CN110570657A - Highway rescue station selection system and method based on big data - Google Patents
Highway rescue station selection system and method based on big data Download PDFInfo
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Abstract
The invention discloses a road rescue station selection system and method based on big data, wherein the station selection system comprises a road section division module, a geographical position analysis module, a vehicle driving analysis module, an accident occurrence statistic module and a rescue station selection module, the geographical position analysis module, the vehicle driving analysis module and the accident occurrence statistic module are connected with the rescue station selection module, the road section division module is used for dividing a road in an interval served by a rescue station into a plurality of small sections of roads, the geographical position analysis module is used for analyzing and counting the geographical position condition of each small section of road, the vehicle driving analysis module is used for analyzing and counting the vehicle driving condition on each small section of road, and the accident occurrence statistic module is used for analyzing and counting the accident occurrence condition on each small section of road.
Description
Technical Field
the invention relates to the field of big data, in particular to a highway rescue station selection system and method based on big data.
Background
with the rapid development of society, the living standard of people is also improved, and more families have private cars. When people go out, the private car is considered to go out firstly, so that more and more vehicles on a traffic road are used, the frequency of traffic accidents is greatly increased, and after the traffic accidents happen, if the accident scene is not processed in time, the passing conditions of other vehicles on the road can be influenced. The rescue station is arranged on the road, so that the accident scene can be timely handled, and the condition that the vehicle communication is slow due to the accident is reduced. However, in the prior art, the rescue stations are not reasonably arranged, so that the rescue speed is not timely enough, and the speed of the vehicle for recovering the normal traffic is low.
disclosure of Invention
The invention aims to provide a road rescue station selection system and method based on big data, and aims to solve the problems in the prior art.
In order to achieve the purpose, the invention provides the following technical scheme:
the road rescue station selection system based on the big data comprises a road section division module, a geographical position analysis module, a vehicle running analysis module, an accident occurrence statistic module and a rescue station selection module, wherein the geographical position analysis module, the vehicle running analysis module and the accident occurrence statistic module are connected with the rescue station selection module, the road section division module is used for dividing a road in a service interval of the rescue station into a plurality of small sections of roads, the geographical position analysis module is used for analyzing and counting the geographical position condition of each small section of road, the vehicle running analysis module is used for analyzing and counting the vehicle running condition on each small section of road, the accident occurrence statistic module is used for analyzing and counting the accident occurrence condition on each small section of road, and the rescue station selection module is used for selecting, counting and counting the accident occurrence condition on each small section of road according to the geographical position condition on each, And selecting the position of the road rescue station according to the vehicle running condition and the accident occurrence condition.
As a preferred scheme, the geographic position analysis module comprises an intersection analysis module, a curve analysis module and a geographic position influence parameter calculation module, the intersection analysis module comprises an intersection number statistic module, an intersection number comparison module and an intersection proportion statistic module, the intersection number statistic module is used for counting the number of intersections on each small section of road, the intersection number comparison module is used for comparing the number of intersections on each small section of road with the intersection reference number and obtaining an intersection influence factor, the intersection proportion statistic module is used for counting the proportion of the number of intersections on each small section of road to the number of intersections on the road in a service interval, the curve analysis module comprises a curve number statistic module, a curve number comparison module and a curve proportion statistic module, the curve number statistic module is used for counting the number of curves on each small section of road, the device comprises a curve number comparison module, a curve proportion calculation module and a geographical position influence parameter calculation module, wherein the curve number comparison module is used for comparing the number of curves on each section of road with a curve reference number and obtaining a curve influence factor, the curve proportion calculation module is used for calculating the proportion of the number of curves on each section of road in a service interval, and the geographical position influence parameter calculation module calculates the geographical position influence parameter according to the turnout influence factor, the curve influence factor, the proportion of the number of turnouts and the proportion of the number of curves.
as a preferable scheme, the vehicle running analysis module comprises a traffic flow analysis module, a vehicle speed analysis module and a vehicle influence parameter calculation module, the traffic flow analysis module comprises a traffic flow statistics module and a traffic flow comparison module, the traffic flow statistics module is used for counting the traffic flow of each small section of highway in a peak time period within a certain period of time, the traffic flow comparison module is used for comparing the traffic flow of each small section of highway with a preset traffic flow value and obtaining a traffic flow influence factor, the vehicle speed analysis module comprises a vehicle speed statistics module and a vehicle speed comparison module, the vehicle speed statistics module is used for counting the average speed of each section of highway in a peak leveling time period within a certain period of time, the vehicle speed comparison module is used for comparing the average speed of each small section of highway with a preset vehicle average speed threshold value and obtaining a vehicle speed influence factor, the vehicle influence parameter calculation module calculates vehicle influence parameters according to the vehicle flow influence factor and the vehicle speed influence factor; the rescue site selection module comprises a selection model establishment module, a model result calculation module, a calculation result sorting module and a rescue site division module, the selection model establishment module is used for establishing a highway rescue site selection model, the model result calculation module is used for calculating highway rescue site selection models on each section of highway respectively, the calculation result sorting module is used for sorting model calculation results on each section of highway in a descending order and selecting a first-sorted highway section and a second-sorted highway section from the model calculation results, and the rescue site division module selects the positions of the highway rescue sites according to the positions of the first-sorted highway section and the second-sorted highway section.
a road rescue station selection method based on big data comprises the following steps:
Step S1: selecting a service section of a rescue station on a highway, and averagely dividing the highway in the service section into m sections of highways with equal length;
Step S2: acquiring the geographical position condition of each section of road;
Step S3: acquiring the driving condition of vehicles on each section of road;
step S4: acquiring the accident occurrence condition of each section of road;
step S5: and selecting the position of the road rescue station according to the geographical position condition, the vehicle running condition and the accident occurrence condition.
preferably, the step S2 further includes the following steps:
step S21: respectively acquiring the number of the intersections on each small section of road, wherein when the number n1 of the intersections on the section of road is less than or equal to 1, the influence factor a1=0, and when the number n1 of the intersections on the section of road is greater than 1, the influence factor a1= 1;
step S22: respectively acquiring the number of curves on each section of road, wherein when the number n2 of curves on the section of road is equal to 0, the curve influence factor a2=0, and when the number n2 of curves on the section of road is greater than 0, the curve influence factor a2= 1;
Step S23: respectively obtaining the proportion a3= n1/s1 of the number of the intersections on each small section of the highway in the service interval, wherein s1 is the sum of the number of the intersections on m small sections of the highway;
step S24: respectively obtaining the proportion a4= n2/s2 of the number of curves on each small section of road in the service interval, wherein s2 is the sum of the number of curves on m small sections of roads;
Step S25: calculating geographic location impact parameters
x =0.3a1+0.3a2+0.2a3+0.2a4, where x has a value range of [0,1], the road conditions of the road are complicated due to the intersection and the curve on the road, and the probability of traffic accidents is high in places with many intersections and curves, so that the intersection and the curve on the road are taken as the consideration factor of the positions of the rescue stations.
preferably, the step S3 further includes the following steps:
Step S31: respectively obtaining the traffic flow of each small section of highway in the next month in the peak time period, wherein when the traffic flow exceeds a preset traffic flow value, a traffic flow influence factor b1=1, otherwise, the traffic flow influence factor b1= 0;
step S32, respectively acquiring the average speed of the vehicles in the peak-averaging time period on each road in the last month, wherein when the average speed of the vehicles exceeds a preset vehicle average speed threshold value, the vehicle speed influence factor b2=1, otherwise, the vehicle speed influence factor b2= 0;
Step S34: calculating a vehicle influence parameter y =0.6b1+04b2, wherein the value range of y is [0,1], the traffic flow is more and more vehicles are more in the peak time period, the probability of accidents is higher, the traffic flow is less in the peak time period, the average speed of the vehicle is higher, and the probability of accidents is higher when the vehicle speed is higher, so that the traffic flow in the peak time period and the average speed of the vehicle in the peak time period are taken as the consideration factors of the positions of rescue stations
preferably, the step S4 further includes: and respectively obtaining the proportion c of the accident occurrence frequency of each section of road in the next month to the accident occurrence frequency of the road in the service interval, wherein the value range of c is [0,1 ].
preferably, the step S1 further includes:
when the current date is a working day, peak time is between 7 and 9 and between 17 and 19, and when the current date is a holiday, peak time is between 8 and 20, and the rest of time is flat peak time.
Preferably, the step S5 further includes the following steps:
S51: establishing a highway rescue station selection model Z = k1x + k2y + k3c, wherein the value range of Z is [0,1], wherein k1 is the weight of x, k2 is the weight of y, and k3 is the weight of c;
s52: respectively calculating the results of the model selected by the road rescue stations on each section of road;
S53: sorting the model calculation results on each section of road in a descending order, selecting the first road section as a first road section, and selecting the second road section as a second road section;
s54: and respectively taking a midpoint P1 of the first road section and a midpoint P2 of the second road section, connecting the point P1 with the point P2 to obtain a connecting line P1P2, taking the midpoint of the connecting line P1P2 as a vertical line with the connecting line P1P2, and taking the intersection point of the vertical line and the road in the service interval, namely the position of the road rescue station.
Compared with the prior art, the invention has the beneficial effects that: the invention selects the position of the highway rescue station by comprehensively considering three aspects of the geographical position condition, the vehicle driving condition and the accident occurrence condition, thereby reasonably setting the position of the highway rescue station, improving the rescue speed after the accident occurs and improving the speed of the vehicle for recovering normal traffic.
drawings
FIG. 1 is a block diagram of a big data based highway rescue site selection system of the present invention;
fig. 2 is a flow chart of a road rescue station selection method based on big data according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, in an embodiment of the present invention, a road rescue station selection system based on big data includes a road section division module, a geographic position analysis module, a vehicle driving analysis module, an accident occurrence statistics module, and a rescue station selection module, where the geographic position analysis module, the vehicle driving analysis module, and the accident occurrence statistics module are connected to the rescue station selection module, the road section division module is configured to divide a road in an area served by a rescue station into a plurality of small sections of roads, the geographic position analysis module is configured to analyze and count a geographic position condition of each small section of road, the vehicle driving analysis module is configured to analyze and count a vehicle driving condition on each small section of road, the accident occurrence statistics module is configured to analyze and count an accident occurrence condition on each small section of road, and the rescue station selection module is configured to select, according to a geographic position condition on each small section of road, And selecting the position of the road rescue station according to the vehicle running condition and the accident occurrence condition.
the geographical position analysis module comprises a turnout analysis module, a curve analysis module and a geographical position influence parameter calculation module, the turnout analysis module comprises a turnout quantity counting module, a turnout quantity comparison module and a turnout proportion counting module, the turnout quantity counting module is used for counting the number of the turnouts on each section of road, the turnout quantity comparison module is used for comparing the number of the turnouts on each section of road with the turnout reference number and obtaining turnout influence factors, the turnout proportion counting module is used for counting the proportion of the number of the turnouts on each section of road to the number of the turnouts on the road in a service interval, the curve analysis module comprises a curve quantity counting module, a curve quantity comparison module and a curve proportion counting module, and the curve quantity counting module is used for counting the number of the curves on each section of road, the device comprises a curve number comparison module, a curve proportion calculation module and a geographical position influence parameter calculation module, wherein the curve number comparison module is used for comparing the number of curves on each section of road with a curve reference number and obtaining a curve influence factor, the curve proportion calculation module is used for calculating the proportion of the number of curves on each section of road in a service interval, and the geographical position influence parameter calculation module calculates the geographical position influence parameter according to the turnout influence factor, the curve influence factor, the proportion of the number of turnouts and the proportion of the number of curves.
The vehicle running analysis module comprises a traffic flow analysis module, a vehicle speed analysis module and a vehicle influence parameter calculation module, the traffic flow analysis module comprises a traffic flow statistic module and a traffic flow comparison module, the traffic flow statistic module is used for counting the traffic flow of each small section of highway in a peak time period within a certain period of time, the traffic flow comparison module is used for comparing the traffic flow of each small section of highway with a preset traffic flow value and obtaining a traffic flow influence factor, the vehicle speed analysis module comprises a vehicle speed statistic module and a vehicle speed comparison module, the vehicle speed statistic module is used for counting the vehicle average speed of each section of highway in a peak leveling time period within a certain period of time, and the vehicle speed comparison module is used for comparing the vehicle average speed of each small section of highway with a preset vehicle average speed threshold value and obtaining a vehicle speed influence factor, the vehicle influence parameter calculation module calculates vehicle influence parameters according to the vehicle flow influence factor and the vehicle speed influence factor; the rescue site selection module comprises a selection model establishment module, a model result calculation module, a calculation result sorting module and a rescue site division module, the selection model establishment module is used for establishing a highway rescue site selection model, the model result calculation module is used for calculating highway rescue site selection models on each section of highway respectively, the calculation result sorting module is used for sorting model calculation results on each section of highway in a descending order and selecting a first-sorted highway section and a second-sorted highway section from the model calculation results, and the rescue site division module selects the positions of the highway rescue sites according to the positions of the first-sorted highway section and the second-sorted highway section.
A road rescue station selection method based on big data comprises the following steps:
step S1: the road section dividing module selects a service section of the rescue station on the road, and averagely divides the road in the service section into m sections of roads with equal length;
Step S2: the geographic position analysis module acquires the geographic position condition on each section of road:
step S21: the method comprises the following steps that a turnout quantity counting module respectively obtains the number of turnouts on each small section of road, a turnout quantity comparison module compares the number n1 of the turnouts on the section of road with 1, when the number n1 of the turnouts on the section of road is less than or equal to 1, an turnout influence factor a1=0, and when the number n1 of the turnouts on the section of road is greater than 1, the turnout influence factor a1= 1;
step S22: the curve number counting module respectively obtains the number of curves on each section of road, and the curve number comparison module compares the number n2 of curves on the section of road with 0, wherein when the number n2 of curves on the section of road is equal to 0, the curve influence factor a2=0, and when the number n2 of curves on the section of road is greater than 0, the curve influence factor a2= 1;
step S23: the turnout proportion statistical module respectively obtains the proportion of the number of the turnouts on each small section of road to the number of the turnouts on the road in the service interval, namely a3= n1/s1, and s1 is the sum of the number of the turnouts on m small sections of roads;
step S24: the curve proportion statistical module respectively obtains the proportion a4= n2/s2 of the number of curves on each section of road in the service interval, and s2 is the sum of the number of curves on m sections of roads;
Step S25: the geographic position influence parameter calculation module calculates the geographic position influence parameter
x =0.3a1+0.3a2+0.2a3+0.2a4, wherein x has a value in the range [0,1 ];
step S3: the vehicle running analysis module obtains the vehicle running condition on each section of road:
step S31: the traffic flow counting module respectively obtains traffic flow of each small segment of highway in the next month in a peak time period, the traffic flow comparison module compares the traffic flow counted by the traffic flow counting module with a preset traffic flow value, when the traffic flow exceeds the preset traffic flow value, a traffic flow influence factor b1=1, and otherwise, the traffic flow influence factor b1= 0;
step S32: the vehicle speed counting module respectively obtains the average speed of the vehicle in the peak-averaging time period on each road in the last month, the vehicle speed comparison module compares the average speed counted by the vehicle speed counting module with a preset vehicle average speed threshold, when the average speed of the vehicle exceeds the preset vehicle average speed threshold, a vehicle speed influence factor b2=1, otherwise, the vehicle speed influence factor b2= 0;
step S34: the vehicle influence parameter calculation module calculates a vehicle influence parameter y =0.6b1+04b2, wherein the value range of y is [0,1 ];
When the current date is a working day, peak time is between 7 and 9 and between 17 and 19, and when the current date is a holiday, peak time is between 8 and 20, and the rest of time is peak-off time.
Step S4: the accident occurrence statistic module acquires the accident occurrence condition on each section of road: the accident occurrence counting module respectively obtains the proportion c of the accident occurrence times of each section of road in a month to the accident occurrence times of the road in the service interval, and the value range of c is [0,1 ];
step S5: the rescue station selection module selects the position of the highway rescue station according to the geographical position condition, the vehicle running condition and the accident occurrence condition:
S51: a selection model establishing module establishes a highway rescue station selection model Z =0.3x +0.2y +0.5c, and the value range of Z is [0,1 ];
s52: the model result calculation module respectively calculates the results of the model selected by the highway rescue stations on each section of highway;
s53: the calculation result sorting module sorts the model calculation results on each section of road in a descending order, selects the first road section as a first road section, and selects the second road section as a second road section;
S54: the rescue station division module is used for respectively taking a midpoint P1 of the first road section and a midpoint P2 of the second road section, connecting a point P1 with a point P2 to obtain a connecting line P1P2, taking the midpoint of the connecting line P1P2 as a vertical line with the connecting line P1P2, and the intersection point of the vertical line and the road in the service interval is the position of the road rescue station.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (9)
1. the utility model provides a highway rescue website selects system based on big data which characterized in that: the station selection system comprises a road section division module, a geographical position analysis module, a vehicle running analysis module, an accident occurrence statistic module and a rescue station selection module, the geographic position analysis module, the vehicle running analysis module and the accident occurrence statistic module are connected with the rescue station selection module, the road section dividing module is used for dividing the road in the service interval of the rescue station into a plurality of small sections of roads, the geographic position analysis module is used for analyzing and counting the geographic position condition of each small section of road, the vehicle running analysis module is used for analyzing and counting the vehicle running condition on each small section of road, the accident occurrence counting module is used for analyzing and counting the accident occurrence condition on each section of road, and the rescue site selecting module selects the position of the road rescue site according to the geographical position condition, the vehicle driving condition and the accident occurrence condition on each section of road.
2. A big data based highway rescue station selection system as claimed in claim 2, wherein: the geographical position analysis module comprises a turnout analysis module, a curve analysis module and a geographical position influence parameter calculation module, the turnout analysis module comprises a turnout quantity counting module, a turnout quantity comparison module and a turnout proportion counting module, the turnout quantity counting module is used for counting the number of the turnouts on each section of road, the turnout quantity comparison module is used for comparing the number of the turnouts on each section of road with the turnout reference number and obtaining turnout influence factors, the turnout proportion counting module is used for counting the proportion of the number of the turnouts on each section of road to the number of the turnouts on the road in a service interval, the curve analysis module comprises a curve quantity counting module, a curve quantity comparison module and a curve proportion counting module, and the curve quantity counting module is used for counting the number of the curves on each section of road, the device comprises a curve number comparison module, a curve proportion calculation module and a geographical position influence parameter calculation module, wherein the curve number comparison module is used for comparing the number of curves on each section of road with a curve reference number and obtaining a curve influence factor, the curve proportion calculation module is used for calculating the proportion of the number of curves on each section of road in a service interval, and the geographical position influence parameter calculation module calculates the geographical position influence parameter according to the turnout influence factor, the curve influence factor, the proportion of the number of turnouts and the proportion of the number of curves.
3. a big data based highway rescue station selection system as claimed in claim 3, wherein: the vehicle running analysis module comprises a traffic flow analysis module, a vehicle speed analysis module and a vehicle influence parameter calculation module, the traffic flow analysis module comprises a traffic flow statistic module and a traffic flow comparison module, the traffic flow statistic module is used for counting the traffic flow of each small section of highway in a peak time period within a certain period of time, the traffic flow comparison module is used for comparing the traffic flow of each small section of highway with a preset traffic flow value and obtaining a traffic flow influence factor, the vehicle speed analysis module comprises a vehicle speed statistic module and a vehicle speed comparison module, the vehicle speed statistic module is used for counting the vehicle average speed of each section of highway in a peak leveling time period within a certain period of time, and the vehicle speed comparison module is used for comparing the vehicle average speed of each small section of highway with a preset vehicle average speed threshold value and obtaining a vehicle speed influence factor, the vehicle influence parameter calculation module calculates vehicle influence parameters according to the vehicle flow influence factor and the vehicle speed influence factor; the rescue site selection module comprises a selection model establishment module, a model result calculation module, a calculation result sorting module and a rescue site division module, the selection model establishment module is used for establishing a highway rescue site selection model, the model result calculation module is used for calculating highway rescue site selection models on each section of highway respectively, the calculation result sorting module is used for sorting model calculation results on each section of highway in a descending order and selecting a first-sorted highway section and a second-sorted highway section from the model calculation results, and the rescue site division module selects the positions of the highway rescue sites according to the positions of the first-sorted highway section and the second-sorted highway section.
4. A highway rescue station selection method based on big data is characterized by comprising the following steps: the distribution method comprises the following steps:
step S1: selecting a service section of a rescue station on a highway, and averagely dividing the highway in the service section into m sections of highways with equal length;
Step S2: acquiring the geographical position condition of each section of road;
Step S3: acquiring the driving condition of vehicles on each section of road;
step S4: acquiring the accident occurrence condition of each section of road;
step S5: and selecting the position of the road rescue station according to the geographical position condition, the vehicle running condition and the accident occurrence condition.
5. a big data-based highway rescue station selection method according to claim 4, wherein the big data-based highway rescue station selection method comprises the following steps: the step S2 further includes the steps of:
step S21: respectively acquiring the number of the intersections on each small section of road, wherein when the number n1 of the intersections on the section of road is less than or equal to 1, the influence factor a1=0, and when the number n1 of the intersections on the section of road is greater than 1, the influence factor a1= 1;
step S22: respectively acquiring the number of curves on each section of road, wherein when the number n2 of curves on the section of road is equal to 0, the curve influence factor a2=0, and when the number n2 of curves on the section of road is greater than 0, the curve influence factor a2= 1;
Step S23: respectively obtaining the proportion a3= n1/s1 of the number of the intersections on each small section of the highway in the service interval, wherein s1 is the sum of the number of the intersections on m small sections of the highway;
Step S24: respectively obtaining the proportion a4= n2/s2 of the number of curves on each small section of road in the service interval, wherein s2 is the sum of the number of curves on m small sections of roads;
Step S25: calculating geographic location impact parameters
x =0.3a1+0.3a2+0.2a3+0.2a4, where x has a value in the range [0,1 ].
6. A big data-based highway rescue station selection method according to claim 5, wherein the big data-based highway rescue station selection method comprises the following steps: the step S3 further includes the steps of:
step S31: respectively obtaining the traffic flow of each small section of highway in the next month in the peak time period, wherein when the traffic flow exceeds a preset traffic flow value, a traffic flow influence factor b1=1, otherwise, the traffic flow influence factor b1= 0;
Step S32, respectively acquiring the average speed of the vehicles in the peak-averaging time period on each road in the last month, wherein when the average speed of the vehicles exceeds a preset vehicle average speed threshold value, the vehicle speed influence factor b2=1, otherwise, the vehicle speed influence factor b2= 0;
Step S34: and calculating a vehicle influence parameter y =0.6b1+04b2, wherein the value range of y is [0,1 ].
7. a big data-based highway rescue station selection method according to claim 6, wherein the big data-based highway rescue station selection method comprises the following steps: the step S4 further includes: and respectively obtaining the proportion c of the accident occurrence frequency of each section of road in the next month to the accident occurrence frequency of the road in the service interval, wherein the value range of c is [0,1 ].
8. a big data-based highway rescue station selection method according to claim 6, wherein the big data-based highway rescue station selection method comprises the following steps: the step S1 further includes:
when the current date is a working day, peak time is between 7 and 9 and between 17 and 19, and when the current date is a holiday, peak time is between 8 and 20, and the rest of time is flat peak time.
9. A big data-based highway rescue station selection method according to claim 7, wherein the big data-based highway rescue station selection method comprises the following steps: the step S5 further includes the steps of:
s51: establishing a highway rescue station selection model Z = k1x + k2y + k3c, wherein the value range of Z is [0,1], wherein k1 is the weight of x, k2 is the weight of y, and k3 is the weight of c;
s52: respectively calculating the results of the model selected by the road rescue stations on each section of road;
s53: sorting the model calculation results on each section of road in a descending order, selecting the first road section as a first road section, and selecting the second road section as a second road section;
s54: and respectively taking a midpoint P1 of the first road section and a midpoint P2 of the second road section, connecting the point P1 with the point P2 to obtain a connecting line P1P2, taking the midpoint of the connecting line P1P2 as a vertical line with the connecting line P1P2, and taking the intersection point of the vertical line and the road in the service interval, namely the position of the road rescue station.
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