Intelligent safety early warning method and system based on multi-source heterogeneous data

Abstract

The invention relates to the technical field of data security and risk early warning, and discloses a security threat early warning method and system based on multi-source data, and the method comprises the steps: carrying out the collection, standardization and mapping processing of the multi-source data, building a unified data matrix, extracting key features, building an association network diagram, and analyzing the distribution of potential threat points. Comprehensive threat evaluation and grading are carried out, the threat evolution trend is predicted in combination with historical data, and finally structured early warning information is generated. According to the method, multi-dimensional data can be effectively integrated, hidden association among the data can be mined, accurate identification, dynamic evaluation and trend prediction of security threats are realized, a reliable basis is provided for security early warning decision, and the accuracy and timeliness of security early warning are improved. The method has an important application value in the aspects of security situation awareness and early warning in a complex environment.

Description

Technical Field

[0001] The present invention relates to the technical field of data security and risk warning, and in particular to an intelligent security warning method and system based on multi-source heterogeneous data. Background Art

[0002] In today's information age, the importance of data security and risk warning is self-evident. It impacts personal privacy, corporate assets, and even national security, and is the cornerstone of ensuring the stable operation of a digital society. With the explosive growth of data volumes and the diversification of data sources, timely identifying potential threats from this vast amount of information and effectively responding to them has become a crucial research topic.

[0003] However, many current solutions have significant shortcomings when dealing with complex data environments. Existing methods are often limited to analyzing a single data source, lack the ability to integrate different types of data, and perform poorly in information correlation and deep mining. This makes it difficult to meet actual needs for accurate and timely warnings.

[0004] In existing technologies, the core challenges facing this field primarily focus on integrating diverse data and analyzing information associations. First, because data comes from a wide range of sources and in various forms, converting data of varying structures into a unified, analyzable representation becomes a fundamental challenge. This problem further extends to uncovering hidden associations within these unified representations. Due to the lack of effective association-building methods, it is often impossible to fully uncover the underlying patterns and risk clues behind the data. This progressive challenge, from integration to association, makes comprehensive decision-making based on multi-dimensional information fusion extremely difficult, which in turn affects the quality of early warning information generation.

[0005] Therefore, how to build an effective association network based on the unified representation of multi-source data and integrate multi-dimensional information for comprehensive analysis has become a key issue in improving security early warning capabilities. Summary of the Invention

[0006] The present invention provides an intelligent security early warning method and system based on multi-source heterogeneous data to enhance the security early warning capability.

[0007] In a first aspect, in order to solve the above technical problems, the present invention provides an intelligent security early warning method based on multi-source heterogeneous data, comprising:

[0008] Perform initial collection of multi-source data streams to obtain preliminary data sets with a unified format;

[0009] According to the format characteristics of the preliminary data group, different types of field contents are mapped into a preset unified framework. If the missing field ratio exceeds a preset ratio threshold, data is supplemented through an interpolation algorithm to obtain a unified data matrix after mapping.

[0010] Extracting key feature vectors from the unified data matrix to obtain a feature subset including core characteristics, and determining a key feature data set based on the feature subset;

[0011] Based on the key feature data set, a hidden correlation network between data points is constructed, and the connection strength between nodes in the network is obtained. If the connection strength is lower than a preset strength threshold, weak connection edges are removed to obtain an optimized correlation network graph;

[0012] Performing a deep traversal analysis on the associated network graph to obtain the distribution of potential threat points between nodes, determine the threat level, and obtain node location information corresponding to the threat level to obtain a list of potential threat points;

[0013] Obtaining dimension conflicts based on the potential threat point list; if the conflict value exceeds a preset conflict threshold, eliminating the conflict through dimensionality reduction processing to obtain a comprehensive threat assessment result;

[0014] Based on the comprehensive threat assessment results, the threat levels are hierarchically processed to obtain high-priority threat categories and corresponding high-risk area distributions;

[0015] Based on the distribution of high-risk areas, historical warning data, and real-time data streams, the evolution trend of potential threats is predicted. If the fluctuation amplitude exceeds a preset amplitude threshold, it is marked as an emergency warning object, and a predicted threat dynamic map is obtained;

[0016] The predicted threat dynamic graph is analyzed at multiple levels to obtain the specific characteristics and distribution patterns of the emergency warning objects and generate structured warning information.

[0017] Preferably, according to the format characteristics of the preliminary data group, different types of field contents are mapped into a preset unified framework. If the missing field ratio exceeds a preset ratio threshold, data is completed by an interpolation algorithm to obtain a mapped unified data matrix, including:

[0018] According to the field characteristics of the preliminary data group, a pre-established mapping rule table is used to map different types of field contents one by one to a preset unified framework;

[0019] Redundant content is cleaned up using a deduplication tool to obtain a deduplicated field set, and the field set is confirmed to have completed preliminary format conversion;

[0020] Calculating the missing proportion of the field using statistical tools for the missing conditions in the field set;

[0021] If the missing value ratio exceeds a preset ratio threshold, the missing content is supplemented with data through an interpolation calculation tool to obtain a supplemented field dataset;

[0022] Based on the completed field data set, a data integration tool is used to reorganize the field content according to a preset matrix structure, and a format verification tool is used to correct format inconsistencies to obtain a reorganized unified data matrix;

[0023] The unified data matrix is ​​compressed using a storage optimization tool, and the matrix is ​​saved in blocks to distributed storage nodes by allocating storage paths. The unified data matrix is ​​judged to complete the final integration based on the access identifier of the node.

[0024] Preferably, extracting key feature vectors from the unified data matrix to obtain a feature subset including core characteristics, and determining a key feature data set based on the feature subset includes:

[0025] Based on the multi-dimensional features in the unified data matrix, a data dimensionality reduction tool is used to perform preliminary processing on the high-dimensional features to obtain an initial feature vector group representative of the data;

[0026] Using a feature screening tool to sort the initial feature vector group by importance, and obtain a selected feature subset including core features;

[0027] Using a data compression tool to perform structural optimization processing on the selected feature subset to obtain a compressed feature subset;

[0028] An association rule mining tool is used to perform relationship analysis on the characteristics in the compressed feature subset to obtain the key feature data set.

[0029] Preferably, the method of constructing a hidden correlation network between data points based on the key feature data set, obtaining the connection strength between nodes in the network, and removing weak connection edges if the connection strength is lower than a preset strength threshold, thereby obtaining an optimized correlation network graph, includes:

[0030] Using a graph processing tool to comprehensively traverse the nodes and edges in the hidden association network to obtain an initial mapping relationship between node distribution and marginal weight;

[0031] The weight comparison tool is used to analyze the marginal weights one by one. If the connection strength of one edge is lower than the preset strength threshold, the weak connection edge is removed to obtain the filtered network structure;

[0032] Performing structural adjustments on the nodes and remaining edges in the screened network structure to obtain an optimized association graph;

[0033] Afterwards, the method further includes: saving the nodes and connection strengths in the optimized association graph to obtain a final association network graph.

[0034] Preferably, the step of obtaining dimension conflicts based on the potential threat point list and eliminating conflicts by dimensionality reduction processing if the conflict value exceeds a preset conflict threshold value to obtain a comprehensive threat assessment result includes:

[0035] Based on the list of potential threat points, using a data integration tool to preliminarily process the threat distribution and the distribution characteristics, and obtaining at least one data set related to a data dimension from the list of potential threat points;

[0036] Compare the data set with the information integration requirements to obtain a preliminary threat distribution record;

[0037] Checking data dimensions and dimension conflicts in the threat distribution record one by one;

[0038] If the conflict value range exceeds the preset conflict threshold, it is adjusted through the dimensionality reduction processing tool to determine the adjusted dimensional data set;

[0039] Associating and mapping the threat level and risk assessment in the adjusted dimensional data set, and obtaining multi-dimensional information in the mapping process;

[0040] The final comprehensive threat assessment result is obtained based on the multi-dimensional information.

[0041] Preferably, the step of performing hierarchical processing on the threat levels based on the comprehensive threat assessment results to obtain high-priority threat categories and corresponding high-risk area distributions includes:

[0042] Comparing the comprehensive threat assessment result with a preset threat threshold to determine the specific range of the high-priority threat category;

[0043] By using the specific range, the high priority threat category is matched with location information in the network;

[0044] If the distribution characteristics of the location information are inconsistent with the category match, the risk weight of the location information is adjusted to obtain a matching record;

[0045] According to the matching records, determining whether the distribution of the risk weights meets the preset weight conditions, and obtaining the preliminary distribution range of the high-risk areas;

[0046] It then includes: using the preliminary distribution range, using a data integration tool to conduct a secondary verification of the high-risk area; if the correlation between the verification result and the high-risk area is lower than a preset correlation threshold, adjusting the boundary of the distribution range to determine the final high-risk area distribution file.

[0047] Preferably, the evolution trend of potential threats is predicted based on the distribution of high-risk areas, historical warning data, and real-time data streams. If the fluctuation amplitude exceeds a preset amplitude threshold, it is marked as an emergency warning object, and a predicted threat dynamic graph is obtained, including:

[0048] According to the distribution of the high-risk areas, a data integration tool is used to extract the characteristics of the data flow from the historical warning data and the real-time data;

[0049] Compare the features with a preset feature threshold. If the fluctuation range of the data flow exceeds the preset fluctuation threshold, mark it as a potential abnormal point and obtain a preliminary abnormal fluctuation record.

[0050] Obtaining a trend corresponding to the preliminary abnormal fluctuation record, obtaining a change characteristic of the trend, determining whether the change characteristic is consistent with the historical warning data, and determining an evolution direction of the potential threat;

[0051] Associating the evolution direction with the regional markers of high-risk areas, and if the distribution of the regional markers is inconsistent with the predicted trend, adjusting the priority to obtain candidates for emergency warnings;

[0052] Based on the candidate objects of the emergency warning, a distribution view of the dynamic map is generated using a visual drawing tool to obtain a final predicted threat dynamic map.

[0053] Preferably, performing multi-level analysis on the predicted threat dynamic graph to obtain specific characteristics and distribution patterns of the emergency warning objects and generate structured warning information includes:

[0054] According to the analytical level of the predicted threat dynamic graph, a data extraction tool is used to separate the original data of the warning characteristics and distribution patterns;

[0055] Comparing the raw data with a preset data threshold, if the fluctuation of the data exceeds the safety threshold, it is marked as a high-risk object, and a preliminary screening threat list is obtained;

[0056] Using the threat list, a data classification tool is used to divide the high-risk objects into regions, and combining the distribution patterns and association mapping, the high-risk objects are matched with geographical ranges to determine a threat distribution view;

[0057] Based on the threat distribution view, using information integration tools to generate structured warning content, grading the warning content according to priority, and obtaining standardized information packages;

[0058] The information package is pushed to the processing module using a data transmission tool. During dynamic updates, if the level of the threat list exceeds a preset level threshold, the priority is adjusted to obtain a final security warning value. In a second aspect, the present invention provides an intelligent security warning system based on multi-source heterogeneous data, comprising:

[0059] The first acquisition module is used to perform initial acquisition of multi-source data streams to obtain a preliminary data group with a unified format;

[0060] A second acquisition module is configured to map different types of field contents into a preset unified framework based on the format characteristics of the preliminary data set. If the proportion of missing fields exceeds a preset threshold, the data is supplemented using an interpolation algorithm to obtain a mapped unified data matrix.

[0061] a determination module, configured to extract key feature vectors from the unified data matrix, obtain a feature subset including core characteristics, and determine a key feature data set based on the feature subset;

[0062] A third acquisition module is used to construct a hidden correlation network between data points based on the key feature data set, obtain the connection strength between nodes in the network, and remove weak connection edges if the connection strength is lower than a preset strength threshold to obtain an optimized correlation network graph;

[0063] a fourth acquisition module, configured to perform a deep traversal analysis of the associated network graph, obtain the distribution of potential threat points between nodes, determine the threat level, and obtain node location information corresponding to the threat level to obtain a list of potential threat points;

[0064] a fifth acquisition module, configured to obtain dimensional conflicts based on the potential threat point list, and if the conflict value exceeds a preset conflict threshold, eliminate the conflict through dimensionality reduction processing to obtain a comprehensive threat assessment result;

[0065] a sixth acquisition module, configured to perform hierarchical processing on the threat levels according to the comprehensive threat assessment result, and obtain high-priority threat categories and corresponding high-risk area distributions therefrom;

[0066] a seventh acquisition module, configured to predict the evolution trend of potential threats based on the distribution of high-risk areas, historical warning data, and real-time data streams; and to mark an emergency warning target if the fluctuation amplitude exceeds a preset amplitude threshold, thereby obtaining a predicted threat dynamic map;

[0067] The generation module is used to perform multi-level analysis on the predicted threat dynamic graph, obtain the specific characteristics and distribution patterns of the emergency warning objects, and generate structured warning information.

[0068] In a third aspect, the present invention also provides an electronic device comprising a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements any one of the above-mentioned intelligent security warning methods based on multi-source heterogeneous data.

[0069] In a fourth aspect, the present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute any one of the above-mentioned intelligent security warning methods based on multi-source heterogeneous data.

[0070] Compared to existing technologies, the present invention provides a security threat early warning method and system based on multi-source data. This method collects, standardizes, and maps multi-source data, constructs a unified data matrix, extracts key features, and establishes a correlation network diagram. It analyzes the distribution of potential threat points, conducts comprehensive threat assessment and classification, and combines historical data to predict threat evolution trends, ultimately generating structured early warning information. The present invention can effectively integrate multi-dimensional data, mine hidden associations between data, and achieve accurate identification, dynamic assessment, and trend prediction of security threats, providing a reliable basis for security early warning decision-making and improving the accuracy and timeliness of security warnings. The present invention has important application value in security situation awareness and early warning in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0071] Figure 1 This is a flow chart of an intelligent security early warning method based on multi-source heterogeneous data provided by the first embodiment of the present invention;

[0072] Figure 2 This is a schematic diagram of the structure of an intelligent safety early warning system based on multi-source heterogeneous data provided by the second embodiment of the present invention. DETAILED DESCRIPTION

[0073] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0074] Reference Figure 1The first embodiment of the present invention provides a flowchart of an intelligent security early warning method based on multi-source heterogeneous data, including the following steps:

[0075] S11, performing initial collection of multi-source data streams to obtain a preliminary data set with a unified format;

[0076] S12, mapping different types of field contents into a preset unified framework based on the format characteristics of the preliminary data set; if the missing field ratio exceeds a preset ratio threshold, performing data completion using an interpolation algorithm to obtain a mapped unified data matrix;

[0077] S13, extracting key feature vectors from the unified data matrix to obtain a feature subset including core characteristics, and determining a key feature data set based on the feature subset;

[0078] S14, constructing a hidden correlation network between data points based on the key feature data set, obtaining the connection strength between nodes in the network, and removing weak connection edges if the connection strength is lower than a preset strength threshold, to obtain an optimized correlation network graph;

[0079] S15, performing a deep traversal analysis on the associated network graph to obtain the distribution of potential threat points between nodes, determine the threat level, and obtain node location information corresponding to the threat level to obtain a list of potential threat points;

[0080] S16, obtaining dimension conflicts based on the potential threat point list, and if the conflict value exceeds a preset conflict threshold, eliminating the conflict through dimensionality reduction processing to obtain a comprehensive threat assessment result;

[0081] S17, based on the comprehensive threat assessment result, the threat levels are hierarchically processed to obtain high-priority threat categories and corresponding high-risk area distributions;

[0082] S18, predicting the evolution trend of potential threats based on the distribution of high-risk areas, historical warning data, and real-time data streams. If the fluctuation amplitude exceeds a preset amplitude threshold, it is marked as an emergency warning object, and a predicted threat dynamic map is obtained;

[0083] S19, performing multi-level analysis on the predicted threat dynamic graph, obtaining specific characteristics and distribution patterns of the emergency warning objects, and generating structured warning information.

[0084] In step S11, the multi-source data streams are initially collected to obtain a preliminary data set with a unified format.

[0085] Initially collecting multi-source data streams, the format of structured and unstructured data from different sources is normalized using pre-established standardized protocols. Key fields are extracted from the multi-source data streams to obtain a preliminarily organized data set, resulting in a uniformly formatted preliminary data set. Based on the content of the preliminary data set, potential missing values ​​and outliers are screened using preset thresholds. If a field value in the preliminary data set is detected to exceed the threshold, the data is marked and isolated, and a marked data subset is obtained to confirm that the outlier data has been identified. The data subset is processed and repaired using a common data cleaning tool. The repaired data subset is then merged with the unmarked data to obtain a complete and consistent data set, and the data set is determined to meet standardization requirements. Based on the storage requirements of the data set, the data set is categorized and stored using a distributed database indexing mechanism. Storage nodes are allocated to different data types, and access paths are obtained from these storage nodes to ensure that the data set can be quickly located.

[0086] For example, during the initial collection of multi-source data streams, consider an internal enterprise data integration scenario involving diverse data from a sales system, a customer feedback platform, and an inventory management system. The sales system provides structured sales records, while unstructured customer feedback is stored in text form, and inventory data may contain image information. For this diverse data source, format normalization is performed using pre-established standardized protocols. This involves unifying the tabular data of sales records into a standard field format, extracting key sentiment terms from text feedback, and annotating image data with recognizable inventory status descriptions, ultimately forming a preliminary, organized data set.

[0087] Specifically, for possible missing values ​​and outliers in the preliminary data set, for example, if the sales field of a certain record in the sales data is empty, or if a customer's feedback score is abnormally 10 times higher than the average, the preset threshold can be set so that the sales field cannot be empty and the score range is between 1 and 5. After detecting data that exceeds the threshold, it is marked and isolated. For example, records with abnormal scores are marked as "pending verification", thereby obtaining a marked data subset and ensuring that abnormal data is effectively distinguished.

[0088] In one embodiment, common data cleaning tools can be used to repair labeled anomaly data. For example, missing sales figures can be filled using historical data averages, or anomaly scores can be corrected through contextual analysis. The repaired data subset is then merged with the unlabeled data to form a complete and consistent data set. Compliance with standardization requirements can be assessed by checking for consistent field formats and data integrity, ensuring smooth subsequent analysis.

[0089] For example, to address the storage needs of a data set, a distributed database indexing mechanism is employed to categorize and store sales data, customer feedback, and inventory data on different nodes. Sales data might be assigned to a high-frequency access node, customer feedback to a text analysis optimization node, and inventory data to an image processing node. Access paths are obtained through storage nodes, ensuring that data sets can be quickly located. For example, when querying sales data through an index, response times are reduced to milliseconds, significantly improving efficiency.

[0090] It's important to note that the implementation methods for each of the aforementioned technical topics revolve around the core goal of data integration and processing, forming a complete chain from collection to cleaning to storage. Format standardization ensures data consistency, outlier processing improves data quality, and distributed storage optimizes access speed. These steps mutually support each other, jointly ensuring high data availability and reliability, ultimately providing precise support for enterprise decision-making and delivering significant technical results and business value.

[0091] In step S12, based on the format characteristics of the preliminary data group, different types of field contents are mapped to a preset unified framework. If the field missing ratio exceeds a preset ratio threshold, data is completed through an interpolation algorithm to obtain a mapped unified data matrix.

[0092] According to the format characteristics of the preliminary data group, different types of field contents are mapped to a preset unified framework. If the missing field ratio exceeds a preset ratio threshold, data is supplemented by an interpolation algorithm to obtain a mapped unified data matrix, including:

[0093] According to the field characteristics of the preliminary data group, a pre-established mapping rule table is used to map different types of field contents one by one to a preset unified framework;

[0094] Redundant content is cleaned up using a deduplication tool to obtain a deduplicated field set, and the field set is confirmed to have completed preliminary format conversion;

[0095] Calculating the missing proportion of the field using statistical tools for the missing conditions in the field set;

[0096] If the missing value ratio exceeds a preset ratio threshold, the missing content is supplemented with data through an interpolation calculation tool to obtain a supplemented field dataset;

[0097] Based on the completed field data set, a data integration tool is used to reorganize the field content according to a preset matrix structure, and a format verification tool is used to correct format inconsistencies to obtain a reorganized unified data matrix;

[0098] The unified data matrix is ​​compressed using a storage optimization tool, and the matrix is ​​saved in blocks to distributed storage nodes by allocating storage paths. The unified data matrix is ​​judged to complete the final integration based on the access identifier of the node.

[0099] For example, in an internal data integration scenario within an enterprise, when processing the field characteristics of the preliminary data group, you can imagine a business environment focused on sales data analysis. Suppose the enterprise needs to integrate data from sales reports in different regions. These data fields include sales date, product category, sales quantity, etc., but the field naming and format of each region are different. To address this situation, a pre-established mapping rule table is used to uniformly map differently named fields such as "SaleDate" and "Sales Date" to the standard field "Date", and at the same time, the content of the product category field is standardized into a unified encoding format. For redundant content that appears in the mapping, such as the same product category being entered multiple times with different names, a deduplication tool is used to clean up duplicate items, and finally a deduplicated field set is obtained to ensure that the field content is concise and consistent.

[0100] For example, when dealing with missing values ​​in a field set, suppose the sales quantity field is empty in some records. Using statistical tools to calculate the missing percentage, it was found that the missing percentage for a certain region reached 20%, exceeding the preset threshold of 10%. To address this, interpolation tools were used to fill in the missing values ​​based on historical sales trends. For example, missing sales quantities for a particular day could be estimated as the average of the two preceding and following days, forming a completed field dataset. When assessing dataset completeness, the missing percentage was checked to ensure that it fell below the threshold, ensuring data availability and laying the foundation for subsequent analysis.

[0101] For example, when reorganizing field datasets, suppose the data needs to be organized into a pre-set matrix structure with dates as rows and product categories as columns. A data integration tool is used to reorganize the field contents into a matrix format. A format validation tool is then used to correct inconsistent date formats, such as unifying "2023-01-01" and "01 / 01 / 2023" into a standard format. This results in a unified reorganized data matrix. Once the matrix meets business adaptation standards, field alignment is ensured during data analysis, preventing misinterpretations due to formatting issues.

[0102] For example, during the final integration phase, storage optimization tools are used to compress the unified data matrix to reduce storage space usage. By assigning storage paths, the matrix is ​​stored in blocks across distributed storage nodes. For example, frequently accessed recent sales data is stored on fast nodes, while other data is stored on standard nodes. After obtaining access identifiers from the nodes, the matrix is ​​determined to be fully integrated, ensuring a clear data storage structure for quick access.

[0103] It's important to note that the implementation methods for each of the aforementioned technical topics closely align around the core goal of data integration, forming a complete process from field mapping to missing data completion, matrix reorganization, and storage optimization. Field mapping and deduplication ensure data consistency, missing data completion improves data integrity, matrix reorganization ensures business adaptability, and storage optimization facilitates data access. These steps support each other, collectively improving data processing efficiency and reliability.

[0104] In step S13, key feature vectors are extracted from the unified data matrix to obtain a feature subset including core characteristics, and a key feature data set is determined based on the feature subset.

[0105] The step of extracting key feature vectors from the unified data matrix to obtain a feature subset including core characteristics, and determining a key feature data set based on the feature subset, includes:

[0106] Based on the multi-dimensional features in the unified data matrix, a data dimensionality reduction tool is used to perform preliminary processing on the high-dimensional features to obtain an initial feature vector group representative of the data;

[0107] Using a feature screening tool to sort the initial feature vector group by importance, and obtain a selected feature subset including core features;

[0108] Using a data compression tool to perform structural optimization processing on the selected feature subset to obtain a compressed feature subset;

[0109] An association rule mining tool is used to perform relationship analysis on the characteristics in the compressed feature subset to obtain the key feature data set.

[0110] For example, in the context of internal sales data analysis, when processing the multidimensional features contained in a unified data matrix, imagine a scenario focused on analyzing regional sales trends. Suppose the company needs to extract key information from multidimensional data to support decision-making. The data matrix contains multiple dimensional features such as sales date, regional distribution, product type, and sales amount.

[0111] For example, when applying data dimensionality reduction tools, one can first understand the principle: Dimensionality reduction aims to reduce data dimensions while retaining key information. In one possible implementation, suppose the original data matrix contains 10 dimensional features. After processing with the dimensionality reduction tool, three initial feature vector groups representing the key information are extracted. These vector groups may focus on core dimensions such as sales amount, regional distribution, and product type. When determining whether these vector groups meet the format requirements for subsequent processing, the data structure can be checked for compatibility with business analysis tools to ensure smooth subsequent steps.

[0112] For example, when using a feature screening tool, when ranking the initial set of feature vectors by importance, one could imagine analyzing the impact of each feature on sales trends, ranking sales amount first, followed by regional distribution, and finally product type, thereby forming a selected feature subset. To determine whether this subset meets the business adaptation criteria, one can check whether it covers core business requirements, such as whether it reflects regional sales differences.

[0113] For example, for the structural optimization processing of data compression tools, its role can be understood from the changes in data volume before and after compression.

[0114] In one possible implementation, assuming the data size of a selected feature subset is large, a compression tool can be used to reduce the data storage requirements from 100MB to 30MB, forming a compressed feature subset. When selecting this feature subset as the basis for association mining input, it can be verified whether it retains key information, ensuring the accuracy of subsequent analysis.

[0115] For example, when applying association rule mining tools, performing relationship analysis on a compressed feature subset, one could potentially uncover correlations between regional distribution and sales figures. For example, discovering consistently high sales figures for a certain product type in a certain region would form a key dataset. When determining whether this dataset meets business adaptation requirements, one could examine whether it can provide a basis for adjusting sales strategies.

[0116] In one possible implementation, the methods for implementing each of the aforementioned topics closely align around the core objective of sales data analysis, forming a complete process from dimensionality reduction to screening, compression, and association mining. Dimensionality reduction reduces the data processing burden, screening focuses on core information, compression optimizes storage structures, and association mining reveals hidden relationships. These steps mutually support each other, providing a solid data foundation for business decision-making.

[0117] In step S14, based on the key feature data set, a hidden correlation network between data points is constructed to obtain the connection strength between nodes in the network. If the connection strength is lower than a preset strength threshold, weak connection edges are removed to obtain an optimized correlation network graph.

[0118] The method constructs a hidden correlation network between data points based on the key feature data set, obtains the connection strength between nodes in the network, and removes weak connection edges if the connection strength is lower than a preset strength threshold to obtain an optimized correlation network graph, including:

[0119] Using a graph processing tool to comprehensively traverse the nodes and edges in the hidden association network to obtain an initial mapping relationship between node distribution and marginal weight;

[0120] The weight comparison tool is used to analyze the marginal weights one by one. If the connection strength of one edge is lower than the preset strength threshold, the weak connection edge is removed to obtain the filtered network structure;

[0121] Performing structural adjustments on the nodes and remaining edges in the screened network structure to obtain an optimized association graph;

[0122] Afterwards, the method further includes: saving the nodes and connection strengths in the optimized association graph to obtain a final association network graph.

[0123] For example, in the business environment of internal sales data analysis, the construction and optimization of hidden correlation networks can be explored from multiple perspectives to explore how to use graph processing tools and related methods to explore the potential relationships between data points. Regarding the processing of network nodes and connection strength, suppose that an enterprise needs to analyze the collaborative relationship between sales teams in different regions. The data contains information such as the frequency of communication between teams and sales performance contributions. By using graph processing tools to comprehensively traverse the nodes and edges, a preliminary network diagram containing all team nodes and connection relationships can be constructed. The nodes represent each sales team, and the edges represent the intensity of collaboration between teams. The initial mapping relationship may reveal that some teams are more closely connected, while others are more distant.

[0124] For example, the application of weight comparison tools.

[0125] In one possible implementation, assuming the threshold for connection strength is set at five communications per week, if two teams only communicate twice per week, this edge is considered a weak connection and removed. This filtered network structure retains only highly collaborative relationships, such as two regional teams that retain a connection due to frequent sharing of sales strategies. This filtering helps focus on the core collaborative relationships that truly impact sales results.

[0126] For example, in the case of structural adjustments to graph layout tools, one could imagine adjusting the node distribution to place frequently collaborating team nodes at the center of the relationship graph, while less frequently connected teams are distributed on the periphery. Imagine, after this adjustment, a core team node is placed in the center of the graph, surrounded by three closely connected regional team nodes. This optimized relationship graph would intuitively reflect the strength of collaborative relationships, making it easier for companies to identify key teams and potential areas of collaboration weaknesses.

[0127] For example, in the application of storage processing tools, suppose the optimized association graph contains 50 nodes and 80 edges. This is persisted to form the final association network diagram. To determine whether it meets business adaptation requirements, the diagram can be checked to see whether it covers key regional teams of the enterprise's interest and whether it can provide a reference for optimizing resource allocation. The final network diagram may reveal the pivotal role of a regional team in the collaborative network, providing data support for the enterprise to adjust its team management strategy.

[0128] For example, the implementation methods for each of the aforementioned topics can be understood in principle: the graph processing tool's traversal aims to fully understand the network structure, the weight comparison tool's screening aims to streamline invalid information, the graph layout tool's adjustment aims to enhance visualization, and the storage processing tool's persistence ensures long-term data usability. These methods are interconnected, collectively constructing a clear and practical relational network diagram, providing a powerful tool for internal sales collaboration analysis.

[0129] In step S15, the association network graph is deeply traversed and analyzed to obtain the distribution of potential threat points between nodes, determine the threat level, and obtain node location information corresponding to the threat level to obtain a list of potential threat points.

[0130] According to the node distribution of the associated network graph, a graph traversal tool is used to perform a comprehensive scan of the network graph, from which the initial mapping relationship between the traversal path and the node position is obtained, and it is determined that the initial mapping relationship can reflect the distribution characteristics of potential threats. With respect to the initial mapping relationship, a weighted calculation tool is used to compare the high-risk nodes one by one. If the association strength of a node is lower than a preset threshold, it is marked as a low-risk category to obtain the screened high-risk node set. Through the screened high-risk node set, a position mapping tool is used to associate the node position with the threat level to obtain a detailed distribution record of the potential threat, and it is determined that the detailed distribution record can reflect the focus of the risk assessment. Based on the detailed distribution record, a data storage tool is used to organize and persistently store the potential threat list, and the final threat point file is obtained by combining the threat level and the position mapping information, and it is determined that the threat point file can reflect the risk distribution in the business.

[0131] For example, in the business environment of internal sales data analysis, for the node distribution of associated network graphs and the identification of potential threats, we can deeply explore from multiple perspectives how to use relevant tools to mine risk distribution characteristics. Regarding the application of graph traversal tools, assuming that the company needs to analyze the possible uneven resource allocation or collaboration risks in the sales team, the graph traversal tool can be used to comprehensively scan the position and path relationship of each node in the network graph. In principle, the traversal tool records the initial mapping relationship between teams by visiting nodes one by one, such as whether a team node is at the edge of the network and whether it has few connections with other nodes. This mapping can preliminarily reflect the distribution characteristics of potential threats, such as the risk of isolation of edge nodes.

[0132] For example, the comparison process for weighted calculation tools.

[0133] In one possible implementation, suppose an enterprise sets a threshold for correlation strength at a 60% weekly collaborative task completion rate. If a team node's collaboration completion rate with other nodes is only 30%, it is labeled as low-risk. By comparing each node individually, a collection of high-risk nodes can be identified. For example, a team identified as high-risk due to chronically inefficient collaboration could be identified. This screening helps focus on core problem areas and provides accurate data support for subsequent risk assessments.

[0134] For example, within the location mapping tool, it's possible to link node locations to threat levels to generate detailed distribution records. For example, if a core team node is located at the center of the network diagram but has a high threat level, this could be due to excessive resource concentration, leading to over-dependence on other teams. This distribution record can intuitively highlight key risk assessment priorities, such as which teams require resource adjustments and which areas may have management gaps. The benefit of this correlation is that companies can more clearly understand the distribution patterns of risk.

[0135] For example, regarding the organization and persistent storage of data storage tools, suppose the final threat point archive contains 20 high-risk nodes and related threat level information. This list is organized into a list using storage tools and combined with location mapping information to form a complete archive record. This archive can reflect the distribution of risks within the business, such as when a regional team is designated as a key concern due to insufficient collaboration. The benefit of persistent storage is that enterprises can track risk changes over time and adjust management strategies in a timely manner.

[0136] For example, from a business context, the distribution of risks within a sales team often stems from uneven resource allocation or poor communication. By applying these tools, we can gradually identify potential threats by starting with the node distribution in the network diagram, ultimately creating an actionable profile. This approach not only improves the comprehensiveness of risk identification but also provides data support for optimizing team collaboration. In particular, during the screening of high-risk node clusters and the generation of distribution records, the application of these tools can help companies quickly identify the root causes of problems, enabling them to develop targeted measures and significantly improve management efficiency.

[0137] In step S16, dimensional conflicts are obtained according to the potential threat point list. If the conflict value exceeds a preset conflict threshold, the conflict is eliminated through dimensionality reduction processing to obtain a comprehensive threat assessment result.

[0138] The dimensional conflict is obtained according to the potential threat point list. If the conflict value exceeds a preset conflict threshold, the conflict is eliminated through dimensionality reduction processing to obtain a comprehensive threat assessment result, including:

[0139] Based on the list of potential threat points, using a data integration tool to preliminarily process the threat distribution and the distribution characteristics, and obtaining at least one data set related to a data dimension from the list of potential threat points;

[0140] Compare the data set with the information integration requirements to obtain a preliminary threat distribution record;

[0141] Checking data dimensions and dimension conflicts in the threat distribution record one by one;

[0142] If the conflict value range exceeds the preset conflict threshold, it is adjusted through the dimensionality reduction processing tool to determine the adjusted dimensional data set;

[0143] Associating and mapping the threat level and risk assessment in the adjusted dimensional data set, and obtaining multi-dimensional information in the mapping process;

[0144] The final comprehensive threat assessment result is obtained based on the multi-dimensional information.

[0145] For example, in a risk analysis scenario involving an enterprise's sales team, the list of potential threat points can be processed from multiple perspectives to explore how to integrate and analyze threat distribution data. For example, in the application of data integration tools, suppose an enterprise has a list of 30 threat points related to the sales team's collaboration efficiency and resource allocation. The data integration tool can initially categorize these threat points by region, team size, and other factors, extracting data sets related to collaboration efficiency. For example, the collaborative task completion rate of a team in a certain region is only 40%, significantly below average. This preliminary analysis helps quickly focus on problem areas and lays the foundation for subsequent analysis.

[0146] For example, a dimension comparison tool can be used to verify data dimension conflicts within the initially organized threat distribution records. For example, an enterprise may discover that a team's collaboration efficiency data and resource allocation data are statistically inconsistent, resulting in a conflicting range of 25%, exceeding the preset threshold of 10%. In this case, the dimensionality reduction tool adjusts the conflicting range, unifying the inconsistent data dimensions to a common statistical standard, such as using monthly data as a benchmark, to generate an adjusted dimensional dataset. This adjustment ensures data accuracy and consistency, providing a reliable basis for subsequent evaluation.

[0147] For example, in the application of comprehensive analysis tools, adjusted dimensional data sets can be used to correlate threat levels with risk assessments. For example, suppose a core sales team's threat level is rated high risk due to excessive resource concentration, which impacts the normal operations of other teams. The comprehensive analysis tool will combine multiple dimensions of information, such as team location and collaboration frequency, to determine whether the assessment meets the requirements. If the assessment results are found to be inconsistent with the actual distribution characteristics, the mapping logic will be further adjusted to ensure the comprehensiveness of the analysis. This correlation mapping can help companies more accurately identify the root causes of risk.

[0148] For example, during the final integration of data storage tools, if the assessment results indicate that five teams are designated as high-risk areas, the data storage tool will compare these results with threat distribution characteristics to create a comprehensive threat assessment profile. For example, if a team is flagged as a key concern due to chronic poor communication, the profile will detail its location and risk level. This integration not only facilitates the company's long-term tracking of risk changes but also provides intuitive basis for management decision-making, significantly improving the efficiency of risk management.

[0149] For example, from a business perspective, sales team risks often stem from uneven resource allocation or information asymmetry. By leveraging these tools, companies can begin with a threat inventory, gradually analyze risk distribution patterns, and ultimately create an actionable profile. This approach not only ensures data consistency but also reveals potential issues from a multi-dimensional perspective, providing strong support for optimizing team management.

[0150] In step S17, the threat levels are hierarchically processed according to the comprehensive threat assessment result, thereby obtaining high-priority threat categories and corresponding high-risk area distributions.

[0151] The threat levels are hierarchically processed based on the comprehensive threat assessment results to obtain high-priority threat categories and corresponding high-risk area distributions, including:

[0152] Comparing the comprehensive threat assessment result with a preset threat threshold to determine the specific range of the high-priority threat category;

[0153] By using the specific range, the high priority threat category is matched with location information in the network;

[0154] If the distribution characteristics of the location information are inconsistent with the category match, the risk weight of the location information is adjusted to obtain a matching record;

[0155] According to the matching records, determining whether the distribution of the risk weights meets the preset weight conditions, and obtaining the preliminary distribution range of the high-risk areas;

[0156] It then includes: using the preliminary distribution range, using a data integration tool to conduct a secondary verification of the high-risk area; if the correlation between the verification result and the high-risk area is lower than a preset correlation threshold, adjusting the boundary of the distribution range to determine the final high-risk area distribution file.

[0157] For example, in a risk management scenario for an enterprise's internal sales team, a data classification tool can be used to categorize threat levels based on the tiered processing of comprehensive threat assessment results. Suppose the assessment results reveal 10 threat points, three of which are marked as high priority, indicating uneven resource allocation within the sales team. The data classification tool compares these three threat points against pre-set thresholds to determine the scope of the high-priority threat category. For example, areas with a threat level exceeding 80% are designated as key areas of concern. This tiered approach helps quickly identify core problem areas.

[0158] For example, using a location mapping tool can further clarify the threat's origin within the scope of a high-priority threat category. For example, suppose a sales team is categorized as high-priority, but their location information shows they are spread across multiple regions, inconsistent with the category. The location mapping tool adjusts the weight of these locations, for example, reducing the weight of marginal areas to 20%, thereby generating more realistic matching records. This adjustment ensures precise localization of the threat's origin.

[0159] For example, when using the regionalization tool, comprehensive comparison of risk areas based on matching records is particularly important. If a region's threat points are concentrated in the team collaboration phase, the regionalization tool will verify whether their distribution characteristics are consistent with pre-defined criteria, such as whether the risk weight exceeds 50%. If a sub-region's weight is only 30%, it is determined not to meet the high-risk criteria, and the initial distribution range will be reduced accordingly. This verification mechanism provides a reliable basis for subsequent analysis.

[0160] For example, using data integration tools to revalidate preliminary distribution ranges can further refine the definition of high-risk areas. For example, suppose the preliminary distribution range for a region covers five teams, but validation results show that two of these teams have a correlation with the risk area below a preset threshold of 40%. The data integration tool adjusts the distribution range boundaries, removing teams with low correlations, and ultimately creating a high-risk area distribution profile. This revalidation effectively improves the accuracy of the distribution profile.

[0161] For example, from a business perspective, sales team risks often stem from poor communication or imbalanced resource allocation. By applying the aforementioned tools, companies can begin with threat assessment results, gradually refine the distribution characteristics of risk areas, and ultimately create a reference profile. This approach not only ensures analytical accuracy but also provides management with a clear risk picture, facilitating the development of targeted strategies.

[0162] For example, for each technical theme, the data classification tool can perform tiered processing based on threat level percentages, the location mapping tool can achieve precise mapping through weight adjustment, the regionalization tool focuses on verifying distribution characteristics, and the data integration tool ensures the reliability of results through boundary adjustment. These methods support each other, forming a complete logical chain from assessment to archiving, providing strong support for enterprise risk management.

[0163] In step S18, the evolution trend of potential threats is predicted based on the distribution of high-risk areas, historical warning data and real-time data streams. If the fluctuation amplitude exceeds the preset amplitude threshold, it is marked as an emergency warning object and a predicted threat dynamic map is obtained.

[0164] The evolution trend of potential threats is predicted based on the distribution of high-risk areas, historical warning data, and real-time data streams. If the fluctuation amplitude exceeds a preset amplitude threshold, it is marked as an emergency warning object, and a predicted threat dynamic map is obtained, including:

[0165] According to the distribution of the high-risk areas, a data integration tool is used to extract the characteristics of the data flow from the historical warning data and the real-time data;

[0166] Compare the features with a preset feature threshold. If the fluctuation range of the data flow exceeds the preset fluctuation threshold, mark it as a potential abnormal point and obtain a preliminary abnormal fluctuation record.

[0167] Obtaining a trend corresponding to the preliminary abnormal fluctuation record, obtaining a change characteristic of the trend, determining whether the change characteristic is consistent with the historical warning data, and determining an evolution direction of the potential threat;

[0168] Associating the evolution direction with the regional markers of high-risk areas, and if the distribution of the regional markers is inconsistent with the predicted trend, adjusting the priority to obtain candidates for emergency warnings;

[0169] Based on the candidate objects of the emergency warning, a distribution view of the dynamic map is generated using a visual drawing tool to obtain a final predicted threat dynamic map.

[0170] For example, in a risk management scenario for an enterprise's internal sales team, data integration tools can extract data flow characteristics from historical warnings and real-time data to identify the distribution of high-risk areas. The data integration tool first analyzes data flow patterns over a period of time, such as whether a sales team's resource request frequency is abnormal within a specific time period. By comparing this with a preset threshold (assuming the threshold is set to fluctuate no more than 30%), if a team's data request frequency fluctuates by 45%, it is marked as a potential anomaly, forming a preliminary record of abnormal fluctuations. This approach helps quickly identify potential problematic links.

[0171] For example, using a time series decomposition tool can further break down data trends based on initial abnormal fluctuation records. For example, if a team's abnormal fluctuation records show a significant increase in resource requests every Monday, the tool will extract this cyclical variation and compare it with historical warning patterns. If the historical patterns also show a high incidence on Mondays, the potential threat may be exhibiting a cyclical evolution. This analysis provides a temporal reference for subsequent warnings.

[0172] For example, by analyzing the evolution of potential threats, data mapping tools can correlate these threats with markers for high-risk regions. For example, if a team is predicted to face a recurrent threat, but its regional marker indicates it is located in a low-risk area, the tool will lower the priority weight of that area, for example, from 50% to 20%, thereby selecting urgent alert candidates that better align with the predicted trend. This correlation mechanism ensures the rational allocation of resources.

[0173] For example, the use of visualization tools is particularly important when generating the final predicted threat dynamic map. For emergency warning candidates, the tool creates a dynamic map based on regional markers and threat directions. For example, if the threat trend in a high-risk area is concentrated on the map, management can intuitively identify key areas of concern. This visual representation provides visual support for rapid decision-making and facilitates cross-departmental communication.

[0174] For example, from a business context, abnormal data flows within sales teams often stem from untimely resource allocation or delayed information feedback. By integrating these tools, companies can establish a complete risk monitoring chain, from data feature extraction to dynamic graph generation. This approach not only improves early warning efficiency but also provides management with an intuitive view of risk distribution, facilitating the development of precise response measures.

[0175] For example, for each technical theme's specific implementation, the data integration tool can flag anomalies based on percentage fluctuations, the time series decomposition tool can identify threat evolution by extracting periodic features, the data mapping tool optimizes priorities by adjusting weights, and the visualization tool presents results intuitively through dynamic graphs. These methods mutually support each other, forming a complete logical chain from data extraction to visual presentation, providing strong support for enterprise risk management.

[0176] In step S19, the predicted threat dynamic graph is subjected to multi-level analysis to obtain the specific characteristics and distribution patterns of the emergency warning objects and generate structured warning information.

[0177] The multi-level analysis of the predicted threat dynamic graph to obtain the specific characteristics and distribution patterns of the emergency warning objects and generate structured warning information includes:

[0178] According to the analytical level of the predicted threat dynamic graph, a data extraction tool is used to separate the original data of the warning characteristics and distribution patterns;

[0179] Comparing the raw data with a preset data threshold, if the fluctuation of the data exceeds the safety threshold, it is marked as a high-risk object, and a preliminary screening threat list is obtained;

[0180] Using the threat list, a data classification tool is used to divide the high-risk objects into regions, and combining the distribution patterns and association mapping, the high-risk objects are matched with geographical ranges to determine a threat distribution view;

[0181] Based on the threat distribution view, using information integration tools to generate structured warning content, grading the warning content according to priority, and obtaining standardized information packages;

[0182] The information package is pushed to the processing module using a data transmission tool. During dynamic updating, if the level of the threat list exceeds a preset level threshold, the priority is adjusted to obtain a final security warning value.

[0183] For example, in the risk management scenario of an enterprise's internal sales team, the analysis of the predictive threat dynamic graph can be used to deeply explore the implementation methods of data processing and early warning mechanisms from multiple perspectives, especially in the context of the distribution of high-risk areas, combining historical early warning data and real-time data streams, focusing on how to extract key information from the graph and form effective early warning content.

[0184] For example, the application of data extraction tools focuses on separating raw data related to warning features and distribution patterns from the predicted threat dynamics graph. Conceptually, such tools primarily extract potential anomalies and distribution patterns by reverse-analyzing the visual data within the graph.

[0185] In one possible implementation, suppose a sales team's region's map shows an unusually high frequency of resource requests within a specific time period. The data extraction tool records this characteristic as raw data and compares it against a pre-set security threshold. If the threshold is set to a fluctuation of no more than 25%, and the actual data fluctuates by 40%, the region is marked as high-risk and included in the initial threat screening list. This approach quickly identifies potential problem areas and lays the foundation for subsequent analysis.

[0186] For example, the purpose of using data classification tools is to divide high-risk objects into regions and match them to geographical ranges. In principle, the tool uses distribution patterns and association mapping to match objects in the threat list with specific regions.

[0187] In one possible implementation, suppose data from a high-risk target shows that its abnormal activity is concentrated in a city's core sales area. Based on historical distribution patterns, the tool matches this activity with the region's high-risk markers to create a threat distribution map. This map intuitively reflects the geographic concentration of threats, helping to accurately allocate resources.

[0188] For example, the implementation of an information integration tool aims to transform the threat distribution view into structured warning content. Conceptually, the tool prioritizes the data in the view and processes it in a hierarchical manner using a push mechanism.

[0189] In one possible implementation, if a threat distribution map for a region shows a high concentration of high-risk targets with frequent fluctuations, the tool would generate alerts with specific time, location, and threat level, sorted by priority from high to low, and form standardized information packages. This structured approach facilitates the rapid dissemination of critical information and improves response efficiency.

[0190] For example, a data transmission tool's task is to push information packets to a processing module and adjust priorities during dynamic updates. In principle, this tool must ensure real-time and accurate data transmission.

[0191] In one possible implementation, if a high-risk object in the threat list rises from level 2 to level 1, exceeding a preset threshold, the tool automatically adjusts its priority, prioritizes the relevant information packets sent to the processing module, and updates the final security warning value. This dynamic adjustment mechanism ensures that urgent situations receive timely attention and avoids delays in handling.

[0192] For example, from a business perspective, sales team risks often stem from uneven regional resource allocation or poor information flow. By integrating these tools, a complete risk monitoring chain, from data extraction to information delivery, is formed. This approach not only improves the accuracy of early warnings but also provides management with a clear basis for decision-making, effectively mitigating potential losses.

[0193] The above only lists some preferred embodiments of the present invention, but the present invention is not limited thereto, and many improvements and modifications can be made. As long as the improvements and modifications are made on the basis of the basic principles of the present invention, they should be considered to fall within the scope of protection of the present invention.

[0194] In summary, the present invention discloses a security threat warning method based on multi-source data, which collects, standardizes and maps multi-source data, constructs a unified data matrix, extracts key features and establishes an associated network diagram, analyzes the distribution of potential threat points, conducts comprehensive threat assessment and classification, combines historical data to predict threat evolution trends, and finally generates structured warning information. The present invention can effectively integrate multi-dimensional data, mine hidden associations between data, and achieve accurate identification, dynamic assessment and trend prediction of security threats, providing a reliable basis for security warning decision-making, and improving the accuracy and timeliness of security warnings. The present invention has important application value in security situation awareness and warning in complex environments.

[0195] Reference Figure 2 The second embodiment of the present invention provides a structure diagram of an intelligent safety early warning system based on multi-source heterogeneous data, including:

[0196] The first acquisition module 201 is used to initially collect multi-source data streams to obtain a preliminary data set with a unified format;

[0197] The second acquisition module 202 is configured to map different types of field contents into a preset unified framework based on the format characteristics of the preliminary data set. If the percentage of missing fields exceeds a preset threshold, the data is supplemented using an interpolation algorithm to obtain a mapped unified data matrix.

[0198] A determination module 203 is configured to extract key feature vectors from the unified data matrix to obtain a feature subset including core characteristics, and determine a key feature data set based on the feature subset;

[0199] The third acquisition module 204 is used to construct a hidden correlation network between data points based on the key feature data set, obtain the connection strength between nodes in the network, and remove weak connection edges if the connection strength is lower than a preset strength threshold to obtain an optimized correlation network graph;

[0200] The fourth acquisition module 205 is used to perform a deep traversal analysis on the associated network graph to obtain the distribution of potential threat points between nodes, determine the threat level, and obtain node location information corresponding to the threat level to obtain a list of potential threat points;

[0201] A fifth acquisition module 206 is configured to obtain dimensional conflicts based on the potential threat point list, and if the conflict value exceeds a preset conflict threshold, eliminate the conflict through dimensionality reduction processing to obtain a comprehensive threat assessment result;

[0202] A sixth acquisition module 207 is configured to perform hierarchical processing on the threat levels according to the comprehensive threat assessment result, and obtain high-priority threat categories and corresponding high-risk area distributions therefrom;

[0203] The seventh acquisition module 208 is configured to predict the evolution trend of potential threats based on the distribution of high-risk areas, historical warning data, and real-time data streams. If the fluctuation amplitude exceeds a preset amplitude threshold, it is marked as an emergency warning object, and a predicted threat dynamic map is obtained;

[0204] The generation module 209 is used to perform multi-level analysis on the predicted threat dynamic graph, obtain the specific characteristics and distribution patterns of the emergency warning objects, and generate structured warning information.

[0205] It should be noted that the intelligent security warning system based on multi-source heterogeneous data provided in an embodiment of the present invention is used to execute all the process steps of the intelligent security warning method based on multi-source heterogeneous data in the above embodiment. The working principles and beneficial effects of the two correspond one to one, so they will not be repeated here.

[0206] An embodiment of the present invention further provides an electronic device. The electronic device includes: a processor, a memory, and a computer program stored in the memory and executable on the processor, such as an intelligent security warning program based on multi-source heterogeneous data. When the processor executes the computer program, the steps in the above-mentioned embodiments of the intelligent security warning method based on multi-source heterogeneous data are implemented, such as Figure 1 Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are realized, such as the first acquisition module.

[0207] Exemplarily, the computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to implement the present invention. The one or more modules / units may be a series of computer program instruction segments capable of implementing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0208] The electronic device may be a computing device such as a desktop computer, notebook, PDA, or smart tablet. The electronic device may include, but is not limited to, a processor and memory. Those skilled in the art will appreciate that the aforementioned components are merely examples of electronic devices and do not constitute a limitation of the electronic device. The electronic device may include more or fewer components than those described above, or a combination of certain components, or different components. For example, the electronic device may also include input / output devices, network access devices, buses, and the like.

[0209] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the electronic device, connecting various parts of the entire electronic device using various interfaces and lines.

[0210] The memory can be used to store the computer programs and / or modules, and the processor realizes various functions of the electronic device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory. The memory can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, at least one application required for a function (such as a sound playback function, an image playback function, etc.); the data storage area can store data created based on the use of the mobile phone (such as audio data, a phone book, etc.). In addition, the memory can include a high-speed random access memory and can also include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other volatile solid-state storage device.

[0211] Wherein, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of each of the above-mentioned method embodiments. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0212] It should be noted that the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed across multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the drawings of the device embodiments provided by the present invention, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. A person of ordinary skill in the art can understand and implement the present invention without inventive effort.

[0213] The specific embodiments described above further illustrate the objectives, technical solutions, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. An intelligent security early warning method based on multi-source heterogeneous data, characterized in that: include: Perform initial collection of multi-source data streams to obtain preliminary data sets with a unified format; According to the format characteristics of the preliminary data group, different types of field contents are mapped into a preset unified framework. If the missing field ratio exceeds a preset ratio threshold, data is supplemented through an interpolation algorithm to obtain a unified data matrix after mapping. Extracting key feature vectors from the unified data matrix to obtain a feature subset including core characteristics, and determining a key feature data set based on the feature subset; Based on the key feature data set, a hidden correlation network between data points is constructed, and the connection strength between nodes in the network is obtained. If the connection strength is lower than a preset strength threshold, weak connection edges are removed to obtain an optimized correlation network graph; Performing a deep traversal analysis on the associated network graph to obtain the distribution of potential threat points between nodes, determine the threat level, and obtain node location information corresponding to the threat level to obtain a list of potential threat points; Obtaining dimension conflicts based on the potential threat point list; if the conflict value exceeds a preset conflict threshold, eliminating the conflict through dimensionality reduction processing to obtain a comprehensive threat assessment result; Based on the comprehensive threat assessment results, the threat levels are hierarchically processed to obtain high-priority threat categories and corresponding high-risk area distributions; Based on the distribution of high-risk areas, historical warning data, and real-time data streams, the evolution trend of potential threats is predicted. If the fluctuation amplitude exceeds a preset amplitude threshold, it is marked as an emergency warning object, and a predicted threat dynamic map is obtained; The predicted threat dynamic graph is analyzed at multiple levels to obtain the specific characteristics and distribution patterns of the emergency warning objects and generate structured warning information.

2. The intelligent safety early warning method based on multi-source heterogeneous data according to claim 1 is characterized in that: According to the format characteristics of the preliminary data group, different types of field contents are mapped to a preset unified framework. If the missing field ratio exceeds a preset ratio threshold, data is supplemented by an interpolation algorithm to obtain a mapped unified data matrix, including: According to the field characteristics of the preliminary data group, a pre-established mapping rule table is used to map different types of field contents one by one to a preset unified framework; Redundant content is cleaned up using a deduplication tool to obtain a deduplicated field set, and the field set is confirmed to have completed preliminary format conversion; Calculating the missing proportion of the field using statistical tools for the missing conditions in the field set; If the missing value ratio exceeds a preset ratio threshold, the missing content is supplemented with data through an interpolation calculation tool to obtain a supplemented field dataset; Based on the completed field data set, a data integration tool is used to reorganize the field content according to a preset matrix structure, and a format verification tool is used to correct format inconsistencies to obtain a reorganized unified data matrix; The unified data matrix is ​​compressed using a storage optimization tool, and the matrix is ​​saved in blocks to distributed storage nodes by allocating storage paths. The unified data matrix is ​​judged to complete the final integration based on the access identifier of the node.

3. The intelligent safety early warning method based on multi-source heterogeneous data according to claim 1 or 2, characterized in that: The step of extracting key feature vectors from the unified data matrix to obtain a feature subset including core characteristics, and determining a key feature data set based on the feature subset, includes: Based on the multi-dimensional features in the unified data matrix, a data dimensionality reduction tool is used to perform preliminary processing on the high-dimensional features to obtain an initial feature vector group representative of the data; Using a feature screening tool to sort the initial feature vector group by importance, and obtain a selected feature subset including core features; Using a data compression tool to perform structural optimization processing on the selected feature subset to obtain a compressed feature subset; An association rule mining tool is used to perform relationship analysis on the characteristics in the compressed feature subset to obtain the key feature data set.

4. The intelligent safety early warning method based on multi-source heterogeneous data according to any one of claims 1 to 3, characterized in that: The method constructs a hidden correlation network between data points based on the key feature data set, obtains the connection strength between nodes in the network, and removes weak connection edges if the connection strength is lower than a preset strength threshold to obtain an optimized correlation network graph, including: Using a graph processing tool to comprehensively traverse the nodes and edges in the hidden association network to obtain an initial mapping relationship between node distribution and marginal weight; The weight comparison tool is used to analyze the marginal weights one by one. If the connection strength of one edge is lower than the preset strength threshold, the weak connection edge is removed to obtain the filtered network structure; Performing structural adjustments on the nodes and remaining edges in the screened network structure to obtain an optimized association graph; Afterwards, the method further includes: saving the nodes and connection strengths in the optimized association graph to obtain a final association network graph.

5. The intelligent security early warning method based on multi-source heterogeneous data according to any one of claims 1 to 4, characterized in that: The dimensional conflict is obtained according to the potential threat point list. If the conflict value exceeds a preset conflict threshold, the conflict is eliminated through dimensionality reduction processing to obtain a comprehensive threat assessment result, including: Based on the list of potential threat points, using a data integration tool to preliminarily process the threat distribution and the distribution characteristics, and obtaining at least one data set related to a data dimension from the list of potential threat points; Compare the data set with the information integration requirements to obtain a preliminary threat distribution record; Checking data dimensions and dimension conflicts in the threat distribution record one by one; If the conflict value range exceeds the preset conflict threshold, it is adjusted through the dimensionality reduction processing tool to determine the adjusted dimensional data set; Associating and mapping the threat level and risk assessment in the adjusted dimensional data set, and obtaining multi-dimensional information in the mapping process; The final comprehensive threat assessment result is obtained based on the multi-dimensional information.

6. The intelligent security early warning method based on multi-source heterogeneous data according to any one of claims 1 to 5, characterized in that: The threat levels are hierarchically processed based on the comprehensive threat assessment results to obtain high-priority threat categories and corresponding high-risk area distributions, including: Comparing the comprehensive threat assessment result with a preset threat threshold to determine the specific range of the high-priority threat category; By using the specific range, the high priority threat category is matched with location information in the network; If the distribution characteristics of the location information are inconsistent with the category match, the risk weight of the location information is adjusted to obtain a matching record; According to the matching records, determining whether the distribution of the risk weights meets the preset weight conditions, and obtaining the preliminary distribution range of the high-risk areas; It then includes: using the preliminary distribution range, using a data integration tool to conduct a secondary verification of the high-risk area; if the correlation between the verification result and the high-risk area is lower than a preset correlation threshold, adjusting the boundary of the distribution range to determine the final high-risk area distribution file.

7. The intelligent security early warning method based on multi-source heterogeneous data according to any one of claims 1 to 6, characterized in that: The evolution trend of potential threats is predicted based on the distribution of high-risk areas, historical warning data, and real-time data streams. If the fluctuation amplitude exceeds a preset amplitude threshold, it is marked as an emergency warning object, and a predicted threat dynamic map is obtained, including: According to the distribution of the high-risk areas, a data integration tool is used to extract the characteristics of the data flow from the historical warning data and the real-time data; Compare the features with a preset feature threshold. If the fluctuation range of the data flow exceeds the preset fluctuation threshold, mark it as a potential abnormal point and obtain a preliminary abnormal fluctuation record. Obtaining a trend corresponding to the preliminary abnormal fluctuation record, obtaining a change characteristic of the trend, determining whether the change characteristic is consistent with the historical warning data, and determining an evolution direction of the potential threat; Associating the evolution direction with the regional markers of high-risk areas, and if the distribution of the regional markers is inconsistent with the predicted trend, adjusting the priority to obtain candidates for emergency warnings; Based on the candidate objects of the emergency warning, a distribution view of the dynamic map is generated using a visual drawing tool to obtain a final predicted threat dynamic map.

8. The intelligent security early warning method based on multi-source heterogeneous data according to any one of claims 1 to 7, characterized in that: The multi-level analysis of the predicted threat dynamic graph to obtain the specific characteristics and distribution patterns of the emergency warning objects and generate structured warning information includes: According to the analytical level of the predicted threat dynamic graph, a data extraction tool is used to separate the original data of the warning characteristics and distribution patterns; Comparing the raw data with a preset data threshold, if the fluctuation of the data exceeds the safety threshold, it is marked as a high-risk object, and a preliminary screening threat list is obtained; Using the threat list, a data classification tool is used to divide the high-risk objects into regions, and combining the distribution patterns and association mapping, the high-risk objects are matched with geographical ranges to determine a threat distribution view; Based on the threat distribution view, using information integration tools to generate structured warning content, grading the warning content according to priority, and obtaining standardized information packages; The information package is pushed to the processing module using a data transmission tool. During dynamic updating, if the level of the threat list exceeds a preset level threshold, the priority is adjusted to obtain a final security warning value.

9. An intelligent safety early warning system based on multi-source heterogeneous data, characterized by: include: The first acquisition module is used to perform initial acquisition of multi-source data streams to obtain a preliminary data group with a unified format; A second acquisition module is configured to map different types of field contents into a preset unified framework based on the format characteristics of the preliminary data set. If the proportion of missing fields exceeds a preset threshold, the data is supplemented using an interpolation algorithm to obtain a mapped unified data matrix. a determination module, configured to extract key feature vectors from the unified data matrix, obtain a feature subset including core characteristics, and determine a key feature data set based on the feature subset; A third acquisition module is used to construct a hidden correlation network between data points based on the key feature data set, obtain the connection strength between nodes in the network, and remove weak connection edges if the connection strength is lower than a preset strength threshold to obtain an optimized correlation network graph; a fourth acquisition module, configured to perform a deep traversal analysis of the associated network graph, obtain the distribution of potential threat points between nodes, determine the threat level, and obtain node location information corresponding to the threat level to obtain a list of potential threat points; a fifth acquisition module, configured to obtain dimensional conflicts based on the potential threat point list, and if the conflict value exceeds a preset conflict threshold, eliminate the conflict through dimensionality reduction processing to obtain a comprehensive threat assessment result; a sixth acquisition module, configured to perform hierarchical processing on the threat levels according to the comprehensive threat assessment result, and obtain high-priority threat categories and corresponding high-risk area distributions therefrom; a seventh acquisition module, configured to predict the evolution trend of potential threats based on the distribution of high-risk areas, historical warning data, and real-time data streams; and to mark an emergency warning target if the fluctuation amplitude exceeds a preset amplitude threshold, thereby obtaining a predicted threat dynamic map; The generation module is used to perform multi-level analysis on the predicted threat dynamic graph, obtain the specific characteristics and distribution patterns of the emergency warning objects, and generate structured warning information.

10. An electronic device, characterized in that: It includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, and when the processor executes the computer program, it implements the intelligent security early warning method based on multi-source heterogeneous data as described in any one of claims 1 to 8.

Images