Rainfall landslide path analysis method and device based on road adjacency relationship

Abstract

The application relates to the technical field of spatial network analysis and path optimization, and discloses a rainfall landslide path analysis method and device based on road adjacency relationship, which comprises the following steps: acquiring road network data of a target area, and preprocessing the road network data; based on a spatial topology judgment rule, automatically identifying other road network units adjacent to each road network unit in space, generating and storing adjacency relationship information corresponding to each road network unit; associating the adjacency relationship information with the corresponding road network unit, and constructing a digital road network model with adjacency attribute information; acquiring disaster influence range data influencing road network passing, and determining impassable road section information in the road network; based on the digital road network model and the impassable road section information, performing network path search, and generating at least one transfer path from a starting point area to an ending point area. The application utilizes automatically constructed road network adjacency relationship to quickly generate a safe transfer path avoiding dangerous road sections.

Description

Technical Field

[0001] This invention relates to the field of spatial network analysis and path optimization technology, specifically to a method and apparatus for analyzing rainfall-induced landslide paths based on road adjacency relationships. Background Technology

[0002] In sudden disasters (such as landslides and mudslides), efficient and safe evacuation of people is a key measure to reduce casualties. As the core carrier of personnel evacuation, the traffic status of urban road networks often changes dynamically under the influence of disasters. For example, some road sections may be interrupted due to water accumulation, landslides, or traffic control. This randomness and dynamism pose a severe challenge to traditional route planning methods.

[0003] In related technologies, personnel evacuation route planning technology mainly faces the following problems: existing route planning methods are insufficiently adaptable to dynamic changes in road networks. Traditional route planning is mostly based on static road network topology, with pre-set fixed evacuation responsibility areas or route schemes. However, in disaster scenarios, the road network structure will change in real time due to dynamic changes such as road blockages and one-way traffic. If evacuation continues to be guided according to fixed routes, it is very easy to cause people to turn back, detour and delay, or even fall into danger, significantly reducing the evacuation success rate. Summary of the Invention

[0004] In view of this, the present invention provides a method and apparatus for analyzing rainfall-induced landslide paths based on road adjacency relationships, in order to solve the problem of being unable to quickly generate safe evacuation paths to avoid dangerous road sections under the dynamic influence of disasters.

[0005] In a first aspect, the present invention provides a method for analyzing rainfall-induced landslide paths based on road adjacency relationships. The method includes acquiring road network data of a target area and preprocessing the road network data, wherein the road network data consists of several road network units. Based on spatial topology determination rules, the system automatically identifies other road network units that are spatially adjacent to each road network unit, and generates and stores the adjacency relationship information corresponding to each road network unit. The adjacency relationship information is associated with the corresponding road network units to construct a digital road network model with adjacency attribute information; Obtain disaster impact range data that affects road network traffic, and determine the information of impassable road sections in the road network based on the disaster impact range data; Based on the digital road network model and the information on impassable road sections, a network path search is performed to generate at least one transfer path from the starting area to the ending area.

[0006] This system automatically identifies the adjacency relationships of road network units based on spatial topology determination rules and constructs a digital road network model. This improves the automation level and topological accuracy of road network data processing, avoiding the inefficiency and error risks associated with manually calibrating each adjacency unit. By associating adjacency attribute information with road network units, the road network model possesses structured semantic expression capabilities, enabling rapid response to local road network changes in subsequent analyses without repeatedly constructing the underlying topology. It dynamically calibrates impassable road sections by integrating disaster impact range data and combines network path search driven by adjacency relationships, allowing the transfer path generation process to avoid blocked units in real time. Simultaneously, it efficiently enumerates optional paths in the remaining feasible road network using adjacency transitivity, effectively balancing path optimality and computational overhead. This shortens the path planning response time and improves the accuracy of road network resilience assessment in emergency evacuation and disaster response scenarios. It also supports the generation of path sets under multi-starting and multi-endpoint conditions, providing reliable and traceable road network accessibility support for decision-making and command under disaster conditions.

[0007] In one optional implementation, the step of automatically identifying other spatially adjacent road network units for each road network unit based on spatial topology determination rules includes: Traverse each road network unit in the road network data; Using the geometric range of the current road network unit as the query condition, perform a spatial intersection query on all road network units; All road network units in the query results other than themselves are identified as road network units adjacent to the current road network unit.

[0008] This system automatically identifies the adjacency relationships of road network units based on spatial topology determination rules and constructs a digital road network model. This improves the automation level and topological accuracy of road network data processing, avoiding the inefficiency and error risks associated with manually calibrating each adjacency unit. By associating adjacency attribute information with road network units, the road network model possesses structured semantic expression capabilities, enabling rapid response to local road network changes in subsequent analyses without repeatedly constructing the underlying topology. It dynamically calibrates impassable road sections by integrating disaster impact range data and combines network path search driven by adjacency relationships, allowing the transfer path generation process to avoid blocked units in real time. Simultaneously, it efficiently enumerates optional paths in the remaining feasible road network using adjacency transitivity, effectively balancing path optimality and computational overhead. This shortens the path planning response time and improves the accuracy of road network resilience assessment in emergency evacuation and disaster response scenarios. It also supports the generation of path sets under multi-starting and multi-endpoint conditions, providing reliable and traceable road network accessibility support for decision-making and command under disaster conditions.

[0009] In one optional implementation, generating and storing the adjacency information corresponding to each of the road network units includes: Extract the unique identifier of the adjacent network units corresponding to each network unit; The unique identifiers are concatenated into a list of adjacent identifiers according to a preset separator; The data is stored in the attribute records of the road network unit in the form of structured data.

[0010] By extracting and concatenating the identifiers of adjacent units into a list string, lightweight storage of adjacency relationship data is achieved. This avoids the need to build complex relational data tables or graph databases separately to store topological relationships, thus significantly reducing data storage overhead and maintenance complexity. The list format concatenated with preset delimiters maintains high data readability and supports quick reconstruction of the adjacent unit set through simple string splitting operations. This eliminates the need to re-execute spatial intersection queries during subsequent path search or topology analysis; all adjacency information can be obtained directly by reading attribute records, significantly reducing the time consumption caused by repeated calculations. By permanently associating adjacency information as attribute fields with road network units, it ensures that adjacency relationships are not lost or distorted during data export, backup, or cross-system migration of the road network model. This enhances the independence and portability of the digital road network model and provides efficient data support for quickly loading road network topology and performing emergency path planning in disaster scenarios.

[0011] In one optional implementation, constructing a digital road network model with adjacency attribute information includes: A road network topology model is constructed using any road network unit as a node and its corresponding adjacency information as the basis for edge connection. In the road network topology model, each node is associated with and stores the geometric information of the road network unit corresponding to that node and a list of adjacent nodes.

[0012] By abstracting road network units as graph nodes and adjacency relationships as graph edges, a standardized road network topology graph model is constructed. This transforms the complex spatial road network problem into a classic graph theory computable problem, enabling the direct reuse of mature path search algorithms such as Dijkstra and A*. This significantly reduces the adaptation and development costs of path planning algorithms in disaster emergency scenarios. The design of storing geometric information and adjacency node lists in association with nodes allows for the simultaneous acquisition of location coordinates and connectivity relationships without repeatedly accessing the original road network data or performing spatial queries during path search. This greatly reduces I / O overhead and real-time computation, improving search efficiency. The graph model's data structure naturally supports dynamic addition and deletion of nodes and edges. When updates to disaster impact range data render certain road sections impassable, the road network status can be quickly updated simply by marking or removing the corresponding edges in the graph, without rebuilding the entire model. This enhances the system's flexibility in responding to dynamic disaster information and provides an efficient and scalable computational foundation for generating reliable transfer paths from the starting area to the ending area.

[0013] In one optional implementation, determining the information on impassable road sections in the road network based on disaster impact range data includes: Spatially overlay the disaster impact range data with the road network data to determine that road network units falling within the disaster impact range are impassable road sections. The disaster impact range data is the range data obtained by binarizing the spatial distribution boundary data or the impact intensity raster data after the disaster event occurs.

[0014] By automatically identifying impassable road sections through spatial overlay analysis, the spatial data of the disaster impact range is directly and automatically compared with the geometric data of road network units. This avoids misjudgments and omissions caused by manual visual interpretation or experience-based estimation, significantly improving the objectivity and accuracy of impassable road section identification. The input format, employing disaster distribution boundary data and binary raster data, can flexibly adapt to disaster monitoring data from different sources and with varying precision (such as flood boundaries interpreted from satellite imagery and wind field ranges output from meteorological models), broadening the applicable scenarios and data compatibility. Using road network units as the smallest granularity of judgment, the judgment results can be seamlessly integrated with the adjacency attribute information and geometric information stored in the digital road network model. Impassable attributes can be quickly mapped to the corresponding edges or nodes in the topology model without additional data conversion, providing real-time updated traffic constraints for subsequent network path searches. This ensures that the generated transfer paths effectively avoid actual disaster-stricken areas, improving the reliability and safety of emergency decision-making.

[0015] In one alternative implementation, a network path search is performed to generate at least one transfer path from the origin region to the destination region, including: The road network units corresponding to the impassable road sections are marked as prohibited from passage or temporarily removed from the digital road network model; Based on the marked or removed road network model, the shortest path search algorithm is used to calculate the path with the minimum total impedance from the starting area to the ending area; The total impedance is calculated based on at least one of the following: path length, travel time, and safety factor.

[0016] By marking or temporarily removing impassable road sections, the road network model can dynamically respond to changes in the scope of disaster impact. Traffic constraints can take effect immediately without rebuilding the entire road network topology, significantly improving the flexibility and response efficiency of road network model updates in disaster scenarios. Retaining impassable units through marking, rather than physical deletion, ensures the complete preservation of original road network data, facilitating rapid restoration of traffic flow after the disaster's impact subsides. It also supports repeated marking and resetting operations in multiple disaster scenario simulations. Using total impedance calculated based on multiple factors such as path length, travel time, and safety factor as the path optimization objective breaks through the limitations of traditional shortest path optimization which relies solely on distance. It can comprehensively balance traffic efficiency and personnel safety in emergency evacuation scenarios. For example, in floods, it selects detour routes with short travel times and safe elevations rather than simply the shortest wading sections. Combined with the established topology model and adjacency information, it can perform efficient searches directly on the graph, ensuring the rapid generation of feasible evacuation paths even at a scale of tens of thousands of road network units. This provides timely, reliable, and multi-objective optimized path decision support for disaster emergency command.

[0017] In one optional implementation, the target area is the area affected by rainfall-induced landslide disasters, the starting point area is the danger zone or personnel gathering area within the area affected by rainfall-induced landslide disasters, and the ending point area is a safe assembly point or refuge outside the area affected by rainfall-induced landslide disasters; the disaster impact range data is a landslide range map obtained based on remote sensing image interpretation, geological data, or real-time monitoring data.

[0018] By clearly defining the starting point as a danger zone or personnel assembly area and the endpoint as an external safe assembly point, the route planning objectives are fully aligned with the actual disaster relief process. The generated evacuation routes can be directly used to guide the scientific evacuation of affected people and the safe deployment of rescue forces, avoiding the risk of secondary disasters caused by blind evacuation. Using remote sensing image interpretation, geological data, or real-time monitoring data as the source of landslide range maps, the determination of impassable road sections is based on the fusion of multi-source heterogeneous data, fully taking into account the timeliness and accuracy of disaster range identification. Remote sensing data ensures large-scale rapid coverage, geological data ensures detailed local characterization, and real-time monitoring ensures dynamic updating capabilities, which can adapt to the data acquisition conditions of different emergency stages (pre-disaster prediction, in-disaster response, and post-disaster assessment). By limiting the starting point to the inside of the disaster-affected area and the endpoint to the outside, the route search naturally has the directional evacuation characteristics from danger to safety. Combined with a multi-factor weighted total impedance model, it can effectively avoid the secondary risks caused by the dynamic expansion of the landslide impact range, providing a highly reliable route decision-making basis with clear spatial orientation and disaster type specificity for geological disaster emergency command.

[0019] Secondly, the present invention provides a rainfall-induced landslide path analysis device based on road adjacency relationships. The device includes an acquisition module for acquiring road network data of a target area and preprocessing the road network data. The identification module is used to automatically identify other road network units that are spatially adjacent to each road network unit based on spatial topology determination rules, and to generate and store the adjacency relationship information corresponding to each road network unit. The association module is used to associate the adjacency relationship information with the corresponding road network units to construct a digital road network model with adjacency attribute information. The determination module is used to acquire disaster impact range data that affects road network traffic, and determine the information of impassable road sections in the road network based on the disaster impact range data; The search module is used to perform network path search based on the digital road network model and the information on impassable road sections, and generate at least one transfer path from the starting area to the ending area.

[0020] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the rainfall-induced landslide path analysis method based on road adjacency relationship described in the first aspect or any corresponding embodiment.

[0021] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the rainfall-based landslide path analysis method based on road adjacency relationships described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0022] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a rainfall-induced landslide path analysis method based on road adjacency relationships according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating another rainfall-induced landslide path analysis method based on road adjacency relationships according to an embodiment of the present invention. Figure 3 This is a flowchart illustrating another rainfall-induced landslide path analysis method based on road adjacency relationships according to an embodiment of the present invention; Figure 4 This is a flowchart illustrating the fourth rainfall-induced landslide path method based on road adjacency relationships according to an embodiment of the present invention. Figure 5 This is a flowchart illustrating the fifth rainfall-induced landslide path analysis method based on road adjacency relationships according to an embodiment of the present invention. Figure 6 This is a flowchart illustrating the sixth rainfall-induced landslide path analysis method based on road adjacency relationships according to an embodiment of the present invention. Figure 7 This is a structural block diagram of a rainfall-induced landslide path analysis device based on road adjacency relationships according to an embodiment of the present invention; Figure 8 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0024] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0025] The application scenario in this invention is to generate safe and feasible personnel transfer routes for emergency decision-makers in the context of sudden natural disasters or public safety incidents, based on the road network adjacency topology and constrained by the impact of the disaster. This is a typical application scenario in the fields of emergency management informatization and intelligent disaster prevention and mitigation.

[0026] Optionally, during the planning of evacuation routes for residents in flood disasters, the city's road network vector data can be obtained, and the roads can be preprocessed by segmenting them into units; the spatial adjacency relationship of each road at the intersection can be automatically identified to construct a complete road network adjacency topology; the adjacency relationship can be associated with the road units to form a digital road network model; flood inundation range prediction data provided by the water resources department can be obtained, and roads in the inundation area can be marked as impassable sections; a route search can be performed with residential communities as the starting point and refuge sites as the ending point to generate multiple selectable evacuation routes.

[0027] This invention provides a rainfall-induced landslide path analysis method based on road adjacency relationships. By automatically constructing the road network adjacency topology, dynamically integrating disaster traffic constraints, and generating graph search paths, the static road network is upgraded into a dynamically computable disaster response model, enabling the rapid, safe, and reliable generation of personnel evacuation paths in disaster scenarios.

[0028] According to an embodiment of the present invention, a method for analyzing rainfall-induced landslide paths based on road adjacency relationships is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a rainfall-induced landslide path analysis method based on road adjacency relationships, which can be used on the aforementioned mobile terminals, such as mobile phones and tablets. Figure 1 This is a flowchart of a rainfall-induced landslide path analysis method based on road adjacency relationships according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Obtain road network data of the target area and preprocess the road network data, which consists of several road network units.

[0030] Specifically, the source of road network data can be any conventional map software, without any restrictions. In practical applications, the road network includes several roads. The preprocessing of road network data includes converting all road network data to the WGS-84 geographic coordinate system, using the D_WGS_1984 geodetic datum to ensure the uniformity of spatial coordinate datum and spatial positioning consistency of the spatial data, and then performing geometric repair and topological preprocessing on the road elements.

[0031] Step S102: Based on the spatial topology determination rules, automatically identify other road network units that are spatially adjacent to each road network unit, and generate and store the adjacency relationship information corresponding to each road network unit.

[0032] Specifically, the implementation process of the spatial topology determination rule can be as follows: input a preprocessed set of road network units (including the geometric coordinates and endpoint information of each unit); the determination rule is based on the common point rule (endpoints coinciding are considered adjacent), the common edge rule (edges overlapping are considered adjacent), and the tolerance proximity rule (distance less than the tolerance error is considered adjacent). The identification process involves traversing each road network unit, calculating its spatial relationship with all other units, automatically determining whether an adjacency relationship exists according to the rules, and finally outputting a list of adjacent units corresponding to each road network unit. This embodiment of the invention transforms the spatial geometric relationships between road network units into a data structure that can be directly called by the algorithm, providing connectivity knowledge for subsequent path search.

[0033] Road network units are the basic elements that constitute a road network, and can be road segments, road intersections, or even finer-grained lanes. In this embodiment, road network units correspond to standardized road segments after preprocessing and segmentation, and each standardized road segment has independent geometric and attribute information.

[0034] Step S103: Associate the adjacency relationship information with the corresponding road network unit to construct a digital road network model with adjacency attribute information.

[0035] Specifically, this embodiment of the invention establishes the link between road segments and adjacent road segments by associating each road network unit with an attribute field. This field stores its neighbor list, transforming the invisible spatial relationships between roads into attribute data. The modeling method of constructing a digital road network model centered on road network units is based on road network units as basic elements, with the adjacency relationships between road network units serving as edges in the digital road network model. This makes the relationship of where road network units come from and where they go more intuitive, and in the subsequent path search process, neighbors can be accessed directly through the nodes of road network units.

[0036] Step S104: Obtain disaster impact range data affecting road network traffic, and determine the information of impassable road sections in the road network based on the disaster impact range data.

[0037] Specifically, the disaster impact range data in the embodiments of the present invention can be the impact range data and intensity data of rainfall-induced landslide disasters. In practical applications, rainfall-induced landslide disaster data can be obtained through high-resolution optical remote sensing images and SAR deformation images. Radiometric correction, geometric correction and cloud and snow removal processing are performed on the images to extract image spectral features, texture features and terrain deformation features. The extracted features are matched and judged with preset landslide disaster standard features to generate rainfall-induced landslide impact range vector data and disaster intensity classification data.

[0038] The process of determining the information on impassable road sections in the road network based on disaster impact range data specifically includes: Based on landslide hazard assessment data and / or real-time rainfall monitoring data, determine the hazard zone and potential landslide impact zone; Spatial overlay analysis is performed between the danger zone and the road network data to identify road units falling within the danger zone and / or potential landslide impact zone as threatened road units, and these threatened road units are marked as impassable road sections.

[0039] Step S105: Based on the digital road network model and the information on impassable road sections, perform a network path search to generate at least one transfer path from the starting area to the ending area.

[0040] Specifically, in practical applications, based on the disaster-adapted digital road network model, the starting point of personnel transfer, and the safe evacuation endpoint, the weighted shortest path algorithm can be used to plan transfer paths. By reading the road adjacency relationship and traffic status information, interrupted road elements are eliminated, a connected topology path is constructed with road length as the weight, the passage cost of feasible paths is calculated, and the optimal transfer path and multiple alternative transfer paths are output.

[0041] Specifically, in the embodiments of the present invention, the endpoint region can be a pre-determined region or multiple pre-determined regions.

[0042] The path analysis method provided in this embodiment automatically identifies the adjacency relationships of road network units and constructs a digital road network model based on spatial topology determination rules. This improves the automation level and topological accuracy of road network data processing, avoids the inefficiency and error risks associated with manually calibrating each adjacency unit, and enables the road network model to have structured semantic expression capabilities by associating adjacency attribute information with road network units. This allows for rapid response to local road network changes in subsequent analysis without repeatedly constructing the underlying topology. It dynamically calibrates impassable road sections by integrating disaster impact range data and combines network path search driven by adjacency relationships to enable the transfer path generation process to avoid blocked units in real time. At the same time, it efficiently enumerates optional paths in the remaining feasible road network by utilizing adjacency transitivity, effectively balancing path optimality and computational overhead. This enables the shortening of path planning response time and the improvement of the accuracy of road network resilience assessment in emergency evacuation and disaster response scenarios. It also supports the generation of path sets under multiple starting and ending point conditions, providing reliable and traceable road network accessibility support for decision-making and command under disaster conditions.

[0043] Specifically, such as Figure 2 As shown, in some optional embodiments, step S102 above includes: Step S1021: Traverse each road network unit in the road network data.

[0044] Iterate through all road network units stored in the road network data to ensure that each road network unit is treated as a processing object.

[0045] Step S1022: Using the geometric range of the current road network unit as the query condition, perform a spatial intersection query on all road network units.

[0046] Using the geometric boundary or spatial coverage of the currently processed road network unit as the search criteria, a spatial intersection query operation is performed in the entire set of road network units. This query uses a spatial indexing mechanism to quickly filter out all other road network units that are geometrically overlapping, contacting, or intersecting with the current road network unit.

[0047] Step S1023: Determine all road network units in the query results other than itself as road network units adjacent to the current road network unit.

[0048] The current road network unit is removed from the result set returned by the query. Each remaining road network unit is then determined to be a unit that is directly adjacent to the current road network unit in space. This embodiment of the invention replaces complex graph theory modeling or manual annotation with pure geometric intersection determination. The adjacency matrix between all road network units can be completely established through one round of traversal and spatial query, providing basic topological data for subsequent road network connectivity analysis.

[0049] By achieving fully automated batch determination of adjacency relationships through spatial intersection queries, the significant manpower and subjective errors required for manual pairwise adjacency determination are completely eliminated. Simultaneously, spatial indexing accelerates the query process, enabling adjacency calculations for tens of thousands of units in large-scale road networks to be completed within seconds. This significantly improves the computational efficiency of the road network preprocessing stage. The strategy combining traversal and querying ensures that the adjacency information of each road network unit is generated independently and completely, without omissions or duplicate determinations, thus guaranteeing the global consistency and integrity of adjacency relationship data. Using geometric range as the query condition rather than relying on attribute field matching allows for adaptation to road network units of arbitrary geometric shapes (such as irregular polygons and linear road segments), exhibiting excellent data compatibility and generalization capabilities. This lays a solid data quality foundation for the reliable association of adjacency attribute information in subsequent digital road network models.

[0050] In some alternative implementations, such as Figure 3 As shown, step S102 above further includes: Step S1024: Extract the unique identifier of the adjacent network units corresponding to each network unit.

[0051] Step S1025: Concatenate the unique identifiers into an adjacency identifier list according to a preset separator.

[0052] Step S1024: Extract the unique identifier (such as unit ID or code) of each adjacent unit from the set of adjacent road network units determined in step S1023 in the road network data; Step S1025: Concatenate the extracted unique identifiers according to the preset separator (such as comma, semicolon or vertical bar) to form a structured adjacent identifier list string. This list completely carries all the adjacent relationships of the current unit in a compact text form.

[0053] Step S1026: Store the data in the attribute record of the road network unit in the form of structured data.

[0054] The concatenated list string is written into the attribute record of the current road network unit in the form of structured data and persistently stored as a new attribute field of the road network unit. This transforms the adjacency relationship from a temporary in-memory calculation result into attribute information that is permanently bound to the road network unit, so that the road network model has topological expression capabilities at the storage level.

[0055] By extracting and concatenating the identifiers of adjacent units into a list string, lightweight storage of adjacency relationship data is achieved. This avoids the need to build complex relational data tables or graph databases separately to store topological relationships, thus significantly reducing data storage overhead and maintenance complexity. The list format concatenated with preset delimiters maintains high data readability and supports quick reconstruction of the adjacent unit set through simple string splitting operations. This eliminates the need to re-execute spatial intersection queries during subsequent path search or topology analysis; all adjacency information can be obtained directly by reading attribute records, significantly reducing the time consumption caused by repeated calculations. By permanently associating adjacency information as attribute fields with road network units, it ensures that adjacency relationships are not lost or distorted during data export, backup, or cross-system migration of the road network model. This enhances the independence and portability of the digital road network model and provides efficient data support for quickly loading road network topology and performing emergency path planning in disaster scenarios.

[0056] In some alternative implementations, such as Figure 4 As shown, step S103 above includes: Step S1031: Construct a road network topology model using any road network unit as a node and its corresponding adjacency information as the basis for edge connection.

[0057] Each road network unit is treated as an independent node in the graph, and the list of adjacency identifiers stored in the attribute record of the road network unit is used as the basis for edge connection. That is, if the adjacency list of unit A contains the unique identifier of unit B, then an undirected or directed edge is constructed between unit A and unit B, thereby transforming the entire road network into a topological graph model composed of nodes and edges.

[0058] Step S1032: Each node in the road network topology model is associated with and stored with the geometric information of the road network unit corresponding to that node and a list of adjacent nodes.

[0059] In this graph model, two types of associated data are attached to each node: first, the geometric information of the road network unit corresponding to the node (such as coordinate sequence, shape length, area, or boundary range); and second, the list of adjacent nodes obtained by parsing the adjacency identifier list, so that each node contains both spatial geometric attributes and topological connectivity attributes. The road network topology graph model constructed in this way integrates spatial geometric data and topological relationship data into a unified graph structure, providing standardized data input for subsequent network analysis.

[0060] By abstracting road network units as graph nodes and adjacency relationships as graph edges, a standardized road network topology graph model is constructed. This transforms the complex spatial road network problem into a classic graph theory computable problem, enabling the direct reuse of mature path search algorithms such as Dijkstra and A*. This significantly reduces the adaptation and development costs of path planning algorithms in disaster emergency scenarios. The design of storing geometric information and adjacency node lists in association with nodes allows for the simultaneous acquisition of location coordinates and connectivity relationships without repeatedly accessing the original road network data or performing spatial queries during path search. This greatly reduces I / O overhead and real-time computation, improving search efficiency. The graph model's data structure naturally supports dynamic addition and deletion of nodes and edges. When updates to disaster impact range data render certain road sections impassable, the road network status can be quickly updated simply by marking or removing the corresponding edges in the graph, without rebuilding the entire model. This enhances the system's flexibility in responding to dynamic disaster information and provides an efficient and scalable computational foundation for generating reliable transfer paths from the starting area to the ending area.

[0061] In some optional implementations, the road network topology model is a directed graph or an undirected graph; when it is a directed graph, the adjacency information includes a travel direction attribute, which is preset according to the road's traffic rules.

[0062] The road network topology model can be flexibly configured into either a directed or undirected graph based on actual road traffic rules. When the road network topology model is an undirected graph, the adjacency relationship between road network units is assumed to be bidirectional, meaning that if unit A is adjacent to unit B, then unit A can reach unit B and vice versa. This is suitable for roads with unrestricted traffic direction or temporary passages that allow bidirectional traffic in emergency situations. When the road network topology model is a directed graph, the adjacency relationship information includes not only adjacent unit identifiers but also additional traffic direction attributes. These attributes are pre-defined according to road traffic rules—for example, marking edges as one-way or bidirectional based on road traffic flow regulations (one-way streets, reversible lanes, time-limited traffic, etc.). This ensures that the connection direction of edges in the graph model strictly corresponds to the permitted driving direction of the actual road, thereby accurately following real-world traffic constraints during path search and avoiding the generation of invalid paths that violate traffic rules.

[0063] By supporting both undirected and directed graph models, this approach endows road network topology representation with flexibility and scenario adaptability. Undirected graphs are suitable for scenarios where road traffic rules in disaster areas are simplified or where two-way evacuation is permitted after traffic control measures are implemented. They can maximize the utilization of available road network resources and simplify graph model construction costs. Directed graphs, on the other hand, can accurately map complex traffic rules such as one-way streets and time-based traffic restrictions on real-world roads, ensuring that the generated transfer paths are fully executable in the real road environment. This avoids the path planning results being unenforceable in actual driving due to the neglect of travel direction. The approach embeds travel direction attributes into the adjacency relationship information and pre-defines them. The design integrates directional constraints and topological connections within the same data structure, allowing the path search algorithm to simultaneously read directional constraints and perform pruning decisions while traversing adjacent nodes. This eliminates the need for additional queries to external traffic rule databases, significantly improving search efficiency and reducing the inconsistency risks associated with multi-source data associations. For traffic directions that may be temporarily adjusted in emergency scenarios involving rainfall and landslide disasters (such as reverse lanes opened for one-way evacuation), rapid response is achieved by dynamically updating the directional attributes in adjacency relationships, eliminating the need to rebuild the graph model and providing solid technical support for flexible traffic organization in emergency response.

[0064] In some alternative implementations, such as Figure 5 As shown, step S104 above includes: Step S1041: Spatial overlay analysis is performed on the disaster impact range data and the road network data to determine that the road network units falling within the disaster impact range are impassable road sections.

[0065] Spatial overlay analysis is performed on disaster impact range data and original road network data. This involves using geometric operations such as spatial intersection, inclusion, or coverage to determine whether the geometric range of each road network unit overlaps with or completely falls within the disaster impact range. Any road network unit that meets this condition is identified as an impassable road segment, thereby enabling the rapid removal of disaster-affected sections from the complete road network.

[0066] Step S1042, the disaster impact range data is the range data obtained by binarizing the spatial distribution boundary data or the impact intensity raster data after the disaster event occurs.

[0067] The sources and forms of disaster impact range data are limited. It can be continuous spatial distribution boundary data (such as flood inundation boundary polygons) obtained after a disaster event through remote sensing monitoring, on-site investigation or model simulation. It can also be range data obtained by binarizing impact intensity raster data (such as seismic intensity grid, landslide probability grid). That is, by setting a threshold, continuous intensity values ​​are converted into 0 / 1 binary regions, where the region with a value of 1 represents the range affected by the disaster. This ensures that the determination of impassable road sections has clear spatial quantitative basis, rather than relying on subjective experience estimation.

[0068] Areas with a disaster intensity probability greater than 30% are marked as 1 (affected), and the rest are marked as 0 (unaffected). This threshold can be dynamically adjusted according to the disaster type and emergency level.

[0069] The disaster impact range data is determined by at least one of the following methods: landslide hazard zones determined based on rainfall threshold models, landslide hazard probability grids generated based on machine learning models, landslide early warning zones triggered by real-time rainfall monitoring, landslide ranges based on remote sensing image interpretation, and landslide impact zones delineated based on geological data results.

[0070] The disaster impact range data is generated based on rainfall monitoring data, combined with topographic and geological data and a landslide hazard assessment model.

[0071] The landslide hazard assessment model is a machine learning model, and the input features of the machine learning model include at least several of the following: rainfall, slope, soil and rock type, vegetation cover, and historical landslide distribution.

[0072] By automatically identifying impassable road sections through spatial overlay analysis, the spatial data of the disaster impact range is directly and automatically compared with the geometric data of road network units. This avoids misjudgments and omissions caused by manual visual interpretation or experience-based estimation, significantly improving the objectivity and accuracy of impassable road section identification. The input format, employing disaster distribution boundary data and binary raster data, can flexibly adapt to disaster monitoring data from different sources and with varying precision (such as flood boundaries interpreted from satellite imagery and wind field ranges output from meteorological models), broadening the applicable scenarios and data compatibility. Using road network units as the smallest granularity of judgment, the judgment results can be seamlessly integrated with the adjacency attribute information and geometric information stored in the digital road network model. Impassable attributes can be quickly mapped to the corresponding edges or nodes in the topology model without additional data conversion, providing real-time updated traffic constraints for subsequent network path searches. This ensures that the generated transfer paths effectively avoid actual disaster-stricken areas, improving the reliability and safety of emergency decision-making.

[0073] In some alternative implementations, such as Figure 6 As shown, step S105 above includes: Step S1051: Mark the road network unit corresponding to the impassable road segment as prohibited from passage or temporarily remove it from the digital road network model.

[0074] The road network units corresponding to the impassable road segments identified in step S1041 are marked as prohibited from passage in the digital road network model or temporarily removed from the topology to ensure that they are not selected in subsequent path searches.

[0075] Step S1052: Based on the marked or removed road network model, the shortest path search algorithm is used to calculate the path with the minimum total impedance from the starting area to the ending area.

[0076] The total impedance is calculated based on at least one of the following: path length, travel time, and safety factor.

[0077] Based on the updated road network model, the shortest path search algorithm (such as Dijkstra's algorithm or A* algorithm) is used to calculate the path with the minimum total impedance from the starting area to the ending area. The total impedance is not based solely on the path length, but is calculated by weighting at least one of the following indicators: path length, travel time, and safety factor. This ensures that the path optimization objective takes into account multiple dimensions such as distance efficiency, timeliness, and safety.

[0078] The safety factor can be calculated by comprehensively weighting factors such as road grade, historical disaster frequency, and elevation information. For example, higher-grade roads are given a higher safety weight.

[0079] In the network path search, the transfer path is the path with the lowest overall travel cost determined based on the risk impedance. The overall travel cost includes at least one or more of the following: landslide hazard level, road traffic status, path distance, and travel time. The transfer path is a safe transfer path, which means a transfer path with the lowest overall travel cost and through which no road segments are marked as impassable.

[0080] The method of using residential areas or gathering points within the danger zone as the starting point for relocation and safe havens outside the danger zone as the destination specifically includes: obtaining information on gathering points within the danger zone and automatically setting these gathering points as the starting point for relocation; obtaining information on safe havens outside the danger zone and automatically setting these safe havens as the destination for relocation.

[0081] Before performing a network path search, the following is also included: Assign a traffic status and risk impedance to each of the aforementioned road network units; The passage status includes normal passage and impassable passage; The factors determining the risk impedance include at least one of the following: whether it is affected by landslides, landslide hazard level, road grade, travel time, distance, slope, and safety factor.

[0082] The execution of network path search generates at least one transfer path from the starting area to the ending area. Specifically, it includes: based on the adjacency relationship and risk impedance of the road network units, searching and determining the transfer path with the minimum comprehensive travel cost as the optimal transfer path, and the transfer path with the second minimum comprehensive travel cost as the alternative path; and outputting the transfer route, transfer distance, estimated time, avoidance points and impassable road sections of the optimal transfer path and the alternative path.

[0083] By marking or temporarily removing impassable road sections, the road network model can dynamically respond to changes in the scope of disaster impact. Traffic constraints can take effect immediately without rebuilding the entire road network topology, significantly improving the flexibility and response efficiency of road network model updates in disaster scenarios. Retaining impassable units through marking, rather than physical deletion, ensures the complete preservation of original road network data, facilitating rapid restoration of traffic flow after the disaster's impact subsides. It also supports repeated marking and resetting operations in multiple disaster scenario simulations. Using total impedance calculated based on multiple factors such as path length, travel time, and safety factor as the path optimization objective breaks through the limitations of traditional shortest path optimization which relies solely on distance. It can comprehensively balance traffic efficiency and personnel safety in emergency evacuation scenarios. For example, in floods, it selects detour routes with short travel times and safe elevations rather than simply the shortest wading sections. Combined with the established topology model and adjacency information, it can perform efficient searches directly on the graph, ensuring the rapid generation of feasible evacuation paths even at a scale of tens of thousands of road network units. This provides timely, reliable, and multi-objective optimized path decision support for disaster emergency command.

[0084] The target area is the area affected by rainfall-induced landslide disasters; the starting point area is the danger zone or personnel gathering area within the area affected by rainfall-induced landslide disasters; and the ending point area is the safe assembly point or refuge outside the area affected by rainfall-induced landslide disasters. The disaster impact range data is a landslide range map obtained based on remote sensing image interpretation, geological data, or real-time monitoring data.

[0085] The rainfall-induced landslide path analysis method based on road adjacency provided in this invention is applicable to emergency evacuation route planning in the context of rainfall-induced landslide disasters. The target area is defined as a specific geographical range affected by rainfall-induced landslide disasters. This area may contain multiple landslide hazard points or sections that have already collapsed. The starting area is defined as a danger zone (i.e., a high-risk or deforming area) or a people gathering area (i.e., a temporary gathering place for affected people awaiting evacuation) within the rainfall-induced landslide disaster-affected area. The ending area is a safe gathering point or refuge site outside the disaster-affected area (i.e., a safe area with temporary resettlement conditions and not threatened by landslides). The disaster-affected area data is specified as a landslide range map obtained based on remote sensing image interpretation (identifying landslide boundaries through comparison of pre- and post-disaster images), geological data (determining landslide distribution through on-site exploration and geological map analysis), or real-time monitoring data (dynamically updating the landslide range through sensor networks such as displacement gauges and rain gauges). This range map accurately depicts the actual spatial distribution of the landslide body and its affected zone in polygon or raster form.

[0086] By clearly defining the starting point as a danger zone or personnel assembly area and the endpoint as an external safe assembly point, the route planning objectives are fully aligned with the actual disaster relief process. The generated evacuation routes can be directly used to guide the scientific evacuation of affected people and the safe deployment of rescue forces, avoiding the risk of secondary disasters caused by blind evacuation. Using remote sensing image interpretation, geological data, or real-time monitoring data as the source of landslide range maps, the determination of impassable road sections is based on the fusion of multi-source heterogeneous data, fully taking into account the timeliness and accuracy of disaster range identification. Remote sensing data ensures large-scale rapid coverage, geological data ensures detailed local characterization, and real-time monitoring ensures dynamic updating capabilities, which can adapt to the data acquisition conditions of different emergency stages (pre-disaster prediction, in-disaster response, and post-disaster assessment). By limiting the starting point to the inside of the disaster-affected area and the endpoint to the outside, the route search naturally has the directional evacuation characteristics from danger to safety. Combined with a multi-factor weighted total impedance model, it can effectively avoid the secondary risks caused by the dynamic expansion of the landslide impact range, providing a highly reliable route decision-making basis with clear spatial orientation and disaster type specificity for geological disaster emergency command.

[0087] This embodiment also provides a rainfall-induced landslide path analysis device based on road adjacency relationships. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0088] This embodiment provides a rainfall-induced landslide path analysis device based on road adjacency relationships, such as... Figure 7 As shown, it includes: The acquisition module 701 is used to acquire road network data of the target area and preprocess the road network data.

[0089] The identification module 702 is used to automatically identify other road network units that are spatially adjacent to each road network unit based on spatial topology determination rules, and to generate and store the adjacency relationship information corresponding to each road network unit.

[0090] The association module 703 is used to associate the adjacency relationship information with the corresponding road network unit to construct a digital road network model with adjacency attribute information.

[0091] The determination module 704 is used to obtain disaster impact range data that affects road network traffic, and determine the information of impassable road sections in the road network based on the disaster impact range data.

[0092] The search module 705 is used to perform network path search based on the digital road network model and the information on impassable road sections, and generate at least one transfer path from the starting area to the ending area.

[0093] In some alternative implementations, the identification module 702 includes: A traversal unit is used to traverse each network unit in the network data. The query unit is used to perform spatial intersection queries on all road network units, using the geometric range of the current road network unit as the query condition. The determination unit is used to determine all road network units in the query results other than itself as road network units adjacent to the current road network unit.

[0094] In some alternative implementations, the identification module 702 further includes: Extraction unit, used to extract the unique identifier of the adjacent network units corresponding to each network unit; A splicing unit is used to splice the unique identifiers into a list of adjacent identifiers according to a preset separator; A storage unit is used to store the attribute records of the road network unit in the form of structured data.

[0095] In some alternative implementations, the association module 703 includes: The building unit is used to construct a road network topology graph model by taking any road network unit as a node and its corresponding adjacency information as the basis for edge connection; The association unit is used to associate and store the geometric information of the road network unit corresponding to the node and the list of adjacent nodes for each node in the road network topology model.

[0096] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0097] In this embodiment, the rainfall-induced landslide path analysis device based on road adjacency is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0098] This invention also provides a computer device having the above-described features. Figure 7 The device shown is a rainfall-induced landslide path analysis device based on road adjacency relationships.

[0099] Please see Figure 8 , Figure 8This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 8 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 8 Take a processor 10 as an example.

[0100] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0101] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0102] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0103] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0104] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 9 Taking the example of a connection between China and Israel via a bus.

[0105] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0106] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0107] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0108] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A rainfall-induced landslide path analysis method based on road adjacency relationships, characterized in that, include: Acquire road network data for the target area and preprocess the road network data, which consists of several road network units; Based on spatial topology determination rules, the system automatically identifies other road network units that are spatially adjacent to each road network unit, and generates and stores the adjacency relationship information corresponding to each road network unit. The adjacency relationship information is associated with the corresponding road network units to construct a digital road network model with adjacency attribute information; Obtain disaster impact range data that affects road network traffic, and determine the information of impassable road sections in the road network based on the disaster impact range data; Based on the digital road network model and the information on impassable road sections, a network path search is performed to generate at least one transfer path from the starting area to the ending area.

2. The method according to claim 1, characterized in that, The spatial topology-based determination rule automatically identifies other spatially adjacent road network units for each road network unit, including: Traverse each road network unit in the road network data; Using the geometric range of the current road network unit as the query condition, perform a spatial intersection query on all road network units; All road network units in the query results other than themselves are identified as road network units adjacent to the current road network unit.

3. The method according to claim 1, characterized in that, The generation and storage of adjacency information corresponding to each of the road network units includes: Extract the unique identifier of the adjacent network units corresponding to each network unit; The unique identifiers are concatenated into a list of adjacent identifiers according to a preset separator; The data is stored in the attribute records of the road network unit in the form of structured data.

4. The method according to claim 1, characterized in that, The construction of the digital road network model with adjacency attribute information includes: A road network topology model is constructed using any road network unit as a node and its corresponding adjacency information as the basis for edge connection. In the road network topology model, each node is associated with and stores the geometric information of the road network unit corresponding to that node and a list of adjacent nodes.

5. The method according to claim 1, characterized in that, The process of determining the information on impassable road sections in the road network based on disaster impact range data includes: Spatially overlay the disaster impact range data with the road network data to determine that road network units falling within the disaster impact range are impassable road sections. The disaster impact range data is the range data obtained by binarizing the spatial distribution boundary data or the impact intensity raster data after the disaster event occurs.

6. The method according to claim 1, characterized in that, Perform a network path search to generate at least one transfer path from the origin region to the destination region, including: The road network units corresponding to the impassable road sections are marked as prohibited from passage or temporarily removed from the digital road network model; Based on the marked or removed road network model, the shortest path search algorithm is used to calculate the path with the minimum total impedance from the starting area to the ending area; The total impedance is calculated based on at least one of the following: path length, travel time, and safety factor.

7. The method according to any one of claims 1-6, characterized in that, The target area is the area affected by rainfall-induced landslide disasters; the starting point area is the danger zone or personnel gathering area within the area affected by rainfall-induced landslide disasters; and the ending point area is the safe assembly point or refuge outside the area affected by rainfall-induced landslide disasters. The disaster impact range data is a landslide range map obtained based on remote sensing image interpretation, geological data, or real-time monitoring data.

8. A rainfall-induced landslide path analysis device based on road adjacency relationships, characterized in that, The device includes: The acquisition module is used to acquire road network data of the target area and preprocess the road network data; The identification module is used to automatically identify other road network units that are spatially adjacent to each road network unit based on spatial topology determination rules, and to generate and store the adjacency relationship information corresponding to each road network unit. The association module is used to associate the adjacency relationship information with the corresponding road network units to construct a digital road network model with adjacency attribute information. The determination module is used to acquire disaster impact range data that affects road network traffic, and determine the information of impassable road sections in the road network based on the disaster impact range data; The search module is used to perform network path search based on the digital road network model and the information on impassable road sections, and generate at least one transfer path from the starting area to the ending area.

9. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the rainfall-induced landslide path analysis method based on road adjacency relationships as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the rainfall-induced landslide path analysis method based on road adjacency relationships as described in any one of claims 1 to 7.

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