CN113591690A - Running monitoring method for network taxi booking/taxi - Google Patents
Running monitoring method for network taxi booking/taxi Download PDFInfo
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Abstract
The invention provides a running monitoring method of a network taxi appointment/taxi, which comprises the following steps: after the network car appointment/taxi receives the order, determining a first moving track of the network car appointment/taxi based on order information of the order; determining a running monitoring mode that the taxi appointment/taxi moves to a first position point corresponding to the first movement track based on the first movement track; and based on the running monitoring mode, running monitoring is carried out on the network car booking/taxi. According to the method for monitoring the running of the network car appointment/taxi, the running route of the network car appointment/taxi is determined through the order, and the running of the network car appointment/taxi is monitored on the basis of the running route, so that the running abnormity can be found, and the safety of personnel and passengers engaged by the network car appointment/taxi drivers can be further protected.
Description
Technical Field
The invention relates to the technical field of running monitoring, in particular to a running monitoring method for a network taxi appointment/taxi.
Background
At present, as the sharing economy is more and more perceived by people, a network appointment car/taxi takes place as the best as the people go out; the network taxi appointment/taxi is short for network taxi appointment. However, the net car booking/taxi driver employees and the passengers are all in good condition, and the quality is greatly different, so how to protect the safety of the net car booking/taxi driver employees and the passengers is a problem which needs to be solved urgently.
Disclosure of Invention
One of the purposes of the invention is to provide a running monitoring method for a network car booking/taxi, which determines a running route of the network car booking/taxi through an order, and monitors the running of the network car booking/taxi based on the running route, so that running abnormity can be found, and the safety of network car booking/taxi driver employees and passengers can be further protected.
The embodiment of the invention provides a method for monitoring running of a network taxi booking/taxi, which comprises the following steps:
after the network car appointment/taxi receives the order, determining a first moving track of the network car appointment/taxi based on order information of the order;
determining a running monitoring mode that the taxi appointment/taxi moves to a first position point corresponding to the first movement track based on the first movement track;
and based on the running monitoring mode, running monitoring is carried out on the network car booking/taxi.
Preferably, the determining the first movement track of the taxi appointment/taxi based on the order information of the order comprises:
analyzing order information and determining a starting point and an end point;
loading map information;
determining a plurality of lines to be selected based on the starting point, the end point and the map information;
sampling position points of a line to be selected to obtain a plurality of sampling position points;
acquiring safety information of sampling position points;
determining the safety degree of the sampling position point based on the safety information;
determining a safety value of the route to be selected based on the safety degree of each sampling position point on the route to be selected;
and taking the route to be selected with the maximum safety value as a first movement track.
Preferably, the acquiring of the safety information of the sampling position point includes:
acquiring peripheral environment information of sampling position points;
determining environmental safety information of a sampling position point based on a preset environmental safety identification template and surrounding environmental information;
and/or the presence of a gas in the gas,
acquiring monitoring and control conditions of sampling position points;
determining the monitoring coverage rate of the sampling position points based on the monitoring deployment and control condition;
and/or the presence of a gas in the gas,
acquiring historical accident conditions of sampling position points;
determining traffic safety information of the sampling position points based on historical accident conditions;
and/or the presence of a gas in the gas,
obtaining complaint information of corresponding sampling position points on a network taxi appointment/taxi platform;
and determining subjective safety information of the sampling position points based on the complaint information.
Preferably, the safety degree of the sampling position point is determined based on the safety information; the method comprises the following steps:
grading the environmental safety information based on a preset environmental safety grading template to obtain a first grading value;
grading the monitoring coverage rate based on a preset monitoring control grading template to obtain a second grading value;
grading the traffic safety information based on a preset traffic safety grading template to obtain a third grading value;
grading the subjective safety information based on a preset subjective safety grading template to obtain a fourth grading value;
determining the safety degree based on the first score value, the second score value, the third score value and the fourth score value, wherein the calculation formula of the safety degree is as follows:
D＝α1P1+α2P2+α3P3+α4P4；
wherein D is the degree of safety, P1Is a first commentA score value; p2Is a second score value; p3Is a third score value; alpha is alpha1A preset first relation coefficient corresponding to the first score value; alpha is alpha2A preset second relation coefficient corresponding to the second score value; alpha is alpha3A preset third relation coefficient corresponding to the third score value; alpha is alpha4Is a predetermined fourth relation coefficient corresponding to a fourth value of credit.
Preferably, position point sampling is carried out on the route to be selected, and a plurality of sampling position points are obtained; the method comprises the following steps:
acquiring a preset first sampling mark list;
determining a segmentation point of the route to be selected based on the first sampling mark list;
segmenting the route to be selected based on the segmentation points to obtain a plurality of segmentation routes;
acquiring vehicle parameters and driver information of a network taxi appointment/taxi;
acquiring a plurality of pieces of first driving data passing through each sectional route through a big data platform based on vehicle parameters and driver information;
analyzing the first driving data, and determining the passing time of each position passing through each sectional route; the passing time of each position is the time for the vehicle to travel from the starting point of the sectional route to each position;
acquiring a preset sampling time interval determination table; the sampling time intervals in the sampling time interval determination table correspond to the standard line parameters one to one;
determining the sampling time of each subsection route based on the line parameter and the sampling time interval determination table of each subsection route;
determining a plurality of passing sampling points based on the sampling time and the passing time of each subsection route;
and taking the starting point, the ending point and the passing sampling point of each segmented route as sampling position points.
Preferably, based on the first sampling mark list, determining the segmentation point of the route to be selected; the method comprises the following steps:
determining mark areas on a plurality of routes to be selected based on the first sampling mark list; the mark area includes: a mapping area corresponding to a first sampling mark in the first sampling mark list on the to-be-selected route and a buffer area before and after the mapping area within a preset first distance;
acquiring a plurality of pieces of second driving data passing through the mark area through a big data platform based on the vehicle parameters and the driver information;
analyzing the second driving data and determining two braking points; the two braking points are respectively positioned at the two ends of the mark area; the braking point is a dividing point of vehicle deceleration and acceleration;
the braking point is taken as a segmentation point.
Preferably, the safety value of the route to be selected is determined based on the safety degree of each sampling position point on the route to be selected; the method comprises the following steps:
acquiring the attribute of a sampling position point;
inquiring a preset weight table based on the attribute, and determining the weight of the sampling position point;
determining a safety value based on the weight of the sampling position point and the safety degree of the sampling position point; the formula for calculating the safety value is as follows:
wherein, P is a safety value; diThe safety degree of the ith sampling position point; beta is aiIs the weight of the ith sample position point;
wherein the attributes of the sampling location points include: whether the sampling position point belongs to the mapping area of the first sampling mark in the first sampling mark list or not, whether the sampling position point is the starting point or the ending point of the segmented route or not, the speed limit condition of the sampling position point and the road surface condition of the sampling position point are combined.
Preferably, the determining the sampling time of each sectional route based on the line parameter and sampling time interval determination table of each sectional route includes:
constructing a first vector based on the line parameters;
constructing a second vector based on the standard line parameters;
calculating the similarity of the first vector and the second vector, wherein the similarity calculation formula is as follows:
wherein X is similarity; a. thejIs the jth parameter value of the first vector; b isjIs the jth parameter value of the second vector; m is the total number of parameters of the first vector or the total number of parameters of the second vector;
and obtaining the sampling time interval corresponding to the standard line parameter corresponding to the second vector with the maximum similarity as the sampling time.
Preferably, based on the first movement track, determining a driving monitoring mode that the taxi is about to be reserved or the taxi moves to a first position point corresponding to the first movement track; the method comprises the following steps:
acquiring a sampling position point in a first moving track;
acquiring a preset safety degree and monitoring mode comparison table;
respectively calculating the distance between the first position point and the sampling position point, and acquiring the safety degree of the sampling position point closest to the first position point as the safety degree of the first position point;
determining a running monitoring mode of the first position point based on the safety degree of the first position point and a comparison table of the safety degree and the detection mode;
wherein the driving monitoring mode includes: the method comprises the steps of obtaining driving data of the network car appointment/taxi, obtaining positioning data of the network car appointment/taxi, obtaining audio data in the network car appointment/taxi, obtaining video data in the network car appointment/taxi and obtaining door opening and closing data of the network car appointment/taxi, wherein one or more of the data are combined.
Preferably, the method for monitoring the driving of the net appointment car/taxi further comprises the following steps:
acquiring first identity information of a taxi appointment/taxi driver and data information of a historical order;
determining a delayed driving monitoring time based on the first identity information and the data information;
after the order is finished, monitoring the running of the network taxi appointment/taxi based on the delayed running monitoring time and a running monitoring mode corresponding to the last sampling position point;
wherein determining the delayed driving monitoring time based on the first identity information and the data information comprises:
acquiring first event data corresponding to a network car appointment/taxi driver from a big data platform based on the first identity information and a preset event extraction table;
evaluating the first event data based on a preset event threat evaluation template to determine a first threat value;
evaluating the first identity information based on a preset identity information evaluation template to determine a second threat value;
acquiring second identity information of a plurality of associated persons associated with the online taxi appointment/taxi driver;
acquiring second event data corresponding to the second identity information from the big data platform based on the second identity information and a preset event extraction table;
evaluating the second event data based on a preset event threat evaluation template to determine a third threat value;
evaluating the second identity information based on a preset identity information evaluation template to determine a fourth threat value;
determining a danger value of a taxi appointment/taxi driver based on the first threat value, the second threat value, the third threat value and the fourth threat value; the risk value is calculated as follows:
wherein WX is a risk value, W1Is a first threat value; w2A second risk value; w3，kA third threat value for a kth associated person; w4，kA fourth threat value for a kth associated person; mu.s1Weight of taxi booking/taxi driver for the preset corresponding network; mu.s2For preset corresponding associated personsThe weight of (c); gamma raykFor a relation coefficient, gamma, determined on the basis of the relation of the kth associated person to the net appointment/taxi driverkQuerying a preset relation coefficient table through the association relation to determine a relation coefficient;
inquiring a preset first extension time table based on the danger value, and determining first extension time;
analyzing the data information and determining complaint information;
determining a complaint score corresponding to each complaint information based on a preset complaint score template;
inquiring a preset second extension time table based on the sum of all the complaint scores, and determining second extension time;
analyzing the data information, and determining the maximum value in time for completing the order clicked by the online taxi appointment/taxi driver in advance as third extension time;
the delayed travel monitoring time is determined based on the first extended time, the second extended time, and the third extended time.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a schematic diagram of a method for monitoring driving of a network taxi appointment/taxi in an embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
The embodiment of the invention provides a method for monitoring running of a network taxi booking/taxi, which comprises the following steps of:
step S1: after the network car appointment/taxi receives the order, determining a first moving track of the network car appointment/taxi based on order information of the order;
step S2: determining a running monitoring mode that the taxi appointment/taxi moves to a first position point corresponding to the first movement track based on the first movement track;
step S3: and based on the running monitoring mode, running monitoring is carried out on the network car booking/taxi.
The working principle and the beneficial effects of the technical scheme are as follows:
when a network car appointment/taxi receives an order, determining a first moving track of the network car appointment/taxi according to the starting point and the end point of order information, road condition information and the like, and driving a car by a driver of the network car appointment/taxi according to the first moving track; during driving, firstly, driving monitoring is carried out on the deviation of an actual driving track and a first moving track; then, the first moving track is refined into each first position point, different driving monitoring modes are selected according to the actual situation of the first position points, and the driving monitoring module can be embodied by adjusting the time interval of data sampling and can also be realized by adjusting the type of the sampled data; for example: vehicle speed, positioning information, in-vehicle images, in-vehicle audio, vehicle door opening and closing conditions and the like; when the network car booking/taxi runs to each first position point, adopting different running monitoring modes to realize running monitoring of the network car booking/taxi; for example: simultaneously acquiring the vehicle speed, the image in the vehicle, the audio in the vehicle, the opening and closing condition of the vehicle door and the positioning information at the starting point and the ending point of the order; and acquiring the vehicle speed and the positioning information during the traveling process. The monitoring mode is adjusted according to different positions in the driving diagram, so that the uploaded data volume and the working time of acquisition equipment are reduced on the premise of ensuring the safety of drivers and passengers, the data acquisition mode is optimized, and the energy utilization rate is improved.
In one embodiment, determining a first movement trajectory of a net appointment/taxi based on order information of an order comprises:
analyzing order information and determining a starting point and an end point;
loading map information;
determining a plurality of lines to be selected based on the starting point, the end point and the map information;
sampling position points of a line to be selected to obtain a plurality of sampling position points;
acquiring safety information of sampling position points;
determining the safety degree of the sampling position point based on the safety information;
determining a safety value of the route to be selected based on the safety degree of each sampling position point on the route to be selected;
and taking the route to be selected with the maximum safety value as a first movement track.
The working principle and the beneficial effects of the technical scheme are as follows:
when the first moving track is determined by the order information, the safety consideration of the route is mainly focused; ensuring that the taxi/taxi ordered by the network runs under a safe path; the safety of the route is mainly shown in whether the route enters a remote area, the time of passing through the remote area, the coverage condition monitored on the route, whether the route passes through a place where a network taxi appointment/taxi dispute event occurs, whether the route passes through a region with multiple accidents and the like.
In one embodiment, obtaining safety information for a sampling location point comprises:
acquiring peripheral environment information of sampling position points;
determining environmental safety information of a sampling position point based on a preset environmental safety identification template and surrounding environmental information;
and/or the presence of a gas in the gas,
acquiring monitoring and control conditions of sampling position points;
determining the monitoring coverage rate of the sampling position points based on the monitoring deployment and control condition;
and/or the presence of a gas in the gas,
acquiring historical accident conditions of sampling position points;
determining traffic safety information of the sampling position points based on historical accident conditions;
and/or the presence of a gas in the gas,
obtaining complaint information of corresponding sampling position points on a network taxi appointment/taxi platform;
and determining subjective safety information of the sampling position points based on the complaint information.
The working principle and the beneficial effects of the technical scheme are as follows:
the safety of the path can be obtained by comprehensively analyzing the surrounding environment information, the monitoring and controlling condition, the historical accident condition and the complaint information of each sampling position point; the surrounding environment conditions mainly include whether a cell exists at the periphery, whether the periphery is a farmland, the lighting conditions of surrounding street lamps and the like; the monitoring and controlling condition is characterized by monitoring coverage rate, namely the ratio of the monitoring area of the monitoring equipment to the area of the sampling position point; the method comprises the steps of determining subjective safety information of a sampling position point based on complaint information; that is, complaint information is screened to obtain required information, for example: the driver presents verbal subjective danger information to the passenger.
In one embodiment, based on the security information, a degree of security of the sampling location point is determined; the method comprises the following steps:
grading the environmental safety information based on a preset environmental safety grading template to obtain a first grading value;
grading the monitoring coverage rate based on a preset monitoring control grading template to obtain a second grading value;
grading the traffic safety information based on a preset traffic safety grading template to obtain a third grading value;
grading the subjective safety information based on a preset subjective safety grading template to obtain a fourth grading value;
determining the safety degree based on the first score value, the second score value, the third score value and the fourth score value, wherein the calculation formula of the safety degree is as follows:
D＝α1P1+α2P2+α3P3+α4P4；
wherein D is the degree of safety, P1Is a first score value; p2Is a second score value; p3Is a third score value; alpha is alpha1A preset first relation coefficient corresponding to the first score value; alpha is alpha2A preset second relation coefficient corresponding to the second score value; alpha is alpha3A preset third relation coefficient corresponding to the third score value; alpha is alpha4Is a predetermined fourth relation coefficient corresponding to a fourth value of credit.
The working principle and the beneficial effects of the technical scheme are as follows:
grading the influence factors of safety through an environmental safety grading template, a monitoring and control grading template, a traffic safety grading template and a subjective safety grading template, and determining the safety degree of each sampling position point through grading; the safety quantification is realized, the computer identification is convenient, and the intellectualization of the platform is improved; among them, for the aspect of environment security information, for example: the periphery has cells, and the first score value is high; farmland is arranged around the farmland, and the first score value is relatively low; the first score value is high when the surrounding has the place to be dispatched; in addition, the situation of people stream is also existed, namely, the situation that people pass through the sampling position point, and if the number of people stream is more, the first scoring value is high; if the flow is low, the first score value is low.
In one embodiment, the method comprises the steps of sampling position points of a line to be selected to obtain a plurality of sampling position points; the method comprises the following steps:
acquiring a preset first sampling mark list;
determining a segmentation point of the route to be selected based on the first sampling mark list;
segmenting the route to be selected based on the segmentation points to obtain a plurality of segmentation routes;
acquiring vehicle parameters and driver information of a network taxi appointment/taxi;
acquiring a plurality of pieces of first driving data passing through each sectional route through a big data platform based on vehicle parameters and driver information;
analyzing the first driving data, and determining the passing time of each position passing through each sectional route; the passing time of each position is the time for the vehicle to travel from the starting point of the sectional route to each position;
acquiring a preset sampling time interval determination table; the sampling time intervals in the sampling time interval determination table correspond to the standard line parameters one to one;
determining the sampling time of each subsection route based on the line parameter and the sampling time interval determination table of each subsection route;
determining a plurality of passing sampling points based on the sampling time and the passing time of each subsection route;
and taking the starting point, the ending point and the passing sampling point of each segmented route as sampling position points.
The working principle and the beneficial effects of the technical scheme are as follows:
according to the first sampling mark list, road section division is carried out; wherein, a plurality of first sampling marks are listed in the first sampling mark list, and the first sampling mark comprises: pedestrian crossings, traffic intersections, community exits, hospital gates, and the like; the reasonable and fine path division is realized; and respectively analyzing each divided sectional route, accurately determining the passing time of each position on the sectional route according to the driving data of the vehicles of the same type and the drivers of the same type on the big data, and then sampling according to the sampling time interval to determine the sampling position point.
In one embodiment, based on the first sampling mark list, determining a segmentation point of the route to be selected; the method comprises the following steps:
determining mark areas on a plurality of routes to be selected based on the first sampling mark list; the mark area includes: a mapping area corresponding to a first sampling mark in the first sampling mark list on the to-be-selected route and a buffer area before and after the mapping area within a preset first distance;
acquiring a plurality of pieces of second driving data passing through the mark area through a big data platform based on the vehicle parameters and the driver information;
analyzing the second driving data and determining two braking points; the two braking points are respectively positioned at the two ends of the mark area; the braking point is a dividing point of vehicle deceleration and acceleration;
the braking point is taken as a segmentation point.
The working principle and the beneficial effects of the technical scheme are as follows:
the traffic data in the mark area is analyzed by using a big data technology, so that segmentation points for path division are determined, accurate path division is realized, and the reasonability of determining sampling position points is improved; the two braking points correspond to the travel data in the two directions, respectively.
In order to realize the calculation of the safety value of the to-be-selected route, in one embodiment, the safety value of the to-be-selected route is determined based on the safety degree of each sampling position point on the to-be-selected route; the method comprises the following steps:
acquiring the attribute of a sampling position point;
inquiring a preset weight table based on the attribute, and determining the weight of the sampling position point;
determining a safety value based on the weight of the sampling position point and the safety degree of the sampling position point; the formula for calculating the safety value is as follows:
wherein, P is a safety value; diThe safety degree of the ith sampling position point; beta is aiIs the weight of the ith sample position point; n is the number of sampling location points;
wherein the attributes of the sampling location points include: whether the sampling position point belongs to the mapping area of the first sampling mark in the first sampling mark list or not, whether the sampling position point is the starting point or the ending point of the segmented route or not, the speed limit condition of the sampling position point and the road surface condition of the sampling position point are combined.
In order to realize the determination of the sampling time, the determination of the sampling time of each subsection route is based on the line parameter and the sampling time interval determination table of each subsection route, and comprises the following steps:
constructing a first vector based on the line parameters;
constructing a second vector based on the standard line parameters;
calculating the similarity of the first vector and the second vector, wherein the similarity calculation formula is as follows:
wherein X is similarity; a. thejIs the jth parameter value of the first vector; b isjIs the jth parameter value of the second vector; m is the total number of parameters of the first vector or the total number of parameters of the second vector;
and obtaining the sampling time interval corresponding to the standard line parameter corresponding to the second vector with the maximum similarity as the sampling time.
In one embodiment, based on the first movement track, determining a driving monitoring mode of the net appointment/taxi moving to a first position point corresponding to the first movement track; the method comprises the following steps:
acquiring a sampling position point in a first moving track;
acquiring a preset safety degree and monitoring mode comparison table;
respectively calculating the distance between the first position point and the sampling position point, and acquiring the safety degree of the sampling position point closest to the first position point as the safety degree of the first position point;
determining a running monitoring mode of the first position point based on the safety degree of the first position point and a comparison table of the safety degree and the detection mode;
wherein the driving monitoring mode includes: the method comprises the steps of obtaining driving data of the network car appointment/taxi, obtaining positioning data of the network car appointment/taxi, obtaining audio data in the network car appointment/taxi, obtaining video data in the network car appointment/taxi and obtaining door opening and closing data of the network car appointment/taxi, wherein one or more of the data are combined.
The working principle and the beneficial effects of the technical scheme are as follows:
the higher the safety degree is, the fewer the equipment called by the driving monitoring mode is, and the less the acquired data amount is; for example: when the area near the sampling position point is a monitoring coverage area, only the positioning information, the speed information and the like of the networked taxi appointment/taxi need to be acquired; when the area near the sampling position point is a farmland area, and the area is not covered by monitoring and has no street lamp, the vehicle speed, the image in the vehicle, the audio frequency in the vehicle, the opening and closing condition of the vehicle door and the positioning information are required to be collected at the same time.
In one embodiment, the method for monitoring the driving of the net appointment/taxi further comprises the following steps:
acquiring first identity information of a taxi appointment/taxi driver and data information of a historical order;
determining a delayed driving monitoring time based on the first identity information and the data information;
after the order is finished, monitoring the running of the network taxi appointment/taxi based on the delayed running monitoring time and a running monitoring mode corresponding to the last sampling position point;
wherein determining the delayed driving monitoring time based on the first identity information and the data information comprises:
acquiring first event data corresponding to a network car appointment/taxi driver from a big data platform based on the first identity information and a preset event extraction table;
evaluating the first event data based on a preset event threat evaluation template to determine a first threat value;
evaluating the first identity information based on a preset identity information evaluation template to determine a second threat value;
acquiring second identity information of a plurality of associated persons associated with the online taxi appointment/taxi driver;
acquiring second event data corresponding to the second identity information from the big data platform based on the second identity information and a preset event extraction table;
evaluating the second event data based on a preset event threat evaluation template to determine a third threat value;
evaluating the second identity information based on a preset identity information evaluation template to determine a fourth threat value;
determining a danger value of a taxi appointment/taxi driver based on the first threat value, the second threat value, the third threat value and the fourth threat value; the risk value is calculated as follows:
wherein WX is a risk value, W1Is a first threat value; w2A second risk value; w3,kA third threat value for a kth associated person; w4,kA fourth threat value for a kth associated person; mu.s1Weight of taxi booking/taxi driver for the preset corresponding network; mu.s2The weight of the preset corresponding associated person is set; gamma raykFor a relation coefficient, gamma, determined on the basis of the relation of the kth associated person to the net appointment/taxi driverkQuerying a preset relation coefficient table through the association relation to determine a relation coefficient;
inquiring a preset first extension time table based on the danger value, and determining first extension time;
analyzing the data information and determining complaint information;
determining a complaint score corresponding to each complaint information based on a preset complaint score template;
inquiring a preset second extension time table based on the sum of all the complaint scores, and determining second extension time;
analyzing the data information, and determining the maximum value in time for completing the order clicked by the online taxi appointment/taxi driver in advance as third extension time;
the delayed travel monitoring time is determined based on the first extended time, the second extended time, and the third extended time.
The working principle and the beneficial effects of the technical scheme are as follows:
the delayed driving monitoring time is determined by comprehensively considering the network taxi appointment/taxi driver and the specific action of the network taxi appointment/taxi driver when the order is completed, so that whether the delayed driving monitoring is started or not and the starting time is ensured according to the situation after the order transaction is completed, the driving dispute that the order is completed and the passenger gets off the taxi in the period of time can not be monitored is prevented, and the comprehensiveness of the driving monitoring is improved. When the conditions of the network car booking/taxi drivers are analyzed, the comprehensive evaluation of the network car booking/taxi drivers is realized through the event data acquired by the big data platform, wherein the event data mainly comprises consumption conditions, whether debt exists, whether house lending car lending exists, whether bad records exist and the like. In addition, in order to ensure the accuracy of comprehensive assessment, information of persons associated with the net appointment/taxi driver, such as family members, friends and the like of the net appointment/taxi driver, is also considered.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A method for monitoring running of a networked taxi/taxi is characterized by comprising the following steps:
after the network car appointment/taxi receives an order, determining a first movement track of the network car appointment/taxi based on order information of the order;
determining a running monitoring mode that the taxi appointment/taxi moves to a first position point corresponding to the first movement track based on the first movement track;
and monitoring the running of the network taxi appointment/taxi based on the running monitoring mode.
2. The method for monitoring the driving of a networked car/taxi according to claim 1, wherein the determining a first movement track of the networked car/taxi based on the order information of the order comprises:
analyzing the order information and determining a starting point and an end point;
loading map information;
determining a plurality of lines to be selected based on the starting point, the end point and the map information;
sampling position points of the route to be selected to obtain a plurality of sampling position points;
acquiring safety information of the sampling position points;
determining the safety degree of the sampling position point based on the safety information;
determining a safety value of the route to be selected based on the safety degree of each sampling position point on the route to be selected;
and taking the route to be selected with the maximum safety value as the first movement track.
3. The method for monitoring the driving of a networked car/taxi according to claim 2, wherein the obtaining of the safety information of the sampling location point comprises:
acquiring peripheral environment information of the sampling position points;
determining environmental safety information of the sampling position point based on a preset environmental safety identification template and the surrounding environmental information;
and/or the presence of a gas in the gas,
acquiring the monitoring and control condition of the sampling position points;
determining the monitoring coverage rate of the sampling position points based on the monitoring deployment and control condition;
and/or the presence of a gas in the gas,
acquiring historical accident conditions of the sampling position points;
determining traffic safety information of the sampling position points based on the historical accident situation;
and/or the presence of a gas in the gas,
obtaining complaint information corresponding to the sampling position points on the network appointment car/taxi platform;
and determining subjective safety information of the sampling position points based on the complaint information.
4. The method for monitoring the driving of a networked car/taxi according to claim 3, wherein the safety of the sampling location point is determined based on the safety information; the method comprises the following steps:
grading the environmental safety information based on a preset environmental safety grading template to obtain a first grading value;
grading the monitoring coverage rate based on a preset monitoring control grading template to obtain a second grading value;
grading the traffic safety information based on a preset traffic safety grading template to obtain a third grading value;
grading the subjective safety information based on a preset subjective safety grading template to obtain a fourth grading value;
determining the safety degree based on the first score value, the second score value, the third score value and the fourth score value, wherein the safety degree is calculated according to the following formula:
D＝α1P1+α2P2+α3P3+α4P4；
wherein D is the degree of safety, P1Is the first score value; p2Is the second score value; p3Is the third score value; alpha is alpha1A preset first relation coefficient corresponding to the first score value; alpha is alpha2A preset second relation coefficient corresponding to the second score value; alpha is alpha3A preset third relation coefficient corresponding to the third score value; alpha is alpha4Is a predetermined fourth relation coefficient corresponding to a fourth value of credit.
5. The method for monitoring the driving of a networked car/taxi according to claim 2, wherein the route to be selected is subjected to position point sampling to obtain a plurality of sampling position points; the method comprises the following steps:
acquiring a preset first sampling mark list;
determining a segmentation point of the route to be selected based on the first sampling mark list;
segmenting the route to be selected based on the segmentation points to obtain a plurality of segmentation routes;
acquiring vehicle parameters and driver information of the online taxi appointment/taxi;
acquiring a plurality of pieces of first driving data passing through each segmented route through a big data platform based on the vehicle parameters and the driver information;
analyzing the first driving data, and determining the passing time of each position passing through each sectional route; the transit time for each location is the time for a vehicle to travel from the starting point of the segmented route to each location;
acquiring a preset sampling time interval determination table; the sampling time intervals in the sampling time interval determination table correspond to standard line parameters one to one;
determining sampling time of each subsection route based on the line parameter of each subsection route and the sampling time interval determination table;
determining a plurality of passing sampling points based on determining the sampling time and the passing time of each segmented route;
and taking the starting point, the ending point and the passing sampling point of each segmented route as the sampling position points.
6. The method for monitoring the driving of a networked car/taxi according to claim 5, wherein the step of determining the section points of the route to be selected based on the first sampling mark list; the method comprises the following steps:
determining marker areas on a plurality of routes to be selected based on the first sampling marker list; the flag region includes: a mapping area corresponding to a first sampling mark in the first sampling mark list on the to-be-selected route and a buffer area in a preset first distance before and after the mapping area are arranged;
acquiring a plurality of pieces of second driving data passing through the mark area through a big data platform based on the vehicle parameters and the driver information;
analyzing the second driving data and determining two braking points; the two braking points are respectively positioned at two ends of the mark area; the braking point is a dividing point of vehicle deceleration and acceleration;
taking the braking point as the segmentation point.
7. The method for monitoring the driving of a taxi appointed by a network according to claim 2, wherein the safety value of the candidate route is determined based on the safety degree of each sampling position point on the candidate route; the method comprises the following steps:
acquiring the attribute of the sampling position point;
inquiring a preset weight table based on the attribute, and determining the weight of the sampling position point;
determining the safety value based on the weight of the sampling location point and the safety degree of the sampling location point; the calculation formula of the safety value is as follows:
wherein P is the security value; diThe safety degree for the ith sampling position point; beta is aiThe weight for the ith said sample location point;
wherein the attributes of the sampling location points include: whether the sampling position point belongs to the mapping area of the first sampling mark in the first sampling mark list, whether the sampling position point is the starting point or the ending point of the segmented route, the speed limit condition of the sampling position point and the road surface condition of the sampling position point are combined.
8. The method for monitoring the driving of a networked car/taxi according to claim 2, wherein the step of determining the sampling time of each of the segment routes based on the route parameters of each of the segment routes and the sampling time interval determination table comprises:
constructing a first vector based on the line parameters;
constructing a second vector based on the standard line parameters;
calculating the similarity between the first vector and the second vector, wherein the similarity calculation formula is as follows:
wherein X is the similarity; a. thejIs the jth parameter value of the first vector; b isjIs the jth parameter value of the second vector; m is the total number of parameters of the first vector or the total number of parameters of the second vector;
and acquiring the sampling time interval corresponding to the standard line parameter corresponding to the second vector with the maximum similarity as the sampling time.
9. The method for monitoring the driving of a net appointment/taxi according to claim 5, wherein the driving monitoring mode for the net appointment/taxi to move to the first position point corresponding to the first movement track is determined based on the first movement track; the method comprises the following steps:
acquiring the sampling position point in the first moving track;
acquiring a preset safety degree and monitoring mode comparison table;
respectively calculating the distance between the first position point and the sampling position point, and acquiring the safety degree of the sampling position point closest to the first position point as the safety degree of the first position point;
determining the driving monitoring mode of the first position point based on the safety degree of the first position point and a comparison table of the safety degree and a detection mode;
wherein the travel monitoring mode includes: the method comprises the steps of obtaining driving data of the network appointment car/taxi, obtaining positioning data of the network appointment car/taxi, obtaining audio data in the network appointment car/taxi, obtaining video data in the network appointment car/taxi and obtaining door switch data of the network appointment car/taxi, wherein one or more of the data are combined.
10. The method for monitoring the driving of a networked taxi appointment/taxi as claimed in claim 1, further comprising:
acquiring first identity information of the online taxi appointment/taxi driver and data information of a historical order;
determining a delayed driving monitoring time based on the first identity information and the data information;
after the order is finished, the network appointment vehicle/taxi is subjected to running monitoring based on the delayed running monitoring time and a running monitoring mode corresponding to the last sampling position point;
wherein determining a delayed travel monitoring time based on the first identity information and the data information comprises:
acquiring first event data corresponding to the network taxi appointment/taxi driver from a big data platform based on the first identity information and a preset event extraction table;
evaluating the first event data based on a preset event threat evaluation template to determine a first threat value;
evaluating the first identity information based on a preset identity information evaluation template to determine a second threat value;
acquiring second identity information of a plurality of associated persons associated with the online taxi appointment/taxi driver;
acquiring second event data corresponding to the second identity information from a big data platform based on the second identity information and a preset event extraction table;
evaluating the second event data based on a preset event threat evaluation template to determine a third threat value;
evaluating the second identity information based on a preset identity information evaluation template to determine a fourth threat value;
determining a risk value for the net appointment/taxi driver based on the first threat value, the second threat value, the third threat value and the fourth threat value; the calculation formula of the risk value is as follows:
wherein WX is the hazard value, W1Is the first threat value; w2Is the second risk value; w3,kA third threat value for a kth associated person; w4,kA fourth threat value for a kth associated person; mu.s1For the preset corresponding network appointment vehicleWeight of taxi driver; mu.s2The weight of the preset corresponding associated person is set; gamma raykFor a relation coefficient, γ, determined on the basis of the relation between the kth associated person and the net appointment/taxi driverkQuerying a preset relation coefficient table through the association relation to determine the relation coefficient;
inquiring a preset first extension time table based on the danger value, and determining first extension time;
analyzing the data information and determining complaint information;
determining a complaint score corresponding to each complaint information based on a preset complaint score template;
inquiring a preset second extension time table based on the sum of all the complaint scores, and determining second extension time;
analyzing the data information, and determining the maximum value in time for completing the order clicking by the online taxi appointment/taxi driver in advance as third extension time;
determining the delayed travel monitoring time based on the first extended time, the second extended time, and the third extended time.
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