CN112052972B - Method and system for visualizing behavior of taxi booking driver - Google Patents
Method and system for visualizing behavior of taxi booking driver Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 42
- 230000006399 behavior Effects 0.000 claims abstract description 109
- 238000012216 screening Methods 0.000 claims abstract description 4
- 238000004458 analytical method Methods 0.000 claims description 31
- 238000012795 verification Methods 0.000 claims description 14
- 238000012800 visualization Methods 0.000 claims description 8
- 230000002159 abnormal effect Effects 0.000 claims description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 230000003542 behavioural effect Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q10/02—Reservations, e.g. for tickets, services or events
- G06Q10/025—Coordination of plural reservations, e.g. plural trip segments, transportation combined with accommodation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/10—File systems; File servers
- G06F16/17—Details of further file system functions
- G06F16/1734—Details of monitoring file system events, e.g. by the use of hooks, filter drivers, logs
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06311—Scheduling, planning or task assignment for a person or group
- G06Q10/063114—Status monitoring or status determination for a person or group
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0833—Tracking
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Abstract
The invention discloses a method and a system for visualizing behaviors of a car booking driver on a network, which comprises the following steps: s1, collecting logs generated by the operation of an APP at a driver end; s2, analyzing the collected logs according to rows to generate data with different dimensions; s3, storing data with different dimensions generated by a driver side APP; s4, inquiring driver behavior data according to the screening conditions of the user; and S5, displaying the driver behavior data on the map by combining the driving track of the driver. The scheme realizes the purpose of inquiring the behavior track of the driver in the service process in the online taxi appointment travel system by one key, and intuitively shows when and where the driver performs what operation on the APP; therefore, a system administrator can conveniently check and analyze whether the driver behavior track has corresponding abnormity in real time or in the future.
Description
Technical Field
The invention belongs to the technical field of data visualization, and particularly relates to a method and a system for visualizing behaviors of a car booking driver on a network.
Background
With the rapid development of cities, short boards of urban traffic are increasingly emerging, and particularly, highway passenger transportation in the urban traffic has insufficient autonomy and personalization for passengers. Although urban traffic is already perfect as a way, the existing urban traffic cannot well meet the requirements of people at present when green travel is advocated, and the one-to-one urban traffic cannot meet the requirements of pursuing customized and personalized travel at present.
The network car booking platform connects people, taxis and private cars to form a service network with autonomous connection of people and cars, meets the requirements of people, greatly fills up short boards of urban traffic, and is gradually approved by the masses as the network car booking platform is gradually accepted by the masses, so that more and more citizens get on the car.
Therefore, the driving behavior of the driver of the network car booking is standardized, the service quality and the safety of the network car booking are ensured, and the method is important. At present, an effective monitoring scheme for the behavior of the car booking driver is not available.
Disclosure of Invention
The invention aims to solve the technical problem that the driving behavior of a net taxi booking driver cannot be monitored, and in order to solve the problem, the invention is realized according to the following technical scheme:
a method for visualizing behavior of a networked car booking driver comprises the following steps:
s1, collecting logs generated by the operation of an APP at a driver end;
s2, analyzing the collected logs according to rows to generate data with different dimensions;
s3, storing data with different dimensions generated by a driver side APP;
s4, inquiring driver behavior data according to the screening conditions of the user;
and S5, displaying the driver behavior data on the map by combining the driving track of the driver.
As a preferable scheme: the data of different dimensions includes geographic position data, operation category data, vehicle speed data and corner data.
As a preferable scheme: when the log is analyzed, the special character strings in the log file are identified, each special character string corresponds to one behavior, and the specific behavior corresponding to the character string is found out after the special character strings are identified, so that each behavior of a driver in the driving process can be analyzed.
As a preferable scheme: the method also comprises the step of analyzing the preference of the driver behavior, which specifically comprises the following steps: and counting the frequency and time of each character string, and substituting the frequency data of each character string into the behavior habit analysis model for calculation and analysis so as to obtain the behavior preference of the driver.
As a preferable scheme: still include the step to driver's dispatch strategy adjustment, specifically do: analyzing and identifying the taxi taking orders served by the driver in a period of time, analyzing the behavior preference of the driver through the behavior data of the driver in the taxi taking orders, and allocating the taxi taking orders according to the normative degree of the behavior preference of the driver.
As a preferable scheme: the method also comprises a step of verifying the abnormal condition, which specifically comprises the following steps: analyzing the behavior preference of the driver at intervals, comparing the results of the two analyses, starting real person verification of the driver when the driver assigns the order of taking the car next time when the behavior preference of the driver is obviously different, and assigning the order of taking the car for the driver after the verification is passed.
A system for visualizing behaviors of car booking drivers on a network is characterized by comprising:
the log uploading module is used for collecting logs generated by the operation of the driver APP terminal and uploading the logs to the online car appointment service platform;
the log analysis module is used for analyzing the log file uploaded to the online car booking service platform and identifying the operation of a driver in the service process;
and the visualization module is used for displaying the time and the place of the driver in the service process in the form of images.
As a preferable scheme: and the behavior characteristic analysis module is used for counting the occurrence frequency of each character string in the log file and substituting the frequency data of each character string into the behavior habit analysis model for calculation and analysis so as to obtain the behavior preference of the driver.
As a preferable scheme: and the dispatching module is used for analyzing and identifying the taxi taking orders served by the driver within a period of time, analyzing the behavior preference of the driver through the behavior data of the driver in the plurality of taxi taking orders, and dispatching the taxi taking orders according to the standard degree of the behavior preference of the driver.
As a preferable scheme: and the driver verification module compares the two results of the driver behavior characteristic analysis, and starts the real person verification of the driver when the driver dispatches the order for taking the car next time when the front and back behavior preferences of the driver have obvious differences.
Compared with the prior art, the invention has the beneficial effects that: the scheme realizes the purpose of inquiring the behavior track of the driver in the service process in the online taxi appointment travel system by one key, and intuitively shows when and where the driver performs what operation on the APP; therefore, a system administrator can conveniently check and analyze whether the driver behavior track has corresponding abnormity in real time or in the future.
Drawings
Embodiments of the invention are described in further detail below with reference to the attached drawing figures, wherein:
FIG. 1 is a flowchart of a method according to a first embodiment.
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 first embodiment is as follows:
a method for visualizing behavior of a networked car booking driver comprises the following steps:
s1, collecting logs generated by the operation of an APP at a driver end;
after the driver meets the order, in the service process, the data such as the operation (including the operation time and the operation type) of the driver on the mobile phone APP and the vehicle speed, the turning angle and the like of the vehicle at each geographic position can be recorded by the APP running log, and each operation type of the driver on the APP can generate a special character string in the running log.
S2, analyzing the collected logs according to rows to generate data with different dimensions;
the running log of a driver APP is uploaded to a monitoring platform, the running log is analyzed by the monitoring platform, namely the running log is read line by line, special character strings in a log file are firstly identified, each special character string corresponds to a behavior, specific behaviors corresponding to the character strings are found out after the special character strings are identified, and therefore all behaviors of the driver in the driving process can be analyzed, so that what operations (such as clicking which buttons, opening which pages, whether the APP enters a background and the like) are carried out on the APP by the driver in the service process, the average vehicle speed in the service process is what, and the turning angle during turning is what … …, namely, data of all dimensions such as operation type data, vehicle speed data and turning angle data are generated.
S3, storing data with different dimensions generated by a driver side APP;
s4, inquiring driver behavior data according to the screening conditions of the user;
when the operation and maintenance or customer service needs to check the behavior of the driver in the service process, the corresponding driver behavior data can be inquired only by inputting the corresponding character string, for example, the inquiry driver clicks the order receiving, and the inquiry driver can inquire the time point at which the driver clicks the order receiving by inputting the character string corresponding to the order receiving operation.
And S5, displaying the driver behavior data on the map by combining the driving track of the driver.
After the behavior data of the driver is inquired, the behavior of the driver is shown in the form of a graph and a table, for example, a driving track in the whole service process is displayed in the map, and the operation of the driver in which place and which time slot is carried out is marked on the form track according to the time sequence, so that the operation and maintenance or customer service can clearly and intuitively know each detail in the service process of the driver and judge whether the driver has illegal behaviors. Therefore, the driving behavior of the driver of the network car booking can be standardized, and the service quality and the safety of the network car booking can be guaranteed.
Example two:
in this embodiment: the method also comprises the step of analyzing the preference of the driver behavior, which specifically comprises the following steps: after the running log of the driver APP is analyzed, the frequency and time of occurrence of each character string (namely the frequency and time of occurrence of each behavior) are counted, and the frequency data of each character string are substituted into the behavior habit analysis model to be calculated and analyzed, so that the behavior preference of the driver is obtained.
In this embodiment, the behavior habit analysis model defines several behavior preferences, and determines the behavior of the driver to determine the behavior preferences of the driver. For example, the time for the driver to click the order receiving operation is obtained after the running log is analyzed, the long order receiving time of the driver after the order is sent is calculated according to the order sending time, the average time for the driver to wait for clicking the order receiving operation is obtained by calculating the time for waiting for clicking the order receiving operation in multiple services, the longer the average time is, the lower the enthusiasm of the driver is, and the average time is compared with the reference time. For example, the reference time is 1 minute, if the average time of the driver does not exceed 1 minute, the driver is judged to belong to the driver of 'receiving an order positively', and the positive degree value is 3; if the average time of the driver is 1-2 minutes, judging that the driver belongs to a normal order receiving driver, and the positive degree value is 2; if the average time of the driver exceeds 2 minutes, the driver is judged to belong to a driver of 'lazy reception order', and the positive degree value is 1. In other embodiments, the division may be more elaborate, depending on the specific requirements.
For another example, when the driver is in the service process, the APP is switched to the background operation for some times, and the driving safety level of the driver is judged by switching the occurrence time and frequency of the background operation. Namely, the driving safety level of a driver is defined in the habit analysis model, the higher the frequency of switching the APP to the background is, or the longer the APP is in the background running time is, the more the driver is not attentive when driving, and the driver can make and receive calls or play mobile phones. For example, the driver is switched to the background for 0 time, the running time of the background is 0 minute, the driver is judged to belong to the driver of 'concentration driving', and the driving concentration degree value is 3; for example, switching to the background for 1 time, judging that the driver belongs to a driver who does not concentrate on driving when the running time of the background is not more than 1 minute, and setting the driving concentration degree value to be 2; for example, the driver is switched to the background for more than 1 time, the running time of the background is more than 1 minute, the driver is judged to belong to the driver of dangerous driving, and the driving concentration degree value is 1.
Similarly, the driving stability and comfort of the driver can be judged by the turning angle and the vehicle speed during turning in the driving process, and the smaller the switching and the vehicle speed, the higher the driving stability and comfort. Corresponding to the analysis results, there are a driver of 'comfortable driving', a driver of 'smooth driving' and a driver of 'aggressive driving'. The riding comfort values are 3, 2 and 1 respectively.
The behavior level analysis of the other dimensions is not enumerated here. This enables analysis of driver's behavioral preferences through behavioral data.
In order to ensure the safety of drivers and passengers and the quality of service, the policy of dispatching orders needs to be adjusted according to the behavior preference of drivers. Specifically, the method comprises the following steps: the driving order served by the driver in a period of time (such as a week or a month) is analyzed and identified, the behavior preference of the driver is analyzed through the behavior data of the driver in a plurality of driving orders, and the driving order is allocated to the driver according to the normative degree of the behavior preference of the driver. For example, for a driver who is actively receiving an order, the priority of the driver for sending the order is improved, so that the driver can receive the order more easily; for drivers who do not depend on the spectrum and drivers who drive aggressively, the priority of the order is reduced, and the drivers are reminded to pay attention to the normative driving, so that the drivers are restrained and normative. And then determining whether to recover the dispatch priority level of the driver according to the result of analyzing the behavior preference of the driver next time.
In this embodiment, the method further includes a step of verifying the abnormal condition, specifically: analyzing the behavior preference of the driver at intervals, and comparing the results of the two analyses, specifically: after the driver behavior frequency data are analyzed, driver behavior preference data are obtained, and the method comprises the following steps: the driver order taking positive degree value A, the driving concentration degree value B and the riding comfort degree value C. The behavior preference data obtained from the last behavior analysis are A1, B1 and C1, the next time are A2, B2 and C2, and the formula P is i | A1-A2| + j | B1-B2| + k | C1-C2|, wherein P is the matching value of the driver behavior, i, j and k are weight coefficients, and i + j + k is 1. The smaller the value of P is, the closer the analysis result of the behavior preference of the front side and the rear side of the driver is; the larger the value of P, the larger the difference of the results of the analysis of the behavior preference of the front side and the rear side of the driver. And comparing the calculated P value with a preset P1 value, judging that the driver is on duty when the current value is larger than the latter value, popping up a real person verification section at the APP when a taxi taking order is dispatched next time, reminding clicking to determine verification, and starting the mobile phone camera to verify the mobile phone camera after the driver clicks. And dispatching the order for taking the car for the car after the verification is successful, and dispatching the order for the car cannot be carried out if the verification fails.
Example three:
a system for network appointment vehicle driver behavior visualization, comprising:
the log uploading module is used for collecting logs generated by the operation of the driver APP terminal and uploading the logs to the online car appointment service platform;
the log analysis module is used for analyzing the log file uploaded to the online car booking service platform and identifying the operation of a driver in the service process;
and the visualization module is used for displaying the time and the place of the driver in the service process in the form of images.
The system also comprises a behavior characteristic analysis module which is used for counting the occurrence frequency of each character string in the log file and substituting the frequency data of each character string into the behavior habit analysis model for calculation and analysis so as to obtain the behavior preference of the driver.
The system also comprises a dispatching adjustment module, wherein the driver analyzes and identifies the driving orders served by the driver within a period of time, analyzes the behavior preference of the driver according to the behavior data of the driver in the plurality of driving orders, and dispatches the driving orders for the driver according to the normative degree of the behavior preference of the driver.
The system also comprises a driver verification module which compares the two results of the analysis of the behavior characteristics of the driver, and when the front behavior preference and the back behavior preference of the driver are obviously different, the driver real person verification is started when the order for taking the car is assigned next time.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, so that any modification, equivalent change and modification made to the above embodiment according to the technical spirit of the present invention are within the scope of the technical solution of the present invention.
Claims (9)
1. A method for visualizing behaviors of a car booking driver on a network is characterized by comprising the following steps:
s1, collecting a log generated by the operation of a driver side APP, wherein in the log recording service process, user operation on the driver side APP, vehicle speed data and corner data of a vehicle at each geographic position are generated, and corresponding special character strings are generated in the log;
s2, analyzing the collected logs according to rows to generate data with different dimensions, wherein the data with different dimensions comprise geographic position data, operation category data, vehicle speed data and corner data;
s3, storing data with different dimensions generated by a driver side APP;
s4, inquiring driver behavior data according to the screening conditions of the user;
and S5, displaying the driver behavior data on the map by combining the driving track of the driver.
2. The method for visualizing driver behavior of a networked car booking according to claim 1, wherein: when the log is analyzed, the special character strings in the log file are identified, each special character string corresponds to one behavior, and the specific behavior corresponding to the character string is found out after the special character strings are identified, so that each behavior of a driver in the driving process can be analyzed.
3. The method for visualizing driver behavior of a networked car appointment as claimed in claim 2, wherein: the method also comprises the step of analyzing the preference of the driver behavior, which specifically comprises the following steps: and counting the frequency and time of each character string, and substituting the frequency data of each character string into the behavior habit analysis model for calculation and analysis so as to obtain the behavior preference of the driver.
4. The method for visualizing driver behavior of a networked car appointment as defined in claim 3, wherein: still include the step to driver's dispatch strategy adjustment, specifically do: analyzing and identifying the taxi taking orders served by the driver in a period of time, analyzing the behavior preference of the driver through the behavior data of the driver in the taxi taking orders, and allocating the taxi taking orders according to the normative degree of the behavior preference of the driver.
5. The method for network car booking driver behavior visualization of claim 4, wherein: the method also comprises a step of verifying the abnormal condition, which specifically comprises the following steps: analyzing the behavior preference of the driver at intervals, comparing the results of the two analyses, starting real person verification of the driver when the driver assigns the order of taking the car next time when the behavior preference of the driver is obviously different, and assigning the order of taking the car for the driver after the verification is passed.
6. A system for visualizing behaviors of car booking drivers on a network is characterized by comprising:
the log uploading module is used for collecting logs generated by running of a driver APP terminal and uploading the logs to the online car appointment service platform, and in the log recording service process, user operation on the driver APP terminal, vehicle speed data and corner data of a vehicle at each geographic position and corresponding special character strings are generated in the logs;
the log analysis module is used for analyzing log files uploaded to the online car booking service platform to generate data with different dimensions, wherein the data with different dimensions comprise geographic position data, operation category data, vehicle speed data and corner data, and identifying the operation of a driver in the service process;
and the visualization module is used for displaying the time and the place of the driver in the service process in the form of images.
7. The system for network car booking driver behavior visualization of claim 6, further comprising:
and the behavior characteristic analysis module is used for counting the occurrence frequency of each character string in the log file and substituting the frequency data of each character string into the behavior habit analysis model for calculation and analysis so as to obtain the behavior preference of the driver.
8. The system for network car booking driver behavior visualization of claim 7, further comprising:
and the dispatching module is used for analyzing and identifying the taxi taking orders served by the driver within a period of time, analyzing the behavior preference of the driver through the behavior data of the driver in the plurality of taxi taking orders, and dispatching the taxi taking orders according to the standard degree of the behavior preference of the driver.
9. The system for network car booking driver behavior visualization of claim 8, further comprising: and the driver verification module compares the two results of the driver behavior characteristic analysis, and starts the real person verification of the driver when the driver dispatches the order for taking the car next time when the front and back behavior preferences of the driver have obvious differences.
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