CN114004663A - Rental rate calculation method, system, storage medium and server for vehicles - Google Patents
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Abstract
The present application relates to a rental rate calculating method, system, storage medium, and server for vehicles, wherein the steps of the method are periodically triggered; the method comprises the following steps: processing the obtained original order data; processing the acquired original vehicle data; based on the processed order data and vehicle data, a rental rate satisfying the set condition is determined. According to the method and the device, the renting rate of any time period, any region and any vehicle type can be calculated, the specific steps of the method are simplified, and the accuracy of the renting rate is improved.
Description
Technical Field
The application relates to the field of big data processing, in particular to a rental rate calculation method, a rental rate calculation system, a storage medium and a server for vehicles.
Background
Before explaining the prior art, the definition of the rental rate needs to be introduced. The rental rate is the ratio of the number of orders divided by the total number of vehicles over a period of time.
At present, both the order and the vehicle are changed at random, and the rental rate is updated for the changed event every time the order or the vehicle is changed.
Due to the expansion of services, orders and vehicle change events are really increased step by step, and the condition that two adjacent data are processed simultaneously in a short time is easy to occur at present, so that dirty data is easy to generate. For example: the event A and the event B simultaneously influence the same vehicle in the same store in the crossed time period, the event B obtains the data and calculates the data after the calculated rental rate of the event A is written into the database in the correct flow, and then the event B is written into the database, when the calculated rental rate of the event A is not written into the database at present, the event B obtains the previous rental rate data, the obtained data of the event B is wrong, and the final result is wrong.
Disclosure of Invention
In order to solve or partially solve the problems in the related art, the invention provides a method, a system, a storage medium and a server for calculating the rental rate of a vehicle, which can calculate the rental rate of any time period, any area and any vehicle type, and the invention simplifies the specific steps of the method for calculating the rental rate, thereby improving the accuracy of the rental rate.
A first aspect of the present application provides a rental rate calculating method for a vehicle, the steps of the method being periodically triggered; the method comprises the following steps:
processing the obtained original order data;
processing the acquired original vehicle data;
based on the processed order data and vehicle data, a rental rate satisfying the set condition is determined.
Optionally, before the processing the acquired original order data, the method further includes:
original order data is obtained.
Optionally, the processing the obtained original order data specifically includes:
preprocessing original order data; and/or
And post-processing the original order data to obtain order data with a time label, wherein the time label is used for indicating the time period of the order data corresponding to the time label.
Optionally, the preprocessing the original order data specifically includes:
eliminating long rental orders;
removing the deleted order;
eliminating cancelled orders;
reserving orders with a starting time and/or an ending time within a first set time of the day and from the day; and/or
The start time and/or end time of the raw order data is formatted to reduce the amount of data for the split order.
Optionally, the first set time is 90 days.
Optionally, the post-processing the original order data to obtain the order data with the time tag specifically includes:
determining date-difference days of each order data based on the starting time and/or the ending time of each order data;
and if the date difference days of the order data are greater than or equal to 1, copying the order data, and adding a time label to each copied order data, wherein the number of the copied order data is equal to the date difference days of the order data, and each order data corresponds to each date difference day of the order data.
Optionally, before the processing the acquired raw vehicle data, the method further includes:
raw vehicle data is acquired.
Optionally, the processing the acquired original vehicle data specifically includes:
preprocessing original vehicle data; and/or
And post-processing the original vehicle data to obtain vehicle data with a time tag, wherein the time tag is used for indicating the time period of the vehicle data corresponding to the time tag.
Optionally, the preprocessing the raw vehicle data specifically includes:
excluding the deleted vehicle;
vehicles with starting time and/or ending time within a second set time of the day and the day are reserved; and/or
The start time and/or end time of the raw vehicle data is formatted to reduce the data volume of the split vehicle.
Optionally, the second set time is 90 days.
Optionally, the performing post-processing on the original vehicle data to obtain the vehicle data with the time tag specifically includes:
determining date-difference days of each order data based on the starting time and/or the ending time of each vehicle data;
and if the date and day difference of the vehicle data is greater than or equal to 1, copying the vehicle data, and adding a time label to each piece of copied order data, wherein the number of the copied vehicle data is equal to the date and day difference of the vehicle data, and each piece of order data corresponds to each day of the date and day difference of the order data.
Optionally, the determining, based on the processed order data and the processed vehicle data, a rental rate that meets the set condition specifically includes:
determining the amount of orders meeting the set conditions based on the processed order data;
determining the number of vehicles meeting the set conditions based on the processed order data;
determining a rental rate satisfying the setting condition based on the amount of orders and the number of vehicles.
Optionally, the setting condition includes: time period conditions, vehicle type conditions, and/or city conditions.
Optionally, after the determining the rental rate meeting the set condition, the method further includes:
and sending the rental rate to a database system.
Optionally, the method is implemented by Spark technology.
Optionally, the method is implemented based on task distribution mechanism and/or automatic resource expansion and recovery.
A second aspect of the present application provides a rental rate calculating system for a vehicle, the system including:
the first processing module is used for periodically processing the acquired original order data;
the second processing module is used for periodically processing the acquired original vehicle data; and
and the calculation module is used for determining the rental rate meeting the set conditions periodically based on the processed order data and the processed vehicle data.
A third aspect of the present application provides a storage medium comprising:
a computer program;
wherein the computer program controls the electronic device where the storage medium is located to execute the rental rate calculating method as described above when running.
A fourth aspect of the present application provides a server, including:
a processor; and
a computer program stored on the memory and executable on the processor;
wherein the processor implements the rental rate calculating method as described above when executing the computer program.
The technical scheme provided by the application can comprise the following beneficial effects:
the method and the device can calculate the rental rate of any time period, any area and any vehicle type, simplify the specific steps of the method, ensure the accuracy of the rental rate and reduce the cost of code maintenance.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a diagram illustrating raw order data according to an embodiment of the present application;
FIG. 2 is a schematic illustration of raw vehicle data in an embodiment of the present application;
FIG. 3 is a flow chart illustrating processing of raw order data according to an embodiment of the present invention;
FIG. 4 is a flow chart of processing raw vehicle data according to an embodiment of the present application;
FIG. 5 is a flow chart of the processing of a raw order data and a raw vehicle data in an embodiment of the present application;
fig. 6 is a flowchart of a rental rate calculating method in the embodiment of the present application.
Detailed Description
Embodiments of the present application will be described in more detail below with reference to the accompanying drawings. While embodiments of the present application are illustrated in the accompanying drawings, it should be understood that the present application may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It should be understood that although the terms "first," "second," "third," etc. may be used herein to describe various information, these information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.
The embodiment of the application provides a method, a system, a storage medium and a server for calculating the rental rate of a vehicle, which can calculate the rental rate of any time period, any area and any vehicle type, and simplifies the specific steps of the rental rate calculating method, thereby improving the accuracy of the rental rate.
The technical solutions of the embodiments of the present application will be described in detail below with reference to the accompanying drawings.
The embodiment provides a rental rate calculation method for vehicles, which correspondingly simplifies each step, does not need to consider order change events and vehicle change events, directly calculates the rental rate according to original order data and original vehicle data, greatly improves the accuracy rate of the calculated rental rate, and each step of the calculation method is triggered regularly and periodically without manual triggering.
Preferably, the steps of the calculation method in this embodiment may be triggered every half hour. It should be noted that the trigger period of each step of the calculation method is not fixed, and a person skilled in the art may adjust the trigger period in real time according to specific situations, which is not specifically limited in this embodiment.
Referring to fig. 6, the rental rate calculating method of the embodiment generally includes the following steps:
s100, processing the acquired original order data;
the raw order data typically includes the order number, vehicle type, city in which it is located, start time and end time of the order.
Taking the original order data shown in fig. 1 as an example, the vehicle type of the order 1 is X, the city is a, the starting time is 10/01/2020, and the ending time is 10/03/2020.
S200, processing the acquired original vehicle data;
the raw vehicle data typically includes the vehicle number, vehicle type, city in which it is located, start time and end time of the vehicle. The unfinished raw vehicle data does not usually include an accurate finish time, but the finish time of the unfinished raw vehicle data may be artificially set to 2099, 01/01 or other feasible dates for calculation.
Taking the original vehicle data shown in fig. 2 as an example, the vehicle type of the vehicle 1 is X, the city is a, the start time is 10/01/2020, and the end time is 10/03/2020.
And S300, determining the rental rate meeting the set conditions based on the processed order data and the processed vehicle data.
The setting conditions are generally set by the user before step S300, and generally include a time period condition, a vehicle type condition, and a city condition. The time period condition can be set to be a specific certain day, a certain week, a certain month or other self-defined time periods, the vehicle type condition can be set to be any vehicle type, preferably to be at least one vehicle type provided by a renter, and the city condition can be set to be any city, preferably to be at least one city covered by a renting platform.
The rental rate calculating method of the embodiment calculates based on the original order data and the original vehicle data, calculates the rental rate meeting the set conditions more accurately, and each step of the rental rate calculating method is triggered periodically without manual triggering, so that the labor cost is reduced.
It should be noted that, in the present embodiment, the sequence of step S100 and step S200 is not specifically limited, and is only exemplarily illustrated in the specific sequence described above, for example, step S200 may be performed before step S100, or simultaneously with step S100.
In this embodiment, before the step S100, the method further includes: original order data is obtained.
In this embodiment, the step S100 includes one or more of the following steps:
preprocessing original order data;
and post-processing the original order data to obtain order data with a time label, wherein the time label is used for indicating the time period of the order data corresponding to the time label.
In this embodiment, the preprocessing the original order data includes one or more of the following:
eliminating long rental orders;
removing the deleted order;
eliminating cancelled orders;
reserving orders with a starting time and/or an ending time within a first set time of the day and from the day; and/or
The start time and/or end time of the raw order data is formatted to reduce the amount of data for the split order.
Wherein, the long lease generally refers to an order with a lease period of more than 1 year or two years, and is basically an order for a company.
The first setting time is not particularly limited in this embodiment, and is only exemplified by the first setting time being 90 days (or 3 months).
In this embodiment, the post-processing the original order data to obtain the order data with the time tag specifically includes:
determining date-difference days of each order data based on the starting time and/or the ending time of each order data;
and if the date difference days of the order data are greater than or equal to 1, copying the order data, and adding a time label to each copied order data, wherein the number of the copied order data is equal to the date difference days of the order data, and each order data corresponds to each date difference day of the order data.
Taking the order data shown in fig. 3 as an example for illustration, the start time and the end time of the order are 2021-10-01 to 2021-10-03, the end time minus the start time obtains the date number of days of difference between the start time and the end time as 2, and the order data is copied three times and added with the marks 0,1, 2. And adding the marked value to the start time 2021-10-01 to obtain the date on which the data appears, namely 2021-10-01,2021-10-02,2021-10-03 respectively, wherein the copied number is the difference value of the start time and the end time, namely +1, and the marked value is the current day number of the start time, namely + 1.
And if the date difference days of the order data are equal to 0, directly adding a time label to the order data, wherein the added time label is the starting time/the ending time of the order.
In this embodiment, before step S200, the method further includes: raw vehicle data is acquired.
In this embodiment, the processing the acquired raw vehicle data includes one or more of the following:
preprocessing original vehicle data;
and post-processing the original vehicle data to obtain vehicle data with a time tag, wherein the time tag is used for indicating the time period of the vehicle data corresponding to the time tag.
In this embodiment, the preprocessing of the raw vehicle data specifically includes one or more of the following:
excluding the deleted vehicle;
vehicles with starting time and/or ending time within a second set time of the day and the day are reserved;
the start time and/or end time of the raw vehicle data is formatted to reduce the data volume of the split vehicle.
The second setting time is not specifically limited in this embodiment, and is only exemplified by the first setting time being 90 days (or 3 months).
In this embodiment, the post-processing the original vehicle data to obtain the vehicle data with the time tag specifically includes:
determining date-difference days of each order data based on the starting time and/or the ending time of each vehicle data;
and if the date and day difference of the vehicle data is greater than or equal to 1, copying the vehicle data, and adding a time label to each piece of copied order data, wherein the number of the copied vehicle data is equal to the date and day difference of the vehicle data, and each piece of order data corresponds to each day of the date and day difference of the order data.
Taking the vehicle data shown in fig. 4 as an example, if the start time and the end time of the vehicle are 2021-10-01 to 2021-10-03, the difference between the start time and the end time obtained by subtracting the start time from the end time is 2, and the data is copied three times and added with the marks 0,1, 2. And adding the marked value to the start time 2021-10-01 to obtain the date on which the data appears, namely 2021-10-01,2021-10-02,2021-10-03 respectively, wherein the copied number is the difference value of the start time and the end time, namely +1, and the marked value is the current day number of the start time, namely + 1.
And if the date and day difference of the vehicle data is equal to 0, directly adding a time tag to the vehicle data, wherein the added time tag is the starting time/the ending time of the vehicle data.
Referring to fig. 5, the processed order data includes an order number, a vehicle type, a city, a start time, an end time, a date difference, a serial number, and a route date, and the processed vehicle data includes an order number, a vehicle type, a city, a start time, an end time, a date difference, a serial number, and a route date.
In this embodiment, the step S300 specifically includes:
s301, determining the amount of orders meeting the set conditions based on the processed order data;
s302, determining the number of vehicles meeting the set conditions based on the processed order data;
and S303, determining the leasing rate meeting the set condition based on the amount of orders and the number of vehicles.
To facilitate an understanding of step S300 of the above method, a specific application is exemplified below:
referring to fig. 5, the vehicle type satellite X is obtained by screening the set conditions of vehicle type X, city A, 2021-10-01 or 2021-10-02, and the order rate of the vehicle type satellite X is obtained by screening the set conditions of city A and date 2021-10-01 or 2021-10-02.
In this embodiment, after step S300, the method further includes: and sending the rental rate to a database system.
As can be seen from the above description, the rental rate calculating method of this embodiment is different from the prior art in that all the customized logics that were previously made for improving performance are discarded, the calculating method is most simplified, and in addition, in order to efficiently process millions of simple data logics, the present embodiment breaks through the performance bottleneck of the current calculating method through the Spark technology, and specifically adopts the following method:
(1) task distribution mechanism
At the beginning of algorithm starting, Spark is enabled to process relevant logic in advance, preparation for executing a current algorithm task is well made, and operation loads are uniformly distributed to each machine according to the current data volume and the number of running machines, so that parallel execution among the machines can be realized, the operation loads are the same, and the execution time is minimized. The mechanism can effectively shorten the execution time of the algorithm as long as the number of machines is continuously increased.
(2) Automatic resource expansion and reclamation
The CPU occupation ratio of each machine or the use amount of the memory is monitored through Spark, an expansion threshold value and a recovery threshold value are set, and when data exceed the expansion threshold value, machine resources are automatically added. And when the data is smaller than the recovery threshold value, automatically recovering the machine resource.
After the 1 and 2 are matched, the number of machines can be automatically or manually increased according to the current calculated task amount and the CPU occupation percentage caused by the current task, the performance can be transversely expanded, and the operation time of the algorithm can be shortened. And when the CPU occupation is reduced, the machine can be automatically recovered, and the resource waste caused by redundant machines is effectively reduced.
And column storage is used for storing the rental rate result, so that the reading performance of the rental rate result is optimized. The column storage can be simply described as that data in a database is stored according to logical storage units on a column basis, the data in a column exists in a continuous storage form in a storage medium, and the advantages of automatic indexing, high cache hit rate, less data reading, more advanced query execution technology and the like are provided.
Based on the same inventive concept, the present embodiment also provides a rental rate calculating system for a vehicle, the system including:
the first processing module is used for periodically processing the acquired original order data;
the second processing module is used for periodically processing the acquired original vehicle data; and
and the calculation module is used for determining the rental rate meeting the set conditions periodically based on the processed order data and the processed vehicle data.
Based on the same inventive concept, the present embodiment further provides a storage medium, including:
a computer program;
wherein the computer program controls the electronic device where the storage medium is located to execute the rental rate calculating method as described above when running.
Based on the same inventive concept, the present embodiment further provides a server, including:
a processor; and
a computer program stored on the memory and executable on the processor;
wherein the processor implements the rental rate calculating method as described above when executing the computer program.
The above description is only an example of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, and improvement made within the spirit and scope of the present application are included in the protection scope of the present application.
Claims (15)
1. A rental rate calculating method for a vehicle, characterized in that the steps of the method are triggered periodically; the method comprises the following steps:
acquiring original order data;
acquiring original vehicle data;
processing the obtained original order data;
processing the acquired original vehicle data;
based on the processed order data and vehicle data, a rental rate satisfying the set condition is determined.
2. A rental rate calculating method for vehicles according to claim 1, wherein the processing of the acquired original order data specifically includes:
preprocessing original order data; and/or
And post-processing the original order data to obtain order data with a time label, wherein the time label is used for indicating the time period of the order data corresponding to the time label.
3. A rental rate calculating method for vehicles according to claim 2, wherein the preprocessing of the original order data specifically includes:
eliminating long rental orders;
removing the deleted order;
eliminating cancelled orders;
reserving orders with a starting time and/or an ending time within a first set time of the day and from the day; and/or
The start time and/or end time of the raw order data is formatted to reduce the amount of data for the split order.
4. A rental rate calculating method for vehicles according to claim 2, wherein the post-processing of the original order data to obtain the order data with the time stamp specifically comprises:
determining date-difference days of each order data based on the starting time and/or the ending time of each order data;
and if the date difference days of the order data are greater than or equal to 1, copying the order data, and adding a time label to each copied order data, wherein the number of the copied order data is equal to the date difference days of the order data, and each order data corresponds to each date difference day of the order data.
5. A rental rate calculating method for vehicles according to claim 1, wherein the processing of the acquired raw vehicle data specifically includes:
preprocessing original vehicle data; and/or
And post-processing the original vehicle data to obtain vehicle data with a time tag, wherein the time tag is used for indicating the time period of the vehicle data corresponding to the time tag.
6. A rental rate calculating method for vehicles according to claim 5, wherein the preprocessing of the raw vehicle data specifically includes:
excluding the deleted vehicle;
vehicles with starting time and/or ending time within a second set time of the day and the day are reserved; and/or
The start time and/or end time of the raw vehicle data is formatted to reduce the data volume of the split vehicle.
7. A rental rate calculating method for vehicles according to claim 5, wherein the post-processing of the original vehicle data to obtain the vehicle data with the time stamp specifically includes:
determining date-difference days of each order data based on the starting time and/or the ending time of each vehicle data;
and if the date and day difference of the vehicle data is greater than or equal to 1, copying the vehicle data, and adding a time label to each piece of copied order data, wherein the number of the copied vehicle data is equal to the date and day difference of the vehicle data, and each piece of order data corresponds to each day of the date and day difference of the order data.
8. A rental rate calculating method for vehicles according to claim 1, wherein the determining of the rental rate satisfying the set condition based on the processed order data and the vehicle data specifically includes:
determining the amount of orders meeting the set conditions based on the processed order data;
determining the number of vehicles meeting the set conditions based on the processed order data;
determining a rental rate satisfying the setting condition based on the amount of orders and the number of vehicles.
9. A rental rate calculating method for vehicles according to claim 1 or 8, wherein the setting condition includes: time period conditions, vehicle type conditions, and/or city conditions.
10. A rental rate calculating method for vehicles according to claim 1, further comprising, after the determining of the rental rate satisfying the set condition:
and sending the rental rate to a database system.
11. A rental rate calculating method for vehicles according to claim 1, wherein the method is implemented by Spark technology.
12. A rental rate calculating method for vehicles according to claim 1, wherein the method is implemented based on a task distribution mechanism and/or automatic resource expansion and recovery.
13. A rental rate calculating system for a vehicle, characterized by comprising:
the first processing module is used for periodically processing the acquired original order data;
the second processing module is used for periodically processing the acquired original vehicle data; and
and the calculation module is used for determining the rental rate meeting the set conditions periodically based on the processed order data and the processed vehicle data.
14. A storage medium, comprising:
a computer program;
wherein the computer program controls an electronic device in which the storage medium is located to execute the rental rate calculating method according to any one of claims 1 to 12 when running.
15. A server, comprising:
a processor; and
a computer program stored on the memory and executable on the processor;
wherein the processor, when executing the computer program, implements the rental rate calculating method according to any one of claims 1 to 12.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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