CN110160550A - A kind of city route bootstrap technique based on the prediction of road ponding - Google Patents

Abstract

The invention proposes an urban route guidance method based on road water accumulation prediction. The method collects the data of the underlying surface and the pipe network in the area to be analyzed, and constructs the city through the analysis of the slope aspect, the generalization of the pipe network, and the division of the sub-catchment areas. The road water accumulation model is combined with the historical rainfall data to design the rainfall process under different return periods as the simulated rainfall conditions. According to the real-time rainfall data, the simulated road water accumulation data is selected according to the trend similarity as the road water accumulation prediction result, and finally the best travel plan is selected. Feedback with stagnant water information to mobile users. Compared with the existing water accumulation detection technology, this method can predict the urban road water accumulation during the entire rainfall duration when the heavy rainfall occurs at the beginning, and correct the predicted value step by step with the increase of the rainfall duration to ensure the accuracy of the prediction results. It can provide water avoidance routes and guidance information for different travel modes, effectively guide mobile users to avoid danger and detour, and ensure the travel safety of urban traffic participants.

Description

An urban route guidance method based on road water accumulation prediction

technical field

The invention belongs to the technical field of road water accumulation prediction, and in particular relates to an urban route guidance method based on road water accumulation prediction.

Background technique

At present, the ways of obtaining road water information are mainly divided into three categories: (1) traditional hydrological methods. The information of urban road water accumulation is obtained based on the data fed back by the rainfall and water level monitoring station. Therefore, the danger can be reported only after the rainstorm has caused road water accumulation. After obtaining the water accumulation data for several times, the distribution of water accumulation points can be guessed according to the empirical method, which is not time-sensitive. , so that accurate information cannot be obtained, and the reference value is low. (2) Water accumulation detection device. The current road water accumulation monitoring device can obtain accurate water accumulation information, but the distribution of water accumulation depends on the detection device and cannot effectively predict the potential accumulation of water on the travel route, so it is difficult to determine a reasonable route to avoid accumulation of water. . (3) Map navigation software. The formation of road surface water mainly depends on rainfall conditions. Existing map navigation software mainly relies on road monitoring to obtain water accumulation information, which can show the distribution of water accumulation on urban roads to a certain extent, but it is impossible to determine the accumulation of water according to the difference of rainfall conditions. The process is effectively quantified, and it is impossible to provide a correct route to avoid water accumulation, and it is difficult to ensure the operation efficiency and safety of urban ground transportation under heavy rainfall weather.

Based on the above status quo, the road water accumulation model can be established through the urban terrain, pipe network and design rainfall data to realize the quantification of road water accumulation; the water accumulation information can be predicted through the trend similarity between the measured rainfall and the designed rainfall; The stagnant water indicator realizes the function of guiding the route to avoid stagnant water.

SUMMARY OF THE INVENTION

Purpose of the invention: In view of the above problems, the present invention proposes an urban route guidance method based on road water accumulation prediction, in order to solve the current situation that it is difficult for citizens to prepare a route to avoid accumulation of water in advance when traveling under heavy rainfall weather. The invention can predict the distribution of water accumulation points and the water accumulation depth on the road, and can effectively guide pedestrians and vehicles to select relatively safe and efficient passage routes according to different traffic modes.

Technical solution: In order to achieve the purpose of the present invention, the technical solution adopted in the present invention is: a city route guidance method based on road water accumulation prediction, the method comprises the following steps:

Step 1: Collect the underlying surface and pipe network data of the area to be analyzed, and carry out the slope aspect analysis and pipe network generalization of the area to be analyzed;

Step 2: Divide the sub-catchments of the hydrological model according to the three principles of singleness, build the urban road water accumulation model in combination with the pipe network, and establish the coordinate conversion relationship with the electronic map;

Step 3: Combine the historical rainfall data of the area to be analyzed to screen the characteristic rainfall process, design the rainfall processes under different return periods as the simulated rainfall conditions, and combine the road water accumulation model in step 2 to simulate the road water accumulation information, and calculate the different design rainfalls Depth of road water accumulation under the process;

Step 4: Record the real-time precipitation process, carry out numerical analysis and trend matching according to the trend similarity and the designed precipitation process, select the predicted precipitation process, take the simulated data of road water accumulation as the water accumulation information prediction result, and analyze the precipitation information one by one according to the change of real-time precipitation conditions. Time period correction, used for the calculation of the passage coefficient of stagnant water in the following steps;

Step 5: Accept the mobile terminal user guidance request, upload the selected starting point to the online electronic map platform, and obtain all feasible travel plans;

Step 6: Obtain the corresponding time period of the rainfall process when the user requests, and combine the road water accumulation prediction result in step 4, take the road water accumulation data in this period, consider different travel modes, calculate the water accumulation passage coefficient of each scheme, and compare and judge, Take the scheme with the smallest stagnant water passing coefficient as the best guide route under each travel mode;

Step 7: Integrate the best travel plan and water accumulation information, and feed it back to the mobile terminal users to complete the route guidance.

Further, the three principles of singleness of sub-catchment division described in step 2 are specifically: (1) The principle of singleness of underlying surface: refers to considering different landforms, vegetation types, lake water systems, man-made buildings and road distribution, so that the same There is only one underlying surface type in the catchment. (2) The principle of singleness of slope aspect: under the condition of (1), the slope aspect in any catchment area shall be consistent; (3) The principle of singleness of slope: under the condition of (2), the control boundary shall be The actual terrain slope value corresponding to the subcatchment changes in Inside, is the average slope value in each subcatchment.

Further, the specific steps for constructing the urban road water accumulation model in step 2 are: (1) according to the type of underlying surface, label the divided subcatchments to distinguish them, and set various parameters: such as slope, Permeability area ratio, Manning's n value, permeability rate, etc.; (2) Generalize the pipe network model, after determining the distribution of artificial ports, connect the artificial ports according to the flow direction of the pipes, and set various parameters of the artificial ports and pipes; (3) The runoff direction is judged according to the slope aspect, and then the confluence relationship between the sub-catchment and the sub-catchment, the sub-catchment and the artificial mouth is established.

Further, the concrete steps of establishing a coordinate conversion relationship in step 2 are: (1) select a point on the electronic map as reference point A, and the latitude and longitude of point A is the reference latitude and longitude; (2) select the road water model corresponding to point A (3) The multi-parameter method is used to transform the two-dimensional plane coordinates of the longitude and latitude and the road water model; (4) According to the plane coordinates of point A and point O, establish the two coordinates in (3) system conversion.

Further, in step 3, the characteristic rainfall process is screened, and the purpose is to select the rainfall process that is likely to cause road water accumulation and cause great harm to the project, and then complete the simulated rainfall design. The characteristic rainfall process needs to meet the following three characteristics of typical rainstorms at the same time: (1) The rainfall is large, which means that the rainfall reaches at least the rainstorm level, that is, the rainfall in 24 hours is greater than 38mm; here, the rainfall can be customized according to actual needs. The value of 38mm is only used as an example; (2) there is a main rain peak, which means that there is a maximum instantaneous rainfall intensity during the complete rainfall process, and its value is not less than 1mm/min; the value here can be customized according to actual needs, (3 ) is behind the main rain peak, which means that the peak ratio is greater than 0.5 in the complete rainfall process. The above specific values can be customized according to actual needs, and the specific values here are only used as examples.

Further, in step 3, the design method for designing the rainfall process under different return periods is as follows: take historical rainfall that meets the characteristics, and obtain the design rainfall process according to the same-time amplification method. The formula for the same-time amplification factor is as follows:

In the formula: X _{characteristic rainfall} is the characteristic rainfall, mm; X _design is the design rainfall under different return periods, mm.

Further, the formula used to calculate the road water depth in step 3 is as follows:

Where: Q is the surface runoff; W is the inherent width of the sub-watershed, m; S is the slope, %; n is the Manning roughness coefficient; _d is the average depth of the pond water, m; .

Further, in step 4, the trend similarity judgment rules and steps specifically include:

Step 4.1: The period-by-period rainfall of the real-time rainfall process is the sequence {P _R }, and the cumulative value of the real-time rainfall is the sequence {P _T }, then there is the formula:

In the formula: P _Ti is the accumulated value of real-time rainfall in the previous i period; P _Ri is the real-time rainfall in the i-th period; k _Ri is the real-time variation coefficient in the i-th period; Δt is the rainfall time recording step, which is 1h.

Step 4.2: Let the period-by-period rainfall of the designed rainfall process in step 3 be the sequence {P′ _R }, and the accumulated value of the designed rainfall is the sequence {P′ _T }, then there is the formula:

In the formula: P′ _Ti is the accumulated value of the design rainfall in the previous i period; P′ _Ri is the design rainfall in the i period; k′ _RI is the real-time variation coefficient of the I period; Δt is the rainfall time recording step, which is 1h.

Step 4.3: I=0, let the initial value of the hourly series of real-time rainfall P _R0 =0; that is, no rainfall occurs in the 0th period, i=i+1;

Step 4.4: Judging the measured value of P _Ri , if P _Ri ≥ 6, then heavy rainfall occurs, go to the next step; if P _Ri <6, then small and medium rainfall occurs, and the water accumulation threshold is not reached, i=i+1, repeat Step 4.4;

Step 4.5: Calculate P _Ti , and select all design rainfall processes that meet the conditions of P' _Ti ∈ (P _Ti -i, P _Ti +i) as alternative rainfall processes under the same rainfall occurrence duration;

Step 4.6: Define the maximum value of variation coefficient k′ _Rimax = k′ _i in the first i period of a certain candidate rainfall process, then for different candidate rainfall processes j, there is a sequence of the maximum variation coefficient within the first i period {k′ _ij }; Calculate the real-time rainfall process variation coefficient sequence {k _R }, and set k _i =max({k _R }), and take the alternative rainfall process represented by | _ki -k′ _ij | _min as the forecast for the first i time periods rainfall process;

Step 4.7: i=i+1, if P _Ri >0 is monitored, repeat steps 4.5 and 4.6, and update the prediction result every time it is repeated; if P _Ri =0, the rainfall stops and the process ends.

Further, step 6 specifically includes:

Step 6.1: Corresponding feasible routes one by one according to the coordinate correspondence in the road water model;

Step 6.2: According to different feasible routes, combined with the road water accumulation prediction results in step 4, take the water accumulation data of the corresponding period of real-time rainfall, and summarize the water accumulation data of all the road sections from the starting point to the end point into a table; The data is the average ponding depth d _x of the road section corresponding to the i-th period;

Step 6.3: Considering the factors of motor vehicles, non-motor vehicles and walking modes of travel, define the risk level of water wading depth for different travel modes, so as to exclude routes with water wading risks under different travel modes;

Step 6.4: Comprehensively consider the factors of the depth of water, the number of road sections and the length of each route, and calculate the water traffic coefficient θ of each route; as shown in the following formula:

In the formula: θ is the stagnant water passage coefficient of each route, and the dimension is 1; _n is the number of road sections of each route; lx is the length of a certain road section, m; _dx is the average ponding depth of the corresponding road section in the i-th period , m.

Step 6.5: Compare the stagnant water passing coefficients of each route, and determine the route with the smallest passing coefficient as the best guiding route.

Beneficial effects: compared with the prior art, the technical solution of the present invention has the following beneficial technical effects:

The beneficial effect of the present invention is that in the early stage of heavy rainfall, the information of urban road water accumulation in the entire rainfall duration can be predicted, and the predicted value can be corrected hourly with the rainfall time, so as to ensure the accuracy of the predicted result; Generate a safer and more efficient guidance route to avoid stagnant water, effectively guide pedestrians and vehicles to avoid danger through the mobile terminal, ensure the safety of traveling vehicles and pedestrians, and reduce the accident rate and accident loss.

Description of drawings

Fig. 1 is the method flow chart of the present invention;

Figure 2 is a case diagram of slope visualization. The size of the slope is distinguished by the depth of the color. The colors from light to dark represent [0, 0.5%], [0.5%, 2%], [2%, 10%], [10%, 30%. ], [30%, 100%];

Figure 3 is a generalized case diagram of the pipeline network;

Figure 4 is a case diagram of the road water model;

Figure 5 shows the characteristic rainfall process of the sample area and its design rainfall process;

Figure 6 is a case diagram of the simulation results of road water distribution in the sample area in the design daily rainfall of 49.2ml and 8 hours after rainfall;

Fig. 7 is a feedback diagram of the best guidance route suggested respectively for different travel modes.

Detailed ways

The technical solutions of the present invention will be further described below with reference to the accompanying drawings and embodiments.

Referring to Figure 1, an urban route guidance method based on road water accumulation prediction includes the following steps:

Step 1: Collect topography and pipe network data in the sample area, conduct slope aspect analysis and pipe network generalization in the sample area, as shown in Figures 2 and 3;

Step 2: 1. Divide the sample area into subcatchments according to the three principles of unity, and combine the divided sample area with the generalized model of the pipe network to form a road water accumulation model, as shown in Figure 4. At the same time, the model coordinate conversion relationship is established: (1) Select a point on the electronic map as the reference point A, and the longitude and latitude of point A as the reference longitude and latitude; (2) Select the position corresponding to point A on the road water model and set it as the origin of the model coordinates O; (3) Multi-parameter method to transform latitude and longitude and plane coordinates; (4) According to the plane coordinates of point A and point O, establish the conversion relationship between the two plane coordinate systems.

Step 3: 1. Refer to the left picture of Figure 5, which is a characteristic rainfall process in the sample area that conforms to the rainfall characteristics. The daily cumulative rainfall is 56.5mm, and the hourly rainfall is shown in Table 1;

Table 1. Rainfall process

Hours (h) 1 2 3 4 5 6 7 8 9 10 11 Rainfall (mm) 0 0 0 0 0 1.2 0.8 4.8 6.6 8.8 9.2 Hours (h) 12 13 14 15 16 17 18 19 20 twenty one twenty two Rainfall (mm) 11.2 6.8 4.4 2.3 0.4 0 0 0 0 0 0

2. The designed daily rainfall under different return periods is shown in Table 2;

Table 2. Design daily rainfall under different return period conditions

Return period (a) 0.5 1 2 3 5 10 20 50 Design daily rainfall (mm) 49.2 75.9 102.5 118.1 135.6 160.6 197.8 230.4

3. After calculating the amplification factor K by the following formula, the designed rainfall process can be obtained by amplifying the hourly rainfall. The right picture of Figure 5 is the timing diagram of the rainfall process under different return periods;

In the formula: X _{characteristic rainfall} is the characteristic rainfall, mm; X _design is the design rainfall under different return periods, mm.

4. Take the designed rainfall process as the simulated rainfall condition, and use the water accumulation model in step 2 to simulate the road water accumulation information. The visualization results are shown in Figure 6. The accumulation water depth can be calculated by the following formula;

Where: Q is the surface runoff; W is the inherent width of the sub-watershed, m; S is the slope, %; n is the Manning roughness coefficient; _d is the average depth of the pond water, m; .

Step 4: 1. Record the real-time rainfall process, as shown in Table 3;

Table 3. Rainfall process record table (0-4h)

time/h 0 1 2 3 4 Rainfall/mm 0 5 7 11 9

2. The period-by-period rainfall of the real-time rainfall process is a sequence {P _R }, and the accumulated value of the real-time rainfall is a sequence {P _T }, then there is the formula:

In the formula: P _Ti is the accumulated value of real-time rainfall in the previous i period; P _Ri is the real-time rainfall in the i-th period; k _Ri is the real-time variation coefficient in the i-th period; Δt is the rainfall time recording step, which is 1h.

3: Let the period-by-period rainfall of the designed rainfall process in step 3 be the sequence {P′ _R }, and the accumulated value of the designed rainfall is the sequence {P′ _T }, then there is the formula:

In the formula: P′ _Ti is the accumulated value of the design rainfall in the previous i period; P′ _Ri is the design rainfall in the i-th period; k′ _Ri is the real-time variation coefficient of the i-th period; Δt is the rainfall time recording step, which is 1h.

4: i=0, the initial value of the real-time rainfall time-by-hour sequence P _R0 =0, i=i+1=1;

5: The real-time rainfall of the real-time rainfall process in the first period P _R1 =5mm<6mm, the water accumulation threshold is not reached, i=i+1=1+1=2; the real-time rainfall of the real-time rainfall process in the second period Quantity P _R2 = 7mm>6mm, that is to say, heavy rainfall is predicted; P _T2 = 12mm; under the same rainfall duration, filter all the parameters that meet P' _T2 ∈ (P _T2 -2, P _T2 +2) = (10mm, 14mm) The designed rainfall process of the conditions is used as the alternative rainfall process; if the maximum value sequence of the variation coefficients of these alternative rainfall processes in the first two time periods {k′ _2j }, calculate the real-time rainfall process variation coefficient sequence {k _R }, let max({ k _R })=k ₂ , take the alternative rainfall process represented by |k ₂ -k′ _2j | _min as the predicted rainfall process in the first two time periods, and take the road stagnant simulation data of the alternative rainfall process as stagnant water forecast result;;

6: In the third period, if P _R3 > 0 is monitored, repeat step 5 to obtain the predicted rainfall process for the first three periods, and update the stagnant water forecast results;

7: In the fourth period, if P _R4 > 0 is monitored, repeat step 5 to obtain the predicted rainfall process for the first four periods, and update the stagnant water forecast results;

8: If P _R5 >0 is monitored in the fifth time period, repeat step 5 to obtain the predicted rainfall process for the first five time periods, and update the stagnant water prediction result; if P _R5 =0, the rainfall stops and the process ends.

Step 5: Accept the mobile terminal user guidance request, and use the existing platform to obtain all travel plans.

Step 6: 1. In the road water model, correspond the feasible routes one by one according to the corresponding coordinate relationship;

2. Combine the road water accumulation prediction results in the fourth step, take the road water accumulation data in the fourth period, and summarize the water accumulation information of all road sections on the feasible route into a table from the starting point to the end point according to different routes;

3. Exclude routes with higher risk levels according to the travel mode. The risk levels of each travel mode are shown in Table 4;

Table 4. Danger level of each travel mode

In Table 4, the assessment of the hazard level of pedestrians and bicycles is based on the water test and experience, and the hazard assessment standard of private cars is based on the investigation of the height of the air intake and exhaust of common models, as shown in Table 5. Water causes the car to stall and other factors, and the height of the exhaust port is selected as the main evaluation standard.

Table 5. Inlet height and outlet height of common models

4. Considering the depth of water, the number of road sections and the length of each route, the traffic coefficient θ of each route is calculated; as shown in the following formula:

In the formula: θ is the traffic coefficient of each route, and the unit is 1; _n is the number of road segments of each route; lx is the length of a road segment, m; _dx is the average water depth of the corresponding road segment in the fourth period, m.

5. Compare the traffic coefficients of each route, determine the route with the smallest traffic coefficient, and use it as the best guiding route.

Step 7: Integrate the best guiding route for the user's travel and the water accumulation information on the route, and feed it back to the mobile terminal user to complete the route guidance. The best guiding route for different travel modes is shown in Figure 7.

Claims (9)

1. an urban route guidance method based on road water accumulation prediction, is characterized in that, this method comprises the following steps: Step 1: Collect the data of the underlying surface and the pipe network in the area to be analyzed, and carry out the analysis of the slope and aspect of the area to be analyzed and the generalization of the pipe network; Step 2: Divide the sub-catchments of the hydrological model according to the three principles of singleness, build an urban road water accumulation model in combination with the pipe network, and establish a coordinate conversion relationship with the electronic map; Step 3: Combine the historical rainfall data of the area to be analyzed to screen the characteristic rainfall processes, design the rainfall processes under different return periods as the simulated rainfall conditions, and combine the road water accumulation model in step 2 to simulate the road water accumulation information, and calculate the rainfall for different designs. Depth of road water accumulation under the process; Step 4: Record the real-time precipitation process, carry out numerical analysis and trend matching according to the trend similarity and the design rainfall process, select the predicted precipitation process, take the road water accumulation simulation data as the precipitation information prediction result, and calculate the precipitation information one by one according to the change of real-time precipitation conditions. Time period correction, used in the calculation of stagnant water passing coefficient in the following steps; Step 5: Accept the mobile terminal user guidance request, upload the selected starting point to the online electronic map platform, and obtain all feasible travel plans; Step 6: Obtain the corresponding time period of the rainfall process at the time of the user's request, and combine the road water accumulation prediction result in step 4, obtain the road water accumulation data in this period, calculate the water accumulation passage coefficient of each scheme, compare and judge, and calculate the accumulation water passage coefficient. The smallest solution is used as the best guiding route for each travel mode; Step 7: Integrate the best travel plan and water accumulation information, and feed it back to the mobile terminal users to complete the route guidance. 2. A kind of urban route guidance method based on road water accumulation prediction according to claim 1, it is characterized in that, described in described step 2, the three principles of singleness of sub-catchment division are specifically: (1) the following The principle of singleness of surface: control the boundary so that there is only one type of underlying surface in the same subcatchment; (2) principle of singleness of slope aspect: under the conditions of (1), control the boundary to make any subcatchment exist The slope aspect in the area is consistent; (3) the principle of slope unity: it means that under the conditions of (2), the control boundary makes the actual terrain slope value corresponding to the subcatchment change within Inside, is the average slope value in each subcatchment. 3. a kind of urban route guidance method based on road water accumulation prediction according to claim 1, is characterized in that, the concrete step of constructing urban road water accumulation model in described step 2 is: (1) according to the type of underlying surface , label the divided subcatchments to distinguish them, and set the slope, permeability area ratio, Manning's n value, and parameter value of infiltration rate; (2) generalize the pipe network model, after determining the distribution of artificial ports, Connect the artificial mouth according to the flow direction of the pipe, and set the parameters of the artificial mouth and the pipeline; (3) Judge the runoff flow direction according to the slope aspect, and establish the confluence relationship between the sub-catchment area and the sub-catchment area, and the sub-catchment area and the artificial mouth. 4. a kind of urban route guidance method based on road water accumulation prediction according to claim 1 or 2 or 3, is characterized in that, the concrete step of establishing coordinate conversion relation in step 2 is: (1) select a point on the electronic map is the reference point A, and the latitude and longitude of point A is the reference latitude and longitude; (2) Select the position corresponding to point A on the road water model and set it as the model coordinate origin O; (3) Multi-parameter method to carry out the longitude and latitude and road water model (4) According to the plane coordinates of point A and point O, establish the conversion relationship between the two coordinate systems in (3). 5. a kind of urban route guidance method based on road water accumulation prediction according to claim 4, is characterized in that, in step 3, screening characteristic rainfall process, characteristic rainfall process needs to satisfy the following three kinds of characteristics of typical rainstorm simultaneously: (1) The rainfall at least reaches the rainstorm level, that is, the rainfall in 24 hours is greater than L mm; (2) There is a main rain peak, which means that there is a maximum instantaneous rainfall intensity in the complete rainfall process, and its value is not less than Xmm/min; (3) The main rain peak is behind, which means that the peak ratio is greater than 0.5 in the complete rainfall process. 6. a kind of urban route guidance method based on road water accumulation prediction according to claim 5, is characterized in that, in step 3, the design method of designing rainfall process under different return periods is specifically: take the historical rainfall that accords with characteristic, The designed rainfall process is obtained according to the same magnification method, and the formula of the same magnification factor is as follows: In the formula: X _{characteristic rainfall} is the characteristic rainfall, mm; X _design is the design rainfall under different return periods, mm. 7. a kind of urban route guidance method based on road water accumulation prediction according to claim 6, is characterized in that, in step 3, the formula used for calculating road water accumulation depth is as follows: Where: Q is the surface runoff; W is the inherent width of the sub-watershed, m; S is the slope, %; n is the Manning roughness coefficient; _d is the average depth of the pond water, m; . 8. a kind of urban route guidance method based on road water accumulation prediction according to claim 1 or 7, is characterized in that, in step 4, trend similarity judgment rule and step specifically comprise: Step 4.1: The period-by-period rainfall of the real-time rainfall process is the sequence {P _R }, and the cumulative value of the real-time rainfall is the sequence {P _T }, then there is the formula: In the formula: P _Ti is the accumulated value of real-time rainfall in the previous i period; P _Ri is the real-time rainfall in the i-th period; k _Ri is the real-time variation coefficient in the i-th period; Δt is the rainfall time recording step; Step 4.2: Let the period-by-period rainfall of the designed rainfall process in step 3 be the sequence {P′ _R }, and the accumulated value of the designed rainfall is the sequence {P′ _T }, then there is the formula: In the formula: P′ _Ti is the accumulated value of the design rainfall in the previous i period; P′ _Ri is the design rainfall in the ith period; k′ _Ri is the real-time variation coefficient of the ith period; Δt is the rainfall time recording step; Step 4.3: i=0, let the initial value of the hourly series of real-time rainfall P _R0 =0; that is, no rainfall occurs in the 0th period, i=i+1; Step 4.4: Determine the measured value of P _Ri , if P _Ri ≥ 6, then heavy rainfall occurs, and go to the next step; if P _Ri <6, then small and medium rainfall occurs, and the water accumulation threshold is not reached, i=i+1, repeat Step 4.4; Step 4.5: Calculate P _Ti , and select all design rainfall processes that meet the conditions of P' _Ti ∈ (P _Ti -i, P _Ti +i) as alternative rainfall processes under the same rainfall occurrence duration; Step 4.6: Define the maximum value of variation coefficient k′ _Rimax = k′ _i in the first i period of a certain candidate rainfall process, then for different candidate rainfall processes j, there is a sequence of the maximum variation coefficient within the first i period {k′ _ij }; Calculate the real-time rainfall process variation coefficient sequence {k _R }, and set k _i =max({k _R }), and take the alternative rainfall process represented by | _ki -k′ _ij | _min as the forecast for the first i time periods rainfall process; Step 4.7: i=i+1, if P _Ri >0 is monitored, repeat steps 4.5 and 4.6, and update the prediction result every time it is repeated; if P _Ri =0, the rainfall stops and the process ends. 9. a kind of urban route guidance method based on road water accumulation prediction according to claim 8, is characterized in that, step 6 specifically comprises the following steps: Step 6.1: Corresponding feasible routes one by one according to the coordinate correspondence in the road water model; Step 6.2: According to different feasible routes, combined with the road water accumulation prediction results in step 4, take the water accumulation data of the corresponding period of real-time rainfall, and summarize the water accumulation data of all road sections from the starting point to the end point into a table; Step 6.3: Define the wading depth danger level of different travel modes, and exclude routes with water wading risks under different travel modes; Step 6.4: Calculate the stagnant water passing coefficient θ of each route based on the factors of the depth of water, the number of road sections and the length of each route; as shown in the following formula: In the formula: θ is the stagnant water passage coefficient of each route, and the dimension is 1; _n is the number of road sections of each route; lx is the length of a certain road section, m; _dx is the average ponding depth of the corresponding road section in the i-th period , m; Step 6.5: Compare the stagnant water passing coefficients of each route, and select the route with the smallest passing coefficient as the best guiding route.