CN111680707A - Card swiping data analysis method based on public transportation system, electronic terminal and storage device - Google Patents
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Abstract
The application discloses a card swiping data analysis method based on a public transport system, an electronic terminal and a storage device, wherein the analysis method comprises the steps of obtaining card swiping records of a preset route, and the card swiping records comprise card swiping time; acquiring the compact points of the card swiping time recorded by the card swiping; and matching the dense points with the station information of the preset route to obtain the corresponding relation between the dense points and the station information. By means of the method, the arrival time of the acquired route can be effectively recorded by swiping the card, and the utilization value of the data is improved.
Description
Technical Field
The application relates to the technical field of data analysis of public transport trips, in particular to a card swiping data analysis method based on a public transport system, an electronic terminal and a storage device.
Background
In recent 10 years, most urban buses and subways have adopted the NFC technology to realize electronic wallet payment and cover most of the commonly-owned passengers. Unlike cash coin and paper tickets, a large number of card swipe transactions deposit a large amount of data. The data are subjected to macroscopic statistical analysis and provided to government authorities and public transport enterprises, so that the data can help them to know the travel rule of passengers and provide auxiliary decisions for traffic management and transportation enterprise promotion services. However, the data recorded by the bus card swiping is far more valuable. The card swiping data also comprises the travel rules of a large number of users, and if the large data analysis can be performed on all card swiping records, the travel rule of each individual card holder can be obtained, so that a valuable data support is provided for commercial operation.
For vehicles such as buses, the position of the running automobile cannot be known by the vehicle-mounted card swiping device, so that the data is difficult to analyze and utilize.
Disclosure of Invention
The technical problem mainly solved by the application is to provide a card swiping data analysis method based on a public transportation system, an electronic device and a storage device, and a station corresponding to each card swiping record can be obtained from card swiping data.
In order to solve the technical problem, the application adopts a technical scheme that: the method comprises the steps of obtaining a card swiping record of a preset route, wherein the card swiping record comprises card swiping time; acquiring the compact points of the card swiping time recorded by the card swiping; and matching the dense points with the station information of the preset route to obtain the corresponding relation between the dense points and the station information.
In order to solve the above technical problem, another technical solution adopted by the present application is: the electronic terminal comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a card swiping record of a preset route, and the card swiping record comprises card swiping time; the second acquisition module is used for acquiring the dense points of the card swiping time of the preset route; and the matching module is used for matching the dense points with the station information of the preset route so as to obtain the corresponding relation between the dense points and the station information.
In order to solve the above technical problem, another technical solution adopted by the present application is: an electronic terminal is provided, which comprises a processor and a communication circuit, wherein the processor is coupled with the communication circuit; the processor is used for acquiring a card swiping record of a preset route through the communication circuit, wherein the card swiping record comprises card swiping time; the processor is used for acquiring the dense points of the card swiping time of the preset route; the processor is used for matching the dense points with the station information of the preset route to obtain the corresponding relation between the dense points and the station information.
In order to solve the above technical problem, another technical solution adopted by the present application is: there is provided an apparatus having a storage function in which program data is stored, the program data being executable to implement a method as in any one of the above.
Compared with the prior art, the beneficial effects of this application are: the method comprises the steps of obtaining a card swiping record of a preset route, obtaining dense points of the card swiping time based on the card swiping time of the card swiping record, and matching the dense points with station information of the preset route, so that the dense points correspond to the station information, and the card swiping time corresponding to the dense points is the arrival time of corresponding stations, so that the stations corresponding to the card swiping record of passengers are conveniently and quickly analyzed, the utilization rate of data resources is improved, and the cost of data analysis is saved.
Drawings
FIG. 1 is a schematic flow chart diagram of an embodiment of the analysis method of the present application;
FIG. 2 is a schematic diagram of a card-swiping record of a certain bus in an embodiment of the analysis method of the present application;
FIG. 3 is a schematic diagram illustrating distribution of user card swiping times in an embodiment of the analysis method of the present application;
FIG. 4 is a schematic flow chart illustrating a process of determining whether the number of dense points and the number of sites are equal in an embodiment of the analysis method of the present application;
FIG. 5 is a schematic flow chart illustrating a mapping relationship between dense points and sites according to an embodiment of an analysis method of the present application;
FIG. 6 is a schematic flow chart diagram of another embodiment of the analysis method of the present application;
FIG. 7 is a schematic structural diagram of an embodiment of an electronic terminal according to the present application;
FIG. 8 is a schematic structural diagram of another embodiment of an electronic terminal according to the present application;
FIG. 9 is a schematic structural diagram of an embodiment of the apparatus with storage function according to the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Through long-term research, the inventor of the application finds that the travel laws of a large number of users are contained in data recorded by swiping a card in a car. For the subway mode, card swiping is needed for the station entering and the station exiting, the card swiping machine of each station is fixed, and the station name can be preset on the card swiping machine. Therefore, the departure site and the arrival site of each user can be known through the card swiping record of the user. However, for a bus (hereinafter referred to as a bus), the vehicle-mounted card swiping device cannot know the running position of the bus, and the card swiping device is generally only responsible for deduction, so that only card swiping records exist in card swiping data, but it is not known in which station each record corresponds to the card to be swiped, and it is difficult to perform subsequent analysis by using the card swiping records to obtain the travel track of the user. Therefore, the following embodiments of the present application can solve the above problems well.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of an analysis method according to the present application. The embodiment comprises the following specific steps:
s11: and acquiring a card swiping record of the preset route, wherein the card swiping record comprises card swiping time.
In this embodiment, the preset route may refer to a planned bus route, and each stop in the route interval is fixed. The preset route can correspond to only one bus or a plurality of buses. Assuming that a part of the plurality of buses corresponding to the preset route is in the route interval of A-B and originates from the station A, and the other part originates from the station B, the departure time points of the buses originating from the station A can be different, and the departure time points of the buses originating from the station B can also be different.
In this embodiment, the obtaining of the card-swiping record of the preset route may be obtaining of a card-swiping record of one bus in the preset route, and certainly, the obtaining of the card-swiping records of multiple buses may also be separately obtained, and the obtained card-swiping records and the corresponding buses perform corresponding analysis. In this embodiment, the mixed analysis is not performed on the card-swiping records of the buses at different departure times on the same route, but of course, in other embodiments, the mixed analysis may be performed. In the embodiment, the same bus can repeatedly run for a plurality of times in the A-B interval line in one day.
Based on the foregoing, the route to be taken by the bus of the preset route is generally fixed. And obtaining the card swiping record of the preset route, namely obtaining the card swiping record of the bus corresponding to the route, wherein the preset route comprises the stop information of the bus needing to stop. The card swiping time is included in the card swiping record, and may include detailed time such as the card swiping date and the card swiping time point, for example, "09: 47:24 in 2, 19 months in 2019". In this embodiment, the card-swiping records can be screened by setting the starting date and the ending date of the card-swiping date to determine the sampling range, such as selecting to obtain the card-swiping records of a certain day, a certain month, consecutive months or years. In some embodiments, the information of the preset route may further include departure time and destination time of the corresponding bus. The departure time and the end time are used as two end points of a time axis, and the acquired card swiping time is sequentially led into the time axis according to the time sequence, so that the card swiping time can be arranged on the time axis in the time sequence.
S12: and acquiring the compact points of the card swiping time recorded by the card swiping.
And clustering the card swiping time through a clustering algorithm, so that dense points recorded by swiping the card can be obtained. In this embodiment, the dense points may be a dense range.
Collecting the card swiping records with similar card swiping times together, for example, obtaining 100 card swiping records of a certain preset route, performing cluster analysis on the card swiping times of the 100 card swiping records, collecting 20 card swiping records in a range of 6:50-7:00, collecting 30 card swiping records in a range of 7:10-7:20, collecting 40 card swiping records in a range of 7:40-7:50, and collecting 10 card swiping records in a range of 8:10-8: 30. Therefore, 6:50-7:00 can be regarded as one dense point (dense range), 7:10-7:20 as one dense point (dense range), 7:40-7:50 as one dense point (dense range), and 8:10-8:30 as one dense point (dense range).
In some embodiments, a clustering algorithm may be used that may be largely divided into four steps: and (4) preprocessing data, defining a distance function for measuring the similarity between data points, clustering or grouping, and evaluating output.
Data preprocessing, including the selection of quantity, type and scale of features, which relies on feature selection and feature extraction, the feature selection selecting important features, the feature extraction converting the input features into a new salient feature, and the data preprocessing also including the removal of outliers from the data, which often lead to biased clustering results, so that they can be culled in order to get the correct clustering.
A distance function is defined for measuring the similarity between data points, and in this embodiment, the similarity between the time points of swiping cards can be used as the distance function. Similarity is the basis for defining a class.
Clustering or grouping, the data objects are grouped into different classes according to different methods. Wherein the method may comprise a partitioning method. The partitioning method generally starts with an initial partitioning and optimization of a clustering criterion. The dividing method generally comprises two technical means of clear clustering and fuzzy clustering: clearly clustering, each data belonging to a separate class; fuzzy clustering: each data may be in any one class, and the partition method clustering is to generate a nested partition series based on a certain standard, so that the similarity between different classes can be measured. In this embodiment, the card swiping data can be divided into different dense points by applying a dividing method.
And evaluating the output for verifying the correctness of the clustering method. In this embodiment, the result of clustering division is verified, and if the verification fails, clustering operation is performed again until a correct clustering result is obtained.
Through the clustering algorithm, the card swiping time data recorded by swiping the card can be processed. Referring to fig. 2, fig. 2 is a schematic diagram of a card swiping record of a certain bus in the embodiment. In the route shown in fig. 2, the preset route has a time axis of 7:00-7:48, and the card swiping times of the preset route are clustered to obtain dense points of the card swiping times, which are respectively concentrated near 7:00, 7:10, 7:21, 7:33 and 7: 48.
S13: and matching the dense points with the station information of the preset route to obtain the corresponding relation between the dense points and the station information.
In a specific application scenario, for a bus, generally, after a bus arrives at a station, a passenger is allowed to get on the bus and swipe a card to form a card swiping record, so that card swiping records within a certain period of time are obtained, and card swiping time is used for clustering to obtain dense points, which can be regarded as time points when the passenger takes the bus and swipes the card intensively.
Since the station information of the preset route is determined, the number of stations is also determined. For example, if a certain route passes through 'A general station-B company-C way-D market-E station', dense points are obtained and then matched with site information of a preset route, the corresponding relation between the dense points and the site information can be obtained. For example, as shown in fig. 2, if the card swiping record is calculated and analyzed to obtain dense spots of 7:00 vicinity, 7:10 vicinity, 7:21 vicinity, 7:33 vicinity and 7:48 vicinity, the dense spots are matched with the site information to obtain a total station a near 7:00, a company B near 7:10, a route C near 7:21, a department store D near 7:33 and a station E near 7: 48.
Even if the bus runs back and forth for multiple times, the total number of passing stations is always fixed, and the dense points which run repeatedly for multiple times are matched, the corresponding relation between the dense points and the station information can still be established.
In the embodiment, the card swiping record of the preset route is obtained, the dense points of the card swiping time are obtained based on the card swiping time of the card swiping record, and the dense points are matched with the station information of the preset route, so that the dense points correspond to the station information, and the card swiping time corresponding to the dense points is the arrival time of the corresponding station, so that the card swiping time of a passenger can be effectively known to be the corresponding station, the utilization rate of data resources is improved, and the cost of data analysis is saved.
Based on an embodiment of the analysis method of the present application, S13 further specifically includes:
alternatively, S23: and judging whether the number of the dense points is equal to the number of the sites in the site information.
Since the number of sites in the site information of the preset route is fixed, the number of dense points needs to be judged. In order to match the number of sites. Referring to fig. 4, fig. 4 is a schematic flow chart illustrating a process of determining whether the number of dense points and the number of sites are equal in an embodiment of the analysis method of the present application.
S231: and if so, mapping the dense points and the sites in the site information one by one according to the time sequence to obtain the corresponding relation of the dense points and the site information.
For a certain bus of a preset route, departure time and ending time are known, and a time sequence, namely a time axis, is formed from the departure time to the ending time. It is also time-sequential for card swiping records.
If the number of the dense points is equal to the site data, mapping the dense points and the site information one by one according to the time sequence, and establishing the corresponding relation between the dense points and the site information, so as to obtain the arrival time point or time range of each site.
For convenience of understanding, as shown in fig. 2, it can be seen from fig. 2 that dense points are mapped one by one with stations in the station information. It can be seen from the figure that after dense points and stations are mapped one by one, the boarding distribution conditions of a large number of users at each station can be obtained, the number of boarding persons at which stations is large, the number of boarding persons at which stations is small, and the like.
In another embodiment, S232: if the number of the dense points is not equal to the number of the sites in the site information, more card swiping records of the preset route can be obtained, and then the execution is returned to judge whether the number of the dense points is equal to the number of the sites in the site information.
In the preset route, card swiping records of a selected certain time range may exist, and card swiping records are not recorded at certain sites. For example, some stations are far away, no person swipes a card to get on the bus within a certain period of time, or the number of people swipes the card is extremely small, the card swiping time does not form dense points, and the obtained dense points cannot correspond to the number of the stations.
Under the condition, more card swiping records can be obtained and then clustering operation is carried out, for example, the time span range and/or the number of vehicles for obtaining the card swiping records are expanded, the card swiping records of the bus preset route in 1 day originally are expanded to be 1 week, or the card swiping records of the bus preset route in 1 bus originally are expanded to be 5 buses and the like.
In this embodiment, part of the sites cannot form dense points due to the absence of card swiping records or too few card swiping records, so that the number of the dense points is not equal to the number of the sites, and more card swiping records can be obtained by expanding the time span range, specifically including the following steps:
s2321: and calculating to obtain the time span range of the card swiping time.
The card swiping data in the vehicle-mounted card swiping equipment is complex, in order to effectively mine the sediment data, the card swiping records need to be sorted, and the time span range of the card swiping time is calculated, wherein the time span range can be understood as the date range of the card swiping records, for example, a batch of card swiping times is sorted and calculated, the earliest card swiping date and the latest card swiping date in the batch of card swiping times are obtained, and thus the range of the batch of card swiping times is obtained. For example, if the earliest card swiping date in the card swiping records of a certain batch is 1 month and 5, and the latest card swiping time is 3 months and 29, the card swiping times of the batch are 1 month to 3 months.
S2322: and judging whether the time span range is larger than a preset time span range or not.
The preset time span range can be set according to the number of the dense points, when the number of the dense points is not equal to the number of the sites, and some sites cannot form the dense points due to too few card swiping records, the span range of the preset time needs to be properly expanded, so that the data form enough dense points. And then judging whether the card swiping time span range of the selected batch is larger than a preset time span range. For example, the preset time span range is set to 1 month-6 months.
S2323: and if not, acquiring the card swiping record within the preset time span range.
Judging whether the calculated time span range is larger than a preset time span range or not, and if not, acquiring a card swiping record in the preset time span range; if so, returning to step S2321 to calculate the time span range of another batch of card swiping time. In an example, the time span range of the card swiping time of the batch is 1 month to 3 months, the preset time span range is 1 month to 6 months, and the time span range of the card swiping time of the batch is smaller than the preset time span range, so that the card swiping record of the batch can be obtained.
In the above embodiment, by setting the preset time span range and adjusting the sample size of the card swiping data, when the number of samples is too small and the number of dense points equal to the number of sites cannot be obtained, the range of the preset time can be properly expanded, so that more samples are obtained for clustering operation, and enough dense points are obtained.
In other embodiments, if the number of dense points is not equal to the number of sites in the site information, the mapping relationship between the dense points and the sites may also be established by calculating the estimated time. Referring to fig. 5, fig. 5 is a schematic flow chart illustrating the process of mapping dense points to sites in the embodiment. The method comprises the following specific steps:
s51: and calculating the estimated arrival time of the station of the preset route.
The driving route of the bus is a fixed preset route, so that the arrival time of each stop can be estimated. And calculating to obtain the estimated arrival time of each station in the preset route.
S52: and matching the estimated station time with the time corresponding to the dense point.
And matching the estimated station time with the time corresponding to the dense points, wherein the time of the dense points can be selected from the time points recorded by swiping any card in the dense points. In order to make the time more accurate, the time point recorded by any card swiping in the central area in the dense point can be selected as the corresponding time of the dense point.
S53: and when the difference value between the estimated station time and the time corresponding to the dense point is within the preset time threshold, establishing a mapping relation between the dense point and the station.
And when the estimated time difference value between the station time and the dense point is within the preset time threshold, establishing a mapping relation between the dense point and the station. The preset time threshold can be adjusted automatically according to actual conditions and can be set to be 3 minutes. For example, 3 dense points are obtained in the preset route, the corresponding time is 7:08, 7:32 and 7:43 respectively, the total number of stations of the preset route is 4, the departure time is 7:00, and the estimated time of arriving 4 stations is 7:10 for station 1, 7:20 for station 2, 7:30 for station 3 and 7:40 for station 4 respectively. According to the mapping relation, the estimated station time of the station 7:08 is close to that of the station 1, so that the first dense point corresponds to the station 1, and so on, the second dense point corresponds to the station 3, and the third dense point corresponds to the station 4. Station number 2 has no corresponding dense spots.
By the method, when the number of the dense points is not consistent with the number of the sites of the site information, the mapping relation between the dense points and the sites can be established by estimating the site time.
In the two embodiments, two mapping methods for the case that the number of processing intensive points is not consistent with the number of sites are provided, and in some embodiments, the two methods may be used alternatively or cooperatively.
The application provides an analysis method of card swiping data based on a bus system, which can be used for analyzing the distribution situation of the number of passengers getting on the bus at each stop of the bus with a preset route and analyzing the travel habits of each user. Referring to fig. 2, fig. 3 and fig. 6, fig. 6 is a schematic flowchart of another embodiment of the card swiping data analysis method of the present application, and the embodiment includes the following specific steps:
s61: and acquiring a card swiping record of the preset card identification on the preset route.
The preset card identifier is, for example, a card number corresponding to a physical card, or a card number of a virtual card (for example, in the form of an application program, a wechat applet, nfc, or the like).
The card swiping record of the card corresponding to the preset card identifier on the preset route refers to the card swiping record of the card corresponding to the preset card identifier on at least part of the buses on the preset route. In this embodiment, the card swiping records of the preset card identifiers on all buses of the preset route may be acquired.
S62: and acquiring the dense points of the card swiping time recorded by the preset card identifier (hereinafter referred to as dense points of the preset card identifier).
And carrying out cluster analysis on the card swiping time recorded by the card swiping of the preset card identification so as to obtain dense points of the preset card identification.
S63: and corresponding the dense points of the preset card identification with the dense points of the card swiping record of the preset route, thereby obtaining the corresponding relation between the dense points of the preset card identification and the site information.
The steps of S61-S63 similar to those of S11-S13 are not repeated here. The card swiping records of the preset card identification on the whole preset route are obtained, and the dense points of the card swiping records of the card identification can be obtained according to the analysis method, so that the dense points of the card swiping records of the card identification are matched with the corresponding relation between the dense points of the card swiping records of the preset route and the station stations, the station and the card swiping time of the card swiping of the preset card identification on the preset route can be obtained, and the station and the time of the user going out are also known.
Fig. 3 shows the dense points of the card swiping records of a certain preset card identification on the preset route. For example, the card-swiping record of the user corresponding to a certain preset card identifier in a certain period of time on a preset route, for example, the card-swiping record in half a year. As shown in fig. 3, for example, there are three dense spots, which are near 07:30, near 18:30, and near 21:30, respectively. One possibility is therefore to conclude that the user has a daily commute time of around 07:30, a daily off-duty time of around 18:30 and a daily off-duty time of around 21: 30. Of course, the embodiment is not limited to this possibility, and there may be many possibilities, for example, the user is going to work at night, or the user is going to school, etc.
In conjunction with fig. 2, fig. 2 shows the correspondence between the card swiping record and the station information on the preset route. In a specific application scenario, the user of the preset card identifier is about 07:30 in the morning, which corresponds to the mapping in fig. 2, which is about swiping a card by company B, and it can be inferred that the user frequently gets on the car at the time point and the station, which may be one of the commuting locations of the target user.
Fig. 2 shows only a part of the travel time and the station of the preset route in one travel direction. In this embodiment, the preset route may include a plurality of buses, and each bus runs a complete one-way trip or a total trip composed of two-way trips back and forth many times, so that the correspondence between the dense point and the station information recorded by swiping the card can be obtained by the method of the embodiment of the present application. The user in fig. 3 swipes his card at 16:30, for example, corresponding to a station on way H (e.g., toward the destination station, station a) not shown in fig. 2. At this point, it can be considered that the H-way can be regarded as another commuting place of the corresponding user for the card identification.
In this embodiment, the commute location may refer to a user's place of residence, place of work, place of school, etc., or other locations that are frequently commuted. Generally, a station going out in the morning is a station of a residential place, a station going out in the evening is a station of a working place, so that the address of a target user is estimated to be near a general station A, and the conventional working riding time is 07: 00; the work site of the target user is near station E and the regular off-duty ride time is 18: 00.
By the method, the card swiping records of the preset card identifications are matched and corresponded with the corresponding relations of the dense points and the site information based on the card swiping records of the preset route, so that the daily travel track of each user, such as the commuting time, the residence place and the commuting place, can be obtained. In addition, users with the same card identification in the commuting place can be marked as users of the same type, and then subsequent data analysis can be carried out. For example, the user who lives at a certain site is mostly on which place to work; meanwhile, the users who work near a certain station mostly live in which place, and the like, and the type division of the users can provide good data support for the public transportation planning of the city.
In other embodiments, the user may have a transfer. The preset route includes a first preset and a second preset route. Wherein at least one of the first predetermined route and the second predetermined route is the same or similar. The sites are identical, namely the names of the sites are completely identical; sites that are close, although named differently, are all in the same area, e.g., "A Garden site" and "A garden west site" are considered similar sites.
Card swiping records of the first preset route and the second preset route are obtained respectively, and the card swiping records comprise card swiping time.
And respectively obtaining the card swiping time dense points corresponding to the card swiping records of the first preset route and the second card swiping record.
And respectively matching the dense points of the first preset route and the second preset route with the station information of the first preset route and the second preset route to respectively obtain the corresponding relation between the dense points and the station information.
In other words, the relationship between the station information of the first preset route and the dense points of the card swiping time and the relationship between the second preset route and the dense points of the card swiping time can be obtained.
The method comprises the following specific steps:
s701: acquiring a first card swiping record of a preset card number on a first preset route.
S702: and acquiring a second card swiping record of the preset card number on a second preset route.
The first preset route and the second preset route have at least one station which is the same or similar.
S703: and acquiring the dense points of the card swiping time of the first card swiping record and the second card swiping record respectively.
And corresponding the dense points of the card swiping time of the first card swiping record with the dense points of the card swiping time of the first preset route to obtain the stations corresponding to the dense points of the card swiping time of the first card swiping record, and then obtaining the first boarding station of the user according to the dense points of the card swiping records of the preset card identifiers. And corresponding the dense points of the card swiping time of the second card swiping record with the dense points of the card swiping time of the second preset route to obtain the stations corresponding to the dense points of the card swiping time of the second card swiping record, and then obtaining the second boarding station of the user according to the dense points of the card swiping records of the preset card identifiers. The first boarding station and the second boarding station are different and dissimilar stations and are commuting stations. Thus, the commuting place, the transfer place and the like of the transfer user can be known.
In the above embodiment, the user transfer is assumed, and the residence place and the work place of the user can be analyzed more accurately. The specific method is to compare two card swiping records with preset card number, wherein the two card swiping records have the preset card number, and whether the card swiping record of the transfer station exists between the two preset routes or not is judged. If yes, judging that transfer exists. In the above embodiment, transfer of a user on two routes is described, in other embodiments, the user may also have transfer on three or more routes, and a specific manner is to compare whether the same station exists in the routes, and the detailed steps are similar to those in the above embodiment and are not described again.
Referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic terminal according to an embodiment of the present application. The electronic terminal 700 comprises a first acquisition module 71, a second acquisition module 72 and a matching module 73. The first obtaining module 71 is configured to obtain a card swiping record of the preset route, where the card swiping record includes card swiping time; the second obtaining module 72 is configured to obtain dense points of the card swiping time of the preset route; the matching module 73 is configured to match the dense point with the station information of the preset route, so as to obtain a corresponding relationship between the dense point and the station information.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 modules may be selected according to actual needs to achieve the purpose of the embodiment.
In addition, each functional module in this embodiment may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
In this embodiment, reference may be made to an embodiment of the analysis method of the present application for more functions of each module of the electronic terminal, which is not described herein again.
Referring to fig. 8, fig. 8 is a schematic structural diagram of another electronic terminal according to an embodiment of the present application. The electronic terminal 800 comprises a processor 81 and a communication circuit 82, the processor 81 being coupled to the communication circuit 82; the processor 81 is used for acquiring a card swiping record of a preset route through the communication circuit 82, wherein the card swiping record comprises card swiping time; the processor 81 is used for acquiring the dense points of the card swiping time of the preset route; the processor 81 is configured to match the dense point with the station information of the preset route to obtain a corresponding relationship between the dense point and the station information.
In the present embodiment, the processor 81 may also be referred to as a CPU (Central Processing Unit). The processor 81 may be an integrated circuit chip having signal processing capabilities. Processor 81 may also be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
For further execution processes of the processor 81 of the electronic terminal in this embodiment, reference may be made to the above-mentioned one embodiment and another embodiment of the analysis method of the present application, which are not described herein again.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an embodiment of a device with a storage function according to the present application, wherein program data 91 is stored in the device 900, and the program data 91 is executed by a processor to implement the analysis method, and the principle and steps of the analysis method are described in detail and are not repeated herein. Further, the apparatus may also be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic tape, or a various media such as an optical disk, which can store program codes.
The application provides an analysis method of card swiping data based on a public transport system, through the method, the travel track, the residence place and the working place of each user can be accurately calculated, the information is combined with other commercialized information, very large commercial value can be generated, meanwhile, the information is correlated with other personal information of the card holding user and then analyzed, the government is facilitated to master the characteristics of the standing population of each area, the government can be helped to better provide service for citizens, and public resources are allocated more reasonably and efficiently.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made by the contents of the specification and the drawings of the present application or are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. A card swiping data analysis method based on a public transportation system is characterized by comprising the following steps:
obtaining a card swiping record of a preset route, wherein the card swiping record comprises card swiping time;
acquiring the compact points of the card swiping time recorded by the card swiping;
and matching the dense point with the station information of the preset route to obtain the corresponding relation between the dense point and the station information.
2. The method of claim 1, wherein: the matching the dense point with the station information of the preset route to obtain the corresponding relation between the dense point and the station information comprises:
judging whether the number of the dense points is equal to the number of the sites in the site information;
and if so, mapping the dense points and the sites in the site information one by one in a time sequence to obtain the corresponding relation between the dense points and the site information.
3. The method of claim 2, wherein: and if not, acquiring more card swiping records of the preset route, and returning to execute the judgment of whether the quantity of the intensive points is equal to the quantity of the sites in the site information.
4. The method of claim 3, wherein: the obtaining of more card swiping records of the preset route comprises:
calculating the time span range of the card swiping time;
judging whether the time span range is larger than a preset time span range or not;
and if not, acquiring the card swiping record within the preset time span range.
5. The method of claim 2, wherein: if not, calculating to obtain the estimated arrival time of the station of the preset route;
matching the estimated arrival time with the time corresponding to the intensive point;
and when the difference value between the estimated station arrival time and the time corresponding to the dense point is within a preset time threshold, establishing a mapping relation between the dense point and the station.
6. The method of claim 1, wherein obtaining a swipe record of the preset route comprises:
acquiring a card swiping record of a preset card identifier on the preset route;
the step of matching the dense point with the station information of the preset route to obtain the corresponding relationship between the dense point and the station information includes:
and marking the site corresponding to the dense point as the commuting place of the user corresponding to the card identifier.
7. The method of claim 6, wherein after marking the sites corresponding to the dense points as commuting locations of the corresponding users of the card identification:
and marking users with the same card identification in the commuting place as users of the same type.
8. An electronic terminal, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a card swiping record of a preset route, and the card swiping record comprises card swiping time;
the second acquisition module is used for acquiring the dense points of the card swiping time of the preset route;
and the matching module is used for matching the dense point with the station information of the preset route so as to obtain the corresponding relation between the dense point and the station information.
9. An electronic terminal comprising a processor and communication circuitry, the processor coupled to the communication circuitry;
the processor is used for acquiring a card swiping record of a preset route through the communication circuit, wherein the card swiping record comprises card swiping time;
the processor is used for acquiring the dense points of the card swiping time of the preset route;
the processor is used for matching the dense point with the station information of the preset route to obtain the corresponding relation between the dense point and the station information.
10. An apparatus having a storage function, characterized in that program data are stored, which program data can be executed to implement the method according to any one of claims 1-7.
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| CN201910181158.6A CN111680707A (en) | 2019-03-11 | 2019-03-11 | Card swiping data analysis method based on public transportation system, electronic terminal and storage device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114038225A (en) * | 2021-11-08 | 2022-02-11 | 深圳市雪球科技有限公司 | Bus route data processing method and device and electronic equipment |
| CN114035580A (en) * | 2021-11-15 | 2022-02-11 | 珠海格力智能装备有限公司 | AGV control method and device, storage medium, processor and AGV equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101540098A (en) * | 2009-04-30 | 2009-09-23 | 北京工业大学 | Matching method based on public transport IC card lines |
| CN101615207A (en) * | 2009-07-10 | 2009-12-30 | 重庆大学 | A kind of method of obtaining bus stations with bus-waiting and bus-IC-card-holding passengers |
| CN102156732A (en) * | 2011-04-11 | 2011-08-17 | 北京工业大学 | Bus IC card data stop matching method based on characteristic stop |
| CN108242146A (en) * | 2016-12-27 | 2018-07-03 | 北京亿阳信通科技有限公司 | Based on mass transit card data analysis passenger ride website it is timely between method and system |
-
2019
- 2019-03-11 CN CN201910181158.6A patent/CN111680707A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101540098A (en) * | 2009-04-30 | 2009-09-23 | 北京工业大学 | Matching method based on public transport IC card lines |
| CN101615207A (en) * | 2009-07-10 | 2009-12-30 | 重庆大学 | A kind of method of obtaining bus stations with bus-waiting and bus-IC-card-holding passengers |
| CN102156732A (en) * | 2011-04-11 | 2011-08-17 | 北京工业大学 | Bus IC card data stop matching method based on characteristic stop |
| CN108242146A (en) * | 2016-12-27 | 2018-07-03 | 北京亿阳信通科技有限公司 | Based on mass transit card data analysis passenger ride website it is timely between method and system |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114038225A (en) * | 2021-11-08 | 2022-02-11 | 深圳市雪球科技有限公司 | Bus route data processing method and device and electronic equipment |
| CN114035580A (en) * | 2021-11-15 | 2022-02-11 | 珠海格力智能装备有限公司 | AGV control method and device, storage medium, processor and AGV equipment |
| CN114035580B (en) * | 2021-11-15 | 2024-04-19 | 珠海格力智能装备有限公司 | AGV control method, AGV control device, storage medium, processor and AGV equipment |
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