CN115953556A - Rainstorm waterlogging road risk AR early warning method and device - Google Patents
Rainstorm waterlogging road risk AR early warning method and device Download PDFInfo
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Abstract
The invention discloses a rainstorm waterlogging road risk AR early warning method and a device, wherein the method comprises the following steps: acquiring a target road section in a visual field range through the road surface information of the urban road network acquired by the mobile terminal; acquiring weather forecast data and basic geographic information in the peripheral range of a target road section to form multi-source data; carrying out urban inland inundation simulation based on the multi-source data to obtain regional ponding inundation information; superposing the target road section and the regional ponding submergence information to obtain road ponding information of the target road section; evaluating the traffic risk level of the target road section based on the road ponding information; and rendering road water accumulation information through augmented reality, and carrying out early warning prompt on the traffic risk level of the target road section. According to the method and the system, augmented reality visualization and early warning can be performed on the road traffic risk at the mobile terminal, so that the perception and evaluation of the traffic risk of the waterlogging in the road area with low terrain by the user are enhanced, and the risk early warning level is improved.
Description
Technical Field
The invention belongs to the technical field of traffic safety and disaster early warning, and particularly relates to an AR early warning method and device for road risks of rainstorm and waterlogging.
Background
With global climate disorders, extreme natural disasters frequently occur. The rainstorm waterlogging disaster has the characteristics of strong emergencies, great harmfulness, wide influence range and the like, and forms great threat to the life and property safety of urban residents. The road is a passage for daily travel and material transportation of residents and is a traffic hub which is vital to urban operation. Under the heavy rain environment, urban roads are the areas most prone to waterlogging due to the fact that the road surfaces of the urban roads are fluctuated and mutually communicated. In particular to the lower terrain areas such as underground passages, tunnels, sunken roads and the like, once the road traffic is interrupted or paralyzed due to rainstorm and waterlogging, the normal operation of cities is blocked, and serious casualties and huge economic losses are easily caused. Therefore, research on urban road waterlogging simulation and traffic risk early warning can provide technical support for waterlogging disaster emergency management, traffic risk management and control, flood control decision support and the like, and is one of important problems which need to be solved urgently in smart city and traffic construction.
The invention patent with publication number CN103489314A discloses a real-time road condition display method and device, and discloses that 3D scanning and 3D modeling are performed on real-time road conditions, and 3D modeling results and real-time road condition images are displayed in a superimposed manner through an augmented reality technology.
Disclosure of Invention
In view of the above, the invention provides an AR early warning method and device for rainstorm waterlogging road risks, which are used for solving the problem that the road risk early warning cannot be accurately performed on low-terrain areas such as sunken roads, underground passages, tunnels and the like in the prior art.
The invention discloses a rainstorm waterlogging road risk AR early warning method, which comprises the following steps:
acquiring a target road section in a visual field range through the road surface information of the urban road network acquired by the mobile terminal;
acquiring weather forecast data and basic geographic information in the peripheral range of a target road section to form multi-source data;
carrying out urban inland inundation simulation based on the multi-source data to obtain regional ponding inundation information;
superposing the target road section and the regional ponding submerging information to obtain road ponding information of the target road section;
evaluating the traffic risk level of the target road section based on the road ponding information;
and rendering road water accumulation information through augmented reality, and carrying out early warning prompt on the traffic risk level of the target road section.
On the basis of the technical scheme, preferably, the mobile terminal has the functions of positioning, camera shooting and screen display, the user positioning and the camera posture are obtained through the mobile terminal, and the target road section is obtained through the earth surface elevation where the mobile terminal is located, the user positioning and the camera posture.
On the basis of the technical scheme, preferably, based on the multi-source data, urban waterlogging simulation is carried out, and the obtaining of regional ponding flooding information specifically comprises:
establishing an urban inland inundation simulation model based on the multi-source data;
calculating rainfall conditions in different recurrence periods based on the Chicago rain pattern;
dividing catchment areas according to rainfall conditions, and performing underground pipe network drainage simulation by using an urban inland inundation simulation model to obtain overflowing pipe network nodes and overflowing water volume;
and performing surface runoff simulation by adopting a cellular automata model based on overflow pipe network nodes and overflow water volume to obtain surface water grid data, and taking the surface water grid data as regional water submerging information.
On the basis of the above technical solution, preferably, the obtaining of the road ponding information of the target road segment by superposing the target road segment and the regional ponding flooding information specifically includes:
performing buffer area analysis according to the number of lanes and the width of the lanes of the two-dimensional road vector line;
rasterizing the road surface subjected to buffer analysis to obtain road area raster data;
and performing superposition analysis on the road area grid data and the surface water grid data, and calculating to obtain road water information, wherein the road water information comprises water depth and area information of each grid.
On the basis of the above technical solution, preferably, the performing buffer area analysis according to the number of lanes of the two-dimensional road vector line and the width of the lane specifically includes:
and setting the starting point of the road line as A and the end point as B, and establishing a two-dimensional Cartesian coordinate system by taking the point A as the starting point along AB as an x axis and perpendicular to AB as a y axis, wherein coordinates of four corner points C, D, E and F of the buffer area after buffer area analysis are respectively as follows:
wherein the number of lanes is n, and the lane width is L.
On the basis of the above technical solution, preferably, the assessing the traffic risk level of the target road segment based on the road water information specifically includes:
acquiring the current vehicle running speed and the current water flow speed, evaluating a road risk value according to the current vehicle running speed, road ponding information and the current water flow speed, and dividing traffic risk grades according to the road risk value;
the formula for evaluating the road risk value is as follows:
where risk is a risk value, tanh is a hyperbolic tangent function, u is a median of a critical water depth for a vehicle to stagnate, v is an attenuation elastic coefficient, x is the water depth, y is the water velocity, and k is a correction coefficient.
On the basis of the above technical scheme, preferably, rendering the road water information through augmented reality specifically includes:
acquiring a camera attitude of the mobile terminal;
acquiring the water depth of each cellular based on the road ponding information, taking the water surface as a reference, stretching the cellular vertical to the water surface downwards to reach the water depth value, constructing a cellular water body cuboid, and fusing all the cellular water body cuboids to form a three-dimensional model of the road ponding;
loading a three-dimensional model of the surface water in a three-dimensional geographic information system, setting a camera view angle of a three-dimensional GIS map, keeping the camera view angle consistent with the camera posture of a mobile terminal, and obtaining a road water scene graph of the three-dimensional GIS;
and superposing the road waterlogging scene graph of the three-dimensional GIS and a camera shooting picture of the mobile terminal in real time to realize augmented reality visualization of waterlogging road risks.
In a second aspect of the present invention, an AR early warning device for road risk of rainstorm and waterlogging is disclosed, the device comprising:
a data acquisition module: the system comprises a mobile terminal, a target road section and a target road section, wherein the mobile terminal is used for acquiring road surface information of an urban road network and acquiring the target road section in a visual field range;
a water accumulation simulation module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring weather forecast data and basic geographic information in a peripheral range of a target road section to form multi-source data; carrying out urban inland inundation simulation based on the multi-source data to obtain regional ponding inundation information;
a risk assessment module: the road waterlogging information of the target road section is obtained by superposing the target road section and the regional waterlogging information; evaluating the traffic risk level of the target road section based on the road ponding information;
AR early warning module: the method is used for rendering the road ponding information through augmented reality and carrying out early warning prompt on the traffic risk level of the target road section.
In a third aspect of the present invention, an electronic device is disclosed, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, which program instructions are invoked by the processor to implement the method according to the first aspect of the invention.
In a fourth aspect of the invention, a computer-readable storage medium is disclosed, which stores computer instructions for causing a computer to implement the method according to the first aspect of the invention.
Compared with the prior art, the invention has the following beneficial effects:
1) According to the method, the target road section in the visual field is obtained through the mobile terminal, the urban waterlogging simulation model is coupled to carry out waterlogging simulation to extract road waterlogging information, the road waterlogging risk grade is calculated based on cellular division, and finally augmented reality visualization and early warning are carried out on road traffic risks on the basis of the road waterlogging information at the mobile terminal.
2) According to the method and the system, the road risk value is evaluated according to the current vehicle driving speed, the road ponding information and the water flow speed, the traffic risk grade is divided according to the road risk value, the quantitative representation of the road risk early warning under the condition of rainstorm and waterlogging is realized, and the accurate road risk early warning can be carried out on lower-lying areas such as a recessed road, an underground passage and a tunnel.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic diagram of a rainstorm waterlogging road traffic risk early warning process according to the present invention;
FIG. 2 is a schematic diagram illustrating a principle of extracting a target road segment range based on a mobile phone AR according to the present invention;
FIG. 3 is a flow chart of a method for simulating surface water based on cellular automata according to the present invention;
FIG. 4 is a schematic diagram of the results of surface water grid data from the waterlogging simulation of the present invention;
FIG. 5 is a method for extracting road ponding based on grid superposition according to the present invention;
FIG. 6 is a schematic diagram of a road ponding three-dimensional model construction according to the present invention;
fig. 7 is an illustration of the waterlogging road risk pre-warning map based on the mobile phone AR according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
Augmented Reality (AR) technology is a technology for skillfully fusing virtual information and a real world, and is generally realized based on a mobile terminal camera image real-time superposition three-dimensional model. The mobile terminal of the embodiment of the invention takes a smart phone as an example, and the prediction information and the real camera scene information can be mutually supplemented through the mobile phone AR, thereby realizing the 'enhancement' of the real world. The rainstorm waterlogging simulation and early warning based on the mobile phone AR can simulate the road waterlogging condition according to rainstorm forecast and evaluate, and has important significance for traffic risk perception, emergency evacuation, reduction of casualties and traffic accidents and the like under the urban road waterlogging condition.
Referring to fig. 1, the invention discloses a rainstorm waterlogging road risk AR early warning method, which includes:
s1, obtaining a target road section in a visual field range through road surface information of an urban road network collected by a mobile terminal.
The mobile terminal adopted by the invention has the functions of positioning, camera shooting and screen display, such as a smart phone, a tablet computer and the like. In this embodiment, taking a smart phone as an example, the user positioning and the camera pose are acquired through the smart phone, and the target road segment is obtained through the earth surface elevation where the mobile terminal is located, the user positioning and the camera pose.
As shown in fig. 2, the user position coordinates obtained by the mobile phone built-in beidou high-precision positioning are longitude lon and latitude lat, and the surface elevation where the person stands is:
alt＝getHeigthByLonLat(lon,lat)
wherein getHeigthByLonLat is an elevation search function, and the elevation of the current position can be obtained by searching global earth surface elevation data through latitude and longitude.
Assuming that the height from the mobile phone to the ground is h, the three-dimensional arc coordinate of the mobile phone can be expressed as (lon, lat, alt + h), and the earth radius is R, the three-dimensional arc coordinate is converted into geocentric three-dimensional cartesian coordinates (X, Y, Z) as follows:
if the gesture of the deflection angle of the mobile phone camera is alpha, beta and gamma according to the mobile phone gyroscope, then combining the camera sight ray equation and the ellipsoid equation can obtain:
wherein O is the position of the mobile phone, V is the sight direction of the mobile phone, and X 0 ,Y 0 ,Z 0 ) Is the coordinates of the center of the earth, and (a, b, c) is the radius on the corresponding coordinate axis.
And solving based on the joint equation, obtaining the coordinate of the central point of the visual field of the mobile phone camera, constructing an outsourcing rectangle with a certain width d based on the coordinate of the central point, intersecting the outsourcing rectangle with the vector data of the two-dimensional road network, and calculating to obtain the target road section.
S2, acquiring weather forecast data and basic geographic information in the peripheral range of the target road section to form multi-source data.
The method comprises the steps of obtaining weather forecast data in a peripheral range of a target road section mainly for obtaining rainfall data of a current time and a future time, forming multi-source data by the weather forecast data and basic geographic information which generally comprises data such as land utilization, terrain, underground pipe networks and road networks, and obtaining road ponding submerging information subsequently.
And S3, carrying out urban waterlogging simulation based on the multi-source data to obtain regional ponding submergence information.
And S31, establishing an urban inland inundation simulation model based on the multi-source data.
Urban inland inundation simulation generally relates to different monitoring departments such as meteorology, hydrology, homeland, planning, traffic and the like, and data of the urban inland inundation simulation have obvious differences in the aspects of spatial scale, sampling frequency, coordinate system, data format and the like. By constructing a unified urban waterlogging simulation model, the changes of waterlogging entities and the interrelations thereof along with time and space are clearly expressed, recorded and managed.
The urban inland inundation simulation model describes the road ponding evolution process from time and space, and comprises three types: geographic objects, spatiotemporal objects, model events. The method comprises the following steps that a geographic object is used for describing static objects, wherein the static objects comprise land utilization, regional landforms, underground pipe networks, urban roads and sub-catchment areas, and the sub-catchment areas are divided based on the land utilization and the landforms and are important inputs of a waterlogging simulation model; the space-time object is used for describing dynamically changing objects, including weather rainfall, dynamic water bodies and time sequences, wherein the dynamic water bodies are represented as the duration of a water accumulation area in time and are represented as the range change of the water accumulation area in space; the model events are used for describing sub models of waterlogging simulation, and the sub models comprise a drainage model and a runoff model. The model events define the inputs, functions, outputs, etc. of the model, facilitating multi-model coupled interactions.
S32, calculating rainfall conditions in different reappearance periods based on Chicago rain patterns, wherein the rainstorm intensity calculation formula is as follows:
wherein i is the rainstorm intensity, P is the rainfall recurrence period, and t is the duration of rainfall.
And S33, dividing catchment areas according to rainfall conditions, and performing underground pipe network drainage simulation by combining the urban inland inundation simulation model to obtain overflow pipe network nodes and overflow water yield.
And S34, performing surface runoff simulation by adopting a cellular automaton model based on overflow pipe network nodes and overflow water volume to obtain surface water grid data, and taking the surface water grid data as regional water flooding information.
Fig. 3 shows a flow chart of a surface runoff simulation method based on a cellular automaton.
The method comprises the steps of dividing the ground surface into two-dimensional cellular spaces consisting of unit square cells along the road trend and the vertical direction based on the water overflowing amount of an underground pipe network, and simulating water body change based on interaction between the cells. And traversing each cell in the cell space in sequence along with the change of time, calculating the state of the cell, calculating the cloud leopard water flow exchange weight and the exchange water volume according to a conversion rule, and after water volume exchange is carried out, calculating the water depth and the flow velocity of the cell according to the cell exchange water volume to obtain information that the cell is submerged, thereby realizing the dynamic simulation of the surface waterlogging.
Wherein, according to the water exchange quantity of the cells, the water depth of the cells is calculated as follows:
wherein f is 0,t+Δt Is the water depth of the central cell at the time t + delta t, f 0,t Is the water depth of the central cell at the t-th time, delta V 0,in The inflow amount of the central cells, Δ V 0,out The outflow water quantity of the central cell is,K i,t+Δt m is a constant coefficient, which is the amount of water exchanged by the ith cell at the time t + Δ t.
According to the amount of water exchanged by the cells, calculating the flow rate of the cells as follows:
wherein e i,t+Δt The flow velocity of the central cell to the ith cell at the t + delta t moment, K i,t+Δt The amount of water exchanged for the ith cell at time t + Δ t, Δ p i Is the boundary length of the ith cell and the central cell, f 0,i,t+Δt The water depth average value of the central cell and the ith cell at the t + delta t moment is obtained.
Fig. 4 is a schematic diagram showing the effect of surface water grid data obtained by surface waterlogging simulation based on cellular automata.
And S4, superposing the target road section and the regional ponding submergence information to obtain road ponding information of the target road section.
Fig. 5 is a flow chart of a road ponding extraction method based on grid stacking, which mainly includes three links of data input, data processing and data output.
Step S4 specifically includes the following sub-steps:
and S41, performing buffer analysis according to the number of lanes and the width of the lanes of the two-dimensional road vector line.
And setting the starting point of the road line as A and the end point as B, constructing a two-dimensional Cartesian coordinate system by taking the point A as the starting point along the axis AB as x and taking the axis AB as y, wherein the number of lanes is n, and the width of the lane is L, and then coordinates of four corner points C, D, E and F of the buffer area after buffer area analysis are respectively as follows:
and S42, performing vector rasterization on the road surface subjected to buffer analysis to obtain road area raster data.
S43, performing superposition analysis on the road area grid data and the surface water grid data, and calculating to obtain road water information, wherein the road water information comprises water depth and area information of each grid.
If the grid cell is a cell, the surface accumulated water output grid is a waterfrid, and then the accumulated water grid cell in the road area needs to meet the conditions:
and S5, evaluating the traffic risk level of the target road section based on the road ponding information.
The influence of waterlogging on the vehicle safety is evaluated by analyzing the influence of the road waterlogging condition on the vehicle running speed as a factor. The height of the car exhaust funnel from the ground in China is about 25-35 cm, and the depth of accumulated water exceeds 30cm, so that the exhaust funnel is easy to enter water to extinguish the car. Thus, the impact of water depth on road traffic can be divided into two cases: when the depth of the road water exceeds 30cm, the road is interrupted, and the vehicle cannot continue to ignite to run; when the road water depth is between 0 and 30 centimeters, the actual driving speed of the vehicle can be reduced. Meanwhile, the influence of the water flow speed on the vehicle running is considered, the traffic risk grade is evaluated, the quantitative representation of the road risk early warning under the condition of rainstorm and waterlogging is realized, and the accurate road risk early warning can be carried out on low-terrain areas such as a sunken road, an underground passage, a tunnel and the like.
Specifically, the current vehicle driving speed and the current water flow speed are obtained, the road risk value is evaluated according to the current vehicle driving speed, the road ponding information and the current water flow speed, the traffic risk grade is divided according to the road risk value, and the formula for evaluating the road risk value is as follows:
where risk is a risk value, tanh is a hyperbolic tangent function, u is a median of a critical water depth for vehicle stagnation, v is an attenuation elastic coefficient, x is the water depth, y is the water velocity, and k is a correction coefficient.
And S6, rendering the road water accumulation information through augmented reality, and carrying out early warning prompt on the traffic risk level of the target road section.
Step S6 specifically includes the following sub-steps:
and S61, acquiring the camera posture of the mobile terminal.
And S62, constructing a three-dimensional model of the surface water.
As shown in fig. 6, the water depth of each cellular is obtained based on the road ponding information, the cellular is stretched downwards to the water depth value perpendicular to the water surface by taking the water surface as a reference, a cellular water body cuboid is constructed, and all cellular water body cuboids are fused to form a three-dimensional model of the surface ponding.
S63, loading the surface water three-dimensional model in the three-dimensional geographic information system, setting a camera view angle of the three-dimensional GIS map, keeping the camera view angle consistent with the camera posture of the mobile terminal, and obtaining a road water scene map of the three-dimensional GIS.
And S64, superposing the road ponding scene graph of the three-dimensional GIS and a camera shooting picture of the mobile terminal in real time to realize augmented reality visualization of waterlogging road risks, wherein the augmented reality visualization effect is shown in fig. 7.
According to the invention, the augmented reality visualization of the waterlogging road risk is realized by the mobile terminal, so that the perception and evaluation of the waterlogging traffic risk of the road area with lower topography are facilitated for the user, and the risk early warning level is improved.
Corresponding to the embodiment of the method, the invention also provides an AR early warning device for the road risk of rainstorm and waterlogging, which comprises the following steps:
a data acquisition module: the system comprises a mobile terminal, a target road section and a target road section, wherein the mobile terminal is used for acquiring road surface information of an urban road network and acquiring the target road section in a visual field range;
a water accumulation simulation module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring weather forecast data and basic geographic information in a peripheral range of a target road section to form multi-source data; performing underground pipe network drainage and surface runoff simulation based on the multi-source data to obtain regional ponding submerging information;
a risk assessment module: the road waterlogging information of the target road section is obtained by superposing the target road section and the regional waterlogging information; evaluating the traffic risk level of the target road section based on the road ponding information;
AR early warning module: the method and the device are used for rendering the road ponding information through the augmented reality and carrying out early warning prompt on the traffic risk level of the target road section.
The above apparatus embodiments and method embodiments are in one-to-one correspondence, and please refer to the method embodiment for the brief description of the apparatus embodiments.
The invention also discloses an electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus; the processor, the memory and the communication interface complete mutual communication through the bus; the memory stores program instructions executable by the processor, which invokes the program instructions to implement the methods of the invention described above.
The invention also discloses a computer readable storage medium which stores computer instructions for causing the computer to implement all or part of the steps of the method of the embodiment of the invention. The storage medium includes: various media capable of storing program codes, such as a U disk, a removable hard disk, a read-only memory ROM, a random access memory RAM, a magnetic disk, or an optical disk.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, i.e. may be distributed over a plurality of network units. Without creative labor, a person skilled in the art can select some or all of the modules according to actual needs to achieve the purpose of the solution of the embodiment.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (10)
1. An AR early warning method for road risk of rainstorm and waterlogging is characterized by comprising the following steps:
acquiring a target road section in a visual field range through the road surface information of the urban road network acquired by the mobile terminal;
acquiring weather forecast data and basic geographic information in the peripheral range of a target road section to form multi-source data;
carrying out urban inland inundation simulation based on the multi-source data to obtain regional ponding inundation information;
superposing the target road section and the regional ponding submerging information to obtain road ponding information of the target road section;
evaluating the traffic risk level of the target road section based on the road ponding information;
and rendering road water accumulation information through augmented reality, and carrying out early warning prompt on the traffic risk level of the target road section.
2. The method for early warning of the AR risk of the rainstorm waterlogging road according to claim 1, wherein the mobile terminal has functions of positioning, camera shooting and screen display, the user positioning and the camera posture are obtained through the mobile terminal, and the target road section is obtained through calculation of the elevation of the earth surface where the mobile terminal is located, the user positioning and the camera posture.
3. The method for AR early warning of rainstorm waterlogging road risks according to claim 1, wherein the urban waterlogging simulation is performed based on the multi-source data, and obtaining regional waterlogging information specifically comprises:
establishing an urban inland inundation simulation model based on the multi-source data;
calculating rainfall conditions in different reappearance periods based on Chicago rain patterns;
dividing catchment areas according to rainfall conditions, and performing underground pipe network drainage simulation by combining an urban inland inundation simulation model to obtain overflowing pipe network nodes and overflowing water volume;
and performing surface runoff simulation by adopting a cellular automata model based on overflow pipe network nodes and overflow water volume to obtain surface water grid data, and taking the surface water grid data as regional water submerging information.
4. The method for AR early warning of road risk of rainstorm waterlogging according to claim 3, wherein the overlaying the target road segment and the regional waterlogging information to obtain road waterlogging information of the target road segment specifically comprises:
performing buffer area analysis according to the number of lanes and the width of the lanes of the two-dimensional road vector line;
rasterizing the road surface subjected to buffer analysis to obtain road area raster data;
and performing superposition analysis on the road area grid data and the surface water grid data, and calculating to obtain road water information, wherein the road water information comprises water depth and area information of each grid.
5. The method for performing AR early warning on the road risk of rainstorm and waterlogging according to claim 4, wherein the step of performing buffer area analysis according to the number of lanes and the width of the lanes of the two-dimensional road vector line specifically comprises the steps of:
and setting the starting point of the road line as A and the end point as B, and establishing a two-dimensional Cartesian coordinate system by taking the point A as the starting point along the axis AB as x and perpendicular to the axis AB as y, wherein coordinates of four corner points C, D, E and F of the buffer area after buffer area analysis are respectively as follows:
wherein the number of lanes is n, and the lane width is L.
6. The method for AR early warning of road risks of rainstorm waterlogging according to claim 4, wherein the evaluating of the traffic risk level of the target road segment based on the road waterlogging information specifically comprises:
acquiring the current vehicle driving speed and the current water flow speed, evaluating a road risk value according to the current vehicle driving speed, road ponding information and the current water flow speed, and dividing traffic risk grades according to the road risk value;
the formula for evaluating the road risk value is as follows:
where risk is a risk value, tanh is a hyperbolic tangent function, u is a median of a critical water depth for vehicle stagnation, v is an attenuation elastic coefficient, x is the water depth, y is the water velocity, and k is a correction coefficient.
7. The method of claim 4, wherein the rendering of the road water logging information through augmented reality specifically comprises:
acquiring a camera posture of the mobile terminal;
acquiring the water depth of each cellular based on the road ponding information, taking the water surface as a reference, stretching the cellular vertical to the water surface downwards to reach the water depth value, constructing a cellular water body cuboid, and fusing all the cellular water body cuboids to form a three-dimensional model of the road ponding;
loading a three-dimensional model of the surface water in a three-dimensional geographic information system, setting a camera view angle of a three-dimensional GIS map, keeping the camera view angle consistent with the camera posture of a mobile terminal, and obtaining a road water scene graph of the three-dimensional GIS;
and superposing the road waterlogging scene graph of the three-dimensional GIS and a camera shooting picture of the mobile terminal in real time, so that the augmented reality visualization of waterlogging road risks is realized.
8. The utility model provides a torrential rain waterlogging road risk AR early warning device which characterized in that, the device includes:
a data acquisition module: the system comprises a mobile terminal, a target road section and a target road section, wherein the mobile terminal is used for acquiring road surface information of an urban road network and acquiring the target road section in a visual field range;
a water accumulation simulation module: the system comprises a data acquisition module, a data processing module and a data processing module, wherein the data acquisition module is used for acquiring weather forecast data and basic geographic information in a peripheral range of a target road section to form multi-source data; performing urban inland inundation simulation based on the multi-source data to obtain regional ponding inundation information;
a risk assessment module: the road ponding information of the target road section is obtained by superposing the target road section and the regional ponding submerging information; evaluating the traffic risk level of the target road section based on the road ponding information;
AR early warning module: the method and the device are used for rendering the road ponding information through the augmented reality and carrying out early warning prompt on the traffic risk level of the target road section.
9. An electronic device, comprising: at least one processor, at least one memory, a communication interface, and a bus;
the processor, the memory and the communication interface complete mutual communication through the bus;
the memory stores program instructions executable by the processor, which invokes the program instructions to implement the method of any of claims 1 to 7.
10. A computer-readable storage medium storing computer instructions for causing a computer to implement the method of any one of claims 1 to 7.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117437759A (en) * | 2023-11-07 | 2024-01-23 | 北京建筑大学 | Rain and flood drainage road risk early warning method and device |
| CN118379697A (en) * | 2024-06-27 | 2024-07-23 | 江西省公路工程检测中心 | Road state risk prediction method and system based on multi-source data analysis |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117437759A (en) * | 2023-11-07 | 2024-01-23 | 北京建筑大学 | Rain and flood drainage road risk early warning method and device |
| CN118379697A (en) * | 2024-06-27 | 2024-07-23 | 江西省公路工程检测中心 | Road state risk prediction method and system based on multi-source data analysis |
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