidity of the traditional static threshold model and classifies the warning level based on the extent to which the node risk value exceeds the standard, such as yellow warning triggering monitoring and red warning triggering emergency response.

[0031] In a preferred embodiment, this application can be further configured as follows: The step of matching the optimal risk handling instruction from a pre-set risk strategy database based on the warning level signal and the risk transmission path, and outputting it to the execution terminal, specifically includes:

[0032] Using the warning level signal as the query key, retrieve the set of risk handling actions from the pre-set risk strategy database;

[0033] The risk transmission path is input into a graph neural network to predict the loss convergence curve of each risk management action;

[0034] The optimal risk management instruction is selected based on the loss convergence curve of each risk management action and output to the execution terminal.

[0035] By adopting the above technical solution, a set of risk handling actions is obtained by retrieving the knowledge graph using the early warning signal as the key. The risk transmission path is associated with the risk handling strategy library through a dynamic matching engine, which reduces the time spent matching risk handling solutions. At the same time, the risk transmission path is input into the graph neural network to predict the loss convergence curve of each risk handling action. Key transmission nodes are located based on the risk heat map to improve the targeting of risk handling actions. The optimal risk handling instruction is selected according to the loss convergence curve of each risk handling action and output to the execution terminal for risk handling execution, which significantly improves the accuracy of risk handling.

[0036] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0037] A big data analytics-based asset operation risk prediction and early warning system, comprising:

[0038] The multi-source asset operation data acquisition and fusion module is used to acquire structured data, unstructured data, time-series data, spatial data and enterprise diagram data related to asset operation in real time based on a distributed acquisition engine, and generate a unified risk feature vector based on the structured data, unstructured data, time-series data, spatial data and enterprise diagram data;

[0039] The dynamic risk graph construction module is used to construct a risk transmission graph network for an enterprise based on the unified risk feature vector. The nodes of the risk transmission graph network represent the asset risk status, the edge weights represent the risk transmission intensity, and the module outputs a risk heat map containing key risk transmission paths.

[0040] An adaptive early warning signal generation module is used to dynamically optimize the early warning threshold based on the risk heatmap using a meta-learning framework to generate an early warning level signal.

[0041] The risk management strategy execution module is used to match the optimal risk management instruction in the preset risk strategy database according to the warning level signal and risk transmission path, and output it to the execution terminal.

[0042] By adopting the above technical solution, in the process of risk prediction for enterprise asset operations, a distributed data acquisition engine integrates structured data (including financial indicators and transaction records), unstructured data (including public opinion texts and contract documents), time-series data (IoT sensor logs), spatial data (supply chain geographic coordinates), and graph data (enterprise equity / guarantee relationships). This multi-type heterogeneous data fusion forms a unified risk feature vector, covering all dimensions of asset operations, thereby improving the accuracy of risk factor identification. Based on this unified risk feature vector, a risk transmission graph network is constructed for the enterprise. In this network, nodes represent asset risk status, and edge weights represent risk transmission intensity. Through dynamic topology generation and edge weight formulas, the output includes relevant data. This system utilizes a risk heatmap to map key risk transmission paths, quantifying the intensity of risk transmission between enterprises and improving the accuracy of locating high-risk transmission areas. It then uses the obtained risk heatmap and a meta-learning framework to dynamically optimize early warning thresholds, generating different levels of early warning signals for tiered risk management. Based on these signals and the risk transmission paths outlined in the heatmap, it matches optimal risk management instructions within a risk strategy database, enabling rapid risk response and improving asset operation risk early warning response time. Through a closed-loop architecture of data fusion, graph construction, dynamic early warning, and intelligent execution, it comprehensively addresses the three major pain points of existing technologies: data silos, rigid early warnings, and delayed responses. This improves the accuracy and response time of asset operation risk early warnings, ultimately enhancing the security of enterprise assets.

[0043] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0044] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described method for asset operation risk prediction and early warning based on big data analysis.

[0045] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0046] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for asset operation risk prediction and early warning based on big data analysis.

[0047] In summary, this application includes at least one of the following beneficial technical effects:

[0048] 1. In the process of risk prediction for enterprise asset operations, a distributed data acquisition engine integrates structured data (including financial indicators and transaction records), unstructured data (including public opinion texts and contract documents), time-series data (IoT sensor logs), spatial data (supply chain geographic coordinates), and graph data (enterprise equity / guarantee relationships). This multi-type heterogeneous data fusion forms a unified risk feature vector, covering all dimensions of asset operations, thereby improving the accuracy of risk factor identification. Based on this unified risk feature vector, a risk transmission graph network is constructed for the enterprise. In this network, nodes represent asset risk status, and edge weights represent the intensity of risk transmission. Through dynamic topology generation and edge weight formulas, the output includes key risk transmission information. The risk heatmap of the path quantifies the intensity of risk transmission between enterprises, improves the accuracy of locating high-risk transmission areas, and uses the obtained risk heatmap to dynamically optimize the early warning threshold based on the meta-learning framework to generate early warning signals of different levels, realizing risk-level disposal. According to the early warning signals of different levels, combined with the risk transmission path of the risk heatmap, the optimal risk disposal instructions are matched in the risk strategy database, thereby enabling rapid response to risk handling and improving the early warning response time of asset operation risks. Through a closed-loop architecture of data fusion, graph construction, dynamic early warning, and intelligent execution, it comprehensively solves the three major pain points of existing technologies: data silos, rigid early warning, and delayed disposal, improves the accuracy and response time of asset operation risk early warning, and enhances the security of enterprise assets.

[0049] 2. By adjusting the edge weight coefficients through a unified risk feature vector, multi-source risk transmission factors are fused in real time. Edge weights are updated using a preset time period, reducing the error rate of edge weight calculation and improving the identification accuracy of risk factors. High-risk sources are located in real time based on the node risk mutation threshold. Cellular automata models are used to simulate the diffusion path of risk in the subgraph, generating a risk heatmap containing key transmission path markers. Through a four-step collaborative approach of dynamic edge weight update mechanism, local subgraph reconstruction trigger rules, cellular automata risk diffusion simulation, and key transmission node annotation, the problems of lag response and fuzzy quantification of traditional static graphs are solved, significantly improving the real-time performance and accuracy of risk transmission prediction.

[0050] 3. Initialize node states and abstract complex risk states into binary infection markers, solving the computational redundancy problem of traditional continuous risk value models. Combine edge weights and initialized node states to calculate the infection influence of neighboring nodes, use the infection influence of neighboring nodes to calculate the infection probability of nodes on the risk transmission path, mark key transmission nodes based on node infection probability, and locate key transmission nodes based on statistical significance. Replace manual experience screening and generate a risk heatmap containing key transmission path markers, improving the accuracy of high-risk hub identification. This breaks through the adaptability limitations of traditional risk transmission models to complex network topologies and improves the efficiency of risk path prediction.

[0051] 4. The initial warning threshold is trained by using historical asset operation data to match the warning benchmark with the industry risk baseline. Real-time market data is integrated, and the threshold sensitivity is dynamically adjusted by an exponential decay function to dynamically update the initial warning threshold and obtain the updated warning threshold. This breaks through the rigidity of the traditional static threshold model. The warning level is divided based on the extent to which the node risk value exceeds the standard, such as yellow warning triggering monitoring and red warning triggering emergency response. Attached Figure Description

[0052] Figure 1 This is a flowchart of an asset operation risk prediction and early warning method based on big data analysis in one embodiment of this application;

[0053] Figure 2 This is a flowchart illustrating the implementation of step S10 in an embodiment of a big data analysis-based method for predicting and warning asset operation risks.

[0054] Figure 3 This is a flowchart illustrating the implementation of step S20 in an embodiment of a big data analysis-based method for predicting and warning asset operation risks.

[0055] Figure 4 This is a flowchart illustrating the implementation of step S23 in an embodiment of a method for predicting and warning asset operation risks based on big data analysis in this application.

[0056] Figure 5 This is a flowchart illustrating the implementation of step S30 in an embodiment of a big data analysis-based method for predicting and warning asset operation risks.

[0057] Figure 6 This is a flowchart illustrating the implementation of step S40 in an embodiment of a method for predicting and warning asset operation risks based on big data analysis in this application.

[0058] Figure 7 This is a principle block diagram of an asset operation risk prediction and early warning system based on big data analysis in one embodiment of this application;

[0059] Figure 8 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0060] The present application will be further described in detail below with reference to the accompanying drawings.

[0061] In one embodiment, such as Figure 1 As shown, this application discloses a method for asset operation risk prediction and early warning based on big data analysis, which specifically includes the following steps:

[0062] S10: Based on a distributed acquisition engine, it acquires structured data, unstructured data, time-series data, spatial data, and enterprise graph data related to asset operation in real time, and generates a unified risk feature vector based on the structured data, unstructured data, time-series data, spatial data, and enterprise graph data.

[0063] Specifically, a distributed data acquisition engine integrates structured data (financial indicators, transaction records), unstructured data (public opinion texts, contract documents), time-series data (IoT sensor logs), spatial data (supply chain geographic coordinates), and graph data (corporate equity / guarantee relationships), fusing five types of heterogeneous data to cover all dimensions of asset operation characteristics, thereby improving the risk factor identification rate. Streaming processing (such as time window alignment technology) is used to eliminate time delays, achieving second-level data updates and solving the problem of delayed early warnings caused by traditional batch data acquisition.

[0064] S20: Construct a risk transmission graph network for the enterprise based on the unified risk feature vector, wherein the nodes of the risk transmission graph network represent the asset risk status, the edge weights represent the risk transmission intensity, and output a risk heat map containing key risk transmission paths.

[0065] Specifically, based on a unified risk feature vector, a risk transmission graph network is constructed that spans enterprises and assets. In this network, nodes represent the risk status of assets, edge weights represent the intensity of risk transmission, the intensity of risk transmission between enterprises is quantified, and a risk heatmap containing key risk transmission paths is output.

[0066] S30: Based on the aforementioned risk heatmap, the warning threshold is dynamically optimized using a meta-learning framework to generate a warning level signal.

[0067] Specifically, by utilizing the obtained risk heatmap and dynamically optimizing the early warning threshold based on the meta-learning framework, the risk early warning threshold was dynamically optimized, and different levels of early warning signals were generated according to the risk early warning threshold, thus realizing risk classification and handling.

[0068] S40: Based on the warning level signal and risk transmission path, match the optimal risk handling instruction in the preset risk strategy database and output it to the execution terminal.

[0069] Specifically, based on different levels of early warning signals and combined with the risk transmission path of the risk heat map, the optimal risk handling instructions are matched in the risk strategy database to quickly respond to risk handling and improve the early warning response time for asset operation risks.

[0070] In this embodiment, during the risk prediction process for an enterprise's asset operations, a distributed data acquisition engine integrates structured data (including financial indicators and transaction records), unstructured data (including public opinion texts and contract documents), time-series data (IoT sensor logs), spatial data (supply chain geographic coordinates), and graph data (enterprise equity / guarantee relationships). This multi-type heterogeneous data fusion forms a unified risk feature vector, covering all dimensions of asset operations, thereby improving the accuracy of risk factor identification. Based on this unified risk feature vector, a risk transmission graph network is constructed for the enterprise. In this network, nodes represent asset risk status, and edge weights represent risk transmission intensity. Through dynamic topology generation and edge weight formulas, the output includes key risk... This system generates a risk heatmap of risk transmission paths, quantifies the intensity of risk transmission between enterprises, and improves the accuracy of locating high-risk transmission areas. Using the obtained risk heatmap and a meta-learning framework, it dynamically optimizes early warning thresholds, generating different levels of early warning signals for tiered risk management. Based on these signals and the risk transmission paths within the risk heatmap, it matches optimal risk management instructions within a risk strategy database, enabling rapid risk response and improving asset operation risk early warning response time. Through a closed-loop architecture of data fusion, graph construction, dynamic early warning, and intelligent execution, it comprehensively addresses the three major pain points of existing technologies: data silos, rigid early warnings, and delayed responses. This improves the accuracy and response time of asset operation risk early warnings, thereby enhancing the security of enterprise assets.

[0071] In one embodiment, such as Figure 2 As shown, in step S10, structured data, unstructured data, time-series data, spatial data, and enterprise diagram data related to asset operation are acquired in real time based on the distributed acquisition engine. A unified risk feature vector is generated based on the structured data, unstructured data, time-series data, spatial data, and enterprise diagram data. Specifically, this includes:

[0072] S11: Align structured data and time-series data with time windows to eliminate time delay differences and generate numerical features of assets.

[0073] Specifically, the sliding time window technique is used to eliminate the time delay difference between structured data (such as transaction logs) and IoT time-series data (such as device sensor logs) to generate numerical features of assets.

[0074] S12: Extract semantic features based on unstructured data and identify key clause risk labels in the semantic features using CNN.

[0075] Specifically, the semantic features of unstructured text (public opinion / contracts) are analyzed using NLP models, and then CNN models are used to classify key contract clauses to identify implicit risk signals, such as contract breach clauses and negative public opinion sentiment, thus forming semantic features.

[0076] S13: Based on spatial topology index, associate spatial data to generate spatial risk density features, and generate a unified risk feature vector according to the asset numerical features, semantic features and spatial risk density features.

[0077] Specifically, based on the R-tree index, the geographic coordinates of spatial data are associated with enterprise nodes to generate spatial risk density distribution characteristics, quantify the regional risk transmission, and use an in-memory data structure to integrate asset numerical features, semantic features and spatial risk density features to generate a unified risk feature vector, realize cross-modal mapping of four types of features, and eliminate the format conversion loss in the traditional ETL process.

[0078] In one embodiment, such as Figure 3 As shown, in step S20, a risk transmission graph network for the enterprise is constructed based on the unified risk feature vector. The nodes of the risk transmission graph network represent the asset risk status, the edge weights represent the risk transmission intensity, and a risk heatmap containing key risk transmission paths is output. Specifically, this includes:

[0079] S21: Based on the unified risk vector features, obtain the proportion of guarantee amount, transaction frequency, and public opinion correlation strength, and update the edge weights according to a preset time period: .

[0080] S22: When the node risk value changes mutably beyond the threshold, local subgraph reconstruction is triggered and high-risk propagation areas are marked.

[0081] S23: Simulate the diffusion path of risk in the subgraph based on the cellular automata model, and generate a risk heatmap containing key transmission path identifiers.

[0082] Specifically, by adjusting industry coefficients, multi-source risk transmission factors are integrated in real time. Edge weights are updated using a preset time period to reduce the error rate of edge weight calculation and improve the identification accuracy of risk factors. High-risk sources are located in real time based on node risk mutation thresholds. Cellular automata models are used to simulate the diffusion path of risk in subgraphs and generate risk heatmaps containing key transmission path markers. Through a four-step collaborative approach of dynamic edge weight update mechanism, local subgraph reconstruction trigger rules, cellular automata risk diffusion simulation, and key transmission node annotation, the problems of lag in response and fuzzy quantitative transmission path of traditional static graphs are solved, significantly improving the real-time performance and accuracy of risk transmission prediction.

[0083] In one embodiment, such as Figure 4 As shown, in step S23, which involves simulating the diffusion path of risk in the subgraph based on the cellular automata model and generating a risk heatmap containing key transmission path identifiers, the specific steps include:

[0084] S231: Initialize the node state, and calculate the influence of neighboring node infection based on the initialized node state and edge weights.

[0085] Specifically, complex risk states are abstracted into binary infection markers. This solves the computational redundancy problem of traditional continuous risk value models by calculating the infection influence of neighboring nodes based on the initial node state and edge weights.

[0086] S232: Calculate the node infection probability based on the infection influence of the neighboring nodes, mark key transmission nodes according to the node infection probability, and generate a risk heat map containing key transmission path identifiers.

[0087] Specifically, the infection probability of nodes on the risk transmission path is calculated by utilizing the infection influence of neighboring nodes. Key transmission nodes are marked based on the infection probability, and key transmission nodes are located based on statistical significance. This replaces manual experience screening and generates a risk heat map containing key transmission path identifiers, thereby improving the accuracy of identifying high-risk hubs.

[0088] In one embodiment, such as Figure 5 As shown, in step S30, based on the risk heatmap, the early warning threshold is dynamically optimized through a meta-learning framework to generate an early warning level signal, specifically including:

[0089] S31: Obtain historical asset operation data and generate an initial early warning threshold based on the historical asset operation data.

[0090] Specifically, the initial early warning threshold is trained using historical asset operation data to match the early warning benchmark with the industry risk baseline.

[0091] S32: Acquire market data in real time, and dynamically update the initial warning threshold based on the market data to obtain the updated warning threshold.

[0092] Specifically, by integrating real-time market data, including real-time market volatility and industry risk indices, the threshold sensitivity is dynamically adjusted through an exponential decay function, and the initial warning threshold is dynamically updated to obtain the updated warning threshold, thus overcoming the rigidity of traditional static threshold models.

[0093] S33: Obtain node risk values ​​based on the risk heatmap. If the node risk value is greater than the updated warning threshold, generate a red warning signal; otherwise, generate a yellow warning signal.

[0094] Specifically, warning levels are determined based on the extent to which node risk values ​​exceed the limits, such as yellow warnings triggering monitoring and red warnings triggering emergency response.

[0095] In one embodiment, such as Figure 6As shown, in step S40, the optimal risk handling instruction is matched in the preset risk strategy database according to the warning level signal and risk transmission path, and then output to the execution terminal. Specifically, this includes:

[0096] S41: Using the warning level signal as the query key, retrieve the set of risk handling actions from the preset risk strategy database.

[0097] S42: Input the risk transmission path into a graph neural network to predict the loss convergence curve of each risk management action.

[0098] S43: Select the optimal risk handling instruction based on the loss convergence curve of each risk handling action and output it to the execution terminal.

[0099] Specifically, the system retrieves a set of risk management actions from a knowledge graph using early warning signals as keys. A dynamic matching engine then links the risk transmission path with the risk management strategy library, reducing the time required for matching risk management solutions. Simultaneously, the risk transmission path is input into a graph neural network to predict the loss convergence curve of each risk management action. Key transmission nodes are located based on a risk heatmap, improving the targeting of risk management actions. The optimal risk management instruction is selected based on the loss convergence curve of each risk management action and output to the execution terminal for risk management execution, significantly improving the accuracy of risk management.

[0100] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0101] In one embodiment, an asset operation risk prediction and early warning system based on big data analysis is provided. This system corresponds one-to-one with the asset operation risk prediction and early warning methods based on big data analysis described in the above embodiments. Figure 7 As shown, this asset operation risk prediction and early warning system based on big data analytics includes a multi-source asset operation data acquisition and fusion module, a dynamic risk map construction module, an adaptive early warning signal generation module, and a risk management strategy execution module. Detailed descriptions of each functional module are as follows:

[0102] The dynamic risk graph construction module is used to construct a risk transmission graph network for an enterprise based on the unified risk feature vector. The nodes of the risk transmission graph network represent the asset risk status, the edge weights represent the risk transmission intensity, and the module outputs a risk heat map containing key risk transmission paths.

[0103] An adaptive early warning signal generation module is used to dynamically optimize the early warning threshold based on the risk heatmap using a meta-learning framework to generate an early warning level signal.

[0104] The risk management strategy execution module is used to match the optimal risk management instruction in the preset risk strategy database according to the warning level signal and risk transmission path, and output it to the execution terminal.

[0105] Preferably, the dynamic risk mapping construction module includes:

[0106] The edge weight update submodule is used to obtain the proportion of guarantee amount, transaction frequency and public opinion correlation strength based on the unified risk vector features, and update the edge weight according to a preset time period.

[0107] The risk node marking submodule is used to trigger local subgraph reconstruction and mark high-risk transmission areas when the risk value of a node suddenly exceeds a threshold.

[0108] The risk heatmap generation submodule is used to simulate the diffusion path of risk in the submap based on the cellular automata model, and generate a risk heatmap containing key transmission path identifiers.

[0109] Specific limitations regarding the asset operation risk prediction and early warning system based on big data analytics can be found in the limitations of the asset operation risk prediction and early warning method based on big data analytics mentioned above, and will not be repeated here. Each module in the aforementioned asset operation risk prediction and early warning system based on big data analytics can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in an electronic device, or stored in the memory of an electronic device as software, so that the processor can call and execute the corresponding operations of each module.

[0110] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 8 As shown, this electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores the database. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for asset operation risk prediction and early warning based on big data analysis.

[0111] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0112] Based on a distributed acquisition engine, structured data, unstructured data, time-series data, spatial data, and enterprise diagram data related to asset operation are acquired in real time. A unified risk feature vector is generated based on the structured data, unstructured data, time-series data, spatial data, and enterprise diagram data.

[0113] The enterprise's risk transmission graph network is constructed based on the unified risk feature vector. The nodes of the risk transmission graph network represent the asset risk status, the edge weights represent the risk transmission intensity, and a risk heat map containing key risk transmission paths is output.

[0114] Based on the aforementioned risk heatmap, the early warning threshold is dynamically optimized using a meta-learning framework to generate early warning level signals.

[0115] Based on the warning level signal and risk transmission path, the optimal risk handling instruction is matched in the preset risk strategy database and output to the execution terminal.

[0116] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0117] Based on a distributed acquisition engine, structured data, unstructured data, time-series data, spatial data, and enterprise diagram data related to asset operation are acquired in real time. A unified risk feature vector is generated based on the structured data, unstructured data, time-series data, spatial data, and enterprise diagram data.

[0118] The enterprise's risk transmission graph network is constructed based on the unified risk feature vector. The nodes of the risk transmission graph network represent the asset risk status, the edge weights represent the risk transmission intensity, and a risk heat map containing key risk transmission paths is output.

[0119] Based on the aforementioned risk heatmap, the early warning threshold is dynamically optimized using a meta-learning framework to generate early warning level signals.

[0120] Based on the warning level signal and risk transmission path, the optimal risk handling instruction is matched in the preset risk strategy database and output to the execution terminal.

[0121] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0122] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0123] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A method for predicting and warning of asset operation risks based on big data analysis, characterized in that, The asset operation risk prediction and early warning method based on big data analysis includes the following steps: Based on a distributed acquisition engine, structured data, unstructured data, time-series data, spatial data, and enterprise diagram data related to asset operation are acquired in real time. A unified risk feature vector is generated based on the structured data, unstructured data, time-series data, spatial data, and enterprise diagram data. The enterprise's risk transmission graph network is constructed based on the unified risk feature vector. The nodes of the risk transmission graph network represent the asset risk status, the edge weights represent the risk transmission intensity, and a risk heat map containing key risk transmission paths is output. Based on the aforementioned risk heatmap, the early warning threshold is dynamically optimized using a meta-learning framework to generate early warning level signals. Based on the warning level signal and risk transmission path, the optimal risk handling instruction is matched in the preset risk strategy database and output to the execution terminal.

2. The asset operation risk prediction and early warning method based on big data analysis according to claim 1, characterized in that, The method involves acquiring structured, unstructured, time-series, spatial, and enterprise graph data related to asset operations in real time using a distributed acquisition engine. A unified risk feature vector is then generated based on these data, specifically including: Align structured data and time-series data with time windows to eliminate time delay differences and generate numerical features of assets; Semantic features are extracted from unstructured data, and risk labels for key terms in the semantic features are identified using CNN. Based on spatial topology indexes, spatial data is associated to generate spatial risk density features. A unified risk feature vector is then generated based on the asset's numerical features, semantic features, and spatial risk density features.

3. The asset operation risk prediction and early warning method based on big data analysis according to claim 1, characterized in that, The process involves constructing a risk transmission graph network for the enterprise based on the unified risk feature vector. Nodes in the risk transmission graph network represent the asset risk status, edge weights represent the risk transmission intensity, and a risk heatmap containing key risk transmission paths is output. Specifically, this includes: Based on the unified risk vector features, the proportion of guaranteed amount, transaction frequency, and public opinion correlation strength are obtained, and the edge weights are updated according to a preset time period: ; When a node's risk value suddenly exceeds a threshold, local subgraph reconstruction is triggered and high-risk propagation areas are marked. Based on the cellular automata model, the diffusion path of risk in the subgraph is simulated, and a risk heatmap containing the identification of key transmission paths is generated.

4. The asset operation risk prediction and early warning method based on big data analysis according to claim 3, characterized in that, The process of simulating the risk diffusion path in the subgraph based on the cellular automata model to generate a risk heatmap containing key transmission path identifiers specifically includes: Initialize the node state, and calculate the influence of neighboring node infection based on the initialized node state and edge weights; The infection probability of a node is calculated based on the infection influence of the neighboring nodes. Key transmission nodes are marked according to the infection probability of the node, and a risk heat map containing key transmission path identifiers is generated.

5. The asset operation risk prediction and early warning method based on big data analysis according to claim 1, characterized in that, The step of dynamically optimizing the early warning threshold and generating an early warning level signal based on the risk heatmap using a meta-learning framework specifically includes: Acquire historical asset operation data, and generate an initial early warning threshold based on the historical asset operation data; Real-time acquisition of market data; dynamic updating of the initial warning threshold based on the market data to obtain the updated warning threshold. The node risk value is obtained based on the risk heat map. When the node risk value is greater than the updated warning threshold, a red warning signal is generated; otherwise, a yellow warning signal is generated.

6. The asset operation risk prediction and early warning method based on big data analysis according to claim 1, characterized in that, The step of matching the optimal risk handling instruction from a pre-set risk strategy database based on the warning level signal and the risk transmission path, and outputting it to the execution terminal, specifically includes: Use the warning level signal as the query key to retrieve a set of risk response actions from a pre-set risk strategy database; The risk transmission path is input into a graph neural network to predict the loss convergence curve of each risk management action; The optimal risk management instruction is selected based on the loss convergence curve of each risk management action and output to the execution terminal.

7. An asset operation risk prediction and early warning system based on big data analysis, characterized in that, The asset operation risk prediction and early warning system based on big data analysis includes: The multi-source asset operation data acquisition and fusion module is used to acquire structured data, unstructured data, time-series data, spatial data and enterprise diagram data related to asset operation in real time based on a distributed acquisition engine, and generate a unified risk feature vector based on the structured data, unstructured data, time-series data, spatial data and enterprise diagram data; The dynamic risk graph construction module is used to construct a risk transmission graph network for an enterprise based on the unified risk feature vector. The nodes of the risk transmission graph network represent the asset risk status, the edge weights represent the risk transmission intensity, and the module outputs a risk heat map containing key risk transmission paths. An adaptive early warning signal generation module is used to dynamically optimize the early warning threshold based on the risk heatmap using a meta-learning framework to generate an early warning level signal. The risk management strategy execution module is used to match the optimal risk management instruction in the preset risk strategy database according to the warning level signal and risk transmission path, and output it to the execution terminal.

8. The asset operation risk prediction and early warning system based on big data analysis according to claim 7, characterized in that, The dynamic risk mapping construction module includes: The edge weight update submodule is used to obtain the proportion of guarantee amount, transaction frequency and public opinion correlation strength based on the unified risk vector features, and update the edge weight according to a preset time period. The risk node marking submodule is used to trigger local subgraph reconstruction and mark high-risk transmission areas when the risk value of a node suddenly exceeds a threshold. The risk heatmap generation submodule is used to simulate the diffusion path of risk in the submap based on the cellular automata model, and generate a risk heatmap containing key transmission path identifiers.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the asset operation risk prediction and early warning method based on big data analysis as described in any one of claims 1 to 6.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the asset operation risk prediction and early warning method based on big data analysis as described in any one of claims 1 to 6.

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