CN111582624A - Train ticket amount pre-dividing method and device, storage medium and computer equipment - Google Patents
Train ticket amount pre-dividing method and device, storage medium and computer equipment Download PDFInfo
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Abstract
The embodiment of the invention provides a train ticket amount pre-classifying method and device, a storage medium and computer equipment, wherein the train ticket amount pre-classifying method comprises the following steps: calculating passenger flow form parameters of the train; calculating the actual passenger seat opening rate of the train; determining the type of the train according to the passenger flow form parameters and the actual passenger seat opening rate; and pre-classifying the ticket amount of the train according to the type of the train. The technical scheme provided by the embodiment of the invention can scientifically pre-divide the train ticket amount.
Description
[ technical field ] A method for producing a semiconductor device
The invention relates to the field of data processing, in particular to a train ticket amount pre-dividing method and device, a storage medium and computer equipment.
[ background of the invention ]
A good ticketing organization strategy cannot avoid deep research and effective analysis on road network requirements, transportation requirements and increasing passenger flow market requirements in different regions and levels in China. Whether the analysis of the passenger flow market is scientific and effective directly influences the quality of ticket organization work, and further has important influence on the matching degree of the demand and supply of the railway passenger transport market, the satisfaction degree of passengers and the like.
The traditional passenger flow analysis method for ticket organization mainly takes the passenger flow market of each station in a line as a basis, focuses on analyzing the passenger flow demand of each station, researches the number of passengers getting on or off the bus at each station, and reasonably arranges and deploys the transport capacity and the ticket amount according to the station passenger flow demand. The traditional analysis method emphasizes the station passenger flow requirement too much, the whole passenger flow state of the whole train is less grasped, and the train ticket amount cannot be scientifically pre-divided.
[ summary of the invention ]
In view of this, the embodiment of the present invention provides a train ticket amount pre-classifying method and apparatus, a storage medium, and a computer device, so as to scientifically pre-classify train tickets.
The embodiment of the invention provides a train ticket amount pre-classifying method, which comprises the following steps: calculating passenger flow form parameters of the train; calculating the actual passenger seat opening rate of the train; determining the type of the train according to the passenger flow form parameters and the actual passenger seat rate; and pre-classifying the ticket amount of the train according to the type of the train.
Further, the passenger flow shape parameters of the train include an initial station seating rate, a station average utilization rate along the way and a station ending seating rate of the train, and the calculating the passenger flow shape parameters of the train includes: and calculating the seating rate of the starting station, the average utilization rate of the station along the way and the seating rate of the terminal station.
Further, the origin station seating rate, the waypoint average utilization rate and the destination station seating rate are calculated according to the following formulas:
r4 ═ avg (R2), where R1 represents the starting station occupancy of the train, N1 represents the number of persons getting on at the starting station of the train, M represents the number of persons getting on at the fixed station of the train, R2 represents the utilization rate of the station along the train, N2 represents the number of persons in the station along the train, R3 represents the rate of seats getting off at the terminal station of the train, N3 represents the number of persons getting off at the terminal station of the train, R4 represents the average utilization rate of the station along the train, and avg represents the average value.
Further, the method can be used for preparing a novel materialAnd calculating the actual driving passenger seat rate of the train according to the following formula:
wherein R5 represents an actual driving passenger seat ratio of the train, M represents the number of passengers loaded on the train, S (i) represents a distance taken by the ith passenger, M represents a member of the train, and S represents a distance between a start station and a final station of the train.
Further, the determining the type of the train according to the passenger flow form parameter and the actual passenger seat occupancy rate includes: judging whether the passenger flow form parameters meet a first preset condition or not; judging whether the actual passenger seat occupancy rate meets a second preset condition or not; and if the passenger flow form parameter meets the first preset condition and the actual passenger seat opening rate meets the second preset condition, determining the type of the train as a target type.
The embodiment of the invention provides a train ticket amount pre-sorting device, which comprises: the first calculation unit is used for calculating passenger flow form parameters of the train; the second calculating unit is used for calculating the actual passenger seat opening rate of the train; the determining unit is used for determining the type of the train according to the passenger flow form parameters and the actual passenger seat driving rate; and the pre-sorting unit is used for pre-sorting the ticket amount of the train according to the type of the train.
Further, the train passenger flow shape parameters include an origin station seating rate, a station average utilization rate along the way, and a station arrival seating rate of the train, and the first calculating unit is configured to: and calculating the initial station seating rate, the average utilization rate of the station along the way and the final station seating rate.
Further, the first calculating unit is configured to calculate the origin station seating rate, the waypoint average utilization rate, and the destination station seating rate according to the following formulas:
r4 ═ avg (R2), where R1 represents the starting station occupancy of the train, N1 represents the number of persons getting on at the starting station of the train, M represents the number of persons getting on at the fixed station of the train, R2 represents the utilization rate of the station along the train, N2 represents the number of persons in the station along the train, R3 represents the rate of seats getting off at the terminal station of the train, N3 represents the number of persons getting off at the terminal station of the train, R4 represents the average utilization rate of the station along the train, and avg represents the average value.
Further, the second calculating unit is used for calculating the actual driving passenger seat rate of the train according to the following formula:
wherein R5 represents an actual driving passenger seat ratio of the train, M represents the number of passengers loaded on the train, S (i) represents a distance taken by the ith passenger, M represents a member of the train, and S represents a distance between a start station and a final station of the train.
Further, the determining unit includes: the first judgment subunit is used for judging whether the passenger flow form parameters meet a first preset condition or not; the second judgment subunit is used for judging whether the actual passenger seat opening rate meets a second preset condition or not; and the determining subunit is configured to determine that the type of the train is a target type if the passenger flow form parameter meets the first preset condition and the actual passenger seat occupancy rate meets the second preset condition.
The embodiment of the invention provides a storage medium, which comprises a stored program, wherein when the program runs, a device where the storage medium is located is controlled to execute the method.
An embodiment of the present invention provides a computer device, including a memory for storing information including program instructions and a processor for controlling execution of the program instructions, where the program instructions are loaded by the processor and executed to implement the steps of the above method.
In the embodiment of the invention, the passenger flow form parameters of the train are calculated; calculating the actual passenger seat opening rate of the train; determining the type of the train according to the passenger flow form parameters and the actual passenger seat opening rate; the method has the advantages that the ticket amount of the train is pre-classified according to the type of the train, the overall grasp on the whole passenger flow state of the train is better, and the ticket amount of the train can be scientifically pre-classified.
[ description of the drawings ]
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a flow chart of a train ticket pre-classifying method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a train ticket pre-sorting device provided by an embodiment of the invention;
fig. 3 is a schematic diagram of a computer device according to an embodiment of the present invention.
[ detailed description ] embodiments
For better understanding of the technical solutions of the present invention, the following detailed descriptions of the embodiments of the present invention are provided with reference to the accompanying drawings.
It should be understood that the described embodiments are only some embodiments of the invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without any inventive step, are within the scope of the present invention.
The terminology used in the embodiments of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the examples of the present invention 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.
Referring to fig. 1, a flow chart of a method for implementing train ticket pre-classification according to an embodiment of the present invention is shown, where the method includes:
step S102: and calculating the passenger flow form parameters of the train.
When the whole-course passenger flow form of the train is analyzed, in order to know the change rule of the passenger flow of the train more intuitively and accurately, the embodiment of the invention carries out index quantification on the passenger flow of the train from three aspects of an initial station, a station along the way and a final station, and creates several indexes as follows: the initial station seating rate, the station utilization rate along the way, the station seating rate at the final station and the average station utilization rate along the way.
And the whole-course passenger flow characteristic of the train can be visually displayed according to three indexes of the initial station seating rate, the station average utilization rate along the way and the final station seating rate.
The passenger flow form parameters of the train comprise the initial station seat-up rate, the station average utilization rate along the way and the station end-to-end seat-down rate of the train, and the calculation of the passenger flow form parameters of the train comprises the following steps: and calculating the seating rate of the initial station, the average utilization rate of the stations along the way and the seating rate of the final station.
Calculating the seating rate of the initial station, the average utilization rate of the station along the way and the seating rate of the final station according to the following formulas:
r4 ═ avg (R2), where R1 represents the train starting station occupancy, N1 represents the number of persons getting on the train starting station, M represents the number of persons getting on the train stopping station, R2 represents the train stop availability, N2 represents the number of persons in the train stop station, R3 represents the train stop end station occupancy, N3 represents the number of persons getting off the train stop end station, R4 represents the train stop end station average availability, and avg represents the averaging value.
For example, suppose the origin station of a certain train is Beijing and the station along the way is: shijiazhuang, Zhengzhou east, Changshan south and Guangzhou south, and Shenzhen north as the final station, assuming that the member of the train is 1000.
TABLE 1
As can be seen from table 1, N1 is 300, M is 1000, and R1 is (300/1000) × 100% is 30%.
According to the table 1, the number of people in the vehicles at the station along the way can be calculated, and the number of people in the vehicles at the Shijiazhuang station is 300+ 50-220-130; the number of people in the vehicle at the east of Zhengzhou is 300+50+ 100-; the number of people in the Changshan south station is 300+50+100+ 220-30-150 ═ 150; the number of people in the south China is 300+50+100+100+ 150-.
R2 (shijiazhuang) ═ 130/1000 × 100% ═ 13%,
r2 (zhengzhou east) ((200/1000) × 100) (-20%),
r2 (changshan) ═ 15% of (150/1000) × 100%,
r2 (southern cantonese) ═ 10% of (100/1000) × 100%,
r4 ═ R2 (shijiazhuang) + R2 (zhengzhou east) + R2 (changshan) + R2 (guanzhou south) ]/4 ═ 14.5% (13% + 20% + 15% + 10%)/4 ═ 14.5%.
R3＝(100/1000)×100％＝10％。
Step S104: and calculating the actual passenger seat opening rate of the train.
Calculating the actual driving passenger seat rate of the train according to the following formula:
where R5 represents the actual driving passenger seat ratio of the train, M represents the number of passengers carried by the train, S (i) represents the distance taken by the ith passenger, M represents the passenger of the train, and S represents the distance between the starting station and the final station of the train.
Step S106: and determining the type of the train according to the passenger flow form parameters and the actual passenger seat opening rate.
Determining the type of the train according to the passenger flow form parameters and the actual passenger seat opening rate, wherein the method comprises the following steps: judging whether the passenger flow form parameters meet a first preset condition or not; judging whether the actual passenger seat opening rate meets a second preset condition or not; and if the passenger flow form parameter meets a first preset condition and the actual passenger seat opening rate meets a second preset condition, determining the type of the train as the target type.
The first digits (or the first digits multiplied by 10) of the train origin station seating rate, the average utilization rate of the station along the way and the final station seating rate are combined to obtain a three-digit number 'XYZ' which can be used for representing the passenger flow form of the train. Therefore, each number represents the number density of the trains in the corresponding section, and the number density is 10 at the maximum and 1 at the minimum, as shown in table 2.
TABLE 2
For trains with fewer stations, the three-digit slot is represented by 0. When the train is a one-stop through train, the train is not stopped midway, and the passenger flow form of the train is represented by X00, wherein X is the first digit of the density in the train in the whole journey, and 00 represents that the train is a one-stop through train; when the train stops at only one stop station except the starting and ending stop station, the train passenger flow form is represented by X0Y, wherein X is the first digit of the train starting stop seating rate, Y is the first digit of the ending stop seating rate, and 0 represents the approach of only one stop.
After the train passenger flow form is obtained and divided, the trains can be classified according to the passenger flow form and the actual passenger seat rate. Taking a certain train on the Jinghui line as an example, the passenger flow situation and the passenger deciding situation are shown in Table 3, and through calculation, the seat occupancy rate of the train at the starting station is 90%, the utilization rate of the train along the station is 99%, and the seat occupancy rate of the train at the final station is 95%, then the first digits of the three indexes are combined to obtain the passenger flow form of the train which is '999'.
TABLE 3
If a certain train is a one-stop train in Beijing-Shanghai, the daily average number density of people in the train in the investigation date is 0.91, and the train type is 900. Similarly, a certain train stops at three stations of Beijing-Jinxi-Shanghai rainbow bridge, the number density of people in the train in two sections of the investigation period is respectively 0.88 and 0.85, and the train type is marked as 808.
Step S108: and pre-classifying the ticket amount of the train according to the type of the train.
After the trains are classified according to the train types, the proper ticket organization strategy can be respectively implemented according to the passenger flow characteristics of each type of train, so that the ticket organization of the trains can more effectively meet the actual passenger flow requirements, and the ticket delivery can more accurately coincide with the whole-course passenger flow form of the trains. The train classification method based on the whole-course passenger flow form of the train can effectively capture the change rule of the passenger flow demand of the train and provide an effective passenger flow rule analysis basis for the optimization realization of the ticket amount pre-distribution.
The method provided by the embodiment of the invention initiatively analyzes the change condition of the passenger flow form of the whole train, knows the dynamic fluctuation of the passenger flow demand of the train at different stations, determines the role and the positioning of the train in the same-direction train and in the historical passenger flow market, classifies the train according to the passenger flow form, and provides a more scientific and effective passenger flow analysis means for the ticket organization and the management of all trains in the same direction.
The invention solves the problem of insufficient understanding degree of the whole train passenger flow in the traditional ticket organization management, effectively realizes the anchoring of the historical passenger flow and the ticket organization on the train level by mainly analyzing the whole train passenger flow form change and guiding the train ticket organization mode, realizes the comprehensive and deep understanding of the train historical passenger flow and provides an effective improvement idea for the train ticket organization. According to the density distribution of the train passenger flow, the method can effectively guide the ticket amount pre-classifying work, assist in describing the whole train operation, classify and summarize the passenger flow form of the train in any section, effectively classify the applicable scheme of the train ticket amount pre-classifying, and improve the ticket amount pre-classifying efficiency and the adaptability.
The embodiment of the invention provides a technology for analyzing the full-range passenger flow form of the train according to the historical passenger flow condition, classifying the train according to different passenger flow forms and providing different effective means for ticket organization management according to different types of trains. The method can effectively comprehensively depict the whole-course passenger flow form of the train under the current ticket amount pre-classifying condition by analyzing the passenger density in the train in different sections during the train running according to the historical passenger flow condition, and can formulate a proper ticket amount pre-classifying sequence according to the train passenger flow form and the actual ticket amount pre-classifying working experience.
The embodiment of the invention provides a comprehensive analysis method for wholly and locally analyzing the passenger flow condition of the whole train journey to the passenger flow condition of the whole train journey on the basis of historical passenger flow, and simultaneously provides a new idea for analyzing the interaction influence of the passenger flow of the train and a ticketing organization by combining with the policy of the ticketing organization. At present, artificial intelligence and big data technology are rapidly developed, the work of ticket organization management and the high-speed train developed by information technology are continuously improved by a new idea, a new method and a new angle, and a big data technology tool realizes more comprehensive and objective understanding and understanding of the railway passenger transportation market, thereby providing a more scientific and comprehensive theoretical basis and analysis method for the optimization analysis of a product supply side. Meanwhile, the applicability and the feasibility of the new technology are best checked and improved through application and check in actual work.
Referring to fig. 2, an embodiment of the present invention provides a train ticket amount pre-sorting apparatus, including: a first calculation unit 10, a second calculation unit 20, a determination unit 30, a pre-classification unit 40.
The first calculating unit 10 is used for calculating the passenger flow form parameters of the train.
And the second calculating unit 20 is used for calculating the actual passenger seat opening rate of the train.
And the determining unit 30 is used for determining the type of the train according to the passenger flow form parameters and the actual passenger seat opening rate.
And the pre-sorting unit 40 is used for pre-sorting the ticket amount of the train according to the type of the train.
Optionally, the passenger flow shape parameters of the train include an origin station seating rate, a waypoint average utilization rate and a destination station seating rate of the train, and the first calculation unit 10 is configured to:
and calculating the seating rate of the initial station, the average utilization rate of the stations along the way and the seating rate of the final station.
Optionally, the first calculating unit 10 is configured to calculate the origin station seating rate, the waypoint average utilization rate and the destination station seating rate according to the following formulas:
R4＝avg(R2)，
wherein R1 represents the train's starting station occupancy, N1 represents the number of persons on the train's starting station, M represents the train's passenger, R2 represents the train's utilization rate of the station, N2 represents the number of persons in the train's station, R3 represents the train's end station occupancy, N3 represents the number of persons off the train's end station, R4 represents the train's average utilization rate of the station, and avg represents the averaging value.
Optionally, the second calculating unit 20 is configured to calculate the actual passenger seat occupancy of the train according to the following formula:
where R5 represents the actual passenger occupancy of the train, M represents the number of passengers carried by the train, S (i) represents the distance taken by the ith passenger, M represents the passenger of the train, and S represents the distance between the starting station and the ending station of the train.
Optionally, the determining unit 30 includes: the device comprises a first judgment subunit, a second judgment subunit and a determination subunit.
And the first judging subunit is used for judging whether the passenger flow form parameters meet a first preset condition.
And the second judgment subunit is used for judging whether the actual passenger seat opening rate meets a second preset condition.
And the determining subunit is used for determining the type of the train as the target type if the passenger flow form parameter meets a first preset condition and the actual passenger seat opening rate meets a second preset condition.
The embodiment of the invention provides a storage medium, which comprises a stored program, wherein when the program runs, equipment where the storage medium is located is controlled to execute the following steps: calculating passenger flow form parameters of the train; calculating the actual passenger seat opening rate of the train; determining the type of the train according to the passenger flow form parameters and the actual passenger seat opening rate; and pre-classifying the ticket amount of the train according to the type of the train.
Optionally, the apparatus for controlling the storage medium when the program runs further performs the following steps: and calculating the seating rate of the initial station, the average utilization rate of the stations along the way and the seating rate of the final station.
Optionally, the apparatus for controlling the storage medium when the program runs further performs the following steps: calculating the seating rate of the initial station, the average utilization rate of the station along the way and the seating rate of the final station according to the following formulas:
r4 ═ avg (R2), where R1 represents the train starting station occupancy, N1 represents the number of persons getting on the train starting station, M represents the train immobilizer, R2 represents the train rate of utilization of the station on the way, N2 represents the number of persons in the station on the way, R3 represents the train rate of getting off the station on the end, N3 represents the number of persons getting off the station on the end, R4 represents the average utilization of the station on the way, and avg represents the average value.
Optionally, the apparatus for controlling the storage medium when the program runs further performs the following steps: according to the followingCalculating the actual passenger seat opening rate of the train according to a formula:
where R5 represents the actual driving passenger seat ratio of the train, M represents the number of passengers carried by the train, S (i) represents the distance taken by the ith passenger, M represents the passenger of the train, and S represents the distance between the starting station and the final station of the train.
Optionally, the apparatus for controlling the storage medium when the program runs further performs the following steps: judging whether the passenger flow morphological parameters meet a first preset condition or not; judging whether the actual passenger seat opening rate meets a second preset condition or not; and if the passenger flow form parameter meets a first preset condition and the actual passenger seat opening rate meets a second preset condition, determining the type of the train as the target type.
An embodiment of the present invention provides a computer device, including a memory and a processor, where the memory is used to store information including program instructions, and the processor is used to control execution of the program instructions, and the program instructions are loaded by the processor and executed to implement the following steps: calculating passenger flow form parameters of the train; calculating the actual passenger seat opening rate of the train; determining the type of the train according to the passenger flow form parameters and the actual passenger seat opening rate; and pre-classifying the ticket amount of the train according to the type of the train.
Optionally, the program instructions when loaded and executed by the processor further implement the steps of: and calculating the seating rate of the initial station, the average utilization rate of the stations along the way and the seating rate of the final station.
Optionally, the program instructions when loaded and executed by the processor further implement the steps of: calculating the seating rate of the initial station, the average utilization rate of the station along the way and the seating rate of the final station according to the following formulas:
r4 ═ avg (R2), where R1 denotes the train origin pick-up rate, N1 denotes the number of persons on board the train origin, M denotes the train stop,r2 represents the utilization rate of the train at the waypoint, N2 represents the number of passengers in the train at the waypoint, R3 represents the rate of leaving seats at the end station of the train, N3 represents the number of passengers getting off the train at the end station, R4 represents the average utilization rate of the train at the waypoint, and avg represents the average value.
Optionally, the program instructions when loaded and executed by the processor further implement the steps of: calculating the actual driving passenger seat rate of the train according to the following formula:
where R5 represents the actual driving passenger seat ratio of the train, M represents the number of passengers carried by the train, S (i) represents the distance taken by the ith passenger, M represents the passenger of the train, and S represents the distance between the starting station and the final station of the train.
Optionally, the program instructions when loaded and executed by the processor further implement the steps of: judging whether the passenger flow form parameters meet a first preset condition or not; judging whether the actual passenger seat opening rate meets a second preset condition or not; and if the passenger flow morphological parameters meet the first preset condition and the actual passenger seat opening rate meets the second preset condition, determining the type of the train as the target type.
Fig. 3 is a schematic diagram of a computer device according to an embodiment of the present invention. As shown in fig. 3, the computer apparatus 50 of this embodiment includes: a processor 51, a memory 52, and a computer program 53 stored in the memory 52 and capable of running on the processor 51, where the computer program 53 is executed by the processor 51 to implement the terminal control method based on emotion recognition in the embodiment, and in order to avoid repetition, details are not repeated here. Alternatively, the computer program is executed by the processor 51 to implement the functions of each model/unit in the terminal control device based on emotion recognition in the embodiment, and in order to avoid repetition, the description is omitted here.
The computing device 50 may be a desktop computer, a notebook, a palm top computer, a cloud server, or other computing device. The computer device may include, but is not limited to, a processor 51, a memory 52. Those skilled in the art will appreciate that fig. 3 is merely an example of a computer device 50 and is not intended to limit the computer device 50 and that it may include more or fewer components than shown, or some components may be combined, or different components, e.g., the computer device may also include input output devices, network access devices, buses, etc.
The Processor 51 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 52 may be an internal storage unit of the computer device 50, such as a hard disk or a memory of the computer device 50. The memory 52 may also be an external storage device of the computer device 50, such as a plug-in hard disk provided on the computer device 50, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the memory 52 may also include both internal and external storage units of the computer device 50. The memory 52 is used to store computer programs and other programs and data required by the computer device. The memory 52 may also be used to temporarily store data that has been output or is to be output.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
The integrated unit implemented in the form of a software functional unit may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, a server, or a network device) or a Processor (Processor) to execute some steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A train fare pre-sorting method, characterized in that the method comprises:
calculating passenger flow form parameters of the train;
calculating the actual passenger seat opening rate of the train;
determining the type of the train according to the passenger flow form parameters and the actual passenger seat opening rate;
and pre-classifying the ticket amount of the train according to the type of the train.
2. The method of claim 1, wherein the train passenger flow shape parameters include an origin station occupancy rate, a waypoint average utilization rate, and a destination station occupancy rate of the train, and wherein the calculating the train passenger flow shape parameters includes:
and calculating the initial station seating rate, the average utilization rate of the station along the way and the final station seating rate.
3. The method of claim 2, wherein the origination station occupancy rate, the waypoint average utilization rate, and the destination station occupancy rate are calculated according to the following formulas:
R4＝avg(R2)，
wherein R1 represents the starting station seating rate of the train, N1 represents the number of persons on the starting station of the train, M represents the number of persons on the starting station of the train, R2 represents the utilization rate of the station on the way of the train, N2 represents the number of persons in the station on the way of the train, R3 represents the seating rate of the station on the end of the train, N3 represents the number of persons off the station on the end of the train, R4 represents the average utilization rate of the station on the way of the train, and avg represents the averaging value.
4. The method of claim 1, wherein the actual rate of passengers on board the train is calculated according to the formula:
wherein R5 represents an actual driving passenger seat ratio of the train, M represents the number of passengers loaded on the train, S (i) represents a distance taken by the ith passenger, M represents a member of the train, and S represents a distance between a start station and a final station of the train.
5. The method according to any one of claims 1 to 4, wherein said determining the type of said train from said traffic pattern parameter and said actual occupancy rate comprises:
judging whether the passenger flow form parameters meet a first preset condition or not;
judging whether the actual passenger seat opening rate meets a second preset condition or not;
and if the passenger flow form parameter meets the first preset condition and the actual passenger seat opening rate meets the second preset condition, determining the type of the train as a target type.
6. A train ticket amount pre-sorting device, the device comprising:
the first calculation unit is used for calculating passenger flow form parameters of the train;
the second calculating unit is used for calculating the actual passenger seat opening rate of the train;
the determining unit is used for determining the type of the train according to the passenger flow form parameters and the actual passenger seat opening rate;
and the pre-sorting unit is used for pre-sorting the ticket amount of the train according to the type of the train.
7. The apparatus according to claim 6, wherein the train passenger flow shape parameters comprise an origin station occupancy rate, a waypoint average utilization rate and a destination station occupancy rate of the train, and the first calculation unit is configured to:
and calculating the initial station seating rate, the average utilization rate of the station along the way and the final station seating rate.
8. The apparatus according to claim 7, wherein the first calculating unit is configured to calculate the origin station seating rate, the waypoint average utilization rate, and the destination station seating rate according to the following formulas:
R4＝avg(R2)，
wherein R1 represents the starting station seating rate of the train, N1 represents the number of persons on the starting station of the train, M represents the number of persons on the starting station of the train, R2 represents the utilization rate of the station on the way of the train, N2 represents the number of persons in the station on the way of the train, R3 represents the seating rate of the station on the end of the train, N3 represents the number of persons off the station on the end of the train, R4 represents the average utilization rate of the station on the way of the train, and avg represents the averaging value.
9. A storage medium, characterized in that the storage medium comprises a stored program, wherein the program, when executed, controls an apparatus in which the storage medium is located to perform the method of any one of claims 1 to 5.
10. A computer device comprising a memory for storing information including program instructions and a processor for controlling execution of the program instructions, characterized in that: the program instructions when loaded and executed by a processor implement the steps of the method of any one of claims 1 to 5.
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