A risk analysis method and system for an important control line of territorial space

Abstract

The application discloses a kind of land space important control line risk analysis method and system, belong to land space planning and monitoring technical field, the method is: obtaining the multi-source spatial data of target area at several preset time points, data correlation is carried out to the multi-source spatial data, constructs space-time cube;Typical risk mode and risk quantization index of each kind of land space important control line are defined, and control line risk rule base is constructed;Based on the control line risk rule base and space-time cube, the corresponding typical risk mode is matched for each space unit in target area, risk identification result and risk unit are obtained, and risk unit is evaluated and evolves in risk grade, risk prediction result is obtained and visualized, and control line early warning analysis report of target area is output.Therefore, by implementing the present application, the problem that the prior art cannot realize dynamic risk early warning and accurate risk evolution of land space important control line can be solved.

Description

Technical Field

[0001] This application belongs to the field of land spatial planning and monitoring technology, specifically involving a risk analysis method and system for important land spatial control lines. Background Technology

[0002] Important control lines for national land space include ecological protection red lines, permanent basic farmland protection red lines, and urban development boundaries in national land space planning. They are core policy tools for ensuring national ecological security, food security, and the intensive and efficient development of cities and towns, and are important bases for implementing land use control. Therefore, monitoring, assessment, and risk identification of these control lines are commonly used to determine whether important control lines for national land space are subject to erosion, damage, or risk of functional degradation.

[0003] Current risk analysis methods for important national spatial control lines mostly focus on static assessments, determining whether human activities have crossed the control line boundaries at a given moment. They rarely provide early warnings about potential threats and evolving risk trends, thus lacking dynamic early warning capabilities. Furthermore, traditional control line monitoring uses overly simplistic spatial rules, merely monitoring boundary crossings, making it difficult to identify more complex risk situations. Additionally, current methods typically treat each control line in isolation and monitor them individually, rarely considering the potential chain reactions that changes around one control line can trigger, impacting other control lines. For example, the uncontrolled expansion of urban boundaries can simultaneously encroach on ecological and agricultural space. Therefore, current technologies lack dynamic early warning capabilities when identifying and analyzing the risk evolution of important national spatial control lines. They employ simplistic spatial rules and neglect the risk transmission and correlation between multiple control lines, leading to inaccurate risk identification and a failure to accurately predict future risk development trends. Summary of the Invention

[0004] This application proposes a risk analysis method and system for important control lines of national territory, which can solve the problem that existing technologies have failed to achieve dynamic risk early warning and accurate risk evolution for important control lines of national territory.

[0005] The first aspect of this application provides a risk analysis method for important control lines in territorial space, the method comprising: Acquire multi-source spatial data of the target area at several preset time points, and construct a spatiotemporal cube by associating the multi-source spatial data; wherein, the multi-source spatial data includes control line vector data, land cover data, land use change data, remote sensing image data, human activity data, and natural geographic data; Define typical risk patterns and risk quantification indicators for various important territorial spatial control lines in terms of time and space, and construct a control line risk rule base; Based on the control line risk rule base and the spatiotemporal cube, the corresponding typical risk pattern is matched for each spatial unit in the target area to obtain the risk identification result; Based on the risk identification results, the risk prediction results of the risk units are obtained by assessing the risk level and trend evolution of the risk units of the space unit. The risk prediction results are visualized, and a control line early warning analysis report for the target area is output.

[0006] The aforementioned scheme first collects multi-source spatial data covering multiple time points in the target area. By constructing a spatiotemporal cube, the multi-source data is linked together, ensuring logical layering, dynamic temporal connection, and cross-verification, providing accurate data support for subsequent risk assessment. Then, typical risk patterns of various control lines are analyzed in the spatiotemporal dimension, generating quantitative risk indicators for each typical risk pattern to define the characteristic conditions triggering risk alarms. A control line risk rule base is constructed based on the analyzed and defined content, providing fundamental rules for subsequent risk type identification. Next, different spatial analyses are performed on each spatial unit in the target area to determine the typical risk patterns that may exist in each spatial unit, accurately identifying the potential risks of each spatial unit and obtaining complete risk identification results. Based on the risk identification results, the risk level of risk units is quantified, and the risk development trend is analyzed to obtain the risk distribution of the entire target area. This enables accurate risk warnings for large-scale, complex geographical environments and accurate prediction of risk development trends, effectively supporting the refined and proactive governance of national land space.

[0007] In one possible implementation of the first aspect, multi-source spatial data of the target region at several preset time points are acquired, and a spatiotemporal cube is constructed by data association of the multi-source spatial data, specifically as follows: At several consecutive preset time points, control line vector data, land cover data, land use change data, remote sensing image data, human activity data, and natural geographic data of the target area are collected to obtain the multi-source spatial data. Knowledge graph technology is used to associate the multi-source spatial data and construct a spatiotemporal cube.

[0008] In one possible implementation of the first aspect, typical risk patterns and risk quantification indicators for various important territorial spatial control lines are defined in both time and spatial dimensions, and a control line risk rule base is constructed, specifically as follows: Feature extraction is performed on the textual descriptions of risk types corresponding to important control lines of national land space to obtain textual feature vectors; Based on the text feature vector, the risk type is analyzed according to the judgment criteria to obtain the risk quantification index of the risk type; Extract words and phrases related to important control lines of national territory from the text feature vector to determine the protection targets of the risk type; By associating the text feature vector, the risk quantification index, and the protection target, typical risk patterns of important control lines in national territorial space are obtained. Integrate all the typical risk models mentioned above to construct a control line risk rule base.

[0009] The above scheme quantifies the criteria for determining risk types through textual descriptions, providing calculable risk quantification indicators for subsequent risk identification. It also defines protection targets for each risk type, further narrowing down the potential risk types for various control lines and improving identification accuracy. By using extracted textual feature vectors, risk quantification indicators, and the aforementioned protection targets, a standardized and structured control line risk rule base is constructed, enabling unified management of different types of typical risk patterns and facilitating their use in subsequent risk identification.

[0010] In one possible implementation of the first aspect, based on the control line risk rule base and the spatiotemporal cube, the corresponding typical risk pattern is matched for each spatial unit in the target region to obtain the risk identification result, specifically as follows: The target area is divided into regular or irregular grids to obtain several spatial units; The risk quantification index is extracted from the control line risk rule base, and the index value of the risk quantification index for each spatial unit is calculated using the spatiotemporal cube; The index values ​​are modeled on a preset time series to obtain the temporal characteristics of the spatial unit; Based on the control line risk rule base, the risk unit and its matching typical risk pattern are identified by calculating the similarity score between the multidimensional indicator feature vector of the spatial unit and the typical risk pattern, thereby obtaining the risk identification result; wherein, the indicator value and the time series feature are fused to obtain the multidimensional indicator feature vector.

[0011] The aforementioned scheme divides the target area into multiple spatial units, enabling refined risk identification and obtaining accurate spatial distribution of risks. Furthermore, during the risk identification process, the static and dynamic characteristics of the spatial units are modeled at continuous time points for different locations along the control line. The resulting multi-dimensional indicator feature vector can more comprehensively characterize the risk state, thereby improving the accuracy of matching with typical risk patterns.

[0012] In one possible implementation of the first aspect, the risk quantification index value of each spatial unit is calculated using the spatiotemporal cube, specifically as follows: Spatiotemporal data corresponding to each spatial analysis method are extracted from the spatiotemporal cube, and the risk quantification index corresponding to each spatial analysis method is determined according to the control line risk rule base; wherein, the spatial analysis method includes buffer analysis, overlay analysis and spatial pattern analysis; For the peripheral area within the spatial unit located outside the important control line of the national territory, buffer analysis is used to calculate the rate of change of the peripheral area using the spatiotemporal data, and the buffer analysis results are obtained. By overlay analysis, the geographic patches provided by the spatiotemporal data are overlaid with the important control lines of the national land space within the spatial unit, and the area ratio of the geographic patches is calculated to obtain the overlay analysis results. For the control line areas where important land spatial control lines of each country are located within the spatial unit, spatial pattern analysis is conducted, and the spatiotemporal data is used to calculate the land fragmentation index of the control line area and the nearest distance to adjacent control line areas to obtain spatial pattern analysis results. Based on the results of buffer analysis, overlay analysis, and spatial pattern analysis, the numerical values ​​of each risk quantification indicator for the spatial unit are obtained.

[0013] The above scheme conducts spatial analysis of spatial units from three different aspects, including the changes in the surrounding area, the analysis of the intersection and attribute changes between spatial units, and the assessment of the integrity and stability of spatial units, making the calculation of risk quantification indicators more scientific and comprehensive.

[0014] In one possible implementation of the first aspect, the index values ​​are modeled on a preset time series to obtain the temporal characteristics of the spatial unit, specifically as follows: The values ​​of each indicator are modeled on a preset time series to obtain the time series of each risk quantification indicator. The slope of the time series of the indicator is calculated using linear regression to obtain the changing trend characteristics of the risk quantification indicator. Calculate the standard deviation or coefficient of variation of the time series of the indicator to obtain the change and fluctuation characteristics of the risk quantification indicator; Based on the spatiotemporal characteristics of the spatial unit, dynamic weights are assigned to the risk quantification indicators; The time series characteristics are obtained by combining the trend characteristics, the fluctuation characteristics, and the dynamic weights.

[0015] The aforementioned scheme uses the dynamic changes of risk quantification indicators over continuous time points to accurately analyze the trends and fluctuations of risk. Furthermore, it analyzes which risk quantification indicators are more important within different spatial units of time and space, adjusting the dynamic weights accordingly to further improve the accuracy of risk identification.

[0016] In one possible implementation of the first aspect, based on the risk identification results, the risk prediction results of the risk units are obtained by assessing the risk level and trend evolution of the risk units of the spatial unit, specifically as follows: Based on the risk identification results, the comprehensive risk index of the risk unit is obtained by weighted summation of the similarity scores between the risk unit and each of the typical risk patterns. Based on the magnitude of the comprehensive risk index, the risk units are classified into risk levels; wherein, the risk levels from high to low are high risk, medium risk, low risk, and concern zone; Using the comprehensive risk index and the risk level, construct the risk evolution path of the risk unit at the preset time point; The risk prediction result is obtained by integrating the risk level and the risk evolution path of the risk unit.

[0017] The aforementioned scheme derives a comprehensive risk index through weighted summation, avoiding the limitations of relying on a single indicator and achieving a comprehensive consideration of multiple risk factors. Simultaneously, the clear risk level classification provides a direct basis for differentiated and precise risk management. Finally, by constructing a risk evolution path, it lays the foundation for understanding future risk developments.

[0018] In one possible implementation of the first aspect, the risk evolution path of the risk unit is constructed at the preset time point using the comprehensive risk index and the risk level, specifically as follows: Extract the comprehensive risk index of the risk unit at each preset time point to determine the typical risk pattern corresponding to the risk unit at each preset time point; Aligning the comprehensive risk index and the risk level at the preset time points yields a risk time series. By describing the risk level and risk index change trends of the risk unit at consecutive preset time points through risk time series, the risk evolution path of the risk unit is obtained.

[0019] The above scheme, by constructing a risk evolution path, intuitively presents the changing trends of risk levels and risk indices, which helps to quickly identify areas where risks continue to deteriorate, improve, or fluctuate repeatedly, providing key information for risk early warning.

[0020] In one possible implementation of the first aspect, the risk prediction results are visualized, and a control line early warning analysis report for the target area is output, specifically as follows: Based on the geographical location of the risk units in the target area, construct the spatial distribution of risks in the target area; Based on the risk level of the risk unit, a list of key risk areas is generated; Using the risk prediction results, risk evolution trend prediction and risk management recommendations are generated for the risk unit; Based on the aforementioned risk spatial distribution, the aforementioned list of key risk areas, trend prediction results, and risk management recommendations, a control line early warning analysis report is constructed.

[0021] The above scheme visualizes the risk prediction results in multiple forms and presentation types, more intuitively showing the risk situation of the control line within the target area, and providing effective suggestions for subsequent homeland security management.

[0022] The second aspect of this application provides a risk analysis system for important control lines of national territory, the system comprising: a data association module, a rule base construction module, a risk identification module, a risk prediction module, and a result visualization module; The data association module is used to acquire multi-source spatial data of the target area at several preset time points, and to construct a spatiotemporal cube by performing data association on the multi-source spatial data; wherein the multi-source spatial data includes control line vector data, land cover data, land use change data, remote sensing image data, human activity data and natural geographic data. The rule base construction module is used to define typical risk patterns and risk quantification indicators for various important control lines of national land space in the time and space dimensions, and to build a control line risk rule base. The risk identification module is used to match the corresponding typical risk pattern for each spatial unit in the target area based on the control line risk rule base and the spatiotemporal cube, so as to obtain the risk identification result; The risk prediction module is used to obtain the risk prediction result of the risk unit by assessing the risk level and trend evolution of the risk unit of the spatial unit based on the risk identification result; The results visualization module is used to visualize the risk prediction results and output a control line early warning analysis report for the target area. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a schematic flowchart illustrating a risk analysis method for important control lines in national land space provided in one embodiment of this application; Figure 2 This is a structural diagram of a risk analysis system for important control lines in national land space provided in an embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] It should be understood that the step numbers used in the text are for ease of description only and are not intended to limit the order in which the steps are performed.

[0027] First Embodiment Monitoring, risk identification, and evolution of key control lines in national land space are crucial technical means to improve the modernization of national land space governance. However, existing technologies targeting control lines can only detect their current state and cannot predict whether human activities at a future point in time will severely impact the boundaries of the control lines. This static assessment method cannot promptly obtain information on potential threats and risk evolution trends facing the control lines, and by the time violations are discovered, irreversible damage has often already occurred. Moreover, traditional detection rules are too simplistic, only able to identify boundary violations at the control lines, and unable to accurately detect risks that substantially affect core production capabilities without altering land use boundaries. Therefore, existing technologies cannot accurately and systematically identify the multi-dimensional and full-process risks faced by key control lines in national land space, nor can they predict future risk evolution trends, making it difficult to provide accurate data support for the refined and proactive governance of national land space.

[0028] like Figure 1 As shown, to address the problem in existing technologies that fail to achieve dynamic risk warning and accurate risk evolution for important control lines of national territory, the first embodiment of this application provides a detailed flowchart of a risk analysis method for important control lines of national territory. The risk analysis method for important control lines of national territory in this embodiment includes steps S1 to S5, detailed below: Step S1: Obtain multi-source spatial data of the target area at several preset time points, and construct a spatiotemporal cube by performing data association on the multi-source spatial data; At several consecutive preset time points, control line vector data, land cover data, land use change data, remote sensing image data, human activity data, and natural geographic data covering the target area are collected. The collected data are then standardized to obtain multi-source spatial data covering the target area.

[0029] Optionally, in this embodiment of the application, data is collected at least at three consecutive preset time points.

[0030] Using knowledge graph technology, standardized multi-source spatial data are correlated to construct a spatiotemporal cube that is logically layered, dynamically connected in time, and mutually verified in business.

[0031] The list of some core data in the spacetime cube is shown in Table 1 below: Table 1 Multi-source data list

[0032] Step S2 defines typical risk patterns and risk quantification indicators for various important control lines of national land space in terms of time and space dimensions, and constructs a control line risk rule base.

[0033] For each important control line of national territory, there is a corresponding risk type. Therefore, by obtaining the textual description of each risk type and extracting its features, a textual feature vector for that risk type is obtained.

[0034] Based on the text feature vector, the judgment criteria and related keywords of each risk type are analyzed to obtain the risk quantification index of the risk type.

[0035] Simultaneously, words and phrases related to the important control lines of national territory are extracted from the text feature vector to determine the protection targets for each risk type. These protection targets are the important control lines of national territory corresponding to each risk type.

[0036] Finally, the obtained text feature vectors, risk quantification indicators, and protection targets are correlated to obtain typical risk patterns of important control lines in national land space. Based on this, the detection rules used for detecting important control lines in national land space are determined. Each rule defines the spatial, temporal, and attribute characteristic conditions that trigger risk alarms.

[0037] By integrating the detection rules representing the typical risk patterns, a scalable control line risk rule base is constructed, providing reliable matching conditions for subsequent risk detection of important territorial spatial control lines, and accurately identifying existing or potential risk categories for each control line.

[0038] For example, Table 2 below shows the contents of some detection rules within the control line risk rule base.

[0039] Table 2 Examples of Control Line Risk Rule Base

[0040] Table 2 above illustrates five different risk types and provides the corresponding protection targets (i.e., target control lines) for each risk type, offering screening criteria for subsequent risk identification. It also presents rule descriptions for each risk type, used for subsequent risk feature matching; examples of quantitative indicators demonstrate the quantitative conditions for triggering risk alerts.

[0041] Step S3: Based on the control line risk rule base and the spatiotemporal cube, match the corresponding typical risk pattern for each spatial unit in the target area to obtain the risk identification result.

[0042] In step S3 of this application embodiment, a feature matching engine capable of understanding complex risk scenarios is constructed. This engine matches typical risk patterns corresponding to the target area through a multi-level, multi-modal intelligent matching process, thereby achieving risk identification.

[0043] The feature matching engine first calculates the value of risk quantification indicators for the target area through spatial analysis, then extracts time-series features, and finally achieves intelligent matching of typical risk patterns through multi-feature fusion, thereby determining the corresponding risk type for each geographical location in the target area.

[0044] First, the target area is divided into regular or irregular grids to obtain several spatial units. For example, the size of a regular spatial unit is 100m × 100m; if the target area is a township or administrative village, it can be divided into irregular spatial units.

[0045] By dividing the spatial area into units, precise risk matching is performed on the target area to obtain a complete spatial risk distribution.

[0046] This application employs three different spatial analysis methods to assess the risk of spatial units: buffer analysis, overlay analysis, and spatial pattern analysis. Buffer analysis is primarily used to calculate the impact of the area surrounding the control line on the control line; overlay analysis is primarily used to identify the intersections and attribute changes between spatial units; and spatial pattern analysis is primarily used to assess the integrity and stability of the spatial pattern.

[0047] First, extract the multi-source spatiotemporal data required for each spatial analysis method from the spatiotemporal cube. Then, determine the risk quantification index used for each spatial analysis method based on the control line risk rule base. Finally, use the multi-source spatiotemporal data to calculate each risk quantification index for each spatial unit.

[0048] When using buffer analysis, the outer areas within a spatial unit that are outside the important control lines of national land space are first located, and these areas are defined as buffer zones. Using acquired multi-source spatiotemporal data, including control line data, land use data, human activity data (such as construction project sites), and natural geographic data, indicators are calculated for the buffer zones to identify the edge effect risks and pressure transmission risks of the spatial unit, thus obtaining the buffer analysis results.

[0049] For example, when identifying edge effect risks, multiple buffer zones, such as 500m and 1km, are established at the ecological protection red line of a spatial unit. Within each buffer zone, the rate of construction land expansion is calculated using multi-source spatiotemporal data, or the slope of the mean NDVI over time is calculated to obtain the vegetation degradation index of the buffer zone. This yields the rate of change within the buffer zone related to a certain risk quantification indicator, which is used as the buffer zone analysis result. The construction land expansion rate can be expressed by the formula "(T...". n Phase 1 construction land area - T n-1 (Construction land area in the first phase) / T n-1 The area of ​​land under construction is calculated; the mean NDVI refers to the normalized vegetation index, which is mainly related to the natural geographic data of multi-source spatiotemporal data.

[0050] For example, when identifying the risk of pressure transmission, a buffer zone is set within a certain range outside the urban development boundary of a certain spatial unit. The centroid coordinates of newly added construction land are added within the buffer zone, and the migration distance and rate of the centroid coordinates towards the ecological protection red line over time are analyzed as one of the results of the buffer zone analysis.

[0051] When using overlay analysis, multi-source spatiotemporal data, including control line data, land use / change survey data, and human activity data, are used to overlay geographic patches provided by the multi-source spatiotemporal data with important control lines of the national land space within the spatial unit. By calculating the area ratio of the geographic patches, the direct encroachment risk and functional degradation risk of the spatial unit can be detected, and the overlay analysis results are obtained.

[0052] For example, when identifying direct encroachment risks, human activity patches of newly approved construction projects within a spatial unit are obtained through multi-source spatiotemporal data. The human activity patches are then spatially intersected with the control line vector surface to directly output the encroachment area and encroachment ratio of the newly approved construction projects, thereby obtaining the overlay analysis results. When identifying functional degradation risks, geographical patches of "planting non-grain crops" in a spatial unit are obtained through altered survey data. These geographical patches are then overlaid with the permanent basic farmland area of ​​the spatial unit to calculate the non-grain crop area ratio of the spatial unit, thereby obtaining the overlay analysis results.

[0053] When using spatial pattern analysis, high-precision land use data is used to calculate the land fragmentation index and the nearest distance between adjacent control line areas for each country's important land spatial control line within a spatial unit. Functional degradation risk and cross-conflict risk are identified for the spatial unit, and spatial pattern analysis results are obtained.

[0054] For example, when identifying functional degradation risks, patch density (PD) or area-weighted average shape index (AWMSI) is used to calculate the fragmentation index of permanent basic farmland areas within a spatial unit. An increase in PD or an abnormal AAWSI value indicates that farmland is fragmented, its morphology is becoming more complex, and the stability of the spatial unit is decreasing. When identifying cross-conflict risks, in areas adjacent to ecological protection red lines and urban development boundaries, the shortest distance between the boundaries of the two adjacent control lines is calculated, and the area of ​​highly overlapping buffer zones is statistically analyzed to obtain spatial pattern analysis results.

[0055] Finally, based on the results of buffer analysis, overlay analysis, and spatial pattern analysis, the numerical values ​​of each risk quantification indicator for the spatial unit are obtained.

[0056] By introducing the above spatial analysis method, identification rules for risk transmission between control lines are introduced, which can reveal the risk correlation within the national spatial system and support systematic and holistic governance decisions.

[0057] For each spatial unit, time-series modeling is performed based on the aforementioned index values ​​to extract deeper time-series features, providing support for subsequent risk evolution and dynamic early warning.

[0058] The values ​​of each indicator are modeled on preset time series T1, T2, ..., Tn to obtain the time series of each risk quantification indicator. Using these time series, the trend characteristics, fluctuation characteristics, and dynamic weights of each risk quantification indicator are calculated to obtain the temporal characteristics of the spatial unit.

[0059] Specifically, the slope of the indicator time series is calculated using linear regression to determine whether it is continuously deteriorating, improving, or passive, thus obtaining the trend characteristics; the standard deviation or coefficient of variation of the indicator time series is calculated to determine the severity of the indicator value change, thus obtaining the fluctuation characteristics. The weights of risk quantification indicators are not fixed. Therefore, in this embodiment, dynamic weights are assigned to the risk quantification indicators based on the spatiotemporal characteristics of the spatial unit, thereby further improving the reliability and accuracy of risk identification.

[0060] For example, for an area that has shown a high risk of encroachment, the weight of its "expansion rate" metric would be dynamically increased over time, because sustained growth is more threatening than a single high value.

[0061] Compared to the simple threshold judgment in existing technologies, the embodiments of this application adopt a typical risk pattern matching method based on multi-feature weighted fusion, which can accurately identify potential abnormal risks of spatial units and has a stronger contextual understanding ability, making it suitable for large-scale, routine monitoring and early warning operations.

[0062] First, when the feature matching engine performs risk matching, its matching object is the multi-dimensional indicator feature vector of each spatial unit. This multi-dimensional indicator feature vector is obtained by fusing the indicator values ​​and the time-series features; in some embodiments, dynamic weights may also be fused. The matching rules used are each detection rule in the control line risk rule base. The feature matching engine invokes the matching rules and calculates the similarity score between the multi-dimensional indicator feature vector of the spatial unit and each typical risk pattern. If the similarity score exceeds a set threshold, it is determined that the spatial unit possesses the risk type of that typical risk pattern.

[0063] Optionally, in this embodiment of the application, the similarity score is calculated using weighted Euclidean distance. For example, for "edge effect type risk", the vector of its typical risk pattern can be defined as [expansion rate (weight 0.6), vegetation degradation index (weight 0.4)]; in other embodiments, the similarity score can also be calculated using a cosine similarity algorithm.

[0064] In this case, a spatial unit can be identified as having / potentially having one or more types of risk.

[0065] By integrating the risk matching results of all spatial units, the risk identification results of the target area are obtained.

[0066] Step S4: Based on the risk identification results, the risk prediction results of the risk units are obtained by assessing the risk level and trend evolution of the risk units of the spatial units.

[0067] Based on the risk identification results of the target area, the spatial distribution of risks in the target area can be preliminarily obtained, thereby identifying risk units within the spatial units. By calculating the comprehensive risk index of each risk unit, these risk units are classified into risk levels, from high risk, medium risk, low risk, and areas of concern, thus quantifying the degree of risk, obtaining a list of key risk areas, and providing timely control recommendations.

[0068] This application embodiment directly calculates the comprehensive risk index based on the risk identification results. The specific calculation formula is as follows: CRI_i = (S_ik * W_k); Wherein, CRI_i is the comprehensive risk index of the i-th spatial unit, S_ik is the similarity score between the i-th spatial unit and the k-th typical risk pattern, and W_k is the global weight of the k-th typical risk pattern, which is determined by the degree of influence of the typical risk pattern on the spatial unit.

[0069] The higher the comprehensive risk index, the higher the corresponding risk level.

[0070] Then, using the comprehensive risk index and the risk level, a risk evolution path is constructed for each spatial unit.

[0071] Specifically, for a risk unit, its comprehensive risk index and its main successfully matched typical risk patterns are extracted at several consecutive preset time points T1, T2, ..., Tn. Based on the typical risk patterns, the comprehensive risk index and the risk level are aligned at each preset time point to obtain a risk time series. The risk time series describes the risk level and risk index change trends of the risk unit at consecutive preset time points, thus obtaining the risk evolution path of the risk unit.

[0072] For example, the risk evolution path of risk unit A is as follows: In period T1, due to a slight increase in the edge effect index, the comprehensive risk index is 0.3, and the risk level is "attention zone"; in period T2, due to the addition of other risk types, the comprehensive risk index rises to 0.6, and the risk level is "medium risk"; in period T3, due to the occurrence of direct encroachment in the risk unit, the comprehensive risk index rises to 0.8, and the risk level is "high risk".

[0073] By integrating the risk levels and risk evolution paths of each risk unit, the risk prediction results for each risk unit are obtained.

[0074] As an improvement to the above solution, the embodiments of this application can also predict the risk level of a risk unit in a future time period through risk time series and preset prediction models, thereby achieving true trend prediction and significantly enhancing the foresight of the early warning.

[0075] Step S5: Visualize the risk prediction results and output a control line early warning analysis report for the target area.

[0076] The risk prediction results for each risk unit and non-risk unit are visualized to generate a control line early warning analysis report for the target area. This report includes the spatial distribution of risks in the target area, a list of key risk areas, main risk types, risk evolution trend predictions, and risk management recommendations.

[0077] Specifically, based on the geographical locations of each risk unit and non-risk unit in the target area, a spatial distribution of risk in the target area is constructed; based on the risk level of the risk units, a list of key risk areas is generated; and using the risk prediction results, risk evolution trend prediction and risk management recommendations are generated for each risk unit.

[0078] The generated control line early warning analysis report can show in detail the risk trends and risk types of the target area in terms of time and space, so as to support the refined and proactive governance of national land space.

[0079] Implementing the embodiments of this application has the following beneficial effects: This application's embodiments first collect multi-source spatial data covering multiple time points in the target area. By constructing a spatiotemporal cube, the multi-source data is linked together, enabling logical layering, dynamic temporal connection, and cross-verification, providing accurate data support for subsequent risk assessment. Then, typical risk patterns of various control lines are analyzed in the spatiotemporal dimension, generating risk quantification indicators for each typical risk pattern to define the characteristic conditions triggering risk alarms. A control line risk rule base is constructed based on the analyzed and defined content, providing fundamental rules for subsequent risk type identification. Next, different spatial analyses are performed on each spatial unit in the target area to determine the typical risk patterns that may exist in each spatial unit, accurately identifying the potential risks of each spatial unit and obtaining complete risk identification results. Based on the risk identification results, the risk level of risk units is quantified, and the risk development trend is analyzed to obtain the risk distribution of the entire target area. This achieves accurate risk warnings for large-scale, complex geographical environments and accurately predicts risk development trends, effectively supporting the refined and proactive governance of national land space.

[0080] Second Embodiment Furthermore, in order to implement the risk analysis system for the important control lines of national land space corresponding to the above method embodiments, and to achieve the corresponding functions and technical effects, Figure 2A structural diagram of a risk analysis system for important control lines of national territory is provided. For ease of explanation, only the parts relevant to this embodiment are shown. The risk analysis system for important control lines of national territory provided in this application embodiment includes: The data association module 201 is used to acquire multi-source spatial data of the target area at several preset time points, and to construct a spatiotemporal cube by performing data association on the multi-source spatial data.

[0081] In this embodiment of the application, control line vector data, land cover data, land use change data, remote sensing image data, human activity data, and natural geographic data covering the target area are collected at several consecutive preset time points. The collected data are then standardized to obtain multi-source spatial data covering the target area.

[0082] Optionally, in this embodiment of the application, data is collected at least at three consecutive preset time points.

[0083] Using knowledge graph technology, standardized multi-source spatial data are correlated to construct a spatiotemporal cube that is logically layered, dynamically connected in time, and mutually verified in business.

[0084] The rule base construction module 202 is used to define typical risk patterns and risk quantification indicators for various important control lines of national land space in the time and space dimensions, and to build a control line risk rule base.

[0085] In this embodiment of the application, each type of important control line in territorial space has a corresponding risk type. Therefore, a textual description of each risk type is obtained and its features are extracted to obtain a textual feature vector for that risk type.

[0086] Based on the text feature vector, the judgment criteria and related keywords of each risk type are analyzed to obtain the risk quantification index of the risk type.

[0087] Simultaneously, words and phrases related to the important control lines of national territory are extracted from the text feature vector to determine the protection targets for each risk type. These protection targets are the important control lines of national territory corresponding to each risk type.

[0088] Finally, the obtained text feature vectors, risk quantification indicators, and protection targets are correlated to obtain typical risk patterns of important control lines in national land space. Based on this, the detection rules used for detecting important control lines in national land space are determined. Each rule defines the spatial, temporal, and attribute characteristic conditions that trigger risk alarms.

[0089] By integrating the detection rules representing the typical risk patterns, a scalable control line risk rule base is constructed, providing reliable matching conditions for subsequent risk detection of important territorial spatial control lines, and accurately identifying existing or potential risk categories for each control line.

[0090] The risk identification module 203 is used to match the corresponding typical risk pattern for each spatial unit in the target area based on the control line risk rule base and the spatiotemporal cube, so as to obtain the risk identification result.

[0091] In this embodiment of the application, the target area is divided into regular or irregular grids to obtain several spatial units; The risk quantification index is extracted from the control line risk rule base, and the index value of the risk quantification index for each spatial unit is calculated using the spatiotemporal cube; The index values ​​are modeled on a preset time series to obtain the temporal characteristics of the spatial unit; Based on the control line risk rule base, the risk unit and its matching typical risk pattern are identified by calculating the similarity score between the multidimensional indicator feature vector of the spatial unit and the typical risk pattern, thereby obtaining the risk identification result; wherein, the indicator value and the time series feature are fused to obtain the multidimensional indicator feature vector.

[0092] The risk prediction module 204 is used to obtain the risk prediction result of the risk unit by assessing the risk level and trend evolution of the risk unit of the spatial unit based on the risk identification result.

[0093] In this embodiment of the application, based on the risk identification results, the comprehensive risk index of the risk unit is obtained by weighted summation of the similarity scores between the risk unit and each of the typical risk patterns; Based on the magnitude of the comprehensive risk index, the risk units are classified into risk levels; wherein, the risk levels from high to low are high risk, medium risk, low risk, and concern zone; Using the comprehensive risk index and the risk level, construct the risk evolution path of the risk unit at the preset time point; The risk prediction result is obtained by integrating the risk level and the risk evolution path of the risk unit.

[0094] The result visualization module 205 is used to visualize the risk prediction results and output a control line early warning analysis report for the target area.

[0095] In this embodiment, the risk prediction results for each risk unit and non-risk unit are visualized to generate a control line early warning analysis report for the target area. The control line early warning analysis report includes the spatial distribution of risks in the target area, a list of key risk areas, main risk types, risk evolution trend predictions, and risk management recommendations.

[0096] Specifically, based on the geographical locations of each risk unit and non-risk unit in the target area, a spatial distribution of risk in the target area is constructed; based on the risk level of the risk units, a list of key risk areas is generated; and using the risk prediction results, risk evolution trend prediction and risk management recommendations are generated for each risk unit.

[0097] The generated control line early warning analysis report can show in detail the risk trends and risk types of the target area in terms of time and space, so as to support the refined and proactive governance of national land space.

[0098] In some embodiments, the risk identification module 203 specifically comprises: This application embodiment constructs a feature matching engine capable of understanding complex risk scenarios. This engine matches typical risk patterns to the target area through a multi-level, multi-modal intelligent matching process, thereby achieving risk identification.

[0099] The feature matching engine first calculates the value of risk quantification indicators for the target area through spatial analysis, then extracts time-series features, and finally achieves intelligent matching of typical risk patterns through multi-feature fusion, thereby determining the corresponding risk type for each geographical location in the target area.

[0100] First, the target area is divided into regular or irregular grids to obtain several spatial units. For example, the size of a regular spatial unit is 100m × 100m; if the target area is a township or administrative village, it can be divided into irregular spatial units.

[0101] By dividing the spatial area into units, precise risk matching is performed on the target area to obtain a complete spatial risk distribution.

[0102] This application employs three different spatial analysis methods to assess the risk of spatial units: buffer analysis, overlay analysis, and spatial pattern analysis. Buffer analysis is primarily used to calculate the impact of the area surrounding the control line on the control line; overlay analysis is primarily used to identify the intersections and attribute changes between spatial units; and spatial pattern analysis is primarily used to assess the integrity and stability of the spatial pattern.

[0103] First, extract the multi-source spatiotemporal data required for each spatial analysis method from the spatiotemporal cube. Then, determine the risk quantification index used for each spatial analysis method based on the control line risk rule base. Finally, use the multi-source spatiotemporal data to calculate each risk quantification index for each spatial unit.

[0104] When using buffer analysis, the outer areas within a spatial unit that are outside the important control lines of national land space are first located, and these areas are defined as buffer zones. Using acquired multi-source spatiotemporal data, including control line data, land use data, human activity data (such as construction project sites), and natural geographic data, indicators are calculated for the buffer zones to identify the edge effect risks and pressure transmission risks of the spatial unit, thus obtaining the buffer analysis results.

[0105] For example, when identifying edge effect risks, multiple buffer zones, such as 500m and 1km, are established at the ecological protection red line of a spatial unit. Within each buffer zone, the rate of construction land expansion is calculated using multi-source spatiotemporal data, or the slope of the mean NDVI over time is calculated to obtain the vegetation degradation index of the buffer zone. This yields the rate of change within the buffer zone related to a certain risk quantification indicator, which is used as the buffer zone analysis result. The construction land expansion rate can be expressed by the formula "(T...". n Phase 1 construction land area - T n-1 (Construction land area in the first phase) / T n-1 The area of ​​land under construction is calculated; the mean NDVI refers to the normalized vegetation index, which is mainly related to the natural geographic data of multi-source spatiotemporal data.

[0106] For example, when identifying the risk of pressure transmission, a buffer zone is set within a certain range outside the urban development boundary of a certain spatial unit. The centroid coordinates of newly added construction land are added within the buffer zone, and the migration distance and rate of the centroid coordinates towards the ecological protection red line over time are analyzed as one of the results of the buffer zone analysis.

[0107] When using overlay analysis, multi-source spatiotemporal data, including control line data, land use / change survey data, and human activity data, are used to overlay geographic patches provided by the multi-source spatiotemporal data with important control lines of the national land space within the spatial unit. By calculating the area ratio of the geographic patches, the direct encroachment risk and functional degradation risk of the spatial unit can be detected, and the overlay analysis results are obtained.

[0108] For example, when identifying direct encroachment risks, human activity patches of newly approved construction projects within a spatial unit are obtained through multi-source spatiotemporal data. The human activity patches are then spatially intersected with the control line vector surface to directly output the encroachment area and encroachment ratio of the newly approved construction projects, thereby obtaining the overlay analysis results. When identifying functional degradation risks, geographical patches of "planting non-grain crops" in a spatial unit are obtained through altered survey data. These geographical patches are then overlaid with the permanent basic farmland area of ​​the spatial unit to calculate the non-grain crop area ratio of the spatial unit, thereby obtaining the overlay analysis results.

[0109] When using spatial pattern analysis, high-precision land use data is used to calculate the land fragmentation index and the nearest distance between adjacent control line areas for each country's important land spatial control line within a spatial unit. Functional degradation risk and cross-conflict risk are identified for the spatial unit, and spatial pattern analysis results are obtained.

[0110] For example, when identifying functional degradation risks, patch density (PD) or area-weighted average shape index (AWMSI) is used to calculate the fragmentation index of permanent basic farmland areas within a spatial unit. An increase in PD or an abnormal AAWSI value indicates that farmland is fragmented, its morphology is becoming more complex, and the stability of the spatial unit is decreasing. When identifying cross-conflict risks, in areas adjacent to ecological protection red lines and urban development boundaries, the shortest distance between the boundaries of the two adjacent control lines is calculated, and the area of ​​highly overlapping buffer zones is statistically analyzed to obtain spatial pattern analysis results.

[0111] Finally, based on the results of buffer analysis, overlay analysis, and spatial pattern analysis, the numerical values ​​of each risk quantification indicator for the spatial unit are obtained.

[0112] By introducing the above spatial analysis method, identification rules for risk transmission between control lines are introduced, which can reveal the risk correlation within the national spatial system and support systematic and holistic governance decisions.

[0113] For each spatial unit, time-series modeling is performed based on the aforementioned index values ​​to extract deeper time-series features, providing support for subsequent risk evolution and dynamic early warning.

[0114] The values ​​of each indicator are modeled on preset time series T1, T2, ..., Tn to obtain the time series of each risk quantification indicator. Using these time series, the trend characteristics, fluctuation characteristics, and dynamic weights of each risk quantification indicator are calculated to obtain the temporal characteristics of the spatial unit.

[0115] Specifically, the slope of the indicator time series is calculated using linear regression to determine whether it is continuously deteriorating, improving, or passive, thus obtaining the trend characteristics; the standard deviation or coefficient of variation of the indicator time series is calculated to determine the severity of the indicator value change, thus obtaining the fluctuation characteristics. The weights of risk quantification indicators are not fixed. Therefore, in this embodiment, dynamic weights are assigned to the risk quantification indicators based on the spatiotemporal characteristics of the spatial unit, thereby further improving the reliability and accuracy of risk identification.

[0116] For example, for an area that has shown a high risk of encroachment, the weight of its "expansion rate" metric would be dynamically increased over time, because sustained growth is more threatening than a single high value.

[0117] Compared to the simple threshold judgment in existing technologies, the embodiments of this application adopt a typical risk pattern matching method based on multi-feature weighted fusion, which can accurately identify potential abnormal risks of spatial units and has a stronger contextual understanding ability, making it suitable for large-scale, routine monitoring and early warning operations.

[0118] First, when the feature matching engine performs risk matching, its matching object is the multi-dimensional indicator feature vector of each spatial unit. This multi-dimensional indicator feature vector is obtained by fusing the indicator values ​​and the time-series features; in some embodiments, dynamic weights may also be fused. The matching rules used are each detection rule in the control line risk rule base. The feature matching engine invokes the matching rules and calculates the similarity score between the multi-dimensional indicator feature vector of the spatial unit and each typical risk pattern. If the similarity score exceeds a set threshold, it is determined that the spatial unit possesses the risk type of that typical risk pattern.

[0119] Optionally, in this embodiment of the application, the similarity score is calculated using weighted Euclidean distance. For example, for "edge effect type risk", the vector of its typical risk pattern can be defined as [expansion rate (weight 0.6), vegetation degradation index (weight 0.4)]; in other embodiments, the similarity score can also be calculated using a cosine similarity algorithm.

[0120] In this case, a spatial unit can be identified as having / potentially having one or more types of risk.

[0121] By integrating the risk matching results of all spatial units, the risk identification results of the target area are obtained.

[0122] In some embodiments, the risk prediction module 204 specifically comprises: Based on the risk identification results of the target area, the spatial distribution of risks in the target area can be preliminarily obtained, thereby identifying risk units within the spatial units. By calculating the comprehensive risk index of each risk unit, these risk units are classified into risk levels, from high risk, medium risk, low risk, and areas of concern, thus quantifying the degree of risk, obtaining a list of key risk areas, and providing timely control recommendations.

[0123] This application embodiment directly calculates the comprehensive risk index based on the risk identification results. The specific calculation formula is as follows: CRI_i = (S_ik * W_k); Wherein, CRI_i is the comprehensive risk index of the i-th spatial unit, S_ik is the similarity score between the i-th spatial unit and the k-th typical risk pattern, and W_k is the global weight of the k-th typical risk pattern, which is determined by the degree of influence of the typical risk pattern on the spatial unit.

[0124] The higher the comprehensive risk index, the higher the corresponding risk level.

[0125] Then, using the comprehensive risk index and the risk level, a risk evolution path is constructed for each spatial unit.

[0126] Specifically, for a risk unit, its comprehensive risk index and its main successfully matched typical risk patterns are extracted at several consecutive preset time points T1, T2, ..., Tn. Based on the typical risk patterns, the comprehensive risk index and the risk level are aligned at each preset time point to obtain a risk time series. The risk time series describes the risk level and risk index change trends of the risk unit at consecutive preset time points, thus obtaining the risk evolution path of the risk unit.

[0127] For example, the risk evolution path of risk unit A is as follows: In period T1, due to a slight increase in the edge effect index, the comprehensive risk index is 0.3, and the risk level is "attention zone"; in period T2, due to the addition of other risk types, the comprehensive risk index rises to 0.6, and the risk level is "medium risk"; in period T3, due to the occurrence of direct encroachment in the risk unit, the comprehensive risk index rises to 0.8, and the risk level is "high risk".

[0128] By integrating the risk levels and risk evolution paths of each risk unit, the risk prediction results for each risk unit are obtained.

[0129] As an improvement to the above solution, the embodiments of this application can also predict the risk level of a risk unit in a future time period through risk time series and preset prediction models, thereby achieving true trend prediction and significantly enhancing the foresight of the early warning.

[0130] Implementing the embodiments of this application has the following beneficial effects: This application's embodiments first collect multi-source spatial data covering multiple time points in the target area. By constructing a spatiotemporal cube, the multi-source data is linked together, enabling logical layering, dynamic temporal connection, and cross-verification, providing accurate data support for subsequent risk assessment. Then, typical risk patterns of various control lines are analyzed in the spatiotemporal dimension, generating risk quantification indicators for each typical risk pattern to define the characteristic conditions triggering risk alarms. A control line risk rule base is constructed based on the analyzed and defined content, providing fundamental rules for subsequent risk type identification. Next, different spatial analyses are performed on each spatial unit in the target area to determine the typical risk patterns that may exist in each spatial unit, accurately identifying the potential risks of each spatial unit and obtaining complete risk identification results. Based on the risk identification results, the risk level of risk units is quantified, and the risk development trend is analyzed to obtain the risk distribution of the entire target area. This achieves accurate risk warnings for large-scale, complex geographical environments and accurately predicts risk development trends, effectively supporting the refined and proactive governance of national land space.

[0131] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of this application. It should be understood that the above descriptions are merely specific embodiments of this application and are not intended to limit the scope of protection of this application. In particular, it should be noted that any modifications, equivalent substitutions, or improvements made by those skilled in the art within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A risk analysis method for important control lines in national land space, characterized in that, include: Acquire multi-source spatial data of the target area at several preset time points, and construct a spatiotemporal cube by associating the multi-source spatial data; wherein, the multi-source spatial data includes control line vector data, land cover data, land use change data, remote sensing image data, human activity data, and natural geographic data; Define typical risk patterns and risk quantification indicators for various important territorial spatial control lines in terms of time and space, and construct a control line risk rule base; Based on the control line risk rule base and the spatiotemporal cube, the corresponding typical risk pattern is matched for each spatial unit in the target area to obtain the risk identification result; Based on the risk identification results, the risk prediction results of the risk units are obtained by assessing the risk level and trend evolution of the risk units of the space unit. The risk prediction results are visualized, and a control line early warning analysis report for the target area is output.

2. The risk analysis method for important control lines of national territory according to claim 1, characterized in that, The process of acquiring multi-source spatial data of the target area at several preset time points, and constructing a spatiotemporal cube by associating the multi-source spatial data, specifically involves: At several consecutive preset time points, control line vector data, land cover data, land use change data, remote sensing image data, human activity data, and natural geographic data of the target area are collected to obtain the multi-source spatial data. Knowledge graph technology is used to associate the multi-source spatial data and construct a spatiotemporal cube.

3. The risk analysis method for important control lines of national territory according to claim 1, characterized in that, The document defines typical risk patterns and quantitative risk indicators for various important territorial spatial control lines in terms of time and space, and constructs a control line risk rule base, specifically as follows: Feature extraction is performed on the textual descriptions of risk types corresponding to important control lines of national land space to obtain textual feature vectors; Based on the text feature vector, the risk type is analyzed according to the judgment criteria to obtain the risk quantification index of the risk type; Extract words and phrases related to important control lines of national territory from the text feature vector to determine the protection targets of the risk type; By associating the text feature vector, the risk quantification index, and the protection target, typical risk patterns of important control lines in national territorial space are obtained. Integrate all the typical risk models mentioned above to construct a control line risk rule base.

4. The risk analysis method for important control lines of national territory according to claim 1, characterized in that, Based on the control line risk rule base and the spatiotemporal cube, the typical risk pattern is matched for each spatial unit in the target area to obtain the risk identification result, specifically as follows: The target area is divided into regular or irregular grids to obtain several spatial units; The risk quantification index is extracted from the control line risk rule base, and the index value of the risk quantification index for each spatial unit is calculated using the spatiotemporal cube; The index values ​​are modeled on a preset time series to obtain the temporal characteristics of the spatial unit; Based on the control line risk rule base, the risk unit and its matching typical risk pattern are identified by calculating the similarity score between the multidimensional indicator feature vector of the spatial unit and the typical risk pattern, thereby obtaining the risk identification result; wherein, the indicator value and the time series feature are fused to obtain the multidimensional indicator feature vector.

5. The risk analysis method for important control lines of national territory according to claim 4, characterized in that, The calculation of the risk quantification index value for each spatial unit using the spatiotemporal cube is specifically as follows: Spatiotemporal data corresponding to each spatial analysis method are extracted from the spatiotemporal cube, and the risk quantification index corresponding to each spatial analysis method is determined according to the control line risk rule base; wherein, the spatial analysis method includes buffer analysis, overlay analysis and spatial pattern analysis; For the peripheral area within the spatial unit located outside the important control line of the national territory, buffer analysis is used to calculate the rate of change of the peripheral area using the spatiotemporal data, and the buffer analysis results are obtained. By overlay analysis, the geographic patches provided by the spatiotemporal data are overlaid with the important control lines of the national land space within the spatial unit, and the area ratio of the geographic patches is calculated to obtain the overlay analysis results. For the control line areas where important land spatial control lines of each country are located within the spatial unit, spatial pattern analysis is conducted, and the spatiotemporal data is used to calculate the land fragmentation index of the control line area and the nearest distance to adjacent control line areas to obtain spatial pattern analysis results. Based on the results of buffer analysis, overlay analysis, and spatial pattern analysis, the numerical values ​​of each risk quantification indicator for the spatial unit are obtained.

6. The risk analysis method for important control lines of national territory according to claim 4, characterized in that, The step of modeling the index values ​​on a preset time series to obtain the temporal characteristics of the spatial unit specifically involves: The values ​​of each indicator are modeled on a preset time series to obtain the time series of each risk quantification indicator. The slope of the time series of the indicator is calculated using linear regression to obtain the changing trend characteristics of the risk quantification indicator. Calculate the standard deviation or coefficient of variation of the time series of the indicator to obtain the change and fluctuation characteristics of the risk quantification indicator; Based on the spatiotemporal characteristics of the spatial unit, dynamic weights are assigned to the risk quantification indicators; The time series characteristics are obtained by combining the trend characteristics, the fluctuation characteristics, and the dynamic weights.

7. The risk analysis method for important control lines of national territory according to claim 1, characterized in that, Based on the risk identification results, the risk prediction results of the risk units are obtained by assessing their risk levels and analyzing their trend evolution. Specifically: Based on the risk identification results, the comprehensive risk index of the risk unit is obtained by weighted summation of the similarity scores between the risk unit and each of the typical risk patterns. Based on the magnitude of the comprehensive risk index, the risk units are classified into risk levels; wherein, the risk levels from high to low are high risk, medium risk, low risk, and concern zone; Using the comprehensive risk index and the risk level, construct the risk evolution path of the risk unit at the preset time point; The risk prediction result is obtained by integrating the risk level and the risk evolution path of the risk unit.

8. The risk analysis method for important control lines of national territory according to claim 7, characterized in that, The method of using the comprehensive risk index and the risk level to construct the risk evolution path of the risk unit at the preset time point specifically includes: Extract the comprehensive risk index of the risk unit at each preset time point to determine the typical risk pattern corresponding to the risk unit at each preset time point; Aligning the comprehensive risk index and the risk level at the preset time points yields a risk time series. By describing the risk level and risk index change trends of the risk unit at consecutive preset time points through risk time series, the risk evolution path of the risk unit is obtained.

9. The risk analysis method for important control lines of national territory according to claim 1, characterized in that, The visualization of the risk prediction results and the output of a control line early warning analysis report for the target area are specifically as follows: Based on the geographical location of the risk units in the target area, construct the spatial distribution of risks in the target area; Based on the risk level of the risk unit, a list of key risk areas is generated; Using the risk prediction results, risk evolution trend prediction and risk management recommendations are generated for the risk unit; Based on the aforementioned risk spatial distribution, the aforementioned list of key risk areas, trend prediction results, and risk management recommendations, a control line early warning analysis report is constructed.

10. A risk analysis system for important control lines in national territory, characterized in that, include: The system includes a data association module, a rule base construction module, a risk identification module, a risk prediction module, and a results visualization module. The data association module is used to acquire multi-source spatial data of the target area at several preset time points, and to construct a spatiotemporal cube by performing data association on the multi-source spatial data; wherein the multi-source spatial data includes control line vector data, land cover data, land use change data, remote sensing image data, human activity data and natural geographic data. The rule base construction module is used to define typical risk patterns and risk quantification indicators for various important control lines of national land space in the time and space dimensions, and to build a control line risk rule base. The risk identification module is used to match the corresponding typical risk pattern for each spatial unit in the target area based on the control line risk rule base and the spatiotemporal cube, so as to obtain the risk identification result; The risk prediction module is used to obtain the risk prediction result of the risk unit by assessing the risk level and trend evolution of the risk unit of the spatial unit based on the risk identification result; The results visualization module is used to visualize the risk prediction results and output a control line early warning analysis report for the target area.

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