Online car-hailing dispatching management platform

Abstract

The invention relates to the field of online car-hailing scheduling management, and discloses an online car-hailing dispatching management platform, which comprises an online vehicle monitoring module, an order module and an analysis module, and is characterized in that historical data and real-time vehicle data of each online vehicle are acquired through the online vehicle monitoring module; receiving real-time order information in a scheduling area through an order module; analyzing the historical data through an analysis module to obtain an online index, a punctuality index and a violation index of each online vehicle; analyzing the online index, the punctuality index, the violation index, the real-time order information and the real-time vehicle data of each online vehicle to obtain a comprehensive evaluation index of each online vehicle; the higher the comprehensive evaluation index is, it shows that the online car-hailing driver of the online car is excellent in all aspects, so that the online car with the highest comprehensive evaluation index is preferentially allocated during order allocation, and the enthusiasm and satisfaction of the excellent online car-hailing driver are improved.

Description

Technical Field

[0001] The present invention relates to the field of online car-hailing dispatching management, and in particular to an online car-hailing dispatching dispatching management platform. Background Art

[0002] As an emerging mode of transportation, online ride-hailing is not only popular among passengers, but also provides a large number of opportunities for free employment. More and more people use their own or rented vehicles to become online ride-hailing drivers.

[0003] With the continuous advancement of Internet technology, the gradual improvement of policies and the rapid development of the economy, more and more people will choose online ride-hailing as their means of transportation. When dispatching orders, online ride-hailing usually uses the distance between the online ride-hailing vehicle and the departure point and the praise rate of the online ride-hailing driver as the main dimensions to determine the priority of dispatching orders, in order to improve customer satisfaction and thus improve the competitiveness of the online ride-hailing platform.

[0004] However, since most passengers will not take the initiative to evaluate online ride-hailing drivers, the praise rate of online ride-hailing drivers cannot truly reflect the service quality of online ride-hailing drivers. Therefore, using the praise rate as the main dimension to determine dispatch orders will cause a certain degree of unfairness, reduce the satisfaction and enthusiasm of online ride-hailing drivers, lead to the loss of online ride-hailing drivers, and ultimately lead to a reduction in online vehicles. Summary of the invention

[0005] The purpose of the present invention is to provide a network car-hailing dispatching and management platform to solve the following technical problems:

[0006] How to improve the motivation and satisfaction of online ride-hailing drivers.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] A dispatching and management platform for online car-hailing, the dispatching and management platform comprising:

[0009] An online vehicle monitoring module is used to monitor all online vehicles in the dispatch area of ​​the online car-hailing platform and obtain data of each online vehicle; the online vehicle data includes historical data and real-time vehicle data;

[0010] The order module is used to receive real-time order information within the dispatch area;

[0011] The analysis module is used to analyze the historical data of online vehicles in the dispatching area to obtain the online index, punctuality index and violation index of each online vehicle; and then obtain the comprehensive evaluation index of each online vehicle by analyzing the online index, punctuality index, violation index, real-time order information and real-time vehicle data of each online vehicle.

[0012] As a further solution of the present invention: the historical data includes daily online time, daily offline time, daily order information and violation information;

[0013] The daily order information includes the total number of orders completed each day, the estimated completion time of each order, and the actual completion time;

[0014] The violation information includes the number of order cancellations, red light runs and complaints per day.

[0015] As a further solution of the present invention: the online index of the online vehicle numbered i is obtained by the formula:

[0016]

[0017] Calculate the online index P of online vehicle number i i ;

[0018] Where N is the preset number of days in the past, n∈N; T oni is the online working time of the online vehicle numbered i on the nth day; T gni is the online time during the peak period of the nth day for the online vehicle numbered i; γ1 is the first online weight coefficient; γ2 is the second online weight coefficient; ε is the unit removal coefficient; μ1 is the first adjustment coefficient.

[0019] As a further solution of the present invention: the punctuality index of the online vehicle numbered i is obtained by the formula:

[0020]

[0021] Calculate the punctuality index H of the online vehicle numbered i i ;

[0022] Among them, H0 is the basic punctuality index; Z ni D is the number of orders that the online vehicle number i has completed without timeout on the nth day; ni is the total number of orders completed by the online vehicle numbered i on the nth day; ρ0 is the standard punctuality rate; μ2 is the second adjustment coefficient.

[0023] As a further solution of the present invention: the violation index of the online vehicle numbered i is obtained by the formula:

[0024]

[0025] Calculate the violation index W i ;

[0026] Among them, C ni is the number of complaints of online vehicle number i on day n; T iis the number of red light violations of the online vehicle numbered i on the nth day; X1 is the first preset coefficient, X1≥1; Q ni The number of order cancellations for online vehicle number i on day n.

[0027] As a further solution of the present invention: the real-time vehicle data includes the real-time location of the vehicle; the real-time order information includes the departure location information.

[0028] As a further solution of the present invention: the comprehensive evaluation index is calculated by the formula:

[0029]

[0030] Calculate the comprehensive evaluation index K of the online vehicle number i i ;

[0031] in, is the first proportionality coefficient; is the second proportionality coefficient; is the third proportionality coefficient; is the fourth proportionality coefficient; The real-time distance between the online vehicle numbered i and the departure point when the order is dispatched.

[0032] As a further solution of the present invention: the analysis process of the comprehensive evaluation index of the online vehicle numbered i is:

[0033] S1: Obtain the daily online working time of the online vehicle numbered i through the daily online time and daily offline time of the online vehicle numbered i;

[0034] S2: Analyze the daily online time, daily offline time and preset peak time of the online vehicle numbered i to obtain the daily peak time online time of the online vehicle numbered i;

[0035] S3: obtaining an online index of the online vehicle numbered i by analyzing the daily online working hours and the daily online hours during the peak hours of the online vehicle numbered i in the past preset days;

[0036] S4; by obtaining the estimated completion time and actual completion time of each order of the online vehicle numbered i, determine whether the order is timed out; by analyzing the daily order information, obtain the number of online vehicle numbered i that has not been completed within the time limit every day;

[0037] S5; by analyzing the number of orders that have not been completed on time and the total number of orders completed on a daily basis for the online vehicle numbered i in the past preset days, the on-time index of the online vehicle numbered i is obtained;

[0038] S6: Obtain a violation index by analyzing the number of red light violations, complaints, and order cancellations of online vehicles on a preset number of days numbered i on a daily basis;

[0039] S7: The real-time distance between the online vehicle numbered i and the departure place when dispatching the order is obtained through the departure place location information and the real-time location of the online vehicle numbered i.

[0040] Beneficial effects of the present invention:

[0041] (1) The present invention monitors all online vehicles in the dispatch area of ​​the online car-hailing platform through an online vehicle monitoring module, and obtains the historical data and real-time vehicle data of each online vehicle; receives real-time order information in the dispatch area through an order module; analyzes the historical data of online vehicles in the dispatch area through an analysis module to obtain the online index, punctuality index and violation index of each online vehicle; then obtains the comprehensive evaluation index of each online vehicle by analyzing the online index, punctuality index, violation index, real-time order information and real-time vehicle data of each online vehicle; evaluates each online vehicle in more dimensions through the online index, punctuality index and violation index. The higher the comprehensive evaluation index, the better the online car-hailing driver of the online vehicle is in all aspects. Therefore, when dispatching orders, priority is given to online vehicles with the highest comprehensive evaluation index, thereby improving the enthusiasm and satisfaction of excellent online car-hailing drivers. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The present invention will be further described below in conjunction with the accompanying drawings.

[0043] Figure 1 A system module framework diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] See also Figure 1 As shown, in one embodiment, a network car-hailing dispatching and management platform is provided, including:

[0046] An online vehicle monitoring module is used to monitor all online vehicles in the dispatch area of ​​the online car-hailing platform and obtain data of each online vehicle; the online vehicle data includes historical data and real-time vehicle data;

[0047] The order module is used to receive real-time order information within the dispatch area;

[0048] The analysis module is used to analyze the historical data of online vehicles in the dispatching area to obtain the online index, punctuality index and violation index of each online vehicle; and then obtain the comprehensive evaluation index of each online vehicle by analyzing the online index, punctuality index, violation index, real-time order information and real-time vehicle data of each online vehicle;

[0049] Each of the online vehicles has a unique corresponding number i;

[0050] Through the above technical scheme, this embodiment monitors all online vehicles in the dispatch area of ​​the online car-hailing platform through the online vehicle monitoring module, and obtains the historical data and real-time vehicle data of each online vehicle; receives real-time order information in the dispatch area through the order module; analyzes the historical data of online vehicles in the dispatch area through the analysis module to obtain the online index, punctuality index and violation index of each online vehicle; then analyzes the online index, punctuality index, violation index, real-time order information and real-time vehicle data of each online vehicle to obtain the comprehensive evaluation index of each online vehicle; evaluates each online vehicle in more dimensions through the online index, punctuality index and violation index. The higher the comprehensive evaluation index, the better the online car-hailing driver of the online vehicle is in all aspects. Therefore, when dispatching orders, priority is given to online vehicles with the highest comprehensive evaluation index, thereby improving the enthusiasm and satisfaction of excellent online car-hailing drivers.

[0051] As an implementation mode of the present invention, the historical data includes daily online time, daily offline time, daily order information and violation information;

[0052] The daily order information includes the total number of orders completed each day, the estimated completion time of each order, and the actual completion time;

[0053] The violation information includes the number of order cancellations, red light violations, and complaints per day;

[0054] The real-time vehicle data of the vehicle includes the real-time position of the vehicle;

[0055] The real-time order information includes departure location information;

[0056] It should be noted that the daily online time, daily offline time, total number of completed orders per day, estimated completion time for each order, actual completion time, number of order cancellations, number of red light runs, number of complaints, real-time vehicle location and methods of obtaining departure location information are all existing technologies and will not be described in detail here.

[0057] As an embodiment of the present invention, the analysis process of the comprehensive evaluation index of the online vehicle numbered i is as follows:

[0058] S1: Obtain the daily online working time of the online vehicle numbered i through the daily online time and daily offline time of the online vehicle numbered i;

[0059] S2: Analyze the daily online time, daily offline time and preset peak time of the online vehicle numbered i to obtain the daily peak time online time of the online vehicle numbered i;

[0060] S3: obtaining an online index of the online vehicle numbered i by analyzing the daily online working hours and the daily online hours during the peak hours of the online vehicle numbered i in the past preset days;

[0061] S4; by obtaining the estimated completion time and actual completion time of each order of the online vehicle numbered i, determine whether the order is timed out; by analyzing the daily order information, obtain the number of online vehicle numbered i that has not been completed within the time limit every day;

[0062] S5; by analyzing the number of orders that have not been completed on time and the total number of orders completed on a daily basis for the online vehicle numbered i in the past preset days, the on-time index of the online vehicle numbered i is obtained;

[0063] S6: Obtain a violation index by analyzing the number of red light violations, complaints, and order cancellations of online vehicles on a preset number of days numbered i on a daily basis;

[0064] S7: obtaining the real-time distance between the online vehicle numbered i and the departure place when dispatching the order through the departure place location information and the real-time location of the online vehicle numbered i;

[0065] S8: Obtaining a comprehensive evaluation index of the online vehicle numbered i by analyzing the online index, punctuality index, violation index, and real-time distance of the online vehicle numbered i;

[0066] Through the above technical scheme, this embodiment first obtains the daily online working time of the online vehicle numbered i through the daily online time and daily offline time of the online vehicle numbered i; then, the daily online time of the online vehicle numbered i during the peak period is analyzed through the daily online time, daily offline time and preset peak period; then, the online index of the online vehicle numbered i is obtained by analyzing the daily online working time and daily peak period online time of the online vehicle numbered i in the past preset days; then, the estimated completion time and actual completion time of each order of the online vehicle numbered i are obtained to determine whether the order is timed out; the order information is obtained by analyzing the daily order information. The number of orders that the online vehicle number i has completed without timeout every day; then the punctuality index of the online vehicle number i is obtained by analyzing the number of orders that the online vehicle number i has completed without timeout every day and the total number of orders completed every day in the past preset days; then the violation index is obtained by analyzing the number of red light runs, complaints and order cancellations of the online vehicle number i every day in the past preset days; then the real-time distance between the online vehicle number i and the departure place when dispatching orders is obtained through the departure place location information and the real-time location of the online vehicle number i; finally, the comprehensive evaluation index of the online vehicle number i is obtained by analyzing the online index, punctuality index, violation index and real-time distance of the online vehicle number i;

[0067] It should be noted that the preset peak period is a preset time period, which is obtained based on experience and will not be described in detail here.

[0068] As an implementation mode of the present invention, in step S3, the online index of the online vehicle numbered i is obtained by the formula:

[0069]

[0070] Calculate the online index P of online vehicle number i i ;

[0071] Where N is the preset number of days in the past, n∈N; T oni is the online working time of the online vehicle numbered i on the nth day; T gni is the online time of the online vehicle numbered i during the peak period of the nth day; γ1 is the first online weight coefficient; γ2 is the second online weight coefficient; ε is the unit removal coefficient; μ1 is the first adjustment coefficient;

[0072] Through the above technical solution, this embodiment The cumulative online working time in the past preset number of days. The longer the cumulative online working time, the higher the online index P. i The higher; The cumulative online time during the peak hours within the preset number of days in the past. The longer the cumulative online time during the peak hours, the higher the online index P.i The higher; is the standard deviation of the online time during peak hours in the past preset days, which is used to reflect the fluctuation of the online time during peak hours of the online vehicle number i in the preset days. The smaller the value, the more stable the online time of the online vehicle numbered i is during the daily peak hours. Therefore, the online index P i The larger the number, the online index is evaluated from three dimensions: cumulative online working time, cumulative online time during peak hours, and whether the online time during peak hours is stable. This allows online car-hailing drivers who want to get priority dispatch to work hard in three directions: cumulative online working time, cumulative online time during peak hours, and stable online time during peak hours to obtain priority dispatch qualifications, while ensuring that there are enough vehicles to meet order demand during peak hours.

[0073] It should be noted that the preset number of days N in the past, the first online weight coefficient γ1, the second online weight coefficient γ2, the unit removal coefficient ε, and the adjustment coefficient μ1 are preset values, which are obtained based on experience and will not be described in detail here.

[0074] As an implementation of the present invention, in step S5, the punctuality index of the online vehicle numbered i is calculated by the formula:

[0075]

[0076] Calculate the punctuality index H of the online vehicle numbered i i ;

[0077] Among them, H0 is the basic punctuality index; Z ni D is the number of orders that the online vehicle number i has completed without timeout on the nth day; ni is the total number of orders completed by the online vehicle numbered i on the nth day; ρ0 is the standard on-time rate; μ2 is the second adjustment coefficient;

[0078] Through the above technical solution, this embodiment is the on-time rate of online vehicle number i on day n; The average punctuality rate of the online vehicle numbered i in the past preset days; It is the ratio of the accumulated online time and accumulated online working time during the peak period of the preset number of days in the past. Since traffic jams are likely to occur during peak hours, which may cause timeouts, Calculate the preset punctuality rate of the online vehicle number i in the past preset days, the ratio of the cumulative online time during the peak hours in the past preset days to the cumulative online working time The larger it is, the smaller the preset punctuality rate is; is the difference between the average punctuality rate of the online vehicle numbered i in the past preset days and the preset punctuality rate; , it means that the average punctuality rate of the online vehicle number i in the past preset days is greater than the preset punctuality rate, and the difference between the average punctuality rate of the online vehicle number i in the past preset days and the preset punctuality rate The larger the punctuality index H is, the i The bigger; when When , it means that the average punctuality rate of the online vehicle number i in the past preset days is equal to the preset punctuality rate, and the punctuality index H i =H0; when When , it means that the average punctuality rate of the online vehicle number i in the past preset days is less than the preset punctuality rate, and the absolute value of the difference between the average punctuality rate of the online vehicle number i in the past preset days and the preset punctuality rate The larger the punctuality index H is, the i The smaller;

[0079] It should be noted that the basic punctuality index H0, the preset punctuality rate ρ0 and the second adjustment coefficient μ2 are preset values, which are obtained based on experience and will not be described in detail here.

[0080] As an embodiment of the present invention, in step S6, the violation index of the online vehicle numbered i is calculated by the formula:

[0081]

[0082] Calculate the violation index W i ;

[0083] Among them, C ni is the number of complaints of online vehicle number i on day n; T i is the number of red light violations of the online vehicle numbered i on the nth day; X1 is the first preset coefficient, X1≥1; Q ni The number of order cancellations on day n for the online vehicle numbered i;

[0084] Through the above technical solution, this embodiment The cumulative number of complaints for the past preset number of days; The cumulative number of red light violations in the past preset number of days; The cumulative number of order cancellations over the past preset number of days; is the cumulative number of violations; the greater the cumulative number of violations, the higher the violation index W i The bigger;

[0085] It should be noted that the first preset coefficient X1 is a preset value, which is obtained based on experience and will not be described in detail here.

[0086] As an implementation of the present invention, in step S8, the comprehensive evaluation index is calculated by the formula:

[0087]

[0088] Calculate the comprehensive evaluation index K of the online vehicle number i i ;

[0089] in, is the first proportionality coefficient; is the second proportionality coefficient; is the third proportionality coefficient; is the fourth proportionality coefficient; The real-time distance between the online vehicle numbered i and the departure point when the order is dispatched.

[0090] Through the above technical solution, the online index P of this embodiment i The higher the comprehensive evaluation index K i The larger the punctuality index H i The higher the comprehensive evaluation index K i The larger the violation index W i The lower the comprehensive evaluation index K i The larger the distance, the real-time distance between the online vehicle with ID i and the departure point when dispatching. The smaller the comprehensive evaluation index K i The larger the value, the higher the platform’s order size. When the distances are similar, the order will be assigned to the online index P first. i High, punctuality index H i High and Violation Index W i Low online vehicles;

[0091] It should be noted that the first proportionality coefficient Second proportionality factor The third proportionality factor and the fourth proportionality factor It is a preset value obtained based on experience and will not be described in detail here.

[0092] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A network car-hailing dispatching and management platform, characterized in that: The dispatch management platform includes: An online vehicle monitoring module is used to monitor all online vehicles in the dispatch area of ​​the online car-hailing platform and obtain data of each online vehicle; the online vehicle data includes historical data and real-time vehicle data; The order module is used to receive real-time order information within the dispatch area; The analysis module is used to analyze the historical data of online vehicles in the dispatching area to obtain the online index, punctuality index and violation index of each online vehicle; and then obtain the comprehensive evaluation index of each online vehicle by analyzing the online index, punctuality index, violation index, real-time order information and real-time vehicle data of each online vehicle.

2. The online car-hailing dispatching and management platform according to claim 1, characterized in that: The historical data includes daily online time, daily offline time, daily order information and violation information; The daily order information includes the total number of orders completed each day, the estimated completion time of each order, and the actual completion time; The violation information includes the number of order cancellations, red light runs and complaints per day.

3. The online car-hailing dispatching and management platform according to claim 2, characterized in that: The online index of the online vehicle numbered i is given by the formula: Calculate the online index P of online vehicle number i i ; Where N is the preset number of days in the past, n∈N; T oni is the online working time of the online vehicle numbered i on the nth day; T gni is the online time during the peak period of the nth day for the online vehicle numbered i; γ1 is the first online weight coefficient; γ2 is the second online weight coefficient; ε is the unit removal coefficient; μ1 is the first adjustment coefficient.

4. The online car-hailing dispatching and management platform according to claim 3, characterized in that: The punctuality index of the online vehicle numbered i is given by the formula: Calculate the punctuality index H of the online vehicle number i i ; Among them, H0 is the basic punctuality index; Z ni D is the number of orders that the online vehicle number i has completed without timeout on the nth day; ni is the total number of orders completed by the online vehicle numbered i on the nth day; ρ0 is the standard punctuality rate; μ2 is the second adjustment coefficient.

5. The online car-hailing dispatching and management platform according to claim 4, characterized in that: The violation index of the online vehicle numbered i is given by the formula: Calculate the violation index W i ; Among them, C ni is the number of complaints of online vehicle number i on day n; T i is the number of red light violations of the online vehicle numbered i on the nth day; X1 is the first preset coefficient, X1≥1; Q ni The number of order cancellations on the nth day for the online vehicle numbered i.

6. The online car-hailing dispatching and management platform according to claim 5, characterized in that: The real-time vehicle data includes the real-time location of the vehicle; the real-time order information includes the departure location information.

7. The online car-hailing dispatching and management platform according to claim 6, characterized in that: The comprehensive evaluation index is calculated by the formula: Calculate the comprehensive evaluation index K of the online vehicle number i i ; Among them, θ1 is the first proportional coefficient; θ2 is the second proportional coefficient; θ3 is the third proportional coefficient; θ4 is the fourth proportional coefficient; The real-time distance between the online vehicle numbered i and the departure point when the order is dispatched.

8. The online car-hailing dispatching and management platform according to claim 7, characterized in that: The analysis process of the comprehensive evaluation index of the online vehicle numbered i is as follows: S1: Obtain the daily online working time of the online vehicle numbered i through the daily online time and daily offline time of the online vehicle numbered i; S2: Analyze the daily online time, daily offline time and preset peak time of the online vehicle numbered i to obtain the daily peak time online time of the online vehicle numbered i; S3: obtaining an online index of the online vehicle numbered i by analyzing the daily online working hours and the daily peak hour online hours of the online vehicle numbered i in the past preset days; S4; by obtaining the estimated completion time and actual completion time of each order of the online vehicle numbered i, determine whether the order is timed out; by analyzing the daily order information, obtain the number of online vehicle numbered i that has not been completed within the time limit every day; S5; by analyzing the number of orders that have not been completed on time and the total number of orders completed on a daily basis for the online vehicle numbered i in the past preset days, the on-time index of the online vehicle numbered i is obtained; S6: Obtain a violation index by analyzing the number of red light violations, complaints, and order cancellations of online vehicles on a preset number of days numbered i on a daily basis; S7: The real-time distance between the online vehicle numbered i and the departure place when dispatching the order is obtained through the departure place location information and the real-time location of the online vehicle numbered i.

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