A waterlogging risk area detour route planning method and system

Abstract

This invention discloses a method and system for planning detour routes in flood-prone areas, relating to the field of intelligent navigation technology. The method includes: obtaining a sequence of water depth changes over time for road segments based on flood risk prediction data; generating basic routes based on the user's origin and destination, determining the estimated arrival time for each segment on each basic route, and determining the water depth at arrival at each segment based on the water depth sequence; determining the risk level of each segment and each basic route based on the water depth at arrival and water depth thresholds, and determining whether the user's selected basic route is at the highest risk level; if it is at the highest risk level and the user needs to detour, searching for alternative routes for the highest-risk segments in the selected basic routes to determine alternative routes. This achieves a proactive detour reminder mechanism that provides advance prediction and guidance, effectively alleviating regional traffic congestion during floods.

Description

Technical Field

[0001] This invention relates to the field of intelligent navigation technology, and in particular to a method and system for planning detour routes in areas at risk of urban flooding. Background Technology

[0002] Urban flooding caused by frequent extreme weather events such as short-duration heavy rainfall not only directly leads to traffic paralysis and property damage such as vehicle damage, but can also cause problems such as road obstruction and vehicle stalling due to flooded roads. When flooding occurs, traffic management departments usually guide traffic through methods such as road flood warning signs and temporary traffic control. However, this guidance information often has a narrow coverage area and limited transmission efficiency, making it difficult to guide travelers to avoid risky areas in advance.

[0003] Currently, mainstream intelligent navigation systems, when responding to sudden disasters such as flooding, have incorporated relevant alert functions based on post-disaster information feedback or manual reporting. The main methods include: Based on macro-level rainstorm warnings issued by meteorological departments, navigation interfaces display text or icon prompts such as "If some areas of the city experience heavy rain in the next hour, please take precautions against flooding." However, this type of alert only provides regional and macro-level risk warnings; its spatial granularity is coarse and its precision is low, failing to pinpoint specific flooded road sections and risk levels.

[0004] Relying on user-reported flooding information, users can upload information about water accumulation on a specific road section through the traffic reporting function of navigation apps. After the platform approves the report, it is pushed to the navigation system. This method is a reactive feedback, which has problems such as high information lag, poor timeliness, strong user dependence, and limited coverage. It cannot match the characteristics of flooding, which is sudden and rapidly changing. By the time the navigation system sends a notification, the user may have already entered a flooded area.

[0005] The data linkage technology for fixed monitoring points, piloted in some cities, integrates real-time water level data into navigation systems by installing water level sensors at flood-prone areas, triggering alerts when the water level exceeds a threshold. However, this technology only covers preset monitoring points and cannot identify road sections without sensors or temporary flooding caused by sudden terrain changes, resulting in serious monitoring blind spots. Summary of the Invention

[0006] To address the aforementioned issues, this invention proposes a method and system for planning detour routes in flood-prone areas. This method deeply integrates flood risk prediction and assessment results with intelligent navigation path planning algorithms to achieve an advance prediction and proactive guidance detour reminder mechanism, effectively alleviating regional traffic congestion during flood periods.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for planning detour routes in areas at risk of urban flooding, comprising: Based on the user's vehicle model, the water depth threshold corresponding to each risk level is determined from the preset vehicle water wading risk level database. Based on the obtained urban flooding risk prediction data, a time-varying sequence of water depth for road sections was obtained; A basic route is generated based on the user's origin and destination. The estimated arrival time of each segment in each basic route is determined, and the water depth at the time of arrival at each segment is determined based on the water depth sequence. Based on the water depth and water depth threshold at the time of arrival at each road segment, the risk level of each road segment and each basic route is determined, and it is determined whether the basic route selected by the user is the highest risk level. If the risk level is the highest and the user needs to take a detour, then alternative routes will be searched for the highest-risk segments in the selected basic route to determine the alternative route.

[0008] As an alternative implementation method, multiple basic routes are generated based on the user's origin and destination, and the travel time of each segment in each basic route is estimated. Combined with the user's planned departure time, the estimated arrival time of each segment in each basic route is predicted.

[0009] As an alternative implementation method, the risk level of the basic route is set to max{risk level of each road segment}. After outputting the basic route to the user, the basic route selected by the user is obtained, and it is determined whether the basic route selected by the user is the highest risk.

[0010] As an alternative implementation method, the process of searching for alternative road segments includes: The parent node of the first road segment with the highest risk level of water accumulation is taken as the starting point n of the road segment. S Take the last sub-node of the road segment with the highest risk level of water accumulation as the end point n of the road segment. E and define the starting point n of the road segment S The corresponding estimated arrival time t_arrive and risk level r_level; Determine n S The adjacent road segment table is used to traverse each node n in the adjacent road segment table and calculate the comprehensive cost f(n) of each node n. It is then determined whether f(n) satisfies the additional time consumption constraint, and n nodes that meet the condition are added to the search road segment table. This process is repeated until the adjacent road segment table is completely traversed. The constraint is f(n) ≤ f(n) S )×(1+set extra time); If the list of road segments to be searched is not empty, then select the node n with the smallest f(n) from the list of road segments to be searched. min , will n min Add an alternative route; if n min =nE If n ≤ n, the search ends and the alternative route is output; if n ≤ n, the search ends and the alternative route is output. min ≠n E Search node n min The adjacent road segment table is used to repeat the steps of calculating the comprehensive cost value.

[0011] As an alternative implementation method, the comprehensive cost value f(n) = g(n) + h(n); g(n) is the actual cumulative cost value from the user's starting point to the current node n; h(n) is the heuristic cost value from the current node n to the user's destination. The cost function is g(n) = α × T(n) + (1-α) × R(n); The time cost T(n) is the actual cumulative travel time from the user's starting point to the current node n: T(n) = T(parent_n) S ) + t(n); where T(parent_n) S ) represents the distance from the user's travel origin to n S Previous node parent_n S The time taken; t(n) is the travel time for the current road segment; Risk cost R(n): R(n) =β×r_level(n,t_arrive) + (1-β)×r_level (n,t_arrive±Δt); Where: β defaults to 0.6, r_level(n,t_arrive) is the quantified risk level of n at arrival time t_arrive; r_level (n,t_arrive±Δt) is the quantified maximum risk level of n at time Δt before and after arrival.

[0012] As an alternative implementation, the heuristic function h(n) = λ×[T_h(n) + R_h(n)]; Among them, the time heuristic T_h(n) estimates the shortest travel time from n to the user's destination by using the ratio of straight-line distance to the maximum speed limit; the risk heuristic R_h(n) predicts the cumulative risk cost of the path from n to the user's destination based on flood risk prediction data.

[0013] Secondly, the present invention provides a detour route planning system for flood-prone areas, comprising: The threshold determination module is configured to determine the water depth threshold corresponding to each risk level based on the user's vehicle model from a preset vehicle model water wading risk level library. The time sequence determination module is configured to obtain a sequence of water depth changes over time for road sections based on the acquired urban flooding risk prediction data; The candidate determination module is configured to generate a basic route based on the user's travel origin and travel destination, determine the estimated arrival time of each segment in each basic route, and thus determine the water depth at the time of arrival at each segment based on the water depth sequence. The judgment module is configured to determine the risk level of each road segment and each basic route based on the water depth and water depth threshold when arriving at each road segment, and to determine whether the basic route selected by the user is the highest risk level. The alternative planning module is configured to search for alternative routes for the highest-risk segments in the selected basic route if the risk level is the highest and the user needs to detour, thereby determining the alternative route.

[0014] Thirdly, the present invention provides an electronic device including a memory and a processor, and computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in the first aspect.

[0015] Fourthly, the present invention provides a computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in the first aspect.

[0016] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect.

[0017] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention provides a method for planning detour routes in flood-prone areas based on intelligent navigation. It integrates flood risk prediction data with intelligent navigation path planning algorithms, combining the prediction and assessment results with the future trend of road flooding risk. When planning travel routes, it avoids sections that may experience flooding, achieving a proactive detour reminder mechanism that anticipates and guides detours in advance. This avoids users being forced to adjust their routes after approaching the risk area, reducing the number of route adjustments and effectively alleviating regional traffic congestion during floods. It addresses the problems of delayed on-site traffic control guidance, delayed and untimely acquisition of road flooding information in existing navigation technologies, and low accuracy of macro-level rainstorm warnings in existing technologies.

[0018] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a flowchart of the detour route planning method for flood-prone areas provided in Embodiment 1 of the present invention; Figure 2 Here is a flowchart of the A* heuristic search algorithm provided in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the road segment between the user's starting point and destination, provided in Embodiment 1 of the present invention. Detailed Implementation

[0021] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0022] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0023] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form as well. Furthermore, it should be understood that the terms “comprising” and “including”, and any variations thereof, are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0024] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0025] Example 1 This embodiment provides a method for planning detour routes in flood-prone areas based on intelligent navigation. It integrates flood risk prediction data with intelligent navigation path planning algorithms, deeply fusing the prediction and assessment results with the actual flood risk over a future period. By considering the changing trends of road flooding risk, the method avoids potentially flooded sections when planning travel routes, achieving a proactive detour reminder mechanism that anticipates and guides detours in advance. This avoids users being forced to adjust their routes after approaching risk areas, reducing the number of route adjustments and effectively alleviating regional traffic congestion during floods. It addresses problems in existing technologies such as delayed on-site traffic control guidance, delayed and untimely acquisition of road flooding information by existing navigation technologies, and low accuracy of macro-level rainstorm warnings.

[0026] like Figure 1 As shown, it includes: S1: Construct a database of vehicle wading risk levels.

[0027] Vehicle types include, but are not limited to, sedans, SUVs, off-road vehicles, and large trucks. The risk level of different wading depths for each vehicle type is analyzed, and extremely high risk (R) is defined. 极高 High risk (R) 高 ), medium risk (R) 中 Low risk (R) 低 The water depth threshold was used to construct a water wading risk level database for vehicle models, as shown in Table 1.

[0028] Table 1. Vehicle Type Water Wading Risk Level Database;

[0029] S2: Obtain user vehicle model data, and determine the risk level R based on the vehicle model in the vehicle wading risk level database. 极高 R 高 R 中 R 低 The threshold for water depth.

[0030] For example, if the current user is driving a sedan, then R 极高 =30cm, R 高 =25cm, R 中 =15cm, R 低 =5cm.

[0031] S3: Obtain time-series data for urban flooding risk prediction, analyze the time-series data, and extract road segments. Time-varying water depth sequence .

[0032] For example, the current water depth of road section n1 5 minutes later, the water depth Water depth after 10 minutes Water depth after 15 minutes The static water depth values ​​are transformed into dynamic time-based water depth correlation data, as shown in Table 2. By extracting the water depth at each time point, a basis for predicting the risk status upon arrival is provided, overcoming the limitation of traditional planning that relies solely on current risk.

[0033] Table 2. Water depth sequence; Serial Number Road segment_time water depth 1 <![CDATA[n1_t(202512161420)]]> 0cm 2 <![CDATA[n1_t(202512161421)]]> 0cm 3 <![CDATA[n1_t(202512161422)]]> 1cm …… …… …… 60 <![CDATA[n1_t(202512161519)]]> 55cm 61 <![CDATA[n2_t(202512161420)]]> 0cm …… …… …… S4: Calculate the arrival time of each segment of the basic route.

[0034] Based on the user's origin and destination, the Dijkstra algorithm is used to generate multiple basic routes, and the travel time of each segment in each basic route is estimated. Combined with the user's planned departure time, the estimated arrival time of each segment in each basic route is predicted.

[0035] S5: Match the water depth at the arrival time of each road segment.

[0036] Based on the estimated arrival time of each road segment, and according to the water depth sequence, the water depth at the time of arrival at each road segment is determined.

[0037] S6: Based on the risk level R corresponding to the user's vehicle model 极高 R 高 R 中 R 低 The water depth threshold was used to determine the water accumulation risk level of each road section, as shown in Table 3.

[0038] Table 3. Waterlogging Risk Levels for Each Road Section; Serial Number node Arrival time water depth Risk of water accumulation 1 <![CDATA[n1]]> 202512161435 0cm Risk-free 2 <![CDATA[n2]]> 202512161440 6cm Low risk 3 <![CDATA[n3]]> 202512161447 24cm Medium risk 4 <![CDATA[n4]]> 202512161451 32cm Extremely high risk …… …… …… …… …… S7: Set the risk level of the basic route = max{risk level of each road segment}, then output the basic route to the user, obtain the basic route selected by the user, and determine whether the basic route selected by the user is extremely high risk.

[0039] If so, based on the risk level of each segment in the user's selected basic route, obtain all extremely high-risk segments (let's say n1, n2, n3), and remind the user, "According to your travel plan time, the current route may encounter deep water accumulation at points n1, n2, and n3. Do you need us to plan a detour for you?"

[0040] It then determines whether the user needs to take a detour; if a detour is required, the A* heuristic search algorithm is used to search for alternative routes for extremely high-risk road sections through customized cost and heuristic functions, and generates a water accumulation risk level table for each road section as shown in Table 3 for the alternative routes, sets the risk level of the alternative routes, and finally outputs the alternative routes to the user.

[0041] like Figure 2 As shown, the specific process of using the A* heuristic search algorithm to search for alternative routes to extremely high-risk road segments is as follows: (1) Take the parent node of the first road segment with an extremely high risk level of water accumulation in the original route (i.e., the basic route selected by the user) as the starting point n of the road segment. S Take the last sub-node of the road segment with an extremely high risk level of water accumulation as the end point n of the road segment. E and the starting point n of the road segment S The corresponding estimated arrival time t_arrive and risk level r_level.

[0042] (2) Initialize the open list (list of road segments to be searched), the closed list (list of road segments already searched), the alternative road segment list, and the starting point n of the road segment at risk of water accumulation. S Adjacent road segment table n * ,like Figure 3 As shown, n S The adjacent road segments are n1, n2, and n3.

[0043] The open list is used to store the road segments to be searched (actually, they should be called nodes). It is initialized to empty and then the starting point n is set. S Add the road segments to be searched to the list and calculate the comprehensive cost f(n). S The list that stores searched road segments is closed; it is empty when initialized.

[0044] Where, f(n) S )=g(n S )+h(n S ), f(n S () represents the distance from the user's starting point to the starting point of the flood-prone road section. S The comprehensive value of g(n) S (n) represents the distance from the user's starting point to the starting point of the flood-prone road section. S The actual cumulative cost includes the time cost T(n) S ) and risk cost R(n S ); h(n S (n) is the starting point of the road section at risk of water accumulation. S The heuristic value of reaching the user's travel destination, based on time series data of urban flooding risk prediction, ensures the directionality of the search.

[0045] In addition, such as Figure 3 As shown, the following concepts are defined: Parent node: n S The parent node of n1, n2, and n3, where n1 is the parent node of n. 1_1 n 1_2 The parent node; Child nodes: n1, n2, n3 are n Schild nodes; Road segment: The line between two nodes is the road segment. In actual calculation, the driving time and water accumulation risk of the road segment are calculated. Adjacent road segments: n S The adjacent road segment of -n1 is n1-n 1_1 n1-n 1_2 For ease of description, road segments are represented by their endpoint nodes; that is, the adjacent road segments of n1 are called n... 1_1 n 1_2 ; Current road segment: The road segment currently being calculated.

[0046] Current node: The endpoint node of the road segment currently being calculated.

[0047] Starting point: The user's starting point for travel.

[0048] Target node: The road segment currently being calculated.

[0049] (3) Calculate the adjacent road segment table n * The total cost of each node n is f(n) = g(n) + h(n).

[0050] Wherein, g(n) is the actual cumulative cost from the user's starting point to the current node n, including the time cost T(n) and the risk cost R(n); h(n) is the heuristic cost from the current node n to the user's destination, which is predicted based on the time series data of urban flooding risk to ensure the directionality of the search.

[0051] (3-1) The cost function is g(n) = α×T(n) + (1-α)×R(n). The time cost and risk cost are balanced by the weight coefficient α. The default value of α is 0.4, which can be adjusted according to the user's risk preference.

[0052] The time cost T(n) is the actual cumulative travel time from the user's starting point to the current node n: T(n) = T(parent_n S ) + t(n); Where, T(parent_n S ) represents the distance from the user's travel origin to n S Previous node parent_n S The time taken is expressed as the cumulative travel time; t(n) is the travel time of the current road segment, which is dynamically corrected in combination with real-time traffic conditions (congestion index k): t(n) = t0(n)×(1 + 0.5k), where t0(n) is the smooth travel time of road segment n.

[0053] The risk cost R(n) is the arrival risk cost calculated based on time series data: R(n) =β×r_level(n,t_arrive) + (1-β)×r_level (n,t_arrive±Δt), Where: β defaults to 0.6, r_level(n,t_arrive) is the quantified risk level of road segment n at arrival time t_arrive (no risk = 0, low risk = 1, medium risk = 3, high risk = 5, extremely high risk = 10); r_level (n,t_arrive±Δt) is the quantified maximum risk level of road segment n within Δt time (Δt = 5 minutes) before and after arrival, which is determined by combining the time series data of urban flooding risk to be the highest level of risk that the road segment will have within 5 minutes before and after arrival time t_arrive; if the r_level(n,t_arrive) of the replacement road segment n is less than or equal to the risk level of the original road segment, then R(n) is reduced by 1, and the selection of the road segment with lower risk is encouraged through the reward mechanism.

[0054] The purpose of this process is to avoid the occurrence of extremely high risks before and after arrival: if a candidate road segment has a low risk at arrival but may have an extremely high risk within 5 minutes before or after arrival, it is still included in the candidate set, and the goal of avoiding extremely high risks before and after arrival is achieved by adjusting the β coefficient of R(n).

[0055] (3-2) The heuristic function h(n) = λ×[T_h(n) + R_h(n)], based on the dual objectives of shortest time and lowest future risk, ensures that the search converges towards avoiding the extremely high risk of arrival: Time-based heuristic T_h(n): The ratio of straight-line distance to the maximum speed limit is used to estimate the shortest travel time from node n to the user's destination, avoiding excessive detours; Risk Heuristic R_h(n): Based on time series data of urban flooding risk, predict the cumulative risk cost along the path from node n to the user's travel destination. R_h(n) = Σ[r_level(m,t_m)]; Where: m is the candidate road segment from n to the user's destination, t_m is the estimated time to reach m, the larger the risk quantification value, the larger R_h(n), and the guidance algorithm avoids extremely high-risk road segments.

[0056] The heuristic function weight coefficient λ is set to 0.8 by default to ensure that h(n) ≤ actual cost, thus satisfying the A* algorithm's adoptability condition.

[0057] (4) Determine whether f(n) satisfies the additional time-consuming constraint and add n that satisfies the condition to the open list.

[0058] The search constraint is as follows: when the f(n) of a candidate alternative road segment ≤ f(original road segment) × (1 + additional time constraint), it is included in the candidate set (open list). (5) Continue executing steps (3) and (4) until the adjacent road segment list n * After the traversal is complete, execute steps (6) and (8).

[0059] (6) If the open list is not empty, select the node n with the smallest f(n) from the open list. min , will n min Add alternative routes, move all nodes from the open list to the closed list, and determine: If n min =n E If the search ends, the alternative route is output. If n min ≠n E If so, then continue with (7).

[0060] (7) Search node n min Adjacent road segments n * Return to step (3) and continue the calculation.

[0061] Among them, the optimal solution selection is as follows: select the road segment with the smallest f(n) from the open list as the optimal alternative road segment. If there are multiple road segments with the same f(n), prioritize the road segment with smaller R(n) to achieve risk priority.

[0062] (8) If the open list is empty, relax the extra time constraint by 5% and return to step (2) to search again.

[0063] It should be noted that all data acquisition is conducted in accordance with laws and regulations and with user consent, and the data is used legally.

[0064] Example 2 This embodiment provides a detour route planning system for areas at risk of urban flooding, including: The threshold determination module is configured to determine the water depth threshold corresponding to each risk level based on the user's vehicle model from a preset vehicle model water wading risk level library. The time sequence determination module is configured to obtain a sequence of water depth changes over time for road sections based on the acquired urban flooding risk prediction data; The candidate determination module is configured to generate a basic route based on the user's travel origin and travel destination, determine the estimated arrival time of each segment in each basic route, and thus determine the water depth at the time of arrival at each segment based on the water depth sequence. The judgment module is configured to determine the risk level of each road segment and each basic route based on the water depth and water depth threshold when arriving at each road segment, and to determine whether the basic route selected by the user is the highest risk level. The alternative planning module is configured to search for alternative routes for the highest-risk segments in the selected basic route if the risk level is the highest and the user needs to detour, thereby determining the alternative route.

[0065] It should be noted that the above modules correspond to the steps described in Embodiment 1, and the examples and application scenarios implemented by the above modules and the corresponding steps are the same, but are not limited to the content disclosed in Embodiment 1. It should also be noted that the above modules, as part of the system, can be executed in a computer system such as a set of computer-executable instructions.

[0066] In further embodiments, the following is also provided: An electronic device includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, wherein the computer instructions, when executed by the processor, perform the method described in Embodiment 1. For brevity, further details are omitted here.

[0067] It should be understood that in this embodiment, the processor can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc.

[0068] Memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of memory may also include non-volatile random access memory. For example, memory may also store information about the device type.

[0069] A computer-readable storage medium for storing computer instructions, which, when executed by a processor, perform the method described in Embodiment 1.

[0070] The method in Example 1 can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor. The software modules can reside in readily available storage media in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method. To avoid repetition, a detailed description is not provided here.

[0071] A computer program product includes a computer program that, when executed by a processor, implements the method described in Embodiment 1.

[0072] The present invention also provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods described above. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0073] The computer program code used to implement the methods of the present invention may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the computer or other programmable data processing device, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0074] In the context of this invention, computer program code or related data may be carried by any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and the like. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0075] Those skilled in the art will recognize that the units and algorithm steps described in connection with the various examples of this embodiment can be implemented in electronic hardware or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this invention.

[0076] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for planning detour routes in areas prone to urban flooding, characterized in that, include: Based on the user's vehicle model, the water depth threshold corresponding to each risk level is determined from the preset vehicle water wading risk level database. Based on the obtained urban flooding risk prediction data, a time-varying sequence of water depth for road sections was obtained; A basic route is generated based on the user's origin and destination. The estimated arrival time of each segment in each basic route is determined, and the water depth at the time of arrival at each segment is determined based on the water depth sequence. Based on the water depth and water depth threshold at the time of arrival at each road segment, the risk level of each road segment and each basic route is determined, and it is determined whether the basic route selected by the user is the highest risk level. If the risk level is the highest and the user needs to take a detour, then alternative routes will be searched for the highest-risk segments in the selected basic route to determine the alternative route.

2. The method for planning detour routes in flood-prone areas as described in claim 1, characterized in that, Based on the user's origin and destination, multiple basic routes are generated, and the travel time of each segment in each basic route is estimated. Combined with the user's planned departure time, the estimated arrival time of each segment in each basic route is predicted.

3. The method for planning detour routes in flood-prone areas as described in claim 1, characterized in that, Set the risk level of the basic route to max{risk level of each road segment}, output the basic route to the user, obtain the basic route selected by the user, and determine whether the basic route selected by the user is the highest risk.

4. The method for planning detour routes in flood-prone areas as described in claim 1, characterized in that, The process of searching for alternative routes includes: The parent node of the first road segment with the highest risk level of water accumulation is taken as the starting point n of the road segment. S Take the last sub-node of the road segment with the highest risk level of water accumulation as the end point n of the road segment. E and define the starting point n of the road segment S The corresponding estimated arrival time t_arrive and risk level r_level; Determine n S The adjacent road segment table is used to traverse each node n in the adjacent road segment table and calculate the comprehensive cost f(n) of each node n. It is then determined whether f(n) satisfies the additional time consumption constraint, and n nodes that meet the condition are added to the search road segment table. This process is repeated until the adjacent road segment table is completely traversed. The constraint is f(n) ≤ f(n) S )×(1+set extra time); If the list of road segments to be searched is not empty, then select the node n with the smallest f(n) from the list of road segments to be searched. min , will n min Add an alternative route; if n min =n E If n ≤ n, the search ends and the alternative route is output; if n ≤ n, the search ends and the alternative route is output. min ≠n E Search node n min The adjacent road segment table is used to repeat the steps of calculating the comprehensive cost value.

5. The method for planning detour routes in flood-prone areas as described in claim 4, characterized in that, The total cost value f(n) = g(n) + h(n); g(n) is the actual cumulative cost value from the user's starting point to the current node n; h(n) is the heuristic cost from the current node n to the user's travel destination; The cost function is g(n) = α × T(n) + (1-α) × R(n); The time cost T(n) is the actual cumulative travel time from the user's starting point to the current node n: T(n) = T(parent_n) S ) + t(n); where T(parent_n) S ) represents the distance from the user's travel origin to n S Previous node parent_n S The time taken; t(n) is the travel time for the current road segment; Risk cost R(n): R(n) =β×r_level(n,t_arrive) + (1-β)×r_level (n,t_arrive±Δt); Where: β defaults to 0.6, r_level(n,t_arrive) is the quantified risk level of n at arrival time t_arrive; r_level (n,t_arrive±Δt) is the quantified maximum risk level of n at time Δt before and after arrival.

6. The method for planning detour routes in flood-prone areas as described in claim 5, characterized in that, The heuristic function is h(n) = λ×[T_h(n) + R_h(n)]; Among them, the time heuristic T_h(n) estimates the shortest travel time from n to the user's destination by using the ratio of straight-line distance to the maximum speed limit; the risk heuristic R_h(n) predicts the cumulative risk cost of the path from n to the user's destination based on flood risk prediction data.

7. A detour route planning system for areas at risk of urban flooding, characterized in that, include: The threshold determination module is configured to determine the water depth threshold corresponding to each risk level based on the user's vehicle model from a preset vehicle model water wading risk level library. The time sequence determination module is configured to obtain a sequence of water depth changes over time for road sections based on the acquired urban flooding risk prediction data; The candidate determination module is configured to generate a basic route based on the user's travel origin and travel destination, determine the estimated arrival time of each segment in each basic route, and thus determine the water depth at the time of arrival at each segment based on the water depth sequence. The judgment module is configured to determine the risk level of each road segment and each basic route based on the water depth and water depth threshold when arriving at each road segment, and to determine whether the basic route selected by the user is the highest risk level. The alternative planning module is configured to search for alternative routes for the highest-risk segments in the selected basic route if the risk level is the highest and the user needs to detour, thereby determining the alternative route.

8. An electronic device, characterized in that, It includes a memory and a processor, as well as computer instructions stored in the memory and running on the processor, which, when executed by the processor, perform the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Used to store computer instructions, which, when executed by a processor, perform the method described in any one of claims 1-6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, implements the method described in any one of claims 1-6.

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