Risk prevention and disposal method and system based on map data fusion

Abstract

The invention is suitable for the technical field of smart cities, and particularly relates to a risk prevention and disposal method and system based on map data fusion, and the method comprises the steps: obtaining multi-dimensional geographic attribute data from a multi-source geographic information database; analyzing the multi-dimensional geographic attribute data to obtain a plurality of grid regions with a risk coupling effect, and taking the grid regions as to-be-checked risk regions; calling the refined geographic monitoring data and the regional response data corresponding to each to-be-checked risk region to analyze the geographic relevance of the regional response; analyzing the refined geographic monitoring data and the regional response data of each to-be-checked risk region, and identifying a specific influence information instance contained in each to-be-checked risk region; according to the specific influence information instance of the to-be-checked risk area, the risk disposal scheme of the specific influence information instance is determined, human, land, event, object and organization elements are woven into a comprehensive safety prevention and control network, and the risk prevention capability is improved.

Description

Technical Field

[0001] The present application belongs to the field of smart city technology, and in particular relates to a risk prevention and disposal method and system based on map data fusion. Background Art

[0002] With the continuous advancement of smart city construction, the integrated application of geographic information systems and multi-source sensing technologies has become a core support for regional risk prevention and control. Among the current mainstream risk prevention methods, technicians can build risk monitoring networks by integrating geospatial databases and deploying monitoring terminals. This allows for the collection and early warning of risk factors in specific areas. For example, they can use map data to divide monitoring grids and combine environmental sensor data to generate risk level distribution maps.

[0003] Existing risk prevention methods require separate invocations of geographic information platforms and regional data analysis systems when combining spatial characteristics with regional feedback for comprehensive assessment. This cumbersome data switching and manual comparison process can easily lead to missed opportunities for risk management. This is particularly true in densely populated areas or emergency scenarios, where the speed of information data transfer and the ability to locate and analyze potential data flows in geographic space are insufficient, leading to delayed early warning responses and inadequately targeted response measures. Summary of the Invention The embodiments of the present application provide a risk prevention and disposal method and system based on map data fusion, which can solve the problems in existing urban governance methods such as untimely early warning responses or insufficiently targeted disposal measures due to the cumbersome data switching and manual comparison processes, insufficient information data flow speed and geographic space positioning and potential flow direction analysis capabilities.

[0004] In a first aspect, embodiments of the present application provide a risk prevention and handling method based on map data fusion, including: Obtain multi-dimensional geographic attribute data corresponding to each preset spatial grid from a preset multi-source geographic information database; Analyzing the multi-dimensional geographic attribute data corresponding to each of the spatial grids to obtain a plurality of grid areas with risk coupling effects as risk areas to be investigated; wherein the grid area is a geographic area corresponding to a single spatial grid; Retrieving the refined geographic monitoring data and regional response data corresponding to each of the risk areas to be checked from the multi-source geographic information database; Analyzing the refined geographic monitoring data and the regional response data for each of the risk areas to be checked to identify specific impact information instances contained in each of the risk areas to be checked; wherein the specific impact information instances are information instances in which the rate of change of information flow exceeds a threshold value within a preset continuous period; A risk handling plan for the specific impact information instance is determined based on the specific impact information instance of the risk area to be checked.

[0005] The above technical solutions in the embodiments of the present application have at least the following technical effects: The risk prevention and disposal method based on map data fusion provided by the embodiment of the present application obtains the multi-dimensional geographic attribute data corresponding to each preset spatial grid from a preset multi-source geographic information database, and accurately grasps the geographic characteristics and potential risk basis of each spatial grid. The multi-dimensional geographic attribute data corresponding to each spatial grid is analyzed to obtain multiple grid areas with risk coupling effects and use them as risk areas to be checked, so as to achieve accurate positioning of regional risks and effectively quantify and analyze the risk correlation characteristics between regions. The refined geographic monitoring data and regional response data corresponding to each risk area to be checked are retrieved from the multi-source geographic information database to provide data support for analyzing the geographic correlation of regional responses. The refined geographic monitoring data and regional response data of each risk area to be checked are analyzed to identify the information instances that may cause risks contained in each risk area to be checked, reduce data switching and manual comparison processes, achieve targeted focus on risk assessment, and improve the accuracy and timeliness of risk identification. Based on the specific impact information instances in the risk areas to be investigated, risk disposal plans for specific impact information instances are determined to form intelligent closed-loop governance capabilities, enhance risk prevention capabilities and emergency response capabilities, and achieve early warning, prediction, prevention, and disposal of risk factors, making the security situation clear at a glance and weaving people, places, events, objects, and organizational elements into a comprehensive security prevention and control network.

[0006] In a second aspect, an embodiment of the present application provides a risk prevention and disposal system based on map data fusion, including: An acquisition unit, configured to acquire multi-dimensional geographic attribute data corresponding to each preset spatial grid from a preset multi-source geographic information database; An analysis unit, configured to analyze the multi-dimensional geographic attribute data corresponding to each of the spatial grids to obtain a plurality of grid areas where risk coupling effects exist and to determine the risk areas to be investigated; wherein the grid area is a geographic area corresponding to a single spatial grid; A retrieving unit, configured to retrieve, from the multi-source geographic information database, the refined geographic monitoring data and regional response data corresponding to each of the risk areas to be checked; an identification unit, configured to analyze the refined geographic monitoring data and the regional response data of each of the risk areas to be checked, and identify specific impact information instances contained in each of the risk areas to be checked; wherein the specific impact information instances are information instances in which the rate of change of information flow exceeds a threshold value within a preset continuous period; The handling unit is used to determine a risk handling plan for the specific impact information instance based on the specific impact information instance of the risk area to be checked.

[0007] In a third aspect, an embodiment of the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described in any one of the first aspects above is implemented.

[0008] In a fourth aspect, an embodiment of the present application provides a computer program product, which, when executed on an electronic device, enables the electronic device to execute the method according to any one of the above aspects.

[0009] It can be understood that the beneficial effects of the second to fourth aspects mentioned above can be found in the relevant descriptions of the above aspects and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0011] Figure 1 This is a flowchart of a risk prevention and disposal method based on map data fusion provided in one embodiment of the present application; Figure 2 This is a schematic diagram of an implementation of a risk prevention and disposal method based on map data fusion provided in one embodiment of the present application; Figure 3 This is a schematic diagram of the structure of a risk prevention and disposal system based on map data fusion provided in one embodiment of the present application; Figure 4 It is a structural diagram of an electronic device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0012] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0013] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0014] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0015] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if the described condition or event is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of the described condition or event" or "in response to detecting the described condition or event," depending on the context.

[0016] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0017] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more but not all embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0018] Existing risk prevention methods require separate invocations of geographic information platforms and regional data analysis systems when combining spatial characteristics with regional feedback for comprehensive assessment. This cumbersome data switching and manual comparison process can easily lead to missed opportunities for risk management. This is particularly true in densely populated areas or emergency scenarios, where the speed of information data transfer and the ability to locate and analyze potential data flows in geographic space are insufficient, leading to delayed early warning responses and inadequately targeted response measures.

[0019] To solve the above problems, the embodiment of the present application provides a risk prevention and disposal method and system based on map data fusion. In this method, by obtaining the multi-dimensional geographic attribute data corresponding to each preset spatial grid from a preset multi-source geographic information database, the geographic characteristics and potential risk basis of each spatial grid are accurately grasped. The multi-dimensional geographic attribute data corresponding to each spatial grid is analyzed to obtain multiple grid areas with risk coupling effects and use them as risk areas to be checked, so as to achieve accurate positioning of regional risks and effectively quantify and analyze the risk correlation characteristics between regions. The refined geographic monitoring data and regional response data corresponding to each risk area to be checked are retrieved from the multi-source geographic information database to provide data support for analyzing the geographic correlation of regional responses. The refined geographic monitoring data and regional response data of each risk area to be checked are analyzed to identify the information instances that may cause risks contained in each risk area to be checked, reduce data switching and manual comparison processes, achieve targeted focus on risk assessment, and improve the accuracy and timeliness of risk identification. Based on the specific impact information instances of the risk area to be investigated, the risk disposal plan for the specific impact information instances is determined to improve the risk prevention and emergency response capabilities, realize the early warning, prediction, prevention and disposal of risk factors, make the security situation clear at a glance, and weave the people, places, things, objects and organizational elements into a comprehensive security prevention and control network.

[0020] The risk prevention and disposal method based on map data fusion provided in the embodiment of the present application can be applied to electronic devices. In this case, the electronic device is the executor of the risk prevention and disposal method based on map data fusion provided in the embodiment of the present application. The embodiment of the present application does not impose any restrictions on the specific type of electronic device.

[0021] For example, the electronic device can be various types of intelligent monitoring devices, including, but not limited to, desktop computers, smart screens, smart TVs, handheld devices with wireless communication capabilities, computing devices, computers, laptop computers, and the like.

[0022] In order to better understand the risk prevention and disposal method based on map data fusion provided in the embodiment of the present application, the specific implementation process of the risk prevention and disposal method based on map data fusion provided in the embodiment of the present application is exemplarily introduced below.

[0023] Figure 1 A schematic flow chart of a risk prevention and disposal method based on map data fusion provided in an embodiment of the present application is shown. Figure 2 A flowchart of the execution steps of a risk prevention and disposal method based on map data fusion provided in an embodiment of the present application is shown. The risk prevention and disposal method based on map data fusion includes: S100: Acquire multi-dimensional geographic attribute data corresponding to each preset spatial grid from a preset multi-source geographic information database.

[0024] A multi-source geographic information database refers to a collection of spatial data from multiple sources, including remote sensing imagery, location sensor data, public service data, trajectory data, event reports, and statistical yearbooks. This database undergoes format standardization, spatial registration, temporal alignment, and semantic mapping. It possesses a comprehensive metadata system and real-time update capabilities, supporting flexible access by time period, region, layer, and data type. A spatial grid is a pre-divided, uniformly sized, regularly shaped analysis unit that divides the target governance area. Each grid has a unique number and boundary information. Common grid sizes include 50m×50m and 100m×100m, enabling high-resolution regional modeling. Multi-dimensional geographic attribute data refers to the descriptive geographic features contained in each spatial grid within a specific monitoring period. These data cover dimensions such as physical topography (e.g., elevation, slope), functional structure (e.g., roads, commercial districts, and industrial zones), demographic characteristics (e.g., mobility density and age composition), facility distribution (e.g., education, healthcare, and transportation nodes), and event frequency (e.g., sudden alarms and facility failures). Spatial grids can be located using spatial indexing techniques (such as R-trees and quadtrees), and then parallel data pulling and caching operations can be performed in multi-source geographic information databases based on their spatiotemporal labels and data layer labels. The acquired multidimensional geographic attribute data is organized in the form of multidimensional feature vectors or tensors, supporting structured analysis and graph computing input, ensuring data integrity, spatial coherence, and temporal synchronization for subsequent risk coupling and identification algorithms. The ultimate effect is to generate a set of spatial grids covering the target analysis area and establish a corresponding multidimensional attribute label cluster for each spatial grid cell, achieving holographic digital modeling of urban space and providing a high-quality spatiotemporal sample foundation for subsequent analysis.

[0025] S200 , analyzing the multi-dimensional geographic attribute data corresponding to each spatial grid to obtain multiple grid areas with risk coupling effects and use them as risk areas to be investigated; wherein the grid area is the geographic area corresponding to a single spatial grid.

[0026] It is understandable that by analyzing the multidimensional geographic attribute data corresponding to each spatial grid, sensitive areas with complex risk superposition structures, known as risk coupling areas, can be identified within the overall prevention and control area. These areas can then be selected as targets for subsequent analysis and governance intervention. Risk coupling areas are geographical regions corresponding to spatial grids where risk factors of different categories and sources co-occur significantly in space and time. These risk factors can potentially create a synergistic amplification effect, manifesting as a complex situation characterized by simultaneous increases in risk, enhanced event propagation efficiency, and increased governance difficulty. Risk status and coupling degree can be modeled and identified for each spatial grid using various methods, including statistical modeling, graph structure analysis, and time series modeling, using the acquired multidimensional geographic attribute data. For example, feature mapping methods can be used to convert information from each risk point within the grid into a time series tensor. Anomalous trends can then be identified using methods such as sliding window statistics, K-means clustering, or isolation forests. Spatial propagation weights can then be introduced using a spatial adjacency matrix to construct a risk transmission network. Based on this, each spatial grid is assessed to determine whether it is simultaneously located in a high-impact zone across multiple risk pathways, or whether it exhibits strong structural coupling with adjacent high-risk areas. This generates output labels for the coupling identification model, marking grid areas that meet a preset coupling strength threshold as areas of potential risk. Subsequent analysis of these areas will focus on their information evolution, information activity, and suitability for intervention measures, enabling early detection of potential risk clusters and providing timely, high-precision data support for resource allocation, intelligent early warning, and emergency response.

[0027] In one possible implementation, S200 analyzes the multi-dimensional geographic attribute data corresponding to each spatial grid to obtain multiple grid areas with risk coupling effects as risk areas to be investigated, including: S210, based on the multi-dimensional geographic attribute data corresponding to each spatial grid, determine the spatial risk situation value sequence and risk spatial correlation sequence corresponding to each spatial grid in the first monitoring period; wherein, the spatial risk situation value sequence is used to reflect the evolution law of the risk level of a single spatial grid in the first monitoring period; the risk spatial correlation sequence is used to reflect the mutual influence intensity and transmission law of risk factors between different spatial grids.

[0028] It can be understood that a spatial risk situation value sequence aggregates risk-related characteristic indicators within a spatial grid over continuous time periods (e.g., daily or hourly) into dynamic trend values. The first monitoring period refers to a predefined data monitoring period, for example, a continuous time period that can be predefined on a daily or hourly basis. The spatial risk situation value sequence reflects the evolution of the risk level of a single spatial grid during the first monitoring period. A weighted aggregation function can be constructed to model the weights of different risk factors within the grid. For example, factors such as building density, population density, traffic node density, and historical event frequency can be linearly or nonlinearly combined according to predefined weights to generate a time-varying continuous value sequence, i.e., the spatial risk situation value sequence. The spatial risk correlation sequence reflects the mutual influence and transmission patterns of risk factors across different spatial grids. This spatial risk correlation sequence can be obtained by establishing a spatial interaction graph model, treating each spatial grid as a graph node and modeling the information flow probability, information resonance strength, or attribute synergy probability between adjacent spatial grids as edge weights. This dynamic sequence, measuring the impact of spatial interaction risk, is then calculated using a sliding window to measure node centrality, structural embedding dimension, or signal strength synergy coefficient at each time point. The spatial risk situation value sequence and the risk spatial correlation degree sequence constitute the input basis for multidimensional space-time analysis, which can be used for subsequent risk coupling function modeling and identification analysis, more accurate key area judgment and the construction of a multidimensional indicator linkage early warning mechanism.

[0029] Optionally, in step S210, determining a spatial risk situation value sequence corresponding to each spatial grid in the first monitoring period based on the multi-dimensional geographic attribute data corresponding to each spatial grid includes: S211, perform a first analysis on the multi-dimensional geographic attribute data corresponding to each spatial grid to obtain a dynamic risk value sequence of each risk point contained in each spatial grid during the first monitoring period; wherein the dynamic risk value sequence is used to reflect the real-time status change trend of a single risk point in a specific spatial grid during the first monitoring period.

[0030] It's understandable that fine-grained time-series decomposition can be performed on the data of different risk points within each spatial grid to extract dynamic trajectories reflecting the fluctuating characteristics of individual risk sources. A risk point is a specific risk unit within the spatial grid with an independent identification number, classification label, and coordinate attributes, such as a high-traffic commercial district, a key facility area, or a densely populated traffic intersection. Risk point data can come from IoT terminals, video surveillance equipment, business reporting systems, or citizen feedback records. Based on the risk point type, feature variables can be generated using pre-set feature extraction rules (such as sensor value standardization, image recognition labeling, and text event structuring). These feature variables are then chronologically organized into a risk value sequence within a specified monitoring period (e.g., the past 7 or 30 days). Dynamic risk values ​​reflect the activity, rate of change, and degree of abnormal spikes of a risk point over time, and are an important signal source for detecting potential risk precursors.

[0031] S212, calculating the spatial risk situation value sequence corresponding to each spatial grid in the first monitoring period based on the dynamic risk value sequence of each risk point included in each spatial grid.

[0032] It can be understood that the dynamic risk value series of multiple risk points within a spatial grid can be aggregated and normalized to form a spatial risk situation value series that represents the temporal evolution of the overall risk status of the grid. This transforms fine-grained information about local individual risks into time series trend data representing a global regional level, facilitating subsequent global situation comparison and dynamic evolution modeling. For each set of risk points within a spatial grid, risk weights can be assigned based on risk point categories (e.g., pedestrian flow, facility, or event). These weights can be set using methods such as expert scoring, the Analytic Hierarchy Process (AHP), or historical regression models. Multiple dynamic risk value series can then be combined to form a unique time series using weighted averaging, principal component extraction, and weighted anomaly fusion. For example, if a region experiences rapid changes in pedestrian density and an increasing frequency of facility failures, these values ​​can be mapped as an "increasing risk" trend, and a continuous spatial risk situation curve can be output with hourly or daily periods. This spatial risk situation value series will serve as input for downstream coupled analysis and risk propagation prediction, enhancing the dynamic perception and responsiveness of subsequent models.

[0033] Optionally, S210, determining, based on the multi-dimensional geographic attribute data corresponding to each spatial grid, a spatial risk situation value sequence and a risk spatial correlation degree sequence corresponding to each spatial grid in the first monitoring period, includes: S213, performing a second analysis on the multi-dimensional geographic attribute data corresponding to each spatial grid to determine the spatial distribution information of the risk points within each spatial grid; wherein the spatial distribution information is used to reflect the spatial distribution pattern of the risk points within the spatial grid.

[0034] It can be understood that spatial distribution information refers to a collection of spatial structural indicator data, including the location coordinates, relative density, distribution balance, cluster center location, and clustering direction of risk points. The second analysis, distinct from the time-series dynamic analysis in S211, utilizes spatial statistical methods to explore structural relationships within the grid. Kernel density estimation (KDE) can be used to calculate heat maps of risk point spatial concentrations; Ripley's K function can be used to analyze clustering or dispersion trends; clustering methods such as DBSCAN, K-Means, and Mean-Shift can be introduced to identify hotspots and distribution cores; or spatial autocorrelation indicators such as Moran's I and Geary's C can be used to evaluate distribution regularity. Spatial distribution information not only provides a basis for subsequent spatial correlation series but can also be used to optimize the spatial layout of intervention resources, improving the spatial precision and efficiency of governance interventions.

[0035] S214 , based on the spatial distribution information of the risk points in each spatial grid and the dynamic risk value sequence of each risk point in the first monitoring period, calculate the risk space correlation degree sequence corresponding to each spatial grid in the first monitoring period.

[0036] It can be understood that the risk spatial correlation sequence reflects whether the risk of a spatial grid within a specific time window is likely to be affected by the diffusion, transmission, and resonance of risks in neighboring areas. Spatial connectivity between grids can be determined by constructing an adjacency matrix or a spatial influence weight map. This spatial connectivity can be determined based on dimensions such as shared boundaries, transportation connectivity, and functional similarity. The spatial distribution characteristics of the current grid (such as the density of risk points, location center, and distribution pattern) are multiplied and weighted with the dynamic risk intensity of adjacent grids to form the external risk input for the current spatial grid at each moment. For example, if the internal risk value of grid A is low, but the risk of its adjacent grid B continues to increase, and A and B have strong functional connectivity (such as a traffic intersection), then the risk spatial correlation of A will be marked as high at the current time. A continuous risk spatial correlation sequence can be calculated using a sliding time window, which can be used to quantify the spatial external sensitivity of nodes. This risk spatial correlation sequence can reveal which areas have strong "risk reception" characteristics in spatiotemporal evolution, helping to construct a topological map of urban risk response, identify potential core areas of linkage, and assist in risk early warning and prevention.

[0037] S220, analyzing the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each spatial grid to obtain the correlation equation between the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each spatial grid; wherein the correlation equation is used to reflect the correlation pattern between the spatial risk situation value sequence and the risk space correlation degree sequence.

[0038] It can be understood that the mapping relationship between two spatial risk situation value sequences and a risk spatial correlation degree sequence can be modeled through a correlation equation. Specifically, it asks whether the changing trend of a spatial grid's risk situation has a measurable functional relationship with its spatial risk transmission intensity. The spatial risk situation value sequence represents the dynamic evolution of internal risks within a region, while the risk spatial correlation degree sequence represents the spatial input influence of external risks. This helps to understand whether a spatial grid is an "internal-dominated risk zone" or a "spatially driven risk zone," providing key indicators for subsequent risk coupling assessment and matching zoning governance strategies. The spatial risk situation value sequence and the risk spatial correlation degree sequence can be fitted using linear, quadratic, exponential, logistic, and support vector regression models. The fitting function is determined based on the best-fit goodness-of-fit (e.g., R², residual sum of squares). For regions with prominent nonlinear relationships, deep fitting methods such as BP neural networks and LSTM can be introduced to uncover implicit multi-order relationships. If the fitting results are stable and the residuals are small, it means that the spatial risk situation of the grid has obvious spatial coupling responsiveness. A set of computable correlation function expressions is output for each spatial grid, including function form, fitting parameters, significance indicators, etc., which serves as an important mathematical basis for subsequent regional screening and model evaluation.

[0039] Optionally, S220, analyzing the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each spatial grid to obtain a correlation equation between the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each spatial grid, including: S221, performing nonlinear correlation calculations on the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each spatial grid, respectively, to obtain a correlation equation between the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each spatial grid.

[0040] Nonlinear correlation calculation refers to the use of statistics or modeling methods with nonlinear mapping capabilities, such as the Maximum Information Coefficient (MIC), mutual information, kernel function correlation coefficient (HSIC), and Granger causality nonlinear tests, to deeply explore relationships between two sets of time series data. This can reveal phenomena such as lagged responses, sudden covariance, and local high-intensity resonance, significantly outperforming linear indices such as the Pearson causal test. The spatial risk situation value series and the risk spatial correlation series for each spatial grid can be uniformly preprocessed (normalization, smoothing, and time alignment). Nonlinear indicator calculations are then performed in parallel to output a correlation score matrix. Significant relationship pairs are then screened based on the scores, and specific functional expressions are generated through spline regression, support vector regression, or tree model fitting, forming a nonlinear correlation equation. This nonlinear correlation equation can be used not only to determine whether a grid belongs to a complex coupling mode area but also to construct a risk response simulator to predict responses to specific external input risks and assess its resilience and vulnerability to internal situational evolution. This significantly improves identification capabilities, enabling the detection of hidden risk linkages, sudden impact areas, and complex vulnerable areas, providing advanced situational awareness support.

[0041] S230: Based on the correlation equation between the spatial risk situation value sequence and the risk spatial correlation degree sequence corresponding to each spatial grid, multiple grid areas with risk coupling effects are obtained and used as risk areas to be checked.

[0042] It is understood that the correlation equations corresponding to each spatial grid can be analyzed, with parameters such as functional structure, fitting performance, and nonlinear characteristics being analyzed to determine whether a coupling relationship with a risk-enhancing and amplifying trend exists. This determination can include criteria such as whether the equation is nonlinearly ascending (e.g., an upward-opening parabola), the correlation strength exceeding a threshold, and the elasticity coefficient of the dependent variable being significantly greater than 1. Furthermore, trend stability indicators (such as sliding window fitting consistency) and response lag parameters can be introduced to determine whether a region constitutes a susceptible transmission zone. Ultimately, spatial grids that meet these coupling conditions are extracted and designated as "risk areas to be investigated," effectively achieving convergence from comprehensive identification to targeted intervention, improving resource allocation efficiency and the precision of risk intervention.

[0043] Optionally, S230, based on the correlation equation between the spatial risk situation value sequence and the risk spatial correlation degree sequence corresponding to each spatial grid, a plurality of grid areas with risk coupling effects are obtained as risk areas to be checked, including: S231, screening is performed based on the correlation equation between the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each spatial grid, and the grid area corresponding to the spatial grid whose correlation equation is a quadratic curve equation is used as the risk area to be checked.

[0044] It can be understood that within the risk evolution mechanism, the structure of the quadratic equation implies that changes in risk status values ​​exhibit a nonlinear acceleration trend against the backdrop of increasing spatial correlation, indicating a significant risk amplification or attenuation effect, depending on the sign of the quadratic coefficient. The correlation function equations for all grids can be uniformly converted to the standard polynomial form ax²+bx+c to identify functions that conform to the quadratic structure. Subsequently, the sign and absolute value of the quadratic coefficient (a) are analyzed to determine the response mechanism represented by the quadratic function. Generally speaking, if a > 0, the risk status exhibits an accelerating upward trend as correlation increases, representing a typical linkage amplification region. If a < 0, spatial self-inhibition or a negative feedback mechanism may exist. By setting an appropriate threshold (e.g., |a| > 0.1), grid regions exhibiting nonlinear amplification mechanisms can be precisely identified. These spatial grids are marked as significant structural coupling regions and added to the list of pending risk areas. These spatial grids are highlighted and outlined in the visualization interface, allowing for priority treatment and resource allocation, providing an efficient means for the nonlinear discovery and rapid localization of complex risks.

[0045] Optionally, S230, based on the correlation equation between the spatial risk situation value sequence and the risk spatial correlation degree sequence corresponding to each spatial grid, a plurality of grid areas with risk coupling effects are obtained as risk areas to be checked, including: S232, performing significance verification on the correlation equation corresponding to each spatial grid, and obtaining the reliability coefficient corresponding to each spatial grid.

[0046] As can be understood, statistical significance tests can be performed on the correlation equation to ensure the mathematical rationality and practical stability of the adopted risk coupling judgment. Statistical metrics such as fitting residuals, mean squared error, goodness-of-fit (R²), and p-value can be calculated for each spatial grid's correlation equation. The p-value verifies whether there is a statistically significant correlation between the dependent variable (risk situation value) and the independent variable (spatial correlation), typically with a threshold of p < 0.05. The goodness-of-fit assesses the adequacy of the overall model's explanatory power. Based on these statistical metrics, a "reliability coefficient" can be defined for each correlation equation. This can be achieved by linearly weighting the various indicators or by employing machine learning methods (such as random forest regression) to output a confidence score. The reliability coefficient facilitates subsequent categorization of candidate risk areas, for example, prioritizing high-reliability areas for intervention while deferring analysis or requiring additional data for areas with less reliable data. Ultimately, this step enhances the model's explanatory power and credibility, establishing a trustworthy defense within the risk assessment system and ensuring the scientific and stable nature of subsequent policy formulation.

[0047] S233, screening is performed based on the reliability coefficient and the correlation equation, and the grid area corresponding to the spatial grid where the quadratic term coefficient of the correlation equation is negative and the reliability coefficient is greater than a preset critical value is selected as the risk area to be checked.

[0048] It can be understood that combining the mathematical characteristics of the correlation equation with the reliability verification results can achieve a dual verification of risk areas. The correlation equation describes the quantitative relationship between a specific indicator and various influencing factors within a spatial grid. The sign of the quadratic coefficient has a clear physical meaning. When the quadratic coefficient is negative, it indicates a nonlinear relationship between the influencing factors within the spatial grid and the safety indicator, with diminishing marginal effects or even negative effects. This means that as the relevant factors change, the safety status may deteriorate (for example, risk increases sharply after a parameter exceeds a threshold). This serves as an important mathematical signal for identifying potential risks. The reliability coefficient measures the goodness of fit or predictive confidence of the correlation equation. Only when the reliability coefficient exceeds a preset critical value can the relationship reflected by the correlation equation be considered statistically significant, thus avoiding misjudgments caused by data noise or accidental correlations. Using a negative quadratic coefficient and a reliability coefficient greater than a preset critical value as joint screening criteria can simultaneously consider the mathematical characteristics of risk and the reliability of the model. The quadratic coefficient inherently identifies grids with a potential risk deterioration trend, while the reliability coefficient provides statistical validity to ensure that this trend is not a spurious correlation. The dual screening mechanism of quadratic coefficient and reliability coefficient not only ensures the sensitivity of risk identification, but also avoids excessive warning, provides a precise and reliable target area for subsequent risk investigation and control, and improves the efficiency and pertinence of risk prevention.

[0049] S300, retrieve the refined geographic monitoring data and regional response data corresponding to each risk area to be checked from the multi-source geographic information database.

[0050] It is understood that refined geographic monitoring data can include: high-definition video images, drone aerial photography data, traffic flow information, personnel trajectory thermal information, real-time sensor data (such as PM2.5, noise, lighting, etc.); regional response data can include information from public channels, third-party monitoring, and related records, and is a collection of information reflecting the risk situation and emergency situations within the grid area. This can be achieved by connecting with multi-source databases, such as retrieving micro-grid data from the city operation brain, smart city Internet of Things platform, and urban operation integrated platform, building a unified data extraction interface, performing coordinate matching and data layered extraction according to the ID of the area to be checked, and retrieving refined geographic monitoring data and regional response data corresponding to each risk area to be checked from the multi-source geographic information database, to achieve real-time, refined and reinforced perception of the risk area to be checked, thereby supporting the next step of information instance identification and enabling situation analysis.

[0051] S400, analyzing the refined geographic monitoring data and regional response data of each risk area to be checked, and identifying specific impact information instances contained in each risk area to be checked; wherein the specific impact information instance is an information instance in which the rate of change of information flow exceeds a threshold within a preset continuous time period.

[0052] It's understandable that refined geographic monitoring data and regional response data can be used to accurately identify key information instances that could significantly impact regional operations or resident perceptions, annotating their location, scope, intensity, theme, and other factors. For example, a pre-built event recognition model can integrate algorithms such as text semantic analysis, time series clustering, and spatial hotspot detection to analyze unstructured data in the target area in real time. An event aggregation model can identify multidimensional manifestations of the same event and correlate them with regional geographic monitoring data (such as crowd gatherings, vehicle anomalies, and elevated noise levels) to eliminate misjudgments and improve recognition accuracy. Specific impact information instances are those where the rate of change in information flow exceeds a threshold within a predetermined continuous period. These instances can be graded based on the information flow index and flow breadth, extracting risky components as specific impact information instances, significantly improving dynamic perception and timely response within the region.

[0053] In one possible implementation, the regional response data includes at least one information instance. At step S400, the refined geographic monitoring data and regional response data for each risk area to be checked are analyzed to identify specific impact information instances contained in each risk area to be checked, including: S410, performing data flow analysis on each information instance of each risk area to be checked, and determining the data flow coefficient of each information instance in the second monitoring period; wherein the data flow coefficient is used to reflect the degree of flow of the information instance in the second monitoring period.

[0054] It can be understood that each spatial grid containing regional response data includes at least one information instance. An information instance is a specific set of information related to a geographic feature, regional activity, or event in the risk area under investigation, transmitted through various information channels within a specific time period. For each identified information instance, its activity across various information channels can be assessed to quantify the intensity and scope of information flow in the short term, outputting the data flow coefficient. A data flow assessment framework can be constructed, consisting of modules such as indicator system construction, entropy weighting, and comprehensive coefficient calculation. The relevant text and metadata for each instance can be collected and merged, extracting core indicators such as flow frequency and unit time growth rate to form an evaluation indicator set. These indicators can then be objectively weighted using the entropy weighting method, with their weights determined by calculating the information entropy of each indicator.

[0055] For example, when the entropy weight method is used, the range normalization formula can be used to process the j-th indicator of the i-th instance: when the indicator is a positive indicator, , when the indicator is negative, ,in is the original indicator value, 、 Respectively The maximum and minimum values ​​of the indicators, is the normalized value (range 0-1). Calculate the information entropy of the j-th indicator: ,in ,in, is the total number of information instances, -6 is a correction term to avoid errors in logarithmic operations. Weight of each indicator: ,in is the total number of indicators, For the The entropy weight of the index (satisfying ), the smaller the information entropy ( The smaller the value), the greater the difference between the indicators in different instances, the more effective information they carry, and the greater the weight ( ) is correspondingly higher, so as to avoid the deviation caused by subjective weighting; at the same time, each indicator is converted into a standardized value between 0 and 1 through the above standardization formula, and the standardized value of each indicator is multiplied by the corresponding entropy weight and then summed to obtain the data flow coefficient: The higher the data flow coefficient, the more active the information instance is during the monitoring period.

[0056] S420, based on the data flow coefficient and the refined geographic monitoring data of each risk area to be checked, determine the geographic impact index of each information instance in the second monitoring period; wherein the geographic impact index is used to reflect the theoretical effect intensity of the information instance on a specific geographic area in the second monitoring period.

[0057] As you can understand, the geographic impact index is calculated by coupling the data flow coefficient with refined geographic monitoring data. The geographic impact index reflects the theoretical impact of an information instance on a specific geographic region during the second monitoring period. The second monitoring period is a predetermined, continuous period in the future. Refined geographic monitoring data can include regional population density, population flow characteristics (such as average daily passenger volume and peak-hour flow rate), functional attribute weights (such as a weight of 1.2 for commercial areas, 1.0 for residential areas, and 0.8 for industrial areas), and the distribution of sensitive locations (such as schools and hospitals, which are given additional weighting coefficients). The geographic data can be normalized to the range of 0-1 and then the entropy weighting method can be used to calculate the geographic impact coefficient. The data flow coefficient and the geographic impact coefficient are then combined using a multiplication formula. The specific implementation formula is as follows: Geographic Impact Index = Data Flow Coefficient × Geographic Impact Coefficient × Regional Risk Base Value (reflecting the region's inherent vulnerability). For example, an instance with a data flow coefficient of 0.8 will have a significantly higher geographic impact index in a commercial area with a population density of 0.9 (after standardization) and a functional attribute weight of 1.2 than the same information instance in an industrial area with a population density of 0.3 and a functional attribute weight of 0.8, thereby quantifying the difference in the theoretical impact intensity of information instances on specific geographical areas.

[0058] S430 , preliminarily screening the information instances of each risk area to be checked based on the geographic impact index to determine a set of candidate information instances of each risk area to be checked.

[0059] It can be understood that the initial screening process based on the geographic impact index achieves a preliminary focus on potentially high-impact information instances through dynamic thresholds or relative ranking. Differentiated screening rules can be set for different types of risk areas to be investigated, with an absolute threshold of an index value ≥ 1.5 times the regional mean applied to core grid areas and a relative threshold of percentages applied to other grids. For example, if there are 20 information instances in a certain area to be investigated, with geographic impact indices ranging from 0.1 to 0.9, information instances with an index ≥ 0.6 (assuming the regional mean is 0.4) can be selected to form a candidate set. This retains instances with significant impact while avoiding the problem of insufficient regional adaptability caused by fixed thresholds, laying the foundation for subsequent refined screening.

[0060] S440 , calculating the flow change rate of each candidate information instance in the candidate information instance set to determine the specific impact information instances included in each risk area to be checked.

[0061] As can be understood, the data flow coefficient and geographic influence index of each candidate information instance can be sampled over multiple consecutive periods to form a time series curve. Sliding window techniques can then be used to calculate indicators such as the growth slope, amplitude, and persistence within each period. Exponential smoothing (such as Holt-Winters) or trend decomposition models (such as STL) can also be used to remove seasonal and trend components from the information instance and extract its true flow change rate. A specific influential information instance is one whose rate of change in information flow exceeds a threshold over a predetermined continuous period. The flow change rate is dynamic trend data that measures the influence of an information instance by tracking changes in its geographic influence index over consecutive periods. The specific calculation formula for the flow change rate is: flow change rate = (geographic influence index for the current period - geographic influence index for the previous period) / geographic influence index for the previous period × 100%. The time period can be pre-set based on the flow rate or type of information instance, such as a 1-hour period or a 6-hour period. By calculating the flow change rate over multiple consecutive periods, it is clear whether the influence of an information instance is increasing, decreasing, or stable. For example, if the flow change rate of a candidate instance in three consecutive time periods is 30%, 25%, and 20%, respectively, it indicates that its influence is continuously increasing and the growth rate is relatively stable. If the change rate is -10%, -15%, and -8%, it indicates that the influence is decreasing. When determining specific impact information instances, instances with a continuously positive flow change rate and an average change rate exceeding a preset value (such as 15%) are selected as specific impact information instances, enabling cross-platform early warning sharing, data visualization, and response decision guidance.

[0062] S500: Determine a risk handling plan for a specific impact information instance based on the specific impact information instance of the risk area to be checked.

[0063] It is understood that identified specific impact information instances can be further transformed into specific intervention response measures to achieve proactive risk responses and intelligent recommendations for intervention strategies. A specific impact information instance refers to a specific information unit with significant impact and importance within a specific geographic region, capable of reflecting the dynamic and changing characteristics of the risk situation in that region. The specific elements of a specific impact information instance can be used as input into a pre-trained risk management solution model. The risk management solution model can be constructed using a supervised learning approach. The training dataset is derived from a historical information instance case library and includes information instance context features (time, space, type), information instance flow indicators, regions, and corresponding optimal response action labels. During the training phase, decision tree-based ensemble learning methods (such as XGBoost and RandomForest) can be used to handle nonlinear data mapping. Alternatively, the Transformer structure from deep learning can be integrated to extract instance semantic context and flow dynamic features, and generate multi-level response recommendations. The risk disposal plan output by the risk disposal plan model not only includes the judgment result of "whether to respond", but is also refined into dimensions such as response levels (such as Level I active guidance, Level II data enhanced monitoring, and Level III collaborative resource intervention), response path recommendations, and automatically matches the mobilized resource units, realizing the transformation of risk management response from "expert experience-driven" to "data model-driven", greatly improving the systematicity, foresight and adaptability of emergency response, and enabling regional risk management agencies to more efficiently respond to multi-type and complex information instances and reduce the probability of potential risks.

[0064] In one possible implementation, S500, based on the specific impact information instance of the risk area to be checked, determines a risk handling solution for the specific impact information instance, including: S510, inputting the specific impact information instance of the risk area to be checked into a preset risk treatment solution model to obtain a risk treatment solution for the specific impact information instance; wherein the risk treatment solution model is a pre-trained machine learning model.

[0065] It can be understood that a specific impact information instance identified within the risk area to be investigated can be used as input, and a pre-trained risk treatment solution model can be used to generate a specific treatment recommendation that best suits the current situation and regional characteristics of that information instance. In other words, the risk treatment solution is a specific risk treatment recommendation generated by the risk treatment solution model for the input specific impact information instance. The risk treatment solution model inputs may include: the current activity value of the information instance, the rate of change of the information instance activity, the information instance expansion change curve, etc.; the output is a set of risk treatment recommendation parameters. The output risk treatment recommendation parameter set is the risk treatment solution. The risk disposal solution model can be implemented based on the Transformer architecture, jointly encoding instance structured information and unstructured text (such as instance content summary) to generate a high-dimensional embedding representation, and then the response generator outputs the recommended strategy; it can also be based on the graph neural network (GNN) to process the propagation graph structure between instances and geographic grids, improve the robustness of instance-region coupling response recommendations, complete the transformation of information instances into strategies, promote regional multi-faceted coordinated responses, and realize the early warning, prediction, prevention, and disposal of risk factors through scientific and technological means, improve risk prevention capabilities, and make the security situation clear at a glance, providing strong support for the formulation of precise prevention measures, combining people, places, things, objects, and organizational elements to weave a comprehensive security control network.

[0066] For example, a training sample set containing historical instances of specific impact information can be prepared. The training data can be sourced from multiple regional information instances, as well as information instance risk management solutions, impact feedback reports, and other sources. The training sample set includes the current activity value of the information instance, the rate of change of information instance activity, and the information instance expansion change curve as input samples, and the risk management solution content and impact feedback reports as output samples. Each training sample contains both structured and unstructured information. Structured information includes the grid number of the instance occurrence area, instance category label, timestamp, data flow curve, and instance impact index; unstructured information includes instance text descriptions and content samples. The training process first normalizes the structured data, including normalizing timestamps, encoding standardized numerical indicators, and category labels, to ensure uniformity and validity of the model input. Simultaneously, natural language processing techniques are used to segment, denoise, and semantically vectorize the unstructured text information to generate a high-dimensional semantic embedding. Next, a supervised learning algorithm is used to train the model on the training samples, optimizing model performance through cross-validation and hyperparameter tuning. For Transformer-based models, the focus is on training their contextual understanding and dynamic feature capture capabilities. For graph neural network-based models, the focus is on strengthening the learning of the relationship between instances and regional grids, improving the ability to express spatial correlations. After training, the models are evaluated using independent test sets. Accuracy, recall, F1 score, and other metrics are used to comprehensively measure the model's performance in practical applications. This ensures that the model can accurately predict specific impact information instances and corresponding treatment strategies, ultimately providing intelligent support for regional risk management methods.

[0067] Corresponding to the risk prevention and disposal method based on map data fusion in the above embodiment, the embodiment of the present application also provides a risk prevention and disposal system based on map data fusion, and each unit of the system can implement each step of the risk prevention and disposal method based on map data fusion. Figure 3 A structural block diagram of a risk prevention and disposal system based on map data fusion provided in an embodiment of the present application is shown. For ease of explanation, only the parts related to the embodiment of the present application are shown.

[0068] Reference Figure 3 The risk prevention and disposal system based on map data fusion includes: An acquisition unit, configured to acquire multi-dimensional geographic attribute data corresponding to each preset spatial grid from a preset multi-source geographic information database; An analysis unit, configured to analyze the multi-dimensional geographic attribute data corresponding to each of the spatial grids to obtain a plurality of grid areas where risk coupling effects exist and to determine the risk areas to be investigated; wherein the grid area is a geographic area corresponding to a single spatial grid; A retrieving unit, configured to retrieve, from the multi-source geographic information database, the refined geographic monitoring data and regional response data corresponding to each of the risk areas to be checked; an identification unit, configured to analyze the refined geographic monitoring data and the regional response data of each of the risk areas to be checked, and identify specific impact information instances contained in each of the risk areas to be checked; wherein the specific impact information instances are information instances in which the rate of change of information flow exceeds a threshold value within a continuous period of time; The handling unit is used to determine a risk handling plan for the specific impact information instance based on the specific impact information instance of the risk area to be checked.

[0069] It should be noted that the information interaction, execution process, etc. between the above-mentioned systems / units are based on the same concept as the method embodiment of this application. Their specific functions and technical effects can be found in the method embodiment section and will not be repeated here.

[0070] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit module can exist physically alone, or two or more unit modules can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0071] The embodiment of the present application also provides an electronic device, Figure 4 This is a schematic diagram of the structure of an electronic device provided in one embodiment of the present application. Figure 4 As shown, the electronic device 6 of this embodiment includes: at least one processor 60 ( Figure 4 Only one is shown), at least one memory 61 ( Figure 4 Only one is shown in the figure) and a computer program 62 stored in the at least one memory 61 and executable on the at least one processor 60. When the processor 60 executes the computer program 62, the electronic device 6 implements the steps of any of the above-mentioned risk prevention and disposal method embodiments based on map data fusion, or implements the functions of each unit in the above-mentioned system embodiments.

[0072] For example, the computer program 62 may be divided into one or more units, which are stored in the memory 61 and executed by the processor 60 to implement the present application. The one or more 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 62 in the electronic device 6.

[0073] The electronic device can be a variety of types of intelligent monitoring devices. The electronic device can include but is not limited to a processor 60 and a memory 61. It will be understood by those skilled in the art that Figure 4 It is only an example of the electronic device 6 and does not constitute a limitation on the electronic device 6. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, it may also include input and output devices, network access devices, buses, etc.

[0074] The processor 60 may be a central processing unit (CPU), or 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, or discrete hardware components. A general-purpose processor may be a microprocessor or any conventional processor.

[0075] In some embodiments, the memory 61 may be an internal storage unit of the electronic device 6, such as a hard drive or memory of the electronic device 6. In other embodiments, the memory 61 may also be an external storage device of the electronic device 6, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the electronic device 6. Furthermore, the memory 61 may include both an internal storage unit of the electronic device 6 and an external storage device. The memory 61 is used to store an operating system, application programs, a boot loader, data, and other programs, such as the program code of the computer program. The memory 61 may also be used to temporarily store data that has been output or is about to be output.

[0076] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in any of the above method embodiments are implemented.

[0077] An embodiment of the present application provides a computer program product. When the computer program product is run on an electronic device, the electronic device implements the steps of any of the above method embodiments.

[0078] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the process steps in the above-mentioned method embodiments by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to an electronic device, a recording medium, computer memory, read-only memory (ROM), random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Examples include a USB flash drive, a removable hard drive, a magnetic disk, or an optical disk.

[0079] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0080] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0081] In the embodiments provided in the present application, it should be understood that the disclosed risk prevention and disposal system / electronic device and method based on map data fusion can be implemented in other ways. For example, the above-described risk prevention and disposal system / electronic device embodiment based on map data fusion is merely illustrative. For example, the division of the units is merely a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0082] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0083] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. A risk prevention and disposal method based on map data fusion, characterized in that: include: Obtain multi-dimensional geographic attribute data corresponding to each preset spatial grid from a preset multi-source geographic information database; Analyzing the multi-dimensional geographic attribute data corresponding to each of the spatial grids to obtain a plurality of grid areas with risk coupling effects as risk areas to be investigated; wherein the grid area is a geographic area corresponding to a single spatial grid; Retrieving the refined geographic monitoring data and regional response data corresponding to each of the risk areas to be checked from the multi-source geographic information database; Analyzing the refined geographic monitoring data and the regional response data for each of the risk areas to be checked to identify specific impact information instances contained in each of the risk areas to be checked; wherein the specific impact information instances are information instances in which the rate of change of information flow exceeds a threshold value within a preset continuous period; A risk handling plan for the specific impact information instance is determined based on the specific impact information instance of the risk area to be checked.

2. The risk prevention and handling method based on map data fusion according to claim 1, characterized in that: The analyzing the multi-dimensional geographic attribute data corresponding to each of the spatial grids to obtain a plurality of grid areas with risk coupling effects as risk areas to be checked includes: Determine, based on the multi-dimensional geographic attribute data corresponding to each of the spatial grids, a spatial risk situation value sequence and a risk spatial correlation degree sequence corresponding to each of the spatial grids during the first monitoring period; wherein the spatial risk situation value sequence is used to reflect the evolution law of the risk level of a single spatial grid during the first monitoring period; and the risk spatial correlation degree sequence is used to reflect the mutual influence intensity and transmission law of risk factors between different spatial grids; Analyzing the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each of the spatial grids to obtain a correlation equation between the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each of the spatial grids; wherein the correlation equation is used to reflect the correlation pattern between the spatial risk situation value sequence and the risk space correlation degree sequence; Based on the correlation equation between the spatial risk situation value sequence corresponding to each of the spatial grids and the risk space correlation degree sequence, a plurality of grid areas with risk coupling effects are obtained and used as risk areas to be checked.

3. The risk prevention and handling method based on map data fusion according to claim 2 is characterized in that: Determining, based on the multi-dimensional geographic attribute data corresponding to each of the spatial grids, a sequence of spatial risk situation values ​​corresponding to each of the spatial grids in a first monitoring period, including: Performing a first analysis on the multi-dimensional geographic attribute data corresponding to each of the spatial grids to obtain a dynamic risk value sequence for each risk point contained in each of the spatial grids during the first monitoring period; wherein the dynamic risk value sequence is used to reflect the real-time state change trend of a single risk point in a specific spatial grid during the first monitoring period; According to the dynamic risk value sequence of each risk point included in each spatial grid, the spatial risk situation value sequence corresponding to each spatial grid in the first monitoring period is calculated.

4. The risk prevention and handling method based on map data fusion according to claim 2 is characterized in that: Determining, based on the multi-dimensional geographic attribute data corresponding to each of the spatial grids, a spatial risk situation value sequence and a risk spatial correlation degree sequence corresponding to each of the spatial grids in the first monitoring period includes: Performing a second analysis on the multi-dimensional geographic attribute data corresponding to each of the spatial grids to determine spatial distribution information of risk points within each of the spatial grids; wherein the spatial distribution information is used to reflect the spatial distribution pattern of risk points within the spatial grid; Based on the spatial distribution information of the risk points in each of the spatial grids and the dynamic risk value sequence of each type of risk point in the first monitoring period, the risk space correlation degree sequence corresponding to each of the spatial grids in the first monitoring period is calculated.

5. The risk prevention and handling method based on map data fusion according to claim 2 is characterized in that: The analyzing the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each of the spatial grids to obtain a correlation equation between the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each of the spatial grids includes: Nonlinear correlation calculation is performed on the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each spatial grid, and a correlation equation between the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each spatial grid is obtained.

6. The risk prevention and handling method based on map data fusion according to claim 2 is characterized in that: The step of obtaining a plurality of grid areas having risk coupling effects as risk areas to be checked based on the correlation equation between the spatial risk situation value sequence and the risk spatial correlation degree sequence corresponding to each of the spatial grids comprises: Based on the correlation equation between the spatial risk situation value sequence and the risk space correlation degree sequence corresponding to each of the spatial grids, screening is performed, and the grid area corresponding to the spatial grid whose correlation equation is a quadratic curve equation is used as the risk area to be checked.

7. The risk prevention and handling method based on map data fusion according to claim 2 is characterized in that: The step of obtaining a plurality of grid areas having risk coupling effects as risk areas to be checked based on the correlation equation between the spatial risk situation value sequence and the risk spatial correlation degree sequence corresponding to each of the spatial grids comprises: Performing significance verification on the correlation equations corresponding to the respective spatial grids to obtain the reliability coefficients corresponding to the respective spatial grids; Screening is performed based on the reliability coefficient and the correlation equation, and the grid area corresponding to the spatial grid in which the quadratic term coefficient of the correlation equation is negative and the reliability coefficient is greater than a preset critical value is taken as the risk area to be checked.

8. The risk prevention and handling method based on map data fusion according to claim 1 is characterized in that: The region response data includes at least one information instance; The analyzing the refined geographic monitoring data and the regional response data of each of the risk areas to be checked to identify specific impact information instances contained in each of the risk areas to be checked includes: Performing data flow analysis on each of the information instances in each of the risk areas to be checked, and determining a data flow coefficient for each of the information instances during the second monitoring period; wherein the data flow coefficient is used to reflect the degree of flow of the information instance during the second monitoring period; Determine, based on the data flow coefficient and the refined geographic monitoring data of each of the risk areas to be checked, a geographic impact index for each of the information instances during the second monitoring period; wherein the geographic impact index is used to reflect the theoretical effect strength of the information instance on a specific geographic area during the second monitoring period; Preliminarily screening the information instances of each of the risk areas to be checked based on the geographic impact index to determine a set of candidate information instances of each of the risk areas to be checked; A flow change rate is calculated for each candidate information instance in the candidate information instance set to determine specific impact information instances included in each risk area to be checked.

9. The risk prevention and handling method based on map data fusion according to claim 1, characterized in that: The step of determining a risk handling plan for the specific impact information instance based on the specific impact information instance of the risk area to be checked includes: The specific impact information instance of the risk area to be checked is input into a preset risk treatment plan model to obtain a risk treatment plan for the specific impact information instance; wherein, the risk treatment plan model is a pre-trained machine learning model.

10. A risk prevention and disposal system based on map data fusion, characterized in that: include: An acquisition unit, configured to acquire multi-dimensional geographic attribute data corresponding to each preset spatial grid from a preset multi-source geographic information database; An analysis unit, configured to analyze the multi-dimensional geographic attribute data corresponding to each of the spatial grids to obtain a plurality of grid areas where risk coupling effects exist and to determine the risk areas to be investigated; wherein the grid area is a geographic area corresponding to a single spatial grid; A retrieving unit, configured to retrieve, from the multi-source geographic information database, the refined geographic monitoring data and regional response data corresponding to each of the risk areas to be checked; an identification unit, configured to analyze the refined geographic monitoring data and the regional response data of each of the risk areas to be checked, and identify specific impact information instances contained in each of the risk areas to be checked; wherein the specific impact information instances are information instances in which the rate of change of information flow exceeds a threshold value within a preset continuous period; The handling unit is used to determine a risk handling plan for the specific impact information instance based on the specific impact information instance of the risk area to be checked.

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