CN117576862B - Urban flood disaster early warning method for navigation vehicle - Google Patents
Urban flood disaster early warning method for navigation vehicle Download PDFInfo
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- G—PHYSICS
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- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
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Abstract
The invention discloses a navigation vehicle-oriented urban flood disaster early warning method, which belongs to the field of urban flood disaster early warning, and comprises the steps of obtaining simulation data and monitoring data, and obtaining an urban flood risk map by utilizing an urban map according to the simulation data and the monitoring data; starting navigation and acquiring a vehicle departure place and a vehicle destination; obtaining the longest vehicle running time by utilizing a navigation system according to the departure place and the destination of the vehicle; and carrying out flood disaster early warning by utilizing the urban flood risk map according to the longest vehicle running time, the vehicle departure place and the vehicle destination. The method solves the problems of accurate early warning and route planning of the navigation vehicle in the flood disasters, and improves the urban flood disaster early warning accuracy facing the navigation vehicle.
Description
Technical Field
The invention belongs to the field of urban flood disaster early warning, and particularly relates to a navigation vehicle-oriented urban flood disaster early warning method.
Background
Under the climate change condition, extreme storm in the city frequently occurs, which threatens the normal operation of the city. For example, people may be trapped in vehicles and cannot escape when a flood disaster occurs. Traffic interruption can also severely impact urban economic activity.
The prior early warning is mainly oriented to residents, individuals and enterprises and public institutions, and from the perspective of disaster chains, road closure and traffic interruption, vehicle damage, increased traffic accident risks, paralysis of a public transportation system and the like caused by the distress of the driving involved in water are important aspects of casualties of urban flood disasters. Avoiding wading distress in vehicles as much as possible is an urgent concern. However, the prior art lacks special early warning technology, and the problem of inaccurate early warning exists.
Disclosure of Invention
Aiming at the defects in the prior art, the urban flood disaster early warning method for the navigation vehicle solves the problems of vehicle navigation and route planning in the flood disasters, and improves the urban flood disaster early warning accuracy for the navigation vehicle.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a city flood disaster early warning method facing navigation vehicles comprises the following steps:
S1, obtaining simulation data and monitoring data, and obtaining an urban flood risk map by utilizing an urban map according to the simulation data and the monitoring data;
s2, starting navigation, and acquiring a vehicle departure place and a vehicle destination;
S3, obtaining the longest vehicle running time by utilizing a navigation system according to the departure place and the destination of the vehicle;
And S4, carrying out flood disaster early warning by utilizing the urban flood risk map according to the longest vehicle running time, the vehicle departure place and the vehicle destination.
The beneficial effects of the invention are as follows: the multi-channel water accumulation depth data are acquired, multi-source water accumulation data are obtained, the defect of insufficient data quantity is overcome, and meanwhile, early-stage guidance is provided for vehicle navigation, so that the vehicle running safety can be ensured, and the early-warning accuracy is improved; and risk assessment is carried out before and during running in real time, travel advice and vehicle route planning are provided, and accurate early warning is achieved.
Further, the step S1 specifically includes:
s101, carrying out urban flood simulation by utilizing a hydrologic model, and obtaining urban ground ponding depth under urban flood simulation to obtain simulation data;
S102, collecting urban ground ponding depth through multiple channels to obtain monitoring data, and integrating the simulation data and the monitoring data to obtain multi-source ponding data;
s103, marking risk points of the urban map according to the multi-source ponding data to obtain an urban flood risk base map;
s104, carrying out risk grade division according to the multi-source ponding data to obtain risk grades of all risk points in the urban flood risk base map;
s105, obtaining the urban flood risk map according to the risk level of each risk point in the urban flood risk map and the urban flood risk map.
The beneficial effects of the above-mentioned further scheme are: the multi-channel water accumulation depth data are obtained, multi-source water accumulation data are obtained, the defect of insufficient data quantity is overcome, and meanwhile, early guidance is provided for navigation vehicles.
Further, the risk class in step S104 is classified into low risk, medium risk, high risk and extremely high risk;
low risk: the water accumulation depth is more than 0cm and less than or equal to 15cm;
risk of (1): 15cm < the depth of accumulated water is less than or equal to 30cm;
high risk: 30cm < the depth of accumulated water is less than or equal to 50cm;
extremely high risk: 50cm < ponding depth.
The beneficial effects of the above-mentioned further scheme are: the threshold value setting of the risk level is carried out according to civil building and vehicle data, so that possible accidents in each risk level can be displayed.
Further, the step S4 specifically includes:
s401, obtaining all driving routes according to a vehicle departure place and a vehicle destination;
s402, obtaining rainfall data in a time period according to the longest vehicle running time;
s403, judging whether a running route which does not involve extremely high risk exists at present according to the urban flood risk map and all running routes, if so, directly entering into the step S404, otherwise, not suggesting travel, and ending flood disaster early warning;
S404, judging whether the overall trend of the urban ponding depth rises in the longest vehicle driving time according to rainfall data in the time period, if so, directly entering a step S405, otherwise, directly entering a step S407;
s405, carrying out flood simulation by utilizing a hydrologic model based on the urban flood risk map to obtain ponding depth data:
Wherein h is ponding depth data; Is the closing speed; u and v are both velocity components; g is gravity acceleration; v t is the momentum diffusion coefficient; z is the free surface elevation; t is time; x is the spatial abscissa; y is the space ordinate; s x and S y are the bottom friction terms in the x and y directions, respectively; s h is the fluid source or sink velocity.
S406, judging whether a travel route which does not involve extremely high risk in the travel process exists or not according to the accumulated water depth data, if so, directly entering into the step S407, otherwise, not suggesting travel, and ending flood disaster early warning;
And S407, performing real-time navigation according to all driving routes and the urban flood risk map to finish flood disaster early warning.
The beneficial effects of the above-mentioned further scheme are: and the navigation software is utilized to obtain the longest vehicle running time, then the risk assessment is carried out according to the urban flood risk map and the rainfall data of the weather bureau, the travel advice is provided, and the travel safety is ensured.
Further, the step S407 specifically includes:
S4071, starting navigation, judging whether the longest vehicle running time is less than the short-distance time, if yes, directly entering a step S4072 to conduct short-distance route planning, otherwise, directly entering a step S4078 to conduct long-distance route planning;
s4072, acquiring all connection road sections of the current intersection according to all driving routes;
S4073, carrying out early warning on the extremely high-risk connecting road sections by utilizing the urban flood risk map according to all the connecting road sections of the current road junction;
S4074, removing the extremely high-risk connection road sections to obtain the rest connection road sections;
S4075, acquiring a risk probability of the highest risk level of each connection link in the remaining connection links:
Wherein, P r is the risk probability of the highest risk level of the r-th connection link in the remaining connection links; n r is the number of the road segments with the highest risk level connected at the exit of the r-th connection road segment in the rest connection road segments; n r is the total number of the connecting road sections at the outlet of the r-th connecting road section in the rest connecting road sections;
S4076, selecting a connection road section with the minimum risk level according to the risk probability of the highest risk level of each connection road section in the rest connection road sections to obtain a road section set to be selected;
S4077, selecting a connection road section with the smallest risk probability as a recommended driving road section according to a road section set to be selected, collecting real-time data in the driving process, updating the risk level of each road section in the urban flood risk map according to all driving routes, returning to step S4072 to calculate the extremely high risk probability of entering the connection road section of the next road port until reaching a destination, and completing flood disaster early warning;
And S4078, carrying out long-distance route planning according to all the driving routes, and completing flood disaster early warning.
The beneficial effects of the above-mentioned further scheme are: and in the short-distance driving process, carrying out extremely high risk probability judgment on each driving road section connected with the intersection in real time, and guaranteeing driving safety in real time.
Further, the step S4078 specifically includes:
S40781, starting navigation to acquire the current running time and the current rainfall intensity;
s40782, calculating trip disaster risk indexes of all drivable roads of the current intersection according to the urban flood risk map and all driving routes:
Rj＝ωh,jωtωr
Wherein R j is a trip disaster risk index of the jth travelable road at the current intersection; omega h,j is the grade index of the ponding depth of the jth travelable road at the current intersection; omega t is the grade index of the current running time; omega r is the grade index of the current rainfall intensity;
S40783, outputting trip disaster risk indexes of all the drivable roads at the current intersection, and selecting a route with the minimum trip disaster risk index as a recommended driving route;
S40784, judging whether the destination is reached, if yes, finishing route planning, and finishing flood disaster early warning, otherwise, returning to the step S40772 to acquire a recommended driving route of the next intersection.
The beneficial effects of the above-mentioned further scheme are: by comprehensively considering the ponding depth, the rainfall intensity and the navigation time, various objective conditions of future driving can be fully combined, so that the navigation early warning can be more in line with the actual situation.
Further, in the step S40782, the value of the grade index of the water accumulation depth of the jth travelable road at the current intersection is specifically: when the accumulated water depth of the jth drivable road at the current intersection is more than or equal to 15cm and is more than 0cm, the grade index is 0.25; when the water accumulation depth of the jth drivable road at the current intersection is more than or equal to 30cm and is more than 15cm, the grade index is 0.5; when the accumulated water depth of the jth drivable road at the current intersection is more than or equal to 50cm and is 30cm, the grade index is 0.75; when the accumulated water depth of the jth travelable road at the current intersection is more than 50cm, the grade index is 1;
The grade index of the current running time is concretely valued as follows: when the current running time is more than 120 minutes, the grade index is 0.25; when the current running time is more than or equal to 120 minutes and is more than 60 minutes, the grade index is 0.5; when 60 minutes is more than or equal to the current running time of more than 30 minutes, the grade index is 0.75; when 30 minutes is more than or equal to the current driving time and is longer than the short-distance time, the grade index is 1;
The current rainfall intensity grade index has the following specific value: when 15mm is more than or equal to the current rainfall intensity of more than 5mm, the grade index is 0.25; when the current rainfall intensity is more than or equal to 30mm and is more than 15mm, the grade index is 0.5; when the current rainfall intensity is more than or equal to 50mm and is more than 30mm, the grade index is 0.75; when the current rainfall intensity is more than 50mm, the grade index is 1.
The beneficial effects of the above-mentioned further scheme are: the water accumulation depth, rainfall intensity and navigation time are further divided, so that flood disaster early warning in the vehicle running process can be more accurate and precise.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of road risk in an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, in one embodiment of the present invention, a method for warning of urban flood disasters for a navigation vehicle includes the following steps:
S1, obtaining simulation data and monitoring data, and obtaining an urban flood risk map by utilizing an urban map according to the simulation data and the monitoring data;
s2, starting navigation, and acquiring a vehicle departure place and a vehicle destination;
S3, obtaining the longest vehicle running time by utilizing a navigation system according to the departure place and the destination of the vehicle;
And S4, carrying out flood disaster early warning by utilizing the urban flood risk map according to the longest vehicle running time, the vehicle departure place and the vehicle destination.
In the embodiment, a special early warning technology is provided for the navigation vehicle, and because the navigation software is basically used by the modern urban vehicles, the condition can be utilized to combine early warning information with the navigation software, so that the accurate early warning is realized, and the probability of wading and distress of the vehicles is greatly reduced.
The step S1 specifically comprises the following steps:
s101, carrying out urban flood simulation by utilizing a hydrologic model, and obtaining urban ground ponding depth under urban flood simulation to obtain simulation data;
S102, collecting urban ground ponding depth through multiple channels to obtain monitoring data, and integrating the simulation data and the monitoring data to obtain multi-source ponding data;
s103, marking risk points of the urban map according to the multi-source ponding data to obtain an urban flood risk base map;
s104, carrying out risk grade division according to the multi-source ponding data to obtain risk grades of all risk points in the urban flood risk base map;
s105, obtaining the urban flood risk map according to the risk level of each risk point in the urban flood risk map and the urban flood risk map.
In this embodiment, the multi-source water logging data may originate from a number of aspects: rainfall prediction, ponding monitoring, remote sensing application, machine vision, road condition identification and public media.
The risk class in step S104 is classified into low risk, medium risk, high risk and extremely high risk;
low risk: the water accumulation depth is more than 0cm and less than or equal to 15cm;
risk of (1): 15cm < the depth of accumulated water is less than or equal to 30cm;
high risk: 30cm < the depth of accumulated water is less than or equal to 50cm;
extremely high risk: 50cm < ponding depth.
The step S4 specifically includes:
s401, obtaining all driving routes according to a vehicle departure place and a vehicle destination;
s402, obtaining rainfall data in a time period according to the longest vehicle running time;
s403, judging whether a running route which does not involve extremely high risk exists at present according to the urban flood risk map and all running routes, if so, directly entering into the step S404, otherwise, not suggesting travel, and ending flood disaster early warning;
S404, judging whether the overall trend of the urban ponding depth rises in the longest vehicle driving time according to rainfall data in the time period, if so, directly entering a step S405, otherwise, directly entering a step S407;
s405, carrying out flood simulation by utilizing a hydrologic model based on the urban flood risk map to obtain ponding depth data:
Wherein h is ponding depth data; Is the closing speed; u and v are both velocity components; g is gravity acceleration; v t is the momentum diffusion coefficient; z is the free surface elevation; t is time; x is the spatial abscissa; y is the space ordinate; s x and S y are the bottom friction terms in the x and y directions, respectively; s h is the fluid source or sink velocity.
S406, judging whether a travel route which does not involve extremely high risk in the travel process exists or not according to the accumulated water depth data, if so, directly entering into the step S407, otherwise, not suggesting travel, and ending flood disaster early warning;
And S407, performing real-time navigation according to all driving routes and the urban flood risk map to finish flood disaster early warning.
The step S407 specifically includes:
S4071, starting navigation, judging whether the longest vehicle running time is less than the short-distance time, if yes, directly entering a step S4072 to conduct short-distance route planning, otherwise, directly entering a step S4078 to conduct long-distance route planning;
s4072, acquiring all connection road sections of the current intersection according to all driving routes;
S4073, carrying out early warning on the extremely high-risk connecting road sections by utilizing the urban flood risk map according to all the connecting road sections of the current road junction;
S4074, removing the extremely high-risk connection road sections to obtain the rest connection road sections;
S4075, acquiring a risk probability of the highest risk level of each connection link in the remaining connection links:
Wherein, P r is the risk probability of the highest risk level of the r-th connection link in the remaining connection links; n r is the number of the road segments with the highest risk level connected at the exit of the r-th connection road segment in the rest connection road segments; n r is the total number of the connecting road sections at the outlet of the r-th connecting road section in the rest connecting road sections;
S4076, selecting a connection road section with the minimum risk level according to the risk probability of the highest risk level of each connection road section in the rest connection road sections to obtain a road section set to be selected;
S4077, selecting a connection road section with the smallest risk probability as a recommended driving road section according to a road section set to be selected, collecting real-time data in the driving process, updating the risk level of each road section in the urban flood risk map according to all driving routes, returning to step S4072 to calculate the extremely high risk probability of entering the connection road section of the next road port until reaching a destination, and completing flood disaster early warning;
And S4078, carrying out long-distance route planning according to all the driving routes, and completing flood disaster early warning.
In this embodiment, as shown in fig. 2, the intersection where the current vehicle is located has three connection sections, one high-risk section, one medium-risk section and one extremely high-risk section; and firstly, removing the extremely high risk road sections to obtain the rest connection road sections, including the high risk road sections and the medium risk road sections. Then respectively calculating the risk probability of the highest risk level of the road sections connected at the exits of the high-risk road sections and the medium-risk road sections; the method comprises the following steps:
① The exit of the high risk highway section is connected with three roads: two are extremely high risks, and the other one is an unknown road section; the highest risk level of the road section connected with the high risk road section is extremely high risk, and the risk probability is
② The exit of the road section of the stroke danger is connected with three roads: two are unknown road sections, and the other one is high risk; so the highest risk level of the road section connected with the medium risk road section is high risk, and the risk probability is
If the vehicle continues to run, a road section with a risk should be selected.
The step S4078 specifically includes:
S40781, starting navigation to acquire the current running time and the current rainfall intensity;
s40782, calculating trip disaster risk indexes of all drivable roads of the current intersection according to the urban flood risk map and all driving routes:
Rj＝ωh,jωtωr
Wherein R j is a trip disaster risk index of the jth travelable road at the current intersection; omega h,j is the grade index of the ponding depth of the jth travelable road at the current intersection; omega t is the grade index of the current running time; omega r is the grade index of the current rainfall intensity;
S40783, outputting trip disaster risk indexes of all the drivable roads at the current intersection, and selecting a route with the minimum trip disaster risk index as a recommended driving route;
S40784, judging whether the destination is reached, if yes, finishing route planning, and finishing flood disaster early warning, otherwise, returning to the step S40772 to acquire a recommended driving route of the next intersection.
The grade index of the water accumulation depth of the jth drivable road at the current intersection in the step S40782 is specifically: when the accumulated water depth of the jth drivable road at the current intersection is more than or equal to 15cm and is more than 0cm, the grade index is 0.25; when the water accumulation depth of the jth drivable road at the current intersection is more than or equal to 30cm and is more than 15cm, the grade index is 0.5; when the accumulated water depth of the jth drivable road at the current intersection is more than or equal to 50cm and is 30cm, the grade index is 0.75; when the accumulated water depth of the jth travelable road at the current intersection is more than 50cm, the grade index is 1;
The grade index of the current running time is concretely valued as follows: when the current running time is more than 120 minutes, the grade index is 0.25; when the current running time is more than or equal to 120 minutes and is more than 60 minutes, the grade index is 0.5; when 60 minutes is more than or equal to the current running time of more than 30 minutes, the grade index is 0.75; when 30 minutes is more than or equal to the current driving time and is longer than the short-distance time, the grade index is 1;
The current rainfall intensity grade index has the following specific value: when 15mm is more than or equal to the current rainfall intensity of more than 5mm, the grade index is 0.25; when the current rainfall intensity is more than or equal to 30mm and is more than 15mm, the grade index is 0.5; when the current rainfall intensity is more than or equal to 50mm and is more than 30mm, the grade index is 0.75; when the current rainfall intensity is more than 50mm, the grade index is 1.
In this embodiment, table 1 is a rank index table.
TABLE 1
Claims (4)
1. The urban flood disaster early warning method for the navigation vehicle is characterized by comprising the following steps of:
S1, obtaining simulation data and monitoring data, and obtaining an urban flood risk map by utilizing an urban map according to the simulation data and the monitoring data;
s2, starting navigation, and acquiring a vehicle departure place and a vehicle destination;
S3, obtaining the longest vehicle running time by utilizing a navigation system according to the departure place and the destination of the vehicle;
S4, carrying out flood disaster early warning by utilizing the urban flood risk map according to the longest vehicle running time, the vehicle departure place and the vehicle destination; the step S4 specifically includes:
s401, obtaining all driving routes according to a vehicle departure place and a vehicle destination;
s402, obtaining rainfall data in a time period according to the longest vehicle running time;
s403, judging whether a running route which does not involve extremely high risk exists at present according to the urban flood risk map and all running routes, if so, directly entering into the step S404, otherwise, not suggesting travel, and ending flood disaster early warning;
S404, judging whether the overall trend of the urban ponding depth rises in the longest vehicle driving time according to rainfall data in the time period, if so, directly entering a step S405, otherwise, directly entering a step S407;
s405, carrying out flood simulation by utilizing a hydrologic model based on the urban flood risk map to obtain ponding depth data:
Wherein h is ponding depth data; Is the closing speed; u and v are both velocity components; g is gravity acceleration; v t is the momentum diffusion coefficient; z is the free surface elevation; t is time; x is the spatial abscissa; y is the space ordinate; s x and S y are the bottom friction terms in the x and y directions, respectively; s h is the fluid source or sink velocity;
s406, judging whether a travel route which does not involve extremely high risk in the travel process exists or not according to the accumulated water depth data, if so, directly entering into the step S407, otherwise, not suggesting travel, and ending flood disaster early warning;
s407, performing real-time navigation according to all driving routes and the urban flood risk map to finish flood disaster early warning; the step S407 specifically includes:
S4071, starting navigation, judging whether the longest vehicle running time is less than the short-distance time, if yes, directly entering a step S4072 to conduct short-distance route planning, otherwise, directly entering a step S4078 to conduct long-distance route planning;
s4072, acquiring all connection road sections of the current intersection according to all driving routes;
S4073, carrying out early warning on the extremely high-risk connecting road sections by utilizing the urban flood risk map according to all the connecting road sections of the current road junction;
S4074, removing the extremely high-risk connection road sections to obtain the rest connection road sections;
S4075, acquiring a risk probability of the highest risk level of each connection link in the remaining connection links:
Wherein, P r is the risk probability of the highest risk level of the r-th connection link in the remaining connection links; n r is the number of the road segments with the highest risk level connected at the exit of the r-th connection road segment in the rest connection road segments; n r is the total number of the connecting road sections at the outlet of the r-th connecting road section in the rest connecting road sections;
S4076, selecting a connection road section with the minimum risk level according to the risk probability of the highest risk level of each connection road section in the rest connection road sections to obtain a road section set to be selected;
S4077, selecting a connection road section with the smallest risk probability as a recommended driving road section according to a road section set to be selected, collecting real-time data in the driving process, updating the risk level of each road section in the urban flood risk map according to all driving routes, returning to step S4072 to calculate the extremely high risk probability of entering the connection road section of the next road port until reaching a destination, and completing flood disaster early warning;
S4078, carrying out long-distance route planning according to all driving routes to finish flood disaster early warning; the step S4078 specifically includes:
S40781, starting navigation to acquire the current running time and the current rainfall intensity;
s40782, calculating trip disaster risk indexes of all drivable roads of the current intersection according to the urban flood risk map and all driving routes:
Rj＝ωh,jωtωr
Wherein R j is a trip disaster risk index of the jth travelable road at the current intersection; omega h,j is the grade index of the ponding depth of the jth travelable road at the current intersection; omega t is the grade index of the current running time; omega r is the grade index of the current rainfall intensity;
S40783, outputting trip disaster risk indexes of all the drivable roads at the current intersection, and selecting a route with the minimum trip disaster risk index as a recommended driving route;
S40784, judging whether the destination is reached, if yes, finishing route planning, and finishing flood disaster early warning, otherwise, returning to the step S40772 to acquire a recommended driving route of the next intersection.
2. The urban flood disaster early warning method for navigation vehicles according to claim 1, wherein the step S1 is specifically:
s101, carrying out urban flood simulation by utilizing a hydrologic model, and obtaining urban ground ponding depth under urban flood simulation to obtain simulation data;
S102, collecting urban ground ponding depth through multiple channels to obtain monitoring data, and integrating the simulation data and the monitoring data to obtain multi-source ponding data;
s103, marking risk points of the urban map according to the multi-source ponding data to obtain an urban flood risk base map;
s104, carrying out risk grade division according to the multi-source ponding data to obtain risk grades of all risk points in the urban flood risk base map;
s105, obtaining the urban flood risk map according to the risk level of each risk point in the urban flood risk map and the urban flood risk map.
3. The method for urban flood disaster warning for navigation vehicles according to claim 2, wherein the risk class is classified into low risk, medium risk, high risk and extremely high risk in step S104;
low risk: the water accumulation depth is more than 0cm and less than or equal to 15cm;
risk of (1): 15cm < the depth of accumulated water is less than or equal to 30cm;
high risk: 30cm < the depth of accumulated water is less than or equal to 50cm;
extremely high risk: 50cm < ponding depth.
4. The urban flood disaster warning method for navigation vehicles according to claim 1, wherein the grade index of the j th travelable road at the current intersection in step S40782 is specifically: when the accumulated water depth of the jth drivable road at the current intersection is more than or equal to 15cm and is more than 0cm, the grade index is 0.25; when the water accumulation depth of the jth drivable road at the current intersection is more than or equal to 30cm and is more than 15cm, the grade index is 0.5; when the accumulated water depth of the jth drivable road at the current intersection is more than or equal to 50cm and is 30cm, the grade index is 0.75; when the accumulated water depth of the jth travelable road at the current intersection is more than 50cm, the grade index is 1;
the grade index of the current running time is concretely valued as follows: when the current running time is more than 120 minutes, the grade index is 0.25; when the current running time is more than or equal to 120 minutes and is more than 60 minutes, the grade index is 0.5; when 60 minutes is more than or equal to the current running time of more than 30 minutes, the grade index is 0.75; when 30 minutes is more than or equal to the current driving time and is longer than the short-distance time, the grade index is 1;
The current rainfall intensity grade index has the following specific value: when 15mm is more than or equal to the current rainfall intensity of more than 5mm, the grade index is 0.25; when the current rainfall intensity is more than or equal to 30mm and is more than 15mm, the grade index is 0.5; when the current rainfall intensity is more than or equal to 50mm and is more than 30mm, the grade index is 0.75; when the current rainfall intensity is more than 50mm, the grade index is 1.
Priority Applications (1)
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| CN111723994A (en) * | 2020-06-24 | 2020-09-29 | 中国平安财产保险股份有限公司 | Vehicle early warning method and system based on disastrous weather |
| CN114202908A (en) * | 2021-12-13 | 2022-03-18 | 中国平安财产保险股份有限公司 | Vehicle early warning method, device, equipment and storage medium based on disaster weather |
| CN115019477A (en) * | 2022-07-18 | 2022-09-06 | 广东广宇科技发展有限公司 | Intelligent urban waterlogging disaster real-time prediction early warning system and method |
| CN116739385A (en) * | 2023-08-09 | 2023-09-12 | 长江三峡集团实业发展(北京)有限公司 | Urban traffic evaluation method and device based on spatial superposition analysis |
| CN117012006A (en) * | 2023-08-28 | 2023-11-07 | 浪潮智慧科技有限公司 | Flood disaster early warning method, equipment and medium for urban road |
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| CN111723994A (en) * | 2020-06-24 | 2020-09-29 | 中国平安财产保险股份有限公司 | Vehicle early warning method and system based on disastrous weather |
| CN114202908A (en) * | 2021-12-13 | 2022-03-18 | 中国平安财产保险股份有限公司 | Vehicle early warning method, device, equipment and storage medium based on disaster weather |
| CN115019477A (en) * | 2022-07-18 | 2022-09-06 | 广东广宇科技发展有限公司 | Intelligent urban waterlogging disaster real-time prediction early warning system and method |
| CN116739385A (en) * | 2023-08-09 | 2023-09-12 | 长江三峡集团实业发展(北京)有限公司 | Urban traffic evaluation method and device based on spatial superposition analysis |
| CN117012006A (en) * | 2023-08-28 | 2023-11-07 | 浪潮智慧科技有限公司 | Flood disaster early warning method, equipment and medium for urban road |
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