WO2021232962A1 - Target site prediction method and apparatus, and electronic device and storage medium - Google Patents
Target site prediction method and apparatus, and electronic device and storage medium Download PDFInfo
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Definitions
- This application relates to the technical field of electronic equipment, and more specifically, to a method, device, electronic equipment, and storage medium for predicting a target site.
- this application proposes a method, device, electronic equipment, and storage medium for predicting a target site, which can improve the above-mentioned problems.
- an embodiment of the present application provides a method for predicting a destination station.
- the method includes: acquiring current station entry information, where the current station station information includes the current station station time and the current station station; and according to historical ride records, Obtain the target historical entry point matching the current pit stop time; when the current entry point is the same as the target historical entry point, according to the historical ride record, the current pit stop time and the current entry point Predict the destination station; when the current inbound station is different from the target historical inbound station, predict the destination station based on the historical ride records and the current inbound station.
- an embodiment of the present application provides a device for predicting a destination site.
- the device includes: a current inbound acquisition module, configured to obtain current inbound information, where the current inbound information includes the current inbound time and the current inbound time. Inbound; the target inbound acquisition module is used to obtain the target historical inbound station that matches the current inbound time according to the historical ride records; the first prediction module is used to when the current inbound station and the target historical inbound When the stations are the same, predict the destination station based on the historical ride record, the current station time and the current station; the second prediction module is used when the current station and the target historical station are not the same , Predict the destination station based on the historical ride records and the current inbound station.
- an embodiment of the present application provides an electronic device, including: one or more processors; a memory; one or more application programs, wherein the one or more application programs are stored in the memory and It is configured to be executed by the one or more processors, and the one or more application programs are configured to execute the method for predicting the target site provided in the first aspect described above.
- an embodiment of the present application provides a computer-readable storage medium.
- the computer-readable storage medium stores program code, and the program code can be invoked by a processor to perform the purpose provided in the first aspect.
- the prediction method of the site is not limited to, but not limited to, but not limited to, but not limited to, but not limited to, but not limited to, but not limited to, but not limited to, but not limited to, but not limited to, but not limited to, the program code, and the program code can be invoked by a processor to perform the purpose provided in the first aspect.
- the prediction method of the site is not limited to perform the site.
- Fig. 1 shows a flowchart of a method for predicting a destination site according to an embodiment of the present application.
- Figure 2 shows a schematic diagram of a database cloud architecture according to the present application.
- Fig. 3 shows a flow chart of a method for predicting a destination site according to another embodiment of the present application.
- Fig. 4 shows a flowchart of step S220 in the method for predicting a destination site according to another embodiment of the present application.
- Fig. 5 shows another flow chart of step S240 in the method for predicting a destination site according to another embodiment of the present application.
- Fig. 6 shows a flowchart of a method for predicting a destination site according to another embodiment of the present application.
- Fig. 7 shows another flow chart of step S330 in the method for predicting a destination site according to another embodiment of the present application.
- Fig. 8 shows a flow chart of a method for predicting a destination site according to still another embodiment of the present application.
- Fig. 9 shows a schematic diagram of an application scenario according to the present application.
- Fig. 10 shows a flowchart of a method for predicting a destination site according to still another embodiment of the present application.
- Fig. 11 shows a schematic diagram of the coverage area of a base station according to the present application.
- Fig. 12 shows a schematic diagram of the overall flow of a base station positioning method according to the present application.
- FIG. 13 shows a schematic block diagram of the overall flow of a method for predicting a destination site according to an embodiment of the present application.
- Fig. 14 shows a block diagram of a device for predicting a destination site according to an embodiment of the present application.
- FIG. 15 is a block diagram of an electronic device for executing the method for predicting a destination site according to an embodiment of the present application according to an embodiment of the present application.
- Fig. 16 is a storage unit for storing or carrying program codes for realizing the prediction method of the destination site according to the embodiment of the present application according to an embodiment of the present application.
- the destination prediction method is usually based on a fixed device or other specific applications to obtain user data for prediction and estimation. It involves sensitive information such as real-time location tracking of the user. In addition, the user needs to provide clear departure and destination information to be effective.
- the inventor has discovered through long-term research that the existing destination prediction method is usually to make simple statistics on the user's historical destinations, and sort the use times of the user's historical destinations in a certain time in descending order, and select The most frequently used historical destination is used as the predicted travel destination; or the last use time of the user's historical destination is sorted from nearest to farthest, and the most recently used historical destination is selected as the predicted travel destination.
- the existing destination prediction methods do not consider the impact of the user's travel time and the user's place of departure on the destination prediction, resulting in inaccurate prediction results, and there is no technical barrier or leading edge.
- the data acquisition in existing prediction methods includes base station, WiFi (Wireless Fidelity), Bluetooth, GPS (Global Positioning System, Global Positioning System) location data, time data, and vehicle id (Identity document, unique identification) Number) and so on, more dependent on user interaction.
- WiFi Wireless Fidelity
- GPS Global Positioning System, Global Positioning System
- time data time data
- vehicle id Identity document, unique identification
- GPS cannot cover all corners of the environment, and its accuracy is limited.
- WiFi and Bluetooth information rely on external equipment, which is costly and unfriendly to the outdoor environment.
- the existing destination prediction schemes usually only target users such as cars, and few or fewer provide corresponding services for users who take subways.
- the inventor has discovered and proposed the method, device, electronic equipment, and storage medium for the prediction of the destination site provided in the embodiments of this application. If the time forecast is not very accurate, you can directly forecast based on the current entry site. Improve the accuracy of the destination site prediction, making the predicted destination more in line with the actual needs of users.
- the prediction method of the destination site in this application is also applicable to subway scenes. The specific target site prediction method will be described in detail in the subsequent embodiments.
- FIG. 1 shows a schematic flowchart of a method for predicting a destination site according to an embodiment of the present application.
- the prediction method can be applied to electronic equipment.
- the electronic device can be a mobile terminal, so that the prediction method can be executed locally; the electronic device can also be a server, so that the prediction method can be executed in the cloud.
- the prediction method of the destination site can be applied to the prediction device 700 of the destination site as shown in FIG. 14 and the electronic equipment configured with the prediction device 700 of the destination site (FIG. 15 ).
- the prediction method of the destination site shown may specifically include the following steps:
- Step S110 Obtain current inbound information, where the current inbound information includes the current inbound time and the current inbound station.
- the electronic device may obtain the user's current inbound information, so as to predict the destination site based on the current inbound information.
- the current inbound information may include the current inbound time and the current inbound station.
- the user's current inbound information can be obtained in multiple ways.
- the user’s current entry information can be identified by swiping identification, NFC (Near Field Communication) identification, GPS identification of the inbound position, and subway driving identification.
- NFC Near Field Communication
- a preset application may be installed on the mobile terminal.
- the preset application can identify the name, route, city, and time of the station entered.
- the preset application can be configured to be suitable for running in a preset traffic scene, and the preset traffic scene can be any traffic scene such as subway, bus, high-speed rail, train, and airplane.
- the preset application may be an application program such as a ride application, a payment application, etc., which is not limited in the embodiment of the present application.
- the preset application may be a bus ride application, which is suitable for running in a bus scene.
- the bus ride application can be used to generate a ride logo for users to swipe the code to ride the bus.
- the name of the site, route, city, and time of the user's boarding can be identified through the bus ride application.
- the preset application can also be a payment application.
- the payment application can be configured with multiple e-cards.
- the e-card can be a radio frequency card simulated based on a traffic card and can support NFC payment.
- the mobile terminal can be built-in
- the NFC module can interact with the NFC reading device based on the NFC module, so that when the user takes a transportation, the user can move the mobile terminal close to the NFC reading device, and the NFC reading device can read the configuration of the payment application The transportation card to make the payment.
- the mobile terminal can obtain the inbound information according to the payment status of the payment application.
- an inbound identification module may be configured on the mobile terminal, and the inbound identification module may be embedded in the android system.
- the inbound identification module may be configured on the mobile terminal, and the inbound identification module may be embedded in the android system.
- the code for example, the Shanghai Metro is a metropolis, Shenzhen has a WeChat applet, Shenzhen Metro, and Beijing uses Yitongxing.
- the mobile terminal will pop up the successful entry, the entry time and the entry site on the mobile terminal.
- the inbound recognition module configured on the mobile terminal can recognize the changes on the screen of the mobile phone to obtain the current inbound information including the inbound time and the inbound station.
- the current inbound information can be stored and recorded, so as to continuously update the database content storing historical ride records, so that more accurate updates can be made based on the continuously updated database content.
- Step S120 According to the historical ride records, obtain the target historical entry point matching the current entry time.
- the user's inbound time may be unstable and fluctuate for a period of time, and sometimes may not be within the long-term inbound time that users are accustomed to. In this case, if combined Predicting the destination site at the current stop time may cause the predicted destination site to be inaccurate and deviate from the actual needs of users.
- the current inbound time can be first predicted according to the current inbound time, so as to determine whether to perform the destination site based on the current inbound time according to the predicted result of the inbound station. predict.
- the target historical entry point matching the current entry time can be obtained.
- the target historical entry site can be understood as the predicted entry site after long-term independent learning of historical ride records.
- the target historical entry point that matches the current entry time based on multiple historical entry times and multiple historical entry points in the long-term historical ride record.
- the matched target historical entry site that is, the predicted current entry site.
- the target historical inbound time that matches the current inbound time may be the same historical inbound time as the current inbound time, or it may be the closest historical inbound time to the current inbound time.
- the current pit-stop time is 8:45 am
- the target historical pit-stop time that matches the current pit-stop time can be the closest 9 am from 8:45 am
- the historical date can be 9 am
- the ride record of the station is extracted, and the entry site entered at 9 am in the ride record is used as the target historical entry site, that is, the predicted current entry site.
- the entry and exit station of the work travel route corresponding to 8 a.m. on weekdays is likely to be different from the entry station of the entertainment travel route corresponding to 8 a.m. on weekends. Therefore, in some embodiments, when there are multiple historical entry sites corresponding to the target historical entry time, the most likely historical entry site can be selected from them as the target historical entry site. As a way, it is possible to select the historical inbound stop with the highest number of occurrences from the multiple historical ride records corresponding to the target historical inbound time as the target historical inbound stop.
- a database may be established to record and store the outbound information and inbound information of the user from the current date on each trip in the past.
- the database can be stored locally or in the cloud, which is not limited here.
- FIG. 2 shows a cloud storage architecture of a subway habit database.
- this storage architecture automatic acquisition and storage of ride records can be realized.
- the specific functions of each module of the architecture are as follows:
- Station recognition module 201 Used to recognize when users enter a subway station. When a user enters a subway station with a mobile phone, he can recognize the name, line, city, and time of entering the subway station through the application.
- Outbound module 202 Used to identify the user when leaving the subway station. When the user leaves the subway station by swiping the code with the mobile phone, the name, line, city, and time of leaving the subway station can be identified through the application.
- Data cloud storage module 203 used for data storage.
- the station entry information the user can scan the code to enter the subway station can be detected
- the corresponding information can be stored, such as the name, line, city and time of the subway station, etc. information.
- the outbound information can be paired with the detected inbound information, it will be stored directly; if the inbound information and outbound information cannot be paired, they will be stored separately. Among them, it can be judged whether the inbound and outbound information matches by judging whether the brush code inbound and outbound appear in pairs, and whether the difference between the used time and the subway running time is less than a certain threshold (which can be preset).
- the locally stored data can be uploaded to the cloud database according to the cloud collaboration strategy.
- Database module 204 an information database used to store the user's subway ride history.
- the content of the database may include information such as location (city), event (inbound, outbound), time (UNIX, accurate to the second).
- the historical ride record may include inbound information and outbound information. Since there may be cases where the inbound information or the outbound information is not recognized and acquired, the historical ride records may also include only the inbound information and only the outbound information.
- the ride record can be stored as shown in Table 1.
- the historical ride records may include information such as city, inbound, outbound, and time (UNIX, accurate to the second). ‘*’ means that the information is empty, and each line represents a matching entry and exit information.
- Step S130 Determine whether the current incoming site and the target historical incoming site are the same. If yes, step S140 can be executed; if not, step S150 can be executed.
- Step S140 Predict the destination station based on the historical ride record, the current station arrival time, and the current station station.
- Step S150 Predict the destination station based on the historical ride records and the current inbound station.
- the destination station after obtaining the target historical inbound site matching the current inbound time, it can be determined whether the current inbound site and the target historical inbound site in the acquired current inbound information are the same. And when the current station is the same as the target historical station, the destination station can be predicted based on the historical ride records, the current station time and the current station. When the current station is different from the target historical station, the destination station can be predicted based on the historical ride records and the current station. The current station time does not participate in the prediction of the destination station.
- the current inbound station that is, the target historical inbound station
- the actual current inbound station that is, the current inbound station in the current inbound information obtained above
- the current pit-stop time may not be the pit-stop time that the user is accustomed to for a long time at the current station, and the current pit-stop time is not suitable for participating in the prediction of the destination station based on habit. Forcible participation may lead to inaccurate prediction results and deviate from the actual needs of users. . Therefore, when the current incoming site is different from the target historical incoming site, the destination site can be directly predicted based on the current incoming site, which effectively guarantees the accuracy of the prediction result.
- the current inbound time predicted based on the current inbound time is the same as the actual current inbound station, it can indicate that the current inbound time may be the user’s long-term habit of entering the site in the current period, and the current inbound time is suitable for participation based on The prediction of the habitual destination site. Therefore, when the current station is the same as the target historical station, the destination station can be predicted based on the historical ride records, the current station time and the current station, which effectively guarantees the accuracy of the prediction result.
- predicting the destination station based on historical ride records, current pit stop time, and current pit stop may be to obtain one or more ride records matching the current pit stop time and current pit stop from the historical ride record, according to The outbound information in the one or more ride records predicts the target station. As a way, it may be to select the outbound station with the highest occurrence from the outbound information in one or more ride records as the predicted target station.
- the specific method of predicting the destination station based on historical ride records, current arrival time, and current arrival station may not be limited in this application. For example, a habit-based Markov prediction model for the target site can be established, and the Markov method can be used to predict the time point of entering and leaving the station and the probability of entering and leaving the station.
- predicting the destination station based on the historical ride records and the current station entry may be to obtain one or more ride records matching the current station entry from the historical ride records, and according to the outbound station in the one or more ride records Station information, predict the target station. As a way, it may be to select the outbound station with the highest occurrence from the outbound information in one or more ride records as the predicted target station.
- the specific method of predicting the destination station based on the historical ride records and the current station entry is not limited in this application. For example, a habit-based Markov prediction model for the target site can be established, and the probability of entering and leaving the site can be predicted by applying the Markov method.
- the method for predicting the destination station obtains the current station information, which can include the current station time and the current station, and then obtains the target that matches the current station time according to the historical ride records Historical entry site, to predict the destination site based on historical ride records, current entry time, and current entry site when the current entry site is the same as the target historical entry site, and when the current entry site is different from the target historical entry site ,
- the destination station can be predicted based on the historical ride records and the current station.
- the forecast of the entry station based on the current pit stop time is accurate, the forecast can be based on the current pit stop time and the current entry station, and based on the current pit stop time In the case that the predicted entry site is not very accurate, it can also be directly predicted based on the current entry site. Improve the accuracy of the destination site prediction, making the predicted destination more in line with the actual needs of users.
- FIG. 3 shows a schematic flowchart of a method for predicting a destination site according to another embodiment of the present application.
- the following will elaborate on the process shown in FIG. 3, and the prediction method of the destination site shown may specifically include the following steps:
- Step S210 Obtain current inbound information, where the current inbound information includes the current inbound time and the current inbound station.
- Step S220 According to the historical ride records, obtain the target historical entry point matching the current entry time.
- step S210 and step S220 can refer to the content of the foregoing embodiment, and will not be repeated here.
- the pit time points of historical ride records may be fragmentary and continuous.
- the prediction results are not typical and have little reference significance. Therefore, in some embodiments, all historical inbound times in the historical ride records can be classified in time segments to obtain multiple time period categories (or time interval categories), which can be based on the time that matches the current inbound time. Segment, to predict entering the site. Since there are multiple time points in each time period, each time period can correspond to multiple historical ride records. Through multiple historical ride records rather than fragmentary historical ride records, predicting entry into the station can make the predicted results have a certain typical Sex, with reference significance.
- step S220 may include:
- Step S221 Obtain an inbound time period matching the current inbound time according to the historical ride record, where the inbound time period is obtained by dividing all historical inbound times in the historical ride record into sections.
- acquiring the inbound time period that matches the current inbound time may be acquiring the time period closest to the current inbound time.
- the current inbound time is 8:45 in the morning
- the inbound time period that matches the current inbound time can be the time period from 7:00 to 9:00.
- the time period may be obtained by dividing all historical inbound times in the historical ride records into sections.
- all historical arrival time data may be extracted from a database storing historical ride records, and then a clustering algorithm is used for analysis to obtain multiple time period categories. For example, categorize the historical inbound time from 6:00 to 10:00 as one time period, and categorize the inbound time from 10:00 to 14:00 as another time period. Multiple time period categories can be recorded as 1, 2, 3,..., KI, and KI is the total number of time period categories.
- the clustering algorithm is an algorithm that involves grouping data in machine learning. A given data set can be divided into different groups through the clustering algorithm.
- the foregoing method of time segmenting all historical inbound times is only an example, and it is not limited here.
- many categories may be generated during the clustering process, but not all categories are uniformly distributed.
- a category with a relatively small probability can be eliminated.
- the probability of each time period category can be counted first, and then only the time period category ki whose probability is greater than a specified threshold (denoted as Thr) is selected.
- the prior probability of each time period category can be counted according to the historical ride records in the data.
- the prior probability can be understood as the ability of each category to affect the prediction result, that is, when the prior probability is large, the greater the impact of the category on the prediction result.
- the calculation formula is as follows:
- Step S222 According to the historical ride records, obtain the entry probabilities of entering different historical entry stops during the entry time period.
- Step S223 Obtain the historical entry site corresponding to the entry probability with the largest value as the target historical entry site.
- the historical inbound probability corresponding to the largest numerical value can be used as the target historical inbound station, that is, the predicted inbound station. It is understandable that when the probability of entering a certain historical entry site is the greatest, it indicates that the user is likely to enter the historical entry site habitually during the subsequent entry period. Therefore, the historical entry site can be habitually entered.
- the entry site is the predicted entry site that the user will enter.
- the target historical entry site can also be directly predicted by the following calculation formula:
- InStation.time is the current inbound time in the current inbound information obtained above
- Ki is the above-mentioned time period category after all historical inbound times are divided into time periods.
- TEStation enters the site for the predicted target history.
- the current pit-stop time used to calculate the distance is the nearest time period class k
- p(k, c) is used to calculate the pit-stop probability of entering station c when the pit-stop time is the nearest time class k
- It is used to obtain the site c corresponding to the maximum probability p(k,c).
- Step S230 Determine whether the current incoming site and the target historical incoming site are the same. If yes, step S240 to step S250 can be performed; if not, step S260 to step S290 can be performed.
- Step S240 Obtain a first probability corresponding to the current inbound station according to the historical ride record, where the first probability is the number of times from the current inbound station to each historical outbound station at the current inbound time Probability.
- the destination station can be predicted based on the historical ride records, the current arrival time and the current arrival station, which effectively guarantees the accuracy of the prediction result.
- the first probability corresponding to the current inbound station may be obtained according to the historical ride records.
- the first probability is the probability of going from the current inbound station to each historical outbound station at the current inbound time.
- step S240 may include:
- Step S241 Obtain a historical pit stop time matching the current pit stop time according to the historical ride record.
- Step S242 Obtain the transition probability from the current inbound station to each historical outbound station under the historical inbound time and different historical outbound times as the first probability corresponding to the current inbound station.
- Markov property and historical ride records can be used to statistically analyze the probability of going from the current inbound station to each historical outbound station at the current inbound time. Specifically, you can first obtain the historical inbound time that matches the current inbound time according to the historical ride records, and then obtain the historical inbound time and different historical outbound times from the current inbound station to each historical outbound station. Transition probability, as the first probability corresponding to the current entry site.
- all historical outbound time data can also be divided into time periods to obtain multiple outbound time periods.
- Multiple time period categories can be recorded as 1, 2, 3,..., KO category, and KO is the total number of time period categories.
- KO is the total number of time period categories.
- the inbound time period that matches the current inbound time can be obtained, and then the transfer from the current inbound station to each historical outbound station under this inbound time period and different outbound time periods can be obtained
- the probability is used as the first probability corresponding to the current incoming station.
- the historical ride records stored in the database can also be pre-calculated in each inbound time period and each outbound time period from different historical inbound stations to different historical outbound stations.
- the transition probability of different historical outbound stations and different historical outbound times corresponding to the inbound time period and the current inbound station can be directly obtained .
- time classes with a relatively small probability may also be eliminated.
- the prior probability of each time period category can be counted according to the historical ride records in the data. The calculation formula is as follows:
- Step S250 Obtain the historical outbound site corresponding to the first probability with the largest value as the predicted destination site.
- the historical outbound station corresponding to the first probability with the largest value can be used as the predicted destination station. It is understandable that the first probability of going from the current inbound station to a historical outbound station at the current inbound time is the greatest, which indicates that the user is likely to get used to it when he subsequently enters the current inbound station at the current inbound time. To go to the historical out-of-site site in a specific manner, therefore, the historical out-of-site site can be used as the predicted destination site that the user will reach.
- the target site can also be directly predicted by the following calculation formula:
- EStation is the predicted destination site, It is used to obtain the outbound station s and outbound time t corresponding to the maximum probability p(c, k, s, t).
- the current arrival time can also be predicted. Specifically, when the first probability of going from the current inbound station to each historical outbound station under the current inbound time is obtained, the historical outbound time corresponding to the first probability with the largest value can be used as the predicted arrival time.
- the inbound time period class corresponding to the current inbound station and the current inbound time can be substituted into c and k in formula (4), so as to obtain the maximum probability p(c, k, s, t) corresponding to The outbound station s and outbound time t can be used as the predicted destination station, and the outbound time t can be used as the predicted arrival time.
- the arrival reminder may also be performed according to the arrival time. As a way, it may be based on the predicted arrival time to trigger the mobile terminal to output prompt information, which is used to remind the user that the user has arrived or is about to arrive.
- the prompt information may include the name and estimated time of the station that has arrived or is about to arrive. It can be output by voice or pop-up in the form of a window, which is not limited here.
- Step S260 Obtain a second probability corresponding to the current station entry according to the historical ride record, where the second probability is the probability of entering the current station entry under different historical entry times.
- the destination station can be predicted directly based on the historical ride records and the current station entry, ensuring the accuracy of the prediction result.
- the second probability corresponding to the current stop can be obtained according to the historical ride record, so as to subsequently predict the destination stop based on the second probability.
- the second probability is the probability of entering the current station at different historical station time. As a way, you can obtain the statistics of the number of historical ride records entering the current station at different historical pit stops, and calculate the ratio of the number of each record to the total number of historical records. The second probability of entering the current station at the historical station time.
- Step S270 Obtain the historical entry time corresponding to the second probability with the largest value as the predicted target entry time.
- the historical pitting time corresponding to the second probability with the largest value can be used as the target pitting time that can participate in the prediction of the destination station. It is understandable that the probability of entering the current station at a certain pit-stop time is the greatest, which indicates that the user is likely to habitually enter the current station at that pit-stop time. Therefore, you can set the highest value
- the historical pit-stop time corresponding to the second probability is used as the target pit-stop time predicted by the participating destination site.
- the second probability of entering the current station when dividing all historical inbound times into time periods, it may also be to obtain the second probability of entering the current station in different inbound time periods, and then obtain the second probability corresponding to the largest value.
- the pit-stop time period is used as the predicted target pit-stop time period. As a method, you can obtain the statistics of the number of historical rides entering the current station during different time periods of inbound stations, and calculate the ratio of the number of each record to the total number of historical records. The second probability of entering the current station during the pit stop time period.
- the target entry time with the highest probability can also be directly obtained through the following calculation formula:
- c is substituted into the current station
- CEStation is the predicted target station time
- Step S280 Obtain a third probability corresponding to the current inbound station according to the historical ride record, where the third probability is the number of times from the current inbound station to each historical outbound station at the target inbound time Probability.
- Step S290 Obtain the historical outbound site corresponding to the third probability with the largest value as the predicted destination site.
- the destination station can be predicted based on historical ride records, target pit station time, and current approach station. Specifically, you can first obtain the third probability corresponding to the current inbound station according to the historical ride records.
- the third probability is the probability of going from the current inbound station to each historical outbound station at the target inbound time, and then obtain the highest numerical value.
- the historical outbound site corresponding to the third probability is used as the predicted destination site.
- to obtain the third probability corresponding to the current entry site, and to obtain the historical exit site corresponding to the third probability with the largest value please refer to the aforementioned acquisition of the first probability corresponding to the current entry site and the historical exit site corresponding to the first probability with the largest value. The relevant content of the site will not be repeated here.
- the current arrival time can also be predicted. Specifically, when the third probability of going from the current inbound station to each historical outbound station under the predicted target inbound time is obtained, the historical outbound time corresponding to the third probability with the largest value can be used as the predicted Arrival time.
- the inbound time period class corresponding to the target inbound time and the current inbound station into c and k in formula (4), so that the outbound corresponding to the maximum probability p(c, k, s, t) can be obtained.
- Station s and outbound time t, the outbound site can be used as the predicted destination site, and the outbound time t can be used as the predicted arrival time.
- the mobile terminal can also be triggered to output prompt information according to the arrival time to remind the user that the station has arrived or is about to arrive.
- the destination station can be temporarily output as empty. After a certain period is met and the database is more complete, the prediction can be continued.
- the method for predicting the destination station obtained by the embodiment of this application obtains the current station information, which can include the current station time and the current station, and then obtains the target that matches the current station time according to the historical ride records Historical entry site, to predict the destination site based on historical ride records, current entry time, and current entry site when the current entry site is the same as the target historical entry site; and when the current entry site is different from the target historical entry site ,
- the pit time can be predicted to obtain the predicted target pit time, and then the destination station can be predicted based on the historical ride records, the predicted target pit time and the current station.
- the forecast of the entry station based on the current pit stop time is accurate, the forecast can be based on the current pit stop time and the current entry station, and based on the current pit stop time In the case that the predicted entry site is not very accurate, it can also be directly predicted based on the current entry site. Improve the accuracy of the destination site prediction, making the predicted destination more in line with the actual needs of users.
- FIG. 6 shows a schematic flowchart of a method for predicting a destination site according to another embodiment of the present application.
- the following will elaborate on the process shown in FIG. 6, and the prediction method of the destination site shown may specifically include the following steps:
- Step S310 Obtain a first destination station predicted based on a first historical ride record, the first historical ride record being a historical ride record dating back to the first date from the current date.
- Step S320 Obtain a second destination station predicted based on a second historical ride record, the second historical ride record being a historical ride record retroactive from the current date to a second date, and the second date is earlier than the first A date.
- the destination site can be predicted separately through learning of recent ride records and long-term ride records, so as to improve the accuracy of prediction. Specifically, it is possible to obtain the first destination station predicted based on the first historical ride record, which is the historical ride record dating back to the first date from the current date, and obtain the prediction based on the second historical ride record
- the second destination site, the second historical ride record is the historical ride record dating back to the second date from the current date.
- the second date is earlier than the first date, that is, the first historical ride record can be understood as a recent ride record, and the second historical ride record can be understood as a long-term ride record.
- first destination site predicted based on the first historical ride record and the second destination site predicted based on the second historical ride record can be predicted based on the aforementioned prediction method, and will not be repeated here.
- the specific retrospective duration of the first historical ride record and the second historical ride record is not limited in this application, and can be set reasonably according to specific scenarios.
- the retrospective duration of the first historical ride record may be within 48 hours
- the retrospective duration of the second historical ride record may be within half a year or within one year.
- Step S330 Determine a predicted destination site according to the first destination site and the second destination site.
- site fusion processing can be performed to obtain the final destination station prediction results.
- step S330 may include:
- Step S331 Obtain the first weight corresponding to the first destination site and the second weight corresponding to the second destination site, respectively.
- Step S332 Obtain a first product of the predicted probability corresponding to the first destination site and the first weight.
- Step S333 Obtain a second product of the predicted probability corresponding to the second destination site and the second weight.
- Step S334 From the first product and the second product, obtain the destination site corresponding to the product with the largest value as the predicted destination site.
- the corresponding first weight and second weight can be acquired respectively. Then the first product of the predicted probability corresponding to the first destination site and the first weight can be calculated, and the second product of the predicted probability corresponding to the second destination site and the second weight can be calculated to obtain from the first product and the second product
- the destination site corresponding to the product with the largest value is used as the predicted destination site.
- the predicted probability corresponding to the first destination site and the predicted probability corresponding to the second destination site can be the maximum probability corresponding to the destination site predicted by the foregoing prediction method, namely
- the weight value corresponding to the recent habit prediction result may be higher than the weight value corresponding to the long-term habit prediction result, that is, the first weight is greater than the second weight.
- the specific weight setting is not limited here, and it can be set reasonably according to the specific forecast demand.
- the method for predicting the destination station uses the aforementioned target station prediction method to predict the first destination station based on long-term historical ride records, and uses the aforementioned target station prediction method to predict the first destination station based on recent historical ride records. Second destination site, and then determine the final predicted destination site based on the first destination site and the second destination site. In this way, when predicting the destination site, both the long-term riding habits of the user and the recent riding habits of the user are taken into consideration. When the recent riding habits change, the inaccurate prediction caused by the single learning long-term riding habits can be avoided. , Improve the accuracy of the destination site prediction, making the predicted destination more suitable for the actual needs of users.
- the arrival reminder function can also be implemented.
- FIG. 8 shows a schematic flowchart of a method for predicting a destination site according to another embodiment of the present application. The following will elaborate on the process shown in FIG. 8.
- the prediction method of the destination site shown may specifically include the following steps:
- Step S410 Determine whether the current site is a neighboring site of the destination site. If yes, step S420 can be executed; if not, step S430 can be executed.
- Step S420 Trigger the mobile terminal to output prompt information, which is used to remind the user that the station is about to arrive.
- Step S430 The step of triggering the mobile terminal to output prompt information is not executed, and the prompt information is used to remind the user that the station is about to arrive.
- the current site can be located in real time, so as to trigger the arrival reminder function when the current site reaches the neighboring site of the destination site.
- the current site can be determined whether the current site is a neighboring site of the destination site. Such as the site before the destination site.
- the mobile terminal can be triggered to output prompt information, which is used to remind the user that the station is about to arrive.
- the mobile terminal may not be triggered to output prompt information.
- Figure 9 shows a schematic diagram of an application scenario where a user enters a station from A and predicts a destination site E. The mobile phone perceives A->B->C->D and reminds the user to arrive when it is approaching E.
- the current site can be located in multiple ways such as GPS, WiFi, Bluetooth, base station, and IMU, which is not limited here.
- the current site can be located based on the base station information.
- the method for predicting the target site of the present application may further include:
- Step S400 Obtain the information of the base station to which the mobile terminal is currently connected.
- the base station information includes the unique Cell ID of the base station, the operator, and other information, so as to query the database based on the base station information.
- Step S401 Acquire a target site corresponding to the base station information according to the corresponding relationship between the base station and the site stored in the base station database.
- FIG. 11 shows a schematic diagram of base station coverage. It can be seen that the coverage of the base station is limited. The coverage range of the base station is usually about 2 kilometers, and generally no more than 5 kilometers. Taking into account the poor signal quality in subway stations, major operators will deploy more base stations in the subway to ensure the quality of calls and Internet access on the subway. Therefore, at different subway stations, mobile phones will be registered to unreachable base stations.
- a base station and site mapping table can be established and stored in the database to obtain the base station database.
- the content of the database table can be obtained by manually going offline to the actual site and scanning the surrounding base station information with a mobile phone. Therefore, the target site corresponding to the information of the base station currently connected to the mobile terminal can be obtained according to the corresponding relationship between the base station and the site stored in the base station database.
- the established mapping table between the base station and the subway station may be as shown in Table 2.
- the database According to the information of the base station to which the mobile terminal is currently connected, query the database to obtain the subway stations covered by the base station. So you can locate the user's current site.
- Base station information Subway station information mcc-460-mnc-00-ci-243174306-pci-303-tac-6227 Shanghai Line 7-Longhua Middle Road mcc-460-mnc-01-ci-11114774-pci-501-tac-6263 Shanghai Line-12-Longcao Road mcc-460-mnc-01-ci-11115275-pci-466-tac-6263 Shanghai Line-12-Longhua Middle Road
- data collection may be incomplete, or the operator may add a new base station during subsequent maintenance, resulting in the base station database not being queried for corresponding base station information, and the target site may not be obtained. At this time, other positioning methods can be used to locate the current site.
- Step S402 Determine the current site according to the target site.
- the target site corresponding to the base station information when the target site corresponding to the base station information is obtained, and the corresponding target site is one, the target site may be used as the current site. It can indicate that the user is about to arrive at the site or is currently at the site at this time.
- step S402 may include:
- Step S4021 Obtain neighboring sites of the previous site.
- Step S4022 Obtain multiple identical sites among the target sites and the neighboring sites as the current site.
- the previously determined site when the current site needs to be located, the previously determined site can be acquired.
- the neighboring site of the previous station can be obtained according to the official route information, and then multiple target sites covered by the base station and the same site in the neighboring site can be obtained as the current site.
- FIG. 12 shows a schematic diagram of an overall flow of base station positioning. It should be noted that at the initial moment of the ride, the determined stop is the pit stop.
- the current station is determined to be B (also known as the previous station), since the subway is always moving forward, the current station needs to be determined in real time, and the adjacent stations of B are set (A, C), if The currently connected base station covers 2 subway stations (C, D), the intersection of the two sets is C, and C is the current station after the subway travels (that is, the next station after B).
- FIG. 13 shows a schematic block diagram of the overall flow of prediction of a destination site. in:
- Inbound recognition module 101 the user enters the subway station to recognize, the methods include mobile phone swiping recognition, mobile phone NFC recognition, inbound location GPS recognition, subway driving recognition, this module is embedded in the android system.
- mobile phone APP uses a mobile phone APP to swipe the code to enter a subway station, for example, the Shanghai subway is a metropolitan city, Shenzhen has a WeChat applet Shenzhen subway, and Beijing uses Yitong. After swiping the code to enter the station, the successful entry and the entry site will pop up on the phone.
- the inbound and outbound recognition module is a part of the customized system, which can identify changes on the mobile phone screen, inbound messages and inbound sites.
- the subway riding habit database module 102 a database of the user's subway riding habit.
- the database is a data collection of the subway riding information of the user for six months or one year.
- Module 103 of subway ride habit within 48 hours A 48-hour database of subway ride habits of users, which is a data collection of users’ recent subway rides.
- the destination site prediction module 104 predicts the user's destination site under the condition of entering the site. For the specific prediction method, refer to the content of the foregoing embodiment.
- Target site fusion result module 105 The user's long-term subway ride habit predicts the target site and the 48-hour subway ride habit predicts the target site to merge to obtain the final target station prediction result.
- Metro station recognition module 106 an algorithm for station recognition in subway mode, which recognizes the information of the current subway station, such as line, name, etc.
- Destination station reminding module 107 Based on the identification of subway mode stations, if the current station and the destination station are adjacent stations, the mobile phone sends notifications and vibrations to remind the user that they are about to arrive at the station and prepare to get off the train.
- the method for predicting the destination site provided by the embodiment of the application, after obtaining the predicted destination site through the aforementioned method for predicting the destination site, the current site can be located in real time, and the current site can be triggered when the current site is the neighboring site of the destination site.
- the mobile terminal outputs prompt information, which is used to remind the user that the station is about to arrive. This eliminates the need for user participation, and provides travel users with humanized services such as station arrival reminders and subway operation status, which solves the pain points of users when traveling and prevents users from passing the station. And there is no need for additional costs, no additional deployment equipment, no dependence on the outside, and feasibility and leadership.
- FIG. 14 shows a structural block diagram of a destination site prediction device 700 provided by an embodiment of the present application.
- the destination site prediction device 700 includes: a current inbound acquisition module 710, a target inbound acquisition module 720, The first prediction module 730 and the second prediction module 740.
- the current inbound acquisition module 710 is used to acquire current inbound information, and the current inbound information includes the current inbound time and the current inbound station;
- the target inbound acquisition module 720 is used to obtain information related to the current inbound station based on historical ride records.
- the target historical entry point with the pit stop time matching; the first prediction module 730 is used for when the current entry point is the same as the target historical entry point, according to the historical ride records, the current pit stop time and the current The inbound station predicts the destination station; the second prediction module 740 is used to predict the destination station based on the historical ride records and the current inbound station when the current inbound station is different from the target historical inbound station.
- the first prediction module 730 may include: a first probability acquisition unit and a first probability prediction unit.
- the first probability obtaining unit is configured to obtain a first probability corresponding to the current arrival station according to the historical ride record, and the first probability is that the current arrival station is to go from the current station at the current arrival time.
- the probability of each historical exit site; the first probability prediction unit is used to obtain the historical exit site corresponding to the first probability with the largest value, as a predicted destination site.
- the first probability obtaining unit may be specifically configured to: obtain a historical inbound time matching the current inbound time according to the historical ride record; obtain the historical inbound time and different historical outbound times. At station time, the transition probability from the current inbound station to each historical outbound station is taken as the first probability corresponding to the current inbound station.
- the prediction device 700 of the destination site may further include: an arrival time prediction module and a prompt triggering module.
- the arrival time prediction module is used to obtain the historical outbound time corresponding to the first probability with the largest value as the predicted arrival time;
- the prompt trigger module is used to trigger the mobile terminal to output prompt information according to the arrival time ,
- the prompt information is used to remind the user that he has arrived at the station or is about to arrive at the station.
- the second prediction module 740 may include: a second probability acquisition unit, a station time prediction unit, a third probability acquisition unit, and a third probability prediction unit.
- the second probability obtaining unit is configured to obtain a second probability corresponding to the current station entry according to the historical ride record, and the second probability is the probability of entering the current station station at different historical pit stops.
- the pit stop time prediction unit is used to obtain the historical pit stop time corresponding to the second probability with the largest value as the predicted target pit stop time;
- the third probability acquisition unit is used to obtain the current pit stop time according to the historical ride record
- the third probability corresponding to the entry site, the third probability is the probability of going from the current entry site to each historical exit site under the target entry time;
- the third probability prediction unit is used to obtain the highest numerical value
- the historical outbound site corresponding to the third probability is used as the predicted destination site.
- the device 700 for predicting a destination site may further include: a first site acquisition module, a second site acquisition module, and a destination site prediction module.
- the first site acquisition module is used to acquire the first destination site predicted based on the first historical ride record, the first historical ride record being the historical ride record dating back to the first date from the current date;
- the second site acquires The module is used to obtain a second destination station predicted based on a second historical ride record, the second historical ride record being a historical ride record retroactive from the current date to a second date, the second date being earlier than the first A date;
- the destination site prediction module is used to determine the predicted destination site based on the first destination site and the second destination site.
- the aforementioned destination site prediction module may be specifically configured to: obtain the first weight corresponding to the first destination site and the second weight corresponding to the second destination site respectively; and obtain the corresponding first destination site.
- the first product of the predicted probability of and the first weight obtain the second product of the predicted probability of the second destination site and the second weight; from the first product and the second product, Obtain the destination site corresponding to the product with the largest value as the predicted destination site.
- the device 700 for predicting the destination site may further include: a base station information acquisition module, a target site acquisition module, and a current site positioning module.
- the base station information acquisition module is used to acquire the base station information that the mobile terminal is currently connected to;
- the target site acquisition module is used to acquire the target site corresponding to the base station information according to the corresponding relationship between the base station and the site stored in the base station database;
- the current site location The module is used to determine the current site according to the target site.
- the above-mentioned current station positioning module may be specifically used to: obtain a neighboring station of the previous station; and obtain the same station among multiple target stations and the neighboring stations as the current station.
- the coupling between the modules may be electrical, mechanical or other forms of coupling.
- the functional modules in the various embodiments of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module.
- the above-mentioned integrated modules can be implemented in the form of hardware or software functional modules.
- the prediction device of the destination site provided in the embodiment of the present application is used to implement the prediction method of the corresponding destination site in the foregoing method embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.
- the electronic device 100 may be a mobile terminal capable of running application programs, such as a notebook computer, a smart phone, a smart watch, or a smart glasses.
- the electronic device 100 in this application may include one or more of the following components: a processor 110, a memory 120, and one or more application programs, where one or more application programs may be stored in the memory 120 and configured to be configured by One or more processors 110 execute, and one or more application programs are configured to execute the methods described in the foregoing method embodiments.
- the processor 110 may include one or more processing cores.
- the processor 110 uses various interfaces and lines to connect various parts of the entire electronic device 100, and executes by running or executing instructions, programs, code sets, or instruction sets stored in the memory 120, and calling data stored in the memory 120.
- Various functions and processing data of the electronic device 100 may adopt at least one of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).
- DSP Digital Signal Processing
- FPGA Field-Programmable Gate Array
- PDA Programmable Logic Array
- the processor 110 may integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a predictor (Graphics Processing Unit, GPU) of a destination site, and a modem.
- CPU Central Processing Unit
- GPU Graphics Processing Unit
- the CPU mainly processes the operating system, user interface, and application programs; the GPU is used for rendering and drawing of display content; the modem is used for processing wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 110, but may be implemented by a communication chip alone.
- the memory 120 may include random access memory (RAM) or read-only memory (Read-Only Memory).
- the memory 120 may be used to store instructions, programs, codes, code sets or instruction sets.
- the memory 120 may include a program storage area and a data storage area, where the program storage area may store instructions for implementing the operating system and instructions for implementing at least one function (such as touch function, sound playback function, image playback function, etc.) , Instructions used to implement the following various method embodiments, etc.
- the storage data area can also store data (such as phone book, audio and video data, chat record data) created by the electronic device 100 during use.
- FIG. 15 is only an example, and the electronic device 100 may also include more or fewer components than those shown in FIG. 15, or have a completely different configuration from that shown in FIG. 15.
- the embodiments of the present application have no limitation on this.
- FIG. 16 shows a structural block diagram of a computer-readable storage medium provided by an embodiment of the present application.
- the computer-readable medium 800 stores program code, and the program code can be invoked by a processor to execute the method described in the foregoing method embodiment.
- the computer-readable storage medium 800 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read Only Memory), EPROM, hard disk, or ROM.
- the computer-readable storage medium 800 includes a non-transitory computer-readable storage medium.
- the computer-readable storage medium 800 has storage space for the program code 810 for executing any method steps in the above-mentioned methods. These program codes can be read from or written into one or more computer program products.
- the program code 810 may be compressed in a suitable form, for example.
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Abstract
The present application relates to the technical field of electronic devices, and provides a target site prediction method and apparatus, and an electronic device and a storage medium. The target site prediction method comprises: obtaining current arrival information, the current arrival information comprising current arrival time and a current arrival point; obtaining, according to a historical riding record, a target historical arrival point matching the current arrival time; when the current arrival point is the same as the target historical arrival point, predicting a target site according to the historical riding record, the current arrival time and the current arrival point; and when the current arrival point is different from the target historical arrival point, predicting a target site according to the historical riding record and the current arrival point. The method can improve the prediction accuracy of the destination site.
Description
相关申请的交叉引用Cross-references to related applications
本申请要求于2020年5月21日提交的申请号为202010437224.4的中国申请的优先权,其在此出于所有目的通过引用将其全部内容并入本文。This application claims the priority of the Chinese application with application number 202010437224.4 filed on May 21, 2020, which is hereby incorporated by reference in its entirety for all purposes.
本申请涉及电子设备技术领域,更具体地,涉及一种目的站点的预测方法、装置、电子设备及存储介质。This application relates to the technical field of electronic equipment, and more specifically, to a method, device, electronic equipment, and storage medium for predicting a target site.
目前,移动终端的功能越来越丰富。为了能够利用移动终端更好地为用户服务,常常需要对用户将要到达的目的地点进行预测。但是,在相关的目的地点的预测方法中,还存在准确性不足的问题。At present, the functions of mobile terminals are becoming more and more abundant. In order to be able to use mobile terminals to better serve users, it is often necessary to predict the destination point the user will reach. However, in the prediction method of the relevant destination point, there is still a problem of insufficient accuracy.
发明内容Summary of the invention
鉴于上述问题,本申请提出了一种目的站点的预测方法、装置、电子设备及存储介质,可改善上述问题。In view of the above-mentioned problems, this application proposes a method, device, electronic equipment, and storage medium for predicting a target site, which can improve the above-mentioned problems.
第一方面,本申请实施例提供了一种目的站点的预测方法,所述方法包括:获取当前进站信息,所述当前进站信息包括当前进站时间和当前进站点;根据历史乘坐记录,获取与所述当前进站时间匹配的目标历史进站点;当所述当前进站点与所述目标历史进站点相同时,根据所述历史乘坐记录、所述当前进站时间及所述当前进站点预测目的站点;当所述当前进站点与所述目标历史进站点不相同时,根据所述历史乘坐记录及所述当前进站点预测目的站点。In the first aspect, an embodiment of the present application provides a method for predicting a destination station. The method includes: acquiring current station entry information, where the current station station information includes the current station station time and the current station station; and according to historical ride records, Obtain the target historical entry point matching the current pit stop time; when the current entry point is the same as the target historical entry point, according to the historical ride record, the current pit stop time and the current entry point Predict the destination station; when the current inbound station is different from the target historical inbound station, predict the destination station based on the historical ride records and the current inbound station.
第二方面,本申请实施例提供了一种目的站点的预测装置,所述装置包括:当前进站获取模块,用于获取当前进站信息,所述当前进站信息包括当前进站时间和当前进站点;目标进站获取模块,用于根据历史乘坐记录,获取与所述当前进站时间匹配的目标历史进站点;第一预测模块,用于当所述当前进站点与所述目标历史进站点相同时,根据所述历史乘坐记录、所述当前进站时间及所述当前进站点预测目的站点;第二预测模块,用于当所述当前进站点与所述目标历史进站点不相同时,根据所述历史乘坐记录及所述当前进站点预测目的站点。In the second aspect, an embodiment of the present application provides a device for predicting a destination site. The device includes: a current inbound acquisition module, configured to obtain current inbound information, where the current inbound information includes the current inbound time and the current inbound time. Inbound; the target inbound acquisition module is used to obtain the target historical inbound station that matches the current inbound time according to the historical ride records; the first prediction module is used to when the current inbound station and the target historical inbound When the stations are the same, predict the destination station based on the historical ride record, the current station time and the current station; the second prediction module is used when the current station and the target historical station are not the same , Predict the destination station based on the historical ride records and the current inbound station.
第三方面,本申请实施例提供了一种电子设备,包括:一个或多个处理器;存储器;一个或多个应用程序,其中所述一个或多个应用程序被存储在所述存储器中并被配置为由所述一个或多个处理器执行,所述一个或多个应用程序配置用于执行上述第一方面提供的目的站点的预测方法。In a third aspect, an embodiment of the present application provides an electronic device, including: one or more processors; a memory; one or more application programs, wherein the one or more application programs are stored in the memory and It is configured to be executed by the one or more processors, and the one or more application programs are configured to execute the method for predicting the target site provided in the first aspect described above.
第四方面,本申请实施例提供了一种计算机可读取存储介质,所述计算机可读取存储介质中存储有程序代码,所述程序代码可被处理器调用执行上述第一方面提供的目的站点的预测方法。In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium. The computer-readable storage medium stores program code, and the program code can be invoked by a processor to perform the purpose provided in the first aspect. The prediction method of the site.
为了更清楚地说明本申请实施例中的技术方案,下面将对实施例描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本申请的一些实施例,对于本领域技术人员来讲,在不付出创造性劳动的前提下,还可以根据这些附图获得其他的附图。In order to more clearly describe the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings that need to be used in the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.
图1示出了根据本申请一个实施例的目的站点的预测方法的一种流程图。Fig. 1 shows a flowchart of a method for predicting a destination site according to an embodiment of the present application.
图2示出了根据本申请的一种数据库云端架构示意图。Figure 2 shows a schematic diagram of a database cloud architecture according to the present application.
图3示出了根据本申请另一个实施例的目的站点的预测方法的一种流程图。Fig. 3 shows a flow chart of a method for predicting a destination site according to another embodiment of the present application.
图4示出了根据本申请另一个实施例的目的站点的预测方法中步骤S220的一种流程图。Fig. 4 shows a flowchart of step S220 in the method for predicting a destination site according to another embodiment of the present application.
图5示出了根据本申请另一个实施例的目的站点的预测方法中步骤S240的另一种流程图。Fig. 5 shows another flow chart of step S240 in the method for predicting a destination site according to another embodiment of the present application.
图6示出了根据本申请又一个实施例的目的站点的预测方法的一种流程图。Fig. 6 shows a flowchart of a method for predicting a destination site according to another embodiment of the present application.
图7示出了根据本申请又一个实施例的目的站点的预测方法中步骤S330的另一种流程图。Fig. 7 shows another flow chart of step S330 in the method for predicting a destination site according to another embodiment of the present application.
图8示出了根据本申请再一个实施例的目的站点的预测方法的一种流程图。Fig. 8 shows a flow chart of a method for predicting a destination site according to still another embodiment of the present application.
图9示出了根据本申请的一种应用场景示意图。Fig. 9 shows a schematic diagram of an application scenario according to the present application.
图10示出了根据本申请还一个实施例的目的站点的预测方法的一种流程图。Fig. 10 shows a flowchart of a method for predicting a destination site according to still another embodiment of the present application.
图11示出了根据本申请的一种基站覆盖范围示意图。Fig. 11 shows a schematic diagram of the coverage area of a base station according to the present application.
图12示出了根据本申请的一种基站定位方法的整体流程示意图。Fig. 12 shows a schematic diagram of the overall flow of a base station positioning method according to the present application.
图13示出了一种根据本申请实施例提供的目的站点的预测方法的整体流程示意框图。FIG. 13 shows a schematic block diagram of the overall flow of a method for predicting a destination site according to an embodiment of the present application.
图14示出了根据本申请一个实施例的目的站点的预测装置的一种框图。Fig. 14 shows a block diagram of a device for predicting a destination site according to an embodiment of the present application.
图15是本申请实施例的用于执行根据本申请实施例的目的站点的预测方法的电子设备的框图。FIG. 15 is a block diagram of an electronic device for executing the method for predicting a destination site according to an embodiment of the present application according to an embodiment of the present application.
图16是本申请实施例的用于保存或者携带实现根据本申请实施例的目的站点的预测方法的程序代码的存储单元。Fig. 16 is a storage unit for storing or carrying program codes for realizing the prediction method of the destination site according to the embodiment of the present application according to an embodiment of the present application.
为了使本技术领域的人员更好地理解本申请方案,下面将结合本申请实施例中的附图,对本申请实施例中的技术方案进行清楚、完整地描述。In order to enable those skilled in the art to better understand the solutions of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the accompanying drawings in the embodiments of the present application.
目前,目的地预测方法通常是基于固定的装置或者其他特定的应用,获取用户数据进行预测和估计,涉及到用户实时位置跟踪等敏感信息,另外需要用户提供明确的出发地和目的地信息才能有效的执行,针对特定的用户群体提供服务。At present, the destination prediction method is usually based on a fixed device or other specific applications to obtain user data for prediction and estimation. It involves sensitive information such as real-time location tracking of the user. In addition, the user needs to provide clear departure and destination information to be effective. The implementation of services for specific user groups.
且发明人经过长期的研究发现,现有的目的地预测方法通常是对用户历史目的地做简单统计,将用户历史目的地的在一定时间的使用次数按从大到小的顺序进行排序,选择使用次数最多的历史目的地作为预测的出行目的地;或者将用户历史目的地最后一次的使用时间按从近到远的顺序进行排序,选择最近使用的历史目的地作为预测的出行目的地。现有的目的地预测方法并没有考虑用户出行时间点和用户出发地对目的地预测的影响,导致预测的结果不准确,不存在技术上壁垒和领先性。In addition, the inventor has discovered through long-term research that the existing destination prediction method is usually to make simple statistics on the user's historical destinations, and sort the use times of the user's historical destinations in a certain time in descending order, and select The most frequently used historical destination is used as the predicted travel destination; or the last use time of the user's historical destination is sorted from nearest to farthest, and the most recently used historical destination is selected as the predicted travel destination. The existing destination prediction methods do not consider the impact of the user's travel time and the user's place of departure on the destination prediction, resulting in inaccurate prediction results, and there is no technical barrier or leading edge.
此外,现有预测方法中的数据获取,包括基站、WiFi(Wireless Fidelity,无线保真)、蓝牙、GPS(Global Positioning System,全球定位系统)位置数据、时间数据和车辆id(Identity document,唯一标识号码)等,比较依赖用户交互。另外GPS并不能全覆盖环境中的各个角落,精度受限,WiFi和蓝牙信息依赖外部设备,成本高,对室外环境不友好。且现有的目的地预测方案通常仅针对乘坐汽车等用户,没有或比较少针对乘坐地铁的用户提供相应的服务。In addition, the data acquisition in existing prediction methods includes base station, WiFi (Wireless Fidelity), Bluetooth, GPS (Global Positioning System, Global Positioning System) location data, time data, and vehicle id (Identity document, unique identification) Number) and so on, more dependent on user interaction. In addition, GPS cannot cover all corners of the environment, and its accuracy is limited. WiFi and Bluetooth information rely on external equipment, which is costly and unfriendly to the outdoor environment. In addition, the existing destination prediction schemes usually only target users such as cars, and few or fewer provide corresponding services for users who take subways.
因此,发明人经过长期的研究发现并提出了本申请实施例提供的目的站点的预测方法、装置、电子设备以及存储介质,可考虑当前进站时间对预测结果的影响,以在基于当前进站时间预测得不太准确的情况下,可以直接根据当前进站点来预测。提升了对目的站点预测的准确性,使得预测的目的地更贴合用户实际需求。此外,本申请的目的站点的预测方式也适用于地铁场景。具体的目的站点的预测方法在后续的实施例中进行详细的说明。Therefore, after long-term research, the inventor has discovered and proposed the method, device, electronic equipment, and storage medium for the prediction of the destination site provided in the embodiments of this application. If the time forecast is not very accurate, you can directly forecast based on the current entry site. Improve the accuracy of the destination site prediction, making the predicted destination more in line with the actual needs of users. In addition, the prediction method of the destination site in this application is also applicable to subway scenes. The specific target site prediction method will be described in detail in the subsequent embodiments.
请参阅图1,图1示出了本申请一个实施例提供的目的站点的预测方法的流程示意图。该预测方法可应用于电子设备。其中,电子设备可以是移动终端,使得该预测方法在本地就可以执行;电子设备也可以是服务器,使得该预测方法在云端执行。在具体的实施例中,该目的站点的预测方法可应用于如图14所示的目的站点的预测装置700以及配置有所述目的站点的预测装置700的电子设备(图15)。下面将针对图1所示的流程进行详细的阐述,所示目的站点的预测方法具体可以包括以下步骤:Please refer to FIG. 1, which shows a schematic flowchart of a method for predicting a destination site according to an embodiment of the present application. The prediction method can be applied to electronic equipment. The electronic device can be a mobile terminal, so that the prediction method can be executed locally; the electronic device can also be a server, so that the prediction method can be executed in the cloud. In a specific embodiment, the prediction method of the destination site can be applied to the prediction device 700 of the destination site as shown in FIG. 14 and the electronic equipment configured with the prediction device 700 of the destination site (FIG. 15 ). The following will elaborate on the process shown in Figure 1. The prediction method of the destination site shown may specifically include the following steps:
步骤S110:获取当前进站信息,所述当前进站信息包括当前进站时间和当前进站点。Step S110: Obtain current inbound information, where the current inbound information includes the current inbound time and the current inbound station.
在本申请实施例中,电子设备可以获取用户的当前进站信息,以根据该当前进站信息进行目的站点的预测。其中,该当前进站信息可以包括当前进站时间和当前进站点。In the embodiment of the present application, the electronic device may obtain the user's current inbound information, so as to predict the destination site based on the current inbound information. Among them, the current inbound information may include the current inbound time and the current inbound station.
在一些实施例中,可以通过多种方式获取到用户的当前进站信息。例如,可以是通过刷码识别,NFC(Near Field Communication,近场通信)识别,进站位置GPS识别,地铁行驶识别等识别到用户的当前进站信息。In some embodiments, the user's current inbound information can be obtained in multiple ways. For example, the user’s current entry information can be identified by swiping identification, NFC (Near Field Communication) identification, GPS identification of the inbound position, and subway driving identification.
在一些实施例中,移动终端上可以安装有预设应用,当用户用移动终端刷码进入预设交通场景时,可以通过预设应用识别到进入的站点的名字、线路、城市和时间等信息。其中,预设应用可被配置为适于在预设交通场景下运行,预设交通场景可以是地铁、公交、高铁、火车、飞机等任一交通场景。In some embodiments, a preset application may be installed on the mobile terminal. When the user enters the preset traffic scene by swiping the code with the mobile terminal, the preset application can identify the name, route, city, and time of the station entered. . Among them, the preset application can be configured to be suitable for running in a preset traffic scene, and the preset traffic scene can be any traffic scene such as subway, bus, high-speed rail, train, and airplane.
在一些实施方式中,预设应用可以为乘车应用、支付应用等应用程序,本申请实施例对此不作限定。In some implementation manners, the preset application may be an application program such as a ride application, a payment application, etc., which is not limited in the embodiment of the present application.
示例性的,预设应用可以为公交车乘车应用,适于在公交车场景下运行。其中,公交车乘车应用可用于生成乘车标识,供用户刷码乘公交车。当用户用移动终端刷码乘上公交车时,可以通过该公交车乘车应用识别到用户上车的站点名字、线路、城市和时间。Exemplarily, the preset application may be a bus ride application, which is suitable for running in a bus scene. Among them, the bus ride application can be used to generate a ride logo for users to swipe the code to ride the bus. When a user uses a mobile terminal to swipe a code to board a bus, the name of the site, route, city, and time of the user's boarding can be identified through the bus ride application.
示例性的,预设应用也可以为支付应用,支付应用可配置由多张电子卡片,电子卡片可以是基于交通卡所模拟的射频卡,可支持NFC支付,在一些示例中,移动终端可内置由NFC模块,则可基于NFC模块与NFC读取设备进行数据交互,从而在用户乘坐交通工具时,用户可将移动终端靠近NFC读取设备,则NFC读取设备可读取到支付应用所配置的交通卡,进行支付。移动终端可根据支付应用的支付情况,获取到进站信息。Exemplarily, the preset application can also be a payment application. The payment application can be configured with multiple e-cards. The e-card can be a radio frequency card simulated based on a traffic card and can support NFC payment. In some examples, the mobile terminal can be built-in The NFC module can interact with the NFC reading device based on the NFC module, so that when the user takes a transportation, the user can move the mobile terminal close to the NFC reading device, and the NFC reading device can read the configuration of the payment application The transportation card to make the payment. The mobile terminal can obtain the inbound information according to the payment status of the payment application.
作为一种具体的实施方式,可以在移动终端上配置有进站识别模块,该进站识别模块可内嵌到android系统中。在地铁交通场景下,当用户使用移动终端上安装的APP(Application,应用程序)刷码进入地铁站时,比如上海地铁是metro大都会,深圳有微信小程序深圳地铁,北京用亿通行等。当用户刷码进站后,移动终端上可弹出进站成功、进站时间和进站站点。移动终端上配置的进站识别模块可以通过识别到手机屏幕上的变化,从而可以获取到当前的包括进站时间和进站站点的进站信息。As a specific implementation manner, an inbound identification module may be configured on the mobile terminal, and the inbound identification module may be embedded in the android system. In the subway traffic scenario, when a user uses an APP (Application) installed on a mobile terminal to enter a subway station by swiping the code, for example, the Shanghai Metro is a metropolis, Shenzhen has a WeChat applet, Shenzhen Metro, and Beijing uses Yitongxing. After the user swipes the code to enter the station, the mobile terminal will pop up the successful entry, the entry time and the entry site on the mobile terminal. The inbound recognition module configured on the mobile terminal can recognize the changes on the screen of the mobile phone to obtain the current inbound information including the inbound time and the inbound station.
在一些实施例中,在获取到当前进站信息后,可以将当前进站信息进行存储记录,以便持续更新存储有历史乘坐记录的数据库内容,从而可根据持续更新的数据库内容,进行更准确更符合当下场景的目的站点预测。作为一种方式,可将获取到当前进站信息以InStation={time,name}格式进行记录,其中,InStation为当前进站信息,time为当前进站时间,name为当前进站点。In some embodiments, after the current inbound information is obtained, the current inbound information can be stored and recorded, so as to continuously update the database content storing historical ride records, so that more accurate updates can be made based on the continuously updated database content. The prediction of the destination site in line with the current scenario. As a way, the acquired current inbound information can be recorded in the format InStation={time,name}, where InStation is the current inbound information, time is the current inbound time, and name is the current inbound station.
步骤S120:根据历史乘坐记录,获取与所述当前进站时间匹配的目标历史进站点。Step S120: According to the historical ride records, obtain the target historical entry point matching the current entry time.
在一些场景中,进入同一个进站点时,用户的进站时间可能会在一段时间内不稳定、波动大,有时可能不在用户长期习惯的进站时间内,而在这种情况下,如果结合当前进站时间对目的站点进行预测,可能会导致预测的目的站点不准确,偏离用户的实际需求。In some scenarios, when entering the same inbound site, the user's inbound time may be unstable and fluctuate for a period of time, and sometimes may not be within the long-term inbound time that users are accustomed to. In this case, if combined Predicting the destination site at the current stop time may cause the predicted destination site to be inaccurate and deviate from the actual needs of users.
因此,在本申请实施例中,在获取到当前进站信息后,可先根据当前进站时间,预测当前的进站点,以根据该进站点预测结果确定是否根据当前进站时间进行目的站点的预测。具体地,可以根据历史乘坐记录,获取与当前进站时间匹配的目标历史进站点。其中,该目标历史进站点可以理解为,对历史乘坐记录进行长时间的自主学习后,所预测出来的进站点。Therefore, in this embodiment of the application, after obtaining the current inbound information, the current inbound time can be first predicted according to the current inbound time, so as to determine whether to perform the destination site based on the current inbound time according to the predicted result of the inbound station. predict. Specifically, according to the historical ride records, the target historical entry point matching the current entry time can be obtained. Among them, the target historical entry site can be understood as the predicted entry site after long-term independent learning of historical ride records.
在一些实施例中,可以根据长时间的历史乘坐记录中的多个历史进站时间和多个历史进站点中,确定与当前进站时间匹配的目标历史进站点。作为一种方式,可以从多个历史进站时间中,获取与当前进站时间匹配的目标历史进站时间,然后将该目标历史进站时间所对应的历史进站点,作为与当前进站时间匹配的目标历史进站点,也即预测的当前的进站点。其中,与当前进站时间匹配的目标历史进站时间,可以是与当前进站时间点相同的历史进站时间点,也可以是距离当前进站时间点最近的历史进站时间点。In some embodiments, it is possible to determine the target historical entry point that matches the current entry time based on multiple historical entry times and multiple historical entry points in the long-term historical ride record. As a way, you can obtain the target historical inbound time that matches the current inbound time from multiple historical inbound times, and then use the historical inbound time corresponding to the target historical inbound time as the current inbound time The matched target historical entry site, that is, the predicted current entry site. Among them, the target historical inbound time that matches the current inbound time may be the same historical inbound time as the current inbound time, or it may be the closest historical inbound time to the current inbound time.
例如,当前进站时间为上午8点45分,与当前进站时间匹配的目标历史进站时间可以是距离上午8点45分最近的上午9点,然后可以将历史日期中是上午9点进站的乘坐记录提取出,并将该乘坐记录中上午9点进入的进站点作为目标历史进站点,也即预测的当前的进站点。For example, the current pit-stop time is 8:45 am, the target historical pit-stop time that matches the current pit-stop time can be the closest 9 am from 8:45 am, and then the historical date can be 9 am The ride record of the station is extracted, and the entry site entered at 9 am in the ride record is used as the target historical entry site, that is, the predicted current entry site.
进一步地,由于可能存在不同历史日期下的同一时间点所对应的历史进站点不同的情况,从而导致与目标历史进站时间对应的历史进站点可能为多个。例如,工作日上午8点对应的上班出行路线的进出站,与周末上午8点对应的娱乐出行路线的进行站很可能不同。因此,在一些实施例中,当目标历史进站时间所对应的历史进站点为多个时,可以从中选取可能性最大的历史进站点,作为目标历史进站点。作为一种方式,可以从目标历史进站时间所对应的多个历史乘坐记录中,选取出现次数最高的历史进站点,作为目标历史进站点。Further, since there may be different historical entry points corresponding to the same time point on different historical dates, there may be multiple historical entry points corresponding to the target historical entry time. For example, the entry and exit station of the work travel route corresponding to 8 a.m. on weekdays is likely to be different from the entry station of the entertainment travel route corresponding to 8 a.m. on weekends. Therefore, in some embodiments, when there are multiple historical entry sites corresponding to the target historical entry time, the most likely historical entry site can be selected from them as the target historical entry site. As a way, it is possible to select the historical inbound stop with the highest number of occurrences from the multiple historical ride records corresponding to the target historical inbound time as the target historical inbound stop.
在一些实施例中,可以通过建立一个数据库,以将用户从当前日期起,过去每次出行乘坐交通工具时的出站信息和进站信息进行记录存储。其中,该数据库可以存储于本地,也可以是存储于云端,此处并不作限定。In some embodiments, a database may be established to record and store the outbound information and inbound information of the user from the current date on each trip in the past. Wherein, the database can be stored locally or in the cloud, which is not limited here.
示例性地,假设于地铁场景,如图2所述,图2示出了一种乘坐地铁习惯数据库云端存储架构。通过该存储架构,可实现乘坐记录的自动获取和存储。其中,架构各模块的具体功能如下:Exemplarily, assuming a subway scene, as shown in FIG. 2, FIG. 2 shows a cloud storage architecture of a subway habit database. Through this storage architecture, automatic acquisition and storage of ride records can be realized. Among them, the specific functions of each module of the architecture are as follows:
进站识别模块201:用于用户进地铁站识别,当用户用手机刷码进入地铁站时,可以通过 应用识别进入地铁站的名字、线路、城市和时间。Station recognition module 201: Used to recognize when users enter a subway station. When a user enters a subway station with a mobile phone, he can recognize the name, line, city, and time of entering the subway station through the application.
出站模块202:用于用户出地铁站识别,当用户用手机刷码离开地铁站时,可以通过应用识别离开地铁站的名字、线路、城市和时间。Outbound module 202: Used to identify the user when leaving the subway station. When the user leaves the subway station by swiping the code with the mobile phone, the name, line, city, and time of leaving the subway station can be identified through the application.
数据云存储模块203:用于数据存储,当检测到进站信息(用户刷码进入地铁站可检测到)时,可把相应的信息进行存储,如地铁站的名字、线路、城市和时间等信息。当检测离开地铁站时,若出站信息能和检测到的进站信息进行配对则进行正对存储,若进站信息和出站信息不能配对则单独存储。其中,可通过判断刷码进站和出站是否成对出现,并且所用时间和地铁运行时间差异是否小于一定阈值(可预先设定),来判断进站信息和出站信息是否匹配。在一些实施例中,本地存储的数据可根据云端协同策略上传至云端数据库。Data cloud storage module 203: used for data storage. When the station entry information is detected (the user can scan the code to enter the subway station can be detected), the corresponding information can be stored, such as the name, line, city and time of the subway station, etc. information. When detecting leaving a subway station, if the outbound information can be paired with the detected inbound information, it will be stored directly; if the inbound information and outbound information cannot be paired, they will be stored separately. Among them, it can be judged whether the inbound and outbound information matches by judging whether the brush code inbound and outbound appear in pairs, and whether the difference between the used time and the subway running time is less than a certain threshold (which can be preset). In some embodiments, the locally stored data can be uploaded to the cloud database according to the cloud collaboration strategy.
数据库模块204:用于存储用户乘坐地铁历史的信息数据库,数据库内容可包括地点(城市),事件(进站、出站),时间(UNIX,精确到秒)等信息。Database module 204: an information database used to store the user's subway ride history. The content of the database may include information such as location (city), event (inbound, outbound), time (UNIX, accurate to the second).
在一些实施例中,历史乘坐记录中可以包括进站信息和出站信息。由于可能存在进站信息或出站信息未识别获取到的情况,因此,历史乘坐记录中也可以仅包括进站信息,仅包括出站信息。In some embodiments, the historical ride record may include inbound information and outbound information. Since there may be cases where the inbound information or the outbound information is not recognized and acquired, the historical ride records may also include only the inbound information and only the outbound information.
示例性地,假设于地铁场景,可以如表1所示对乘坐记录进行存储。其中,历史乘坐记录中可包括城市、进站、出站、时间(UNIX,精确到秒)等信息。‘*’代表信息为空,每一行代表一次匹配的进出站信息。Illustratively, assuming a subway scene, the ride record can be stored as shown in Table 1. Among them, the historical ride records may include information such as city, inbound, outbound, and time (UNIX, accurate to the second). ‘*’ means that the information is empty, and each line represents a matching entry and exit information.
表1Table 1
城市city | 进站Pit stop | 时间time | 出站Outbound | 时间time |
上海Shanghai | 2号线-陆家嘴Line 2-Lujiazui | 15698880001569888000 | 2号线-淞虹路Line 2-Songhong Road | 15698898001569889800 |
上海Shanghai | 2号线-淞虹路Line 2-Songhong Road | 15704082001570408200 | ** | ** |
上海Shanghai | ** | ** | 10号线-豫园Line 10-Yu Garden | 15703254001570325400 |
在一些实施例中,在进行目的站点预测时,可以将数据库中信息为空的历史乘坐记录剔除,可保证预测的有效性。In some embodiments, when the destination site is predicted, historical ride records with empty information in the database can be eliminated, which can ensure the effectiveness of the prediction.
步骤S130:判断所述当前进站点与所述目标历史进站点是否相同。若是,则可执行步骤S140;若否,则可执行步骤S150。Step S130: Determine whether the current incoming site and the target historical incoming site are the same. If yes, step S140 can be executed; if not, step S150 can be executed.
步骤S140:根据所述历史乘坐记录、所述当前进站时间及所述当前进站点预测目的站点。Step S140: Predict the destination station based on the historical ride record, the current station arrival time, and the current station station.
步骤S150:根据所述历史乘坐记录及所述当前进站点预测目的站点。Step S150: Predict the destination station based on the historical ride records and the current inbound station.
在本申请实施例中,在获取到与当前进站时间匹配的目标历史进站点后,可以判断上述获取到的当前进站信息中的当前进站点与目标历史进站点是否相同。并在当前进站点与目标历史进站点相同时,可根据历史乘坐记录、当前进站时间及当前进站点预测目的站点。而在当前进站点与目标历史进站点不同时,可根据历史乘坐记录及当前进站点预测目的站点,当前进站时间并不参与目的站点的预测。In the embodiment of the present application, after obtaining the target historical inbound site matching the current inbound time, it can be determined whether the current inbound site and the target historical inbound site in the acquired current inbound information are the same. And when the current station is the same as the target historical station, the destination station can be predicted based on the historical ride records, the current station time and the current station. When the current station is different from the target historical station, the destination station can be predicted based on the historical ride records and the current station. The current station time does not participate in the prediction of the destination station.
可以理解的是,如果根据当前进站时间预测得到的当前的进站点(即目标历史进站点),与实际的当前进站点(即上述获取到的当前进站信息中的当前进站点)不同,则可表明当前进站时间可能不是用户于当前进站点长期习惯的进站时间,当前进站时间不适合参与基于习惯的目的站点的预测,强行参与可能会导致预测结果不准确,偏离用户实际需求。因此,在当前进站点与目标历史进站点不同时,可直接根据当前进站点对目的站点进行预测,有效保证了预测结果的准确性。It is understandable that if the current inbound station (that is, the target historical inbound station) predicted based on the current inbound time is different from the actual current inbound station (that is, the current inbound station in the current inbound information obtained above), It can indicate that the current pit-stop time may not be the pit-stop time that the user is accustomed to for a long time at the current station, and the current pit-stop time is not suitable for participating in the prediction of the destination station based on habit. Forcible participation may lead to inaccurate prediction results and deviate from the actual needs of users. . Therefore, when the current incoming site is different from the target historical incoming site, the destination site can be directly predicted based on the current incoming site, which effectively guarantees the accuracy of the prediction result.
如果根据当前进站时间预测得到的当前的进站点,与实际的当前进站点相同,则可表明当前进站时间可能是用户于当期进站点长期习惯的进站时间,当前进站时间适合参与基于习惯的目的站点的预测。因此,在当前进站点与目标历史进站点相同时,可根据历史乘坐记录、当前进站时间及当前进站点预测目的站点,有效保证了预测结果的准确性。If the current inbound time predicted based on the current inbound time is the same as the actual current inbound station, it can indicate that the current inbound time may be the user’s long-term habit of entering the site in the current period, and the current inbound time is suitable for participation based on The prediction of the habitual destination site. Therefore, when the current station is the same as the target historical station, the destination station can be predicted based on the historical ride records, the current station time and the current station, which effectively guarantees the accuracy of the prediction result.
在一些实施例中,根据历史乘坐记录、当前进站时间及当前进站点预测目的站点,可以是从历史乘坐记录中获取与当前进站时间及当前进站点匹配的一条或多条乘坐记录,根据该一条或多条乘坐记录中的出站信息,预测目标站点。作为一种方式,可以是从一条或多条乘坐记录中的出站信息中,选取出现次数最高的出站站点,作为预测的目标站点。具体根据历史乘坐记 录、当前进站时间及当前进站点预测目的站点的方式,在本申请中可以不作为限定。例如,可以建立基于习惯的目标站点马尔科夫预测模型,通过应用马尔科夫方法对进出站时间点及进出站点的概率进行预测。In some embodiments, predicting the destination station based on historical ride records, current pit stop time, and current pit stop may be to obtain one or more ride records matching the current pit stop time and current pit stop from the historical ride record, according to The outbound information in the one or more ride records predicts the target station. As a way, it may be to select the outbound station with the highest occurrence from the outbound information in one or more ride records as the predicted target station. The specific method of predicting the destination station based on historical ride records, current arrival time, and current arrival station may not be limited in this application. For example, a habit-based Markov prediction model for the target site can be established, and the Markov method can be used to predict the time point of entering and leaving the station and the probability of entering and leaving the station.
在一些实施例中,根据历史乘坐记录及当前进站点预测目的站点,可以是从历史乘坐记录中获取与当前进站点匹配的一条或多条乘坐记录,根据该一条或多条乘坐记录中的出站信息,预测目标站点。作为一种方式,可以是从一条或多条乘坐记录中的出站信息中,选取出现次数最高的出站站点,作为预测的目标站点。具体根据历史乘坐记录及当前进站点预测目的站点的方式,在本申请中可以不作为限定。例如,可以建立基于习惯的目标站点马尔科夫预测模型,通过应用马尔科夫方法对进出站点的概率进行预测。In some embodiments, predicting the destination station based on the historical ride records and the current station entry may be to obtain one or more ride records matching the current station entry from the historical ride records, and according to the outbound station in the one or more ride records Station information, predict the target station. As a way, it may be to select the outbound station with the highest occurrence from the outbound information in one or more ride records as the predicted target station. The specific method of predicting the destination station based on the historical ride records and the current station entry is not limited in this application. For example, a habit-based Markov prediction model for the target site can be established, and the probability of entering and leaving the site can be predicted by applying the Markov method.
本申请实施例提供的目的站点的预测方法,通过获取当前进站信息,该当前进站信息可包括当前进站时间和当前进站点,然后根据历史乘坐记录,获取与当前进站时间匹配的目标历史进站点,以在当前进站点与该目标历史进站点相同时,可根据历史乘坐记录、当前进站时间及当前进站点预测目的站点,而在当前进站点与该目标历史进站点不相同时,可根据历史乘坐记录及当前进站点预测目的站点。如此,考虑到了当前进站时间对预测结果的影响,在基于当前进站时间预测进站点预测得准确的情况下,可以根据当前进站时间和当前进站点来预测,而在基于当前进站时间预测进站点预测得不太准确的情况下,还可以直接根据当前进站点来预测。提升了对目的站点预测的准确性,使得预测的目的地更贴合用户实际需求。The method for predicting the destination station provided by the embodiment of this application obtains the current station information, which can include the current station time and the current station, and then obtains the target that matches the current station time according to the historical ride records Historical entry site, to predict the destination site based on historical ride records, current entry time, and current entry site when the current entry site is the same as the target historical entry site, and when the current entry site is different from the target historical entry site , The destination station can be predicted based on the historical ride records and the current station. In this way, taking into account the influence of the current pit stop time on the prediction result, if the forecast of the entry station based on the current pit stop time is accurate, the forecast can be based on the current pit stop time and the current entry station, and based on the current pit stop time In the case that the predicted entry site is not very accurate, it can also be directly predicted based on the current entry site. Improve the accuracy of the destination site prediction, making the predicted destination more in line with the actual needs of users.
请参阅图3,图3示出了本申请另一个实施例提供的目的站点的预测方法的流程示意图。下面将针对图3所示的流程进行详细的阐述,所示目的站点的预测方法具体可以包括以下步骤:Please refer to FIG. 3, which shows a schematic flowchart of a method for predicting a destination site according to another embodiment of the present application. The following will elaborate on the process shown in FIG. 3, and the prediction method of the destination site shown may specifically include the following steps:
步骤S210:获取当前进站信息,所述当前进站信息包括当前进站时间和当前进站点。Step S210: Obtain current inbound information, where the current inbound information includes the current inbound time and the current inbound station.
步骤S220:根据历史乘坐记录,获取与所述当前进站时间匹配的目标历史进站点。Step S220: According to the historical ride records, obtain the target historical entry point matching the current entry time.
在本申请实施例中,步骤S210和步骤S220可以参阅前述实施例的内容,此处不再赘述。In the embodiment of the present application, step S210 and step S220 can refer to the content of the foregoing embodiment, and will not be repeated here.
在一些实施例中,由于进入站点的时间点具备随机性,所以历史乘坐记录的进站时间点可能是零碎的、连续的,如果根据这些零碎的、连续的历史进站时间点,来预测进站点,预测结果不具备典型性,参考意义不太。因此,在一些实施例中,可以对历史乘坐记录中的所有历史进站时间进行时间分段分类,得到多个时间段类别(或时间区间类别),从而可以根据与当前进站时间匹配的时间段,来预测进站点。由于每个时间段内存在多个时间点,因此每个时间段可对应多个历史乘坐记录,通过多个历史乘坐记录而非零碎的历史乘坐记录,预测进站点,可使预测结果具备一定典型性,具备参考意义。In some embodiments, due to the randomness of the time point of entering the station, the pit time points of historical ride records may be fragmentary and continuous. For the site, the prediction results are not typical and have little reference significance. Therefore, in some embodiments, all historical inbound times in the historical ride records can be classified in time segments to obtain multiple time period categories (or time interval categories), which can be based on the time that matches the current inbound time. Segment, to predict entering the site. Since there are multiple time points in each time period, each time period can correspond to multiple historical ride records. Through multiple historical ride records rather than fragmentary historical ride records, predicting entry into the station can make the predicted results have a certain typical Sex, with reference significance.
具体地,请参阅图4,步骤S220可以包括:Specifically, referring to FIG. 4, step S220 may include:
步骤S221:根据历史乘坐记录,获取与所述当前进站时间匹配的进站时间段,所述进站时间段为对历史乘坐记录中的所有历史进站时间进行分段划分后得到时间段。Step S221: Obtain an inbound time period matching the current inbound time according to the historical ride record, where the inbound time period is obtained by dividing all historical inbound times in the historical ride record into sections.
在一些实施例中,获取与当前进站时间匹配的进站时间段,可以是获取距离当前进站时间最近的时间段。例如,当前进站时间为上午8点45分,与当前进站时间匹配的进站时间段可以是7:00~9:00这个时间段。该时间段可以是对历史乘坐记录中的所有历史进站时间进行分段划分后得到的。In some embodiments, acquiring the inbound time period that matches the current inbound time may be acquiring the time period closest to the current inbound time. For example, the current inbound time is 8:45 in the morning, and the inbound time period that matches the current inbound time can be the time period from 7:00 to 9:00. The time period may be obtained by dividing all historical inbound times in the historical ride records into sections.
作为一种实施方式,可以是从存储有历史乘坐记录的数据库中提取出所有的历史进站时间数据,然后采用聚类算法进行分析得到多个时间段类别。例如,将历史进站时间为6:00~10:00归为一个时间段,将进站时间为10:00~14:00的归为另一个时间段。多个时间段类别依次可记为1,2,3,…,KI类,KI为时间段类总个数。其中,聚类算法是机器学习中涉及对数据进行分组的一种算法,可通过聚类算法将给定的数据集分成一些不同的组。As an implementation manner, all historical arrival time data may be extracted from a database storing historical ride records, and then a clustering algorithm is used for analysis to obtain multiple time period categories. For example, categorize the historical inbound time from 6:00 to 10:00 as one time period, and categorize the inbound time from 10:00 to 14:00 as another time period. Multiple time period categories can be recorded as 1, 2, 3,..., KI, and KI is the total number of time period categories. Among them, the clustering algorithm is an algorithm that involves grouping data in machine learning. A given data set can be divided into different groups through the clustering algorithm.
可以理解的是,上述对所有历史进站时间进行时间分段的方式仅为举例,此处并不作限定。例如,也可以是将一天24小时以固定时长进行均分,从而得到多个时间段。如将一天24小时以一小时进行均分,可以得到24个时间段。It is understandable that the foregoing method of time segmenting all historical inbound times is only an example, and it is not limited here. For example, it is also possible to divide 24 hours a day with a fixed time length to obtain multiple time periods. If the 24 hours a day is divided into one hour, 24 time periods can be obtained.
在一些实施例中,聚类过程中可能会产生很多类别,但是不是所有类别都分布的很均匀,为了保证预测的准确性,可以将概率比较小的类剔除。作为一种方式,可以先统计各个时间段类的概率,然后仅选取概率大于指定阈值(记为Thr)的时间段类别ki。In some embodiments, many categories may be generated during the clustering process, but not all categories are uniformly distributed. In order to ensure the accuracy of the prediction, a category with a relatively small probability can be eliminated. As a way, the probability of each time period category can be counted first, and then only the time period category ki whose probability is greater than a specified threshold (denoted as Thr) is selected.
可选地,可以根据数据中的历史乘坐记录,统计各个时间段类的先验概率。该先验概率可以理解为每个类别对预测结果的影响能力,也即先验概率大的情况下,该类别对预测结果的影响越大。其计算公式如下:Optionally, the prior probability of each time period category can be counted according to the historical ride records in the data. The prior probability can be understood as the ability of each category to affect the prediction result, that is, when the prior probability is large, the greater the impact of the category on the prediction result. The calculation formula is as follows:
其中,
可以理解为数据库中历史进站时间t归为k类的乘坐记录条数,
可以理解为历史进站时间t归为1,2,3,…,KI类的乘坐记录条数,也可以理解为数据库中所有乘坐记录条数。从而可以将小于指定阈值的P(k)所对应的时间段类k剔除。
in, It can be understood as the number of ride records whose historical pit stop time t is classified as k category in the database, It can be understood that the historical pit stop time t is classified as 1, 2, 3,..., the number of KI ride records, or it can be understood as the number of all ride records in the database. Therefore, the time period category k corresponding to P(k) that is less than the specified threshold can be eliminated.
步骤S222:根据历史乘坐记录,获取在所述进站时间段下,进入不同历史进站点的进站概率。Step S222: According to the historical ride records, obtain the entry probabilities of entering different historical entry stops during the entry time period.
在一些实施例中,在获取到与当前进站时间匹配的进站时间段后,可以先从存储有历史乘坐记录否数据库中,获取历史进站时间在该进站时间段内的每个历史进站点的记录条数,然后计算每个历史进站点的记录条数和总历史乘坐记录条数的比值,即可得到在进站时间段下进入到每个历史进站点的进站概率。In some embodiments, after obtaining the inbound time period that matches the current inbound time, you can first obtain each history of the historical inbound time in the inbound time period from the database storing historical ride records. Enter the number of records in the station, and then calculate the ratio of the number of records in each historical station to the total number of historical ride records, and then the probability of entering each historical station in the inbound time period can be obtained.
可选的,用数学公式可表示为:p(k,c),c=1,2,…,C,C为地铁站总数目。p(k,c)可以理解为当进站时间为时间类k的时候,进入站点c的概率,即p(进站时间类为k且进站点=c)。Optionally, the mathematical formula can be expressed as: p(k,c), c=1, 2,...,C, where C is the total number of subway stations. p(k,c) can be understood as the probability of entering station c when the inbound time is time class k, that is, p (the inbound time class is k and inbound station=c).
在一些实施例中,也可以预先计算好在每个时间段下,进入不同历史进站点的进站概率,从而在获取到与当前进站时间匹配的进站时间段时,可直接获取到与该进站时间段对应的不同历史进站点的进站概率。In some embodiments, it is also possible to pre-calculate the inbound probability of entering different historical inbound stations in each time period, so that when the inbound time period that matches the current inbound time is obtained, the and The inbound probability of different historical sites corresponding to the inbound time period.
步骤S223:获取数值最大的所述进站概率对应的历史进站点,作为目标历史进站点。Step S223: Obtain the historical entry site corresponding to the entry probability with the largest value as the target historical entry site.
在获取到进站时间段下,进入不同历史进站点的进站概率后,可以将数值最大的进站概率所对应的历史进站点,作为目标历史进站点,即预测的进站点。可以理解的是,当进入到某个历史进站点的进站概率最大,表明用户后续在该进站时间段内,很大可能会习惯性地进入到该历史进站点,因此,可以将该历史进站点作为预测出的用户将要进入的进站点。After obtaining the inbound time period, after entering the inbound probabilities of different historical inbound stations, the historical inbound probability corresponding to the largest numerical value can be used as the target historical inbound station, that is, the predicted inbound station. It is understandable that when the probability of entering a certain historical entry site is the greatest, it indicates that the user is likely to enter the historical entry site habitually during the subsequent entry period. Therefore, the historical entry site can be habitually entered. The entry site is the predicted entry site that the user will enter.
在一些实施例中,也可以通过如下计算公式直接预测得到目标历史进站点:In some embodiments, the target historical entry site can also be directly predicted by the following calculation formula:
其中,InStation.time为上述获取到的当前进站信息中的当前进站时间,Ki为上述对所有历史进站时间进行时间段划分后的时间段类别。TEStation为预测的目标历史进站点。
用于计算距离的当前进站时间最近的时间段类k,p(k,c)用于计算当进站时间为最近的时间类k的时候,进入站点c的进站概率,
用于获取最大概率p(k,c)所对应的站点c。
Among them, InStation.time is the current inbound time in the current inbound information obtained above, and Ki is the above-mentioned time period category after all historical inbound times are divided into time periods. TEStation enters the site for the predicted target history. The current pit-stop time used to calculate the distance is the nearest time period class k, p(k, c) is used to calculate the pit-stop probability of entering station c when the pit-stop time is the nearest time class k, It is used to obtain the site c corresponding to the maximum probability p(k,c).
需要说明的是,s.t.是subject to的缩写,其意义为“使得...满足...”,可以理解为在满足第二个公式
的条件下,再计算第一个公式
It should be noted that st is the abbreviation of subject to, and its meaning is "to satisfy...", which can be understood as satisfying the second formula Under the conditions, calculate the first formula
步骤S230:判断所述当前进站点与所述目标历史进站点是否相同。若是,则可执行步骤S240至步骤S250;若否,则可执行步骤S260至步骤S290。Step S230: Determine whether the current incoming site and the target historical incoming site are the same. If yes, step S240 to step S250 can be performed; if not, step S260 to step S290 can be performed.
步骤S240:根据所述历史乘坐记录,获取所述当前进站点对应的第一概率,所述第一概率为在所述当前进站时间下,由所述当前进站点前往每个历史出站点的概率。Step S240: Obtain a first probability corresponding to the current inbound station according to the historical ride record, where the first probability is the number of times from the current inbound station to each historical outbound station at the current inbound time Probability.
由于在实际的当前进站点与预测的目标历史进站点相同时,可认为当前进站时间适合参与基于习惯的目的站点的预测。因此,可根据历史乘坐记录、当前进站时间及当前进站点预测目的站点,有效保证了预测结果的准确性。可选的,当前进站点与目标历史进站点相同,可以用数学公式表示为:InStation==TEStation。Since the actual current inbound site is the same as the predicted target historical site inbound, it can be considered that the current inbound time is suitable for participating in the prediction of the target site based on habit. Therefore, the destination station can be predicted based on the historical ride records, the current arrival time and the current arrival station, which effectively guarantees the accuracy of the prediction result. Optionally, the current incoming station is the same as the target historical incoming station, which can be expressed by a mathematical formula: InStation==TEStation.
在一些实施例中,可根据历史乘坐记录,获取当前进站点对应的第一概率,第一概率为在当前进站时间下,由当前进站点前往每个历史出站点的概率。作为一种方式,可以获取分别统计在当前进站时间下,由当前进站点前往每个历史出站点的历史乘坐记录条数,并分别计算每个记录条数与总历史记录条数的比值,即可得到在当前进站时间下,由当前进站点前往每个历史出站点的第一概率。In some embodiments, the first probability corresponding to the current inbound station may be obtained according to the historical ride records. The first probability is the probability of going from the current inbound station to each historical outbound station at the current inbound time. As a way, you can obtain the statistics of the number of historical ride records from the current inbound station to each historical outbound station under the current inbound time, and calculate the ratio of the number of each record to the total number of historical records. You can get the first probability of going from the current inbound station to each historical outbound station under the current inbound time.
在一些实施例中,也可以根据不同的历史出站时间,对应获取所述当前进站点对应的第一 概率。具体的,请参阅图5,步骤S240可以包括:In some embodiments, the first probability corresponding to the current inbound station may be correspondingly obtained according to different historical outbound times. Specifically, referring to FIG. 5, step S240 may include:
步骤S241:根据所述历史乘坐记录,获取与所述当前进站时间匹配的历史进站时间。Step S241: Obtain a historical pit stop time matching the current pit stop time according to the historical ride record.
步骤S242:获取在所述历史进站时间及不同历史出站时间下,由所述当前进站点前往每个历史出站点的转移概率,作为所述当前进站点对应的第一概率。Step S242: Obtain the transition probability from the current inbound station to each historical outbound station under the historical inbound time and different historical outbound times as the first probability corresponding to the current inbound station.
在一些实施例中,可以利用马尔可夫性和历史乘坐记录,统计分析出在所述当前进站时间下,由所述当前进站点前往每个历史出站点的概率。具体地,可以先根据历史乘坐记录,获取与当前进站时间匹配的历史进站时间,然后获取在该历史进站时间及不同历史出站时间下,由当前进站点前往每个历史出站点的转移概率,作为当前进站点对应的第一概率。In some embodiments, Markov property and historical ride records can be used to statistically analyze the probability of going from the current inbound station to each historical outbound station at the current inbound time. Specifically, you can first obtain the historical inbound time that matches the current inbound time according to the historical ride records, and then obtain the historical inbound time and different historical outbound times from the current inbound station to each historical outbound station. Transition probability, as the first probability corresponding to the current entry site.
作为一种方式,可以分别统计在当前进站时间及不同历史出站时间下,由当前进站点前往每个历史出站点的历史乘坐记录条数,并分别计算每个记录条数与总历史记录条数的比值,即可得到在当前进站时间及不同历史出站时间下,由当前进站点前往每个历史出站点的第一概率。As a way, you can separately count the number of historical ride records from the current inbound station to each historical outbound station under the current inbound time and different historical outbound times, and calculate the number of each record and the total historical records separately The ratio of the number of entries can get the first probability of going from the current inbound station to each historical outbound station under the current inbound time and different historical outbound times.
在一些实施例中,由于出站时间也具备随机性,也可能会呈零碎的、连续的,这样可能会导致需要计算大量的转移概率。因此,也可以对所有历史出站时间数据进行时间段划分,得到多个出站时间段。作为一种方式,可以从存储有历史乘坐记录的数据库中提取出所有的历史出站时间数据,然后采用聚类算法进行分析得到多个出站时间段类别。多个时间段类别依次可记为1,2,3,…,KO类,KO为时间段类总个数。具体划分方式可参阅历史进站时间的划分,此处不再赘述。In some embodiments, since the outbound time is also random, it may also be fragmented and continuous, which may result in the need to calculate a large number of transition probabilities. Therefore, all historical outbound time data can also be divided into time periods to obtain multiple outbound time periods. As a way, it is possible to extract all historical outbound time data from a database storing historical ride records, and then use a clustering algorithm for analysis to obtain multiple outbound time period categories. Multiple time period categories can be recorded as 1, 2, 3,..., KO category, and KO is the total number of time period categories. For the specific division method, please refer to the division of historical inbound time, which will not be repeated here.
因此,可根据历史乘坐记录,获取与当前进站时间匹配的进站时间段,然后可获取在该进站时间段及不同出站时间段下,由当前进站点前往每个历史出站点的转移概率,作为所述当前进站点对应的第一概率。作为一种方式,可以通过获取在该进站时间段及目标出站时间段t下,由当前进站点前往目标历史出站点c的乘坐记录条数,并通过计算该乘坐记录条数与总记录条数的比值,即可得到在该进站时间段及目标出站时间段t下,由当前进站点前往目标历史出站点c的转移概率,其中,t值可取1,2,3,…,KO,c可取s=1,2,…,C,C为地铁站总数目。从而可以得到在不同出站时间段,不同出站点下,当前进站点对应的多个第一概率。Therefore, according to the historical ride records, the inbound time period that matches the current inbound time can be obtained, and then the transfer from the current inbound station to each historical outbound station under this inbound time period and different outbound time periods can be obtained The probability is used as the first probability corresponding to the current incoming station. As a way, it is possible to obtain the number of ride records from the current inbound station to the target historical outbound station c under the inbound time period and the target outbound time period t, and calculate the number of ride records and the total record The ratio of the number of entries can be used to obtain the transition probability from the current inbound station to the target historical outbound station c under the inbound time period and the target outbound time period t, where the value of t can be 1, 2, 3, ..., KO, c can take s=1,2,...,C, C is the total number of subway stations. In this way, multiple first probabilities corresponding to the current inbound station in different outbound time periods and different outbound stations can be obtained.
在一些实施例中,也可以数据库存储的历史乘坐记录,预先计算好在每个进站时间段、每个出站时间段下,从不同历史进站点前往不同的历史出站点的转移概率,从而在获取到与当前进站时间匹配的进站时间段和当前进站点时,可直接获取到与该进站时间段和当前进站点对应的,不同历史出站点和不同历史出站时间的转移概率。可选的,用数学公式可表示为:p(c,k,s,t),c=1,…,C,k=1,…,KI,s=1,…,C;t=1,…,KO,其中,c为进站点,k为进站时间段类,s为出站点,t为出站时间段类,C为地铁站总数目。In some embodiments, the historical ride records stored in the database can also be pre-calculated in each inbound time period and each outbound time period from different historical inbound stations to different historical outbound stations. When the inbound time period and the current inbound station that match the current inbound time are obtained, the transition probability of different historical outbound stations and different historical outbound times corresponding to the inbound time period and the current inbound station can be directly obtained . Optionally, the mathematical formula can be expressed as: p(c,k,s,t), c=1,...,C,k=1,...,KI,s=1,...,C; t=1, …, KO, where c is the inbound station, k is the inbound time period category, s is the outbound station, t is the outbound time period category, and C is the total number of subway stations.
同理,在一些实施例中,也可以将概率比较小的时间类剔除。作为一种方式,可以根据数据中的历史乘坐记录,统计各个时间段类的先验概率。其计算公式如下:In the same way, in some embodiments, time classes with a relatively small probability may also be eliminated. As a way, the prior probability of each time period category can be counted according to the historical ride records in the data. The calculation formula is as follows:
其中,
可以理解为数据库中历史出站时间t归为k类的乘坐记录条数,
可以理解为历史出站时间t归为1,2,3,…,KO类的乘坐记录条数,也可以理解为数据库中所有乘坐记录条数。从而可以将小于指定阈值的P(k)所对应的时间段类k剔除。
in, It can be understood as the number of ride records whose historical outbound time t is classified as category k in the database, It can be understood that the historical outbound time t is classified as 1, 2, 3,..., the number of ride records in the KO category, or it can be understood as the number of all ride records in the database. Therefore, the time period category k corresponding to P(k) that is less than the specified threshold can be eliminated.
步骤S250:获取数值最大的所述第一概率对应的历史出站点,作为预测的目的站点。Step S250: Obtain the historical outbound site corresponding to the first probability with the largest value as the predicted destination site.
当获取到在当前进站时间下,由当前进站点前往每个历史出站点的第一概率后,可以将数值最大的第一概率所对应的历史出站点,作为预测的目的站点。可以理解的是,当在当前进站时间下,由当前进站点前往某个历史出站点的第一概率最大,可表明用户后续在当前进站时间进入到当前进站点时,很大可能会习惯性地前往该历史出站点,因此,可以将该历史出站点作为预测出的用户将要到达的目的站点。After obtaining the first probability of going from the current inbound station to each historical outbound station at the current inbound time, the historical outbound station corresponding to the first probability with the largest value can be used as the predicted destination station. It is understandable that the first probability of going from the current inbound station to a historical outbound station at the current inbound time is the greatest, which indicates that the user is likely to get used to it when he subsequently enters the current inbound station at the current inbound time. To go to the historical out-of-site site in a specific manner, therefore, the historical out-of-site site can be used as the predicted destination site that the user will reach.
在一些实施例中,也可以通过如下计算公式直接预测得到目的站点:In some embodiments, the target site can also be directly predicted by the following calculation formula:
其中,EStation为预测得到的目的站点,
用于获取最大概率p(c,k,s,t)所对应的出站点s和出站时间t。
Among them, EStation is the predicted destination site, It is used to obtain the outbound station s and outbound time t corresponding to the maximum probability p(c, k, s, t).
在一些实施例中,也可以对当前的到站时间进行预测。具体地,当获取到在当前进站时间下,由当前进站点前往每个历史出站点的第一概率后,可以将数值最大的第一概率所对应的历史出站时间,作为预测的到站时间。作为一种方式,可以将当前进站点和当前进站时间对应的进站时间段类,代入公式(4)中的c和k,从而可以获取最大概率p(c,k,s,t)对应的出站点s和出站时间t,该出站点可作为预测的目的站点,该出站时间t可作为预测的到站时间。In some embodiments, the current arrival time can also be predicted. Specifically, when the first probability of going from the current inbound station to each historical outbound station under the current inbound time is obtained, the historical outbound time corresponding to the first probability with the largest value can be used as the predicted arrival time. As a way, the inbound time period class corresponding to the current inbound station and the current inbound time can be substituted into c and k in formula (4), so as to obtain the maximum probability p(c, k, s, t) corresponding to The outbound station s and outbound time t can be used as the predicted destination station, and the outbound time t can be used as the predicted arrival time.
在一些实施例中,在获取到预测的到站时间后,也可以根据该到站时间进行到站提醒。作为一种方式,可以是根据预测的到站时间,触发移动终端输出提示信息,该提示信息用于提醒用户已到站或者即将到站。其中,提示信息可以包含已到站或者即将到站的站点名称和预计时间。可以采用语音方式输出,也可以采用窗口的形式弹出,此处不做限定。In some embodiments, after the predicted arrival time is obtained, the arrival reminder may also be performed according to the arrival time. As a way, it may be based on the predicted arrival time to trigger the mobile terminal to output prompt information, which is used to remind the user that the user has arrived or is about to arrive. Among them, the prompt information may include the name and estimated time of the station that has arrived or is about to arrive. It can be output by voice or pop-up in the form of a window, which is not limited here.
步骤S260:根据所述历史乘坐记录,获取所述当前进站点对应的第二概率,所述第二概率为在不同历史进站时间下,进入所述当前进站点的概率。Step S260: Obtain a second probability corresponding to the current station entry according to the historical ride record, where the second probability is the probability of entering the current station entry under different historical entry times.
由于在实际的当前进站点与预测的目标历史进站点不相同时,可认为当前进站时间不适合参与基于习惯的目的站点的预测。因此,可直接根据历史乘坐记录及当前进站点预测目的站点,保证了预测结果的准确性。可选的,当前进站点与目标历史进站点不相同,可以用数学公式表示为:InStation!=TEStation。Since the actual current station is not the same as the predicted target historical station, it can be considered that the current station time is not suitable for participating in the prediction of the target station based on habits. Therefore, the destination station can be predicted directly based on the historical ride records and the current station entry, ensuring the accuracy of the prediction result. Optionally, the current incoming station is different from the target historical incoming station, which can be expressed by a mathematical formula: InStation! = TEStation.
在一些实施例中,可根据历史乘坐记录,获取当前进站点对应的第二概率,以便后续根据该第二概率预测目的站点。其中,该第二概率为在不同历史进站时间下,进入当前进站点的概率。作为一种方式,可以获取分别统计在不同历史进站时间下,进入当前进站点的历史乘坐记录条数,并分别计算每个记录条数与总历史记录条数的比值,即可得到在不同历史进站时间下,进入当前进站点的第二概率。In some embodiments, the second probability corresponding to the current stop can be obtained according to the historical ride record, so as to subsequently predict the destination stop based on the second probability. Among them, the second probability is the probability of entering the current station at different historical station time. As a way, you can obtain the statistics of the number of historical ride records entering the current station at different historical pit stops, and calculate the ratio of the number of each record to the total number of historical records. The second probability of entering the current station at the historical station time.
步骤S270:获取数值最大的所述第二概率对应的历史进站时间,作为预测的目标进站时间。Step S270: Obtain the historical entry time corresponding to the second probability with the largest value as the predicted target entry time.
当获取到在不同历史进站时间下,进入当前进站点的第二概率后,可以将数值最大的第二概率所对应的历史进站时间,作为可参与目的站点预测的目标进站时间。可以理解的是,当在某个进站时间下,进入当前进站点的概率最大,可表明用户很大可能会习惯性地在该进站时间进入当前进站点,因此,可以将数值最大的第二概率所对应的历史进站时间作为参与目的站点预测的目标进站时间。After obtaining the second probability of entering the current station under different historical pitting times, the historical pitting time corresponding to the second probability with the largest value can be used as the target pitting time that can participate in the prediction of the destination station. It is understandable that the probability of entering the current station at a certain pit-stop time is the greatest, which indicates that the user is likely to habitually enter the current station at that pit-stop time. Therefore, you can set the highest value The historical pit-stop time corresponding to the second probability is used as the target pit-stop time predicted by the participating destination site.
在一些实施例中,当对所有历史进站时间进行时间段划分时,也可以是获取在不同进站时间段下,进入当前进站点的第二概率,然后获取数值最大的第二概率对应的进站时间段,作为预测的目标进站时间段。作为一种方式,可以获取分别统计在不同进站时间段下,进入当前进站点的历史乘坐记录条数,并分别计算每个记录条数与总历史记录条数的比值,即可得到在不同进站时间段下,进入当前进站点的第二概率。In some embodiments, when dividing all historical inbound times into time periods, it may also be to obtain the second probability of entering the current station in different inbound time periods, and then obtain the second probability corresponding to the largest value. The pit-stop time period is used as the predicted target pit-stop time period. As a method, you can obtain the statistics of the number of historical rides entering the current station during different time periods of inbound stations, and calculate the ratio of the number of each record to the total number of historical records. The second probability of entering the current station during the pit stop time period.
在一些实施例中,也可以通过如下计算公式直接获取到概率最大的目标进站时间:In some embodiments, the target entry time with the highest probability can also be directly obtained through the following calculation formula:
其中,c代入当前进站点,CEStation为预测得到的的目标进站时间,p(c,k)用于计算当进入的站点是c的时候各个时间段的概率,k=1,2,3…,KI。即p(进站点=c且进站时间类为k)。
用于获取最大概率p(c,k)所对应的进站时间段类k。
Among them, c is substituted into the current station, CEStation is the predicted target station time, p(c,k) is used to calculate the probability of each time period when the station is c, k=1, 2, 3... , KI. That is, p (incoming stop = c and the incoming time class is k). It is used to obtain the inbound time category k corresponding to the maximum probability p(c,k).
步骤S280:根据所述历史乘坐记录,获取所述当前进站点对应的第三概率,所述第三概率为在所述目标进站时间下,由所述当前进站点前往每个历史出站点的概率。Step S280: Obtain a third probability corresponding to the current inbound station according to the historical ride record, where the third probability is the number of times from the current inbound station to each historical outbound station at the target inbound time Probability.
步骤S290:获取数值最大的所述第三概率对应的历史出站点,作为预测的目的站点。Step S290: Obtain the historical outbound site corresponding to the third probability with the largest value as the predicted destination site.
在根据当前进站点,获取到最大概率的目标进站时间后,可以根据历史乘坐记录、目标进站时间及当前进站点预测目的站点。具体地,可以先根据历史乘坐记录,获取当前进站点对应的第三概率,该第三概率为在目标进站时间下,由当前进站点前往每个历史出站点的概率,然后获取数值最大的第三概率对应的历史出站点,作为预测的目的站点。其中,获取当前进站点对应的第三概率,以及获取数值最大的第三概率对应的历史出站点,可以参阅前述获取当前进站点对应的第一概率以及获取数值最大的第一概率对应的历史出站点的相关内容,此处不再赘述。After obtaining the most probable target pit stop time based on the current approach station, the destination station can be predicted based on historical ride records, target pit station time, and current approach station. Specifically, you can first obtain the third probability corresponding to the current inbound station according to the historical ride records. The third probability is the probability of going from the current inbound station to each historical outbound station at the target inbound time, and then obtain the highest numerical value. The historical outbound site corresponding to the third probability is used as the predicted destination site. Among them, to obtain the third probability corresponding to the current entry site, and to obtain the historical exit site corresponding to the third probability with the largest value, please refer to the aforementioned acquisition of the first probability corresponding to the current entry site and the historical exit site corresponding to the first probability with the largest value. The relevant content of the site will not be repeated here.
在一些实施例中,也可以对当前的到站时间进行预测。具体地,当获取到在预测的目标进站时间下,由当前进站点前往每个历史出站点的第三概率后,可以将数值最大的第三概率所对应的历史出站时间,作为预测的到站时间。In some embodiments, the current arrival time can also be predicted. Specifically, when the third probability of going from the current inbound station to each historical outbound station under the predicted target inbound time is obtained, the historical outbound time corresponding to the third probability with the largest value can be used as the predicted Arrival time.
当前,也可以将目标进站时间对应的进站时间段类和当前进站点代入公式(4)中的c和k,从而可以获取到最大概率p(c,k,s,t)对应的出站点s和出站时间t,该出站点可作为预测的目的站点,该出站时间t可作为预测的到站时间。在一些实施例中,在获取到预测的到站时间后,也可以根据该到站时间触发移动终端输出提示信息,以提醒用户已到站或者即将到站。At present, it is also possible to substitute the inbound time period class corresponding to the target inbound time and the current inbound station into c and k in formula (4), so that the outbound corresponding to the maximum probability p(c, k, s, t) can be obtained. Station s and outbound time t, the outbound site can be used as the predicted destination site, and the outbound time t can be used as the predicted arrival time. In some embodiments, after the predicted arrival time is obtained, the mobile terminal can also be triggered to output prompt information according to the arrival time to remind the user that the station has arrived or is about to arrive.
在一些实施例中,如果存在最大概率相同的预测结果,可能表明用户的乘坐习惯发生改变,可以暂时输出目的站为空,待满足一定周期,数据库更完善之后,再继续进行预测。本申请实施例提供的目的站点的预测方法,通过获取当前进站信息,该当前进站信息可包括当前进站时间和当前进站点,然后根据历史乘坐记录,获取与当前进站时间匹配的目标历史进站点,以在当前进站点与该目标历史进站点相同时,可根据历史乘坐记录、当前进站时间及当前进站点预测目的站点;而在当前进站点与该目标历史进站点不相同时,可先根据历史乘坐记录和当前进站点,对进站时间进行预测,得到预测的目标进站时间,然后再根据历史乘坐记录、预测的目标进站时间及当前进站点预测目的站点。如此,考虑到了当前进站时间对预测结果的影响,在基于当前进站时间预测进站点预测得准确的情况下,可以根据当前进站时间和当前进站点来预测,而在基于当前进站时间预测进站点预测得不太准确的情况下,还可以直接根据当前进站点来预测。提升了对目的站点预测的准确性,使得预测的目的地更贴合用户实际需求。In some embodiments, if there is a prediction result with the same maximum probability, it may indicate that the user's riding habits have changed, and the destination station can be temporarily output as empty. After a certain period is met and the database is more complete, the prediction can be continued. The method for predicting the destination station provided by the embodiment of this application obtains the current station information, which can include the current station time and the current station, and then obtains the target that matches the current station time according to the historical ride records Historical entry site, to predict the destination site based on historical ride records, current entry time, and current entry site when the current entry site is the same as the target historical entry site; and when the current entry site is different from the target historical entry site , According to the historical ride records and the current station, the pit time can be predicted to obtain the predicted target pit time, and then the destination station can be predicted based on the historical ride records, the predicted target pit time and the current station. In this way, taking into account the influence of the current pit stop time on the prediction result, if the forecast of the entry station based on the current pit stop time is accurate, the forecast can be based on the current pit stop time and the current entry station, and based on the current pit stop time In the case that the predicted entry site is not very accurate, it can also be directly predicted based on the current entry site. Improve the accuracy of the destination site prediction, making the predicted destination more in line with the actual needs of users.
请参阅图6,图6示出了本申请又一个实施例提供的目的站点的预测方法的流程示意图。下面将针对图6所示的流程进行详细的阐述,所示目的站点的预测方法具体可以包括以下步骤:Please refer to FIG. 6, which shows a schematic flowchart of a method for predicting a destination site according to another embodiment of the present application. The following will elaborate on the process shown in FIG. 6, and the prediction method of the destination site shown may specifically include the following steps:
步骤S310:获取根据第一历史乘坐记录预测得到的第一目的站点,所述第一历史乘坐记录为从当前日期起追溯至第一日期的历史乘坐记录。Step S310: Obtain a first destination station predicted based on a first historical ride record, the first historical ride record being a historical ride record dating back to the first date from the current date.
步骤S320:获取根据第二历史乘坐记录预测得到的第二目的站点,所述第二历史乘坐记录为从当前日期起追溯至第二日期的历史乘坐记录,所述第二日期早于所述第一日期。Step S320: Obtain a second destination station predicted based on a second historical ride record, the second historical ride record being a historical ride record retroactive from the current date to a second date, and the second date is earlier than the first A date.
在一些实施例中,可以通过对近期乘坐记录和长期乘坐记录的学习,来分别预测目的站点,以提高预测的准确性。具体地,可以获取根据第一历史乘坐记录预测得到的第一目的站点,该第一历史乘坐记录为从当前日期起追溯至第一日期的历史乘坐记录,并获取根据第二历史乘坐记录预测得到的第二目的站点,第二历史乘坐记录为从当前日期起追溯至第二日期的历史乘坐记录。其中,第二日期早于第一日期,也即第一历史乘坐记录可以理解为近期乘坐记录,第二历史乘坐记录可以理解为长期乘坐记录。In some embodiments, the destination site can be predicted separately through learning of recent ride records and long-term ride records, so as to improve the accuracy of prediction. Specifically, it is possible to obtain the first destination station predicted based on the first historical ride record, which is the historical ride record dating back to the first date from the current date, and obtain the prediction based on the second historical ride record The second destination site, the second historical ride record is the historical ride record dating back to the second date from the current date. The second date is earlier than the first date, that is, the first historical ride record can be understood as a recent ride record, and the second historical ride record can be understood as a long-term ride record.
可以理解的是,根据第一历史乘坐记录预测得到的第一目的站点,及根据第二历史乘坐记录预测得到的第二目的站点,可以是根据前述的预测方式预测得到,此处不再赘述。It is understandable that the first destination site predicted based on the first historical ride record and the second destination site predicted based on the second historical ride record can be predicted based on the aforementioned prediction method, and will not be repeated here.
需要说明的是,第一历史乘坐记录和第二历史乘坐记录具体的追溯的时长在本申请并不作限定,根据具体场景合理设置即可。例如,第一历史乘坐记录的追溯时长可以是48小时内,第二历史乘坐记录的追溯时长可以是半年或一年内。It should be noted that the specific retrospective duration of the first historical ride record and the second historical ride record is not limited in this application, and can be set reasonably according to specific scenarios. For example, the retrospective duration of the first historical ride record may be within 48 hours, and the retrospective duration of the second historical ride record may be within half a year or within one year.
步骤S330:根据所述第一目的站点和所述第二目的站点,确定预测的目的站点。Step S330: Determine a predicted destination site according to the first destination site and the second destination site.
在获取到近期乘坐记录和长期乘坐记录预测得到的第一目的站点和第二目的站点后,可以进行站点融合处理,得到最终的目的站预测结果。After obtaining the first destination site and the second destination site predicted by the recent ride records and long-term ride records, site fusion processing can be performed to obtain the final destination station prediction results.
在一些实施例中,可以对近期习惯预测结果和长期习惯预测结果分配不同的权重,以根据权重确定最终预测的目的站点。具体的,请参阅图7,步骤S330可以包括:In some embodiments, different weights may be assigned to the short-term habit prediction results and the long-term habit prediction results, so as to determine the final predicted destination site according to the weights. Specifically, referring to FIG. 7, step S330 may include:
步骤S331:分别获取所述第一目的站点对应的第一权重、所述第二目的站点对应的第二权重。Step S331: Obtain the first weight corresponding to the first destination site and the second weight corresponding to the second destination site, respectively.
步骤S332:获取所述第一目的站点对应的预测概率与所述第一权重的第一乘积。Step S332: Obtain a first product of the predicted probability corresponding to the first destination site and the first weight.
步骤S333:获取所述第二目的站点对应的预测概率与所述第二权重的第二乘积。Step S333: Obtain a second product of the predicted probability corresponding to the second destination site and the second weight.
步骤S334:从所述第一乘积和所述第二乘积中,获取数值最大的乘积所对应的目的站点,作为预测的目的站点。Step S334: From the first product and the second product, obtain the destination site corresponding to the product with the largest value as the predicted destination site.
在一些实施例中,在获取到第一目的站点和第二目的站点后,可以分别获取对应的第一权重和第二权重。然后可分别计算第一目的站点对应的预测概率与第一权重的第一乘积,第二目的站点对应的预测概率与第二权重的第二乘积,以从第一乘积和第二乘积中,获取数值最大的乘积所对应的目的站点,作为预测的目的站点。其中,第一目的站点对应的预测概率和第二目的站点对应的预测概率可以是利用前述预测方法预测得到目的站点所对应的最大概率即
In some embodiments, after the first destination site and the second destination site are acquired, the corresponding first weight and second weight can be acquired respectively. Then the first product of the predicted probability corresponding to the first destination site and the first weight can be calculated, and the second product of the predicted probability corresponding to the second destination site and the second weight can be calculated to obtain from the first product and the second product The destination site corresponding to the product with the largest value is used as the predicted destination site. Among them, the predicted probability corresponding to the first destination site and the predicted probability corresponding to the second destination site can be the maximum probability corresponding to the destination site predicted by the foregoing prediction method, namely
在一些实施例中,为了使预测结果比较符合当下用户的出行习惯,可以是近期习惯预测结果对应的权重值,高于长期习惯预测结果对应的权重值,即第一权重大于第二权重。当然,具体的权重设置,此处并不作限定,根据具体预测需求合理设置即可。In some embodiments, in order to make the prediction result more consistent with the current user's travel habits, the weight value corresponding to the recent habit prediction result may be higher than the weight value corresponding to the long-term habit prediction result, that is, the first weight is greater than the second weight. Of course, the specific weight setting is not limited here, and it can be set reasonably according to the specific forecast demand.
本申请实施例提供的目的站点的预测方法,通过基于长期历史乘坐记录,采用前述的目标站点预测方法预测得到第一目的站点,并基于近期历史乘坐记录,采用前述的目标站点预测方法预测得到第二目的站点,然后根据该第一目的站点和第二目的站点,确定最终预测的目的站点。如此,在对目的站点进行预测时,既考虑到了用户的长期乘坐习惯,也考虑了用户的近期乘坐习惯,可在近期乘坐习惯发生改变时,避免因单一学习长期乘坐习惯而导致的预测不准确,提升了对目的站点预测的准确性,使得预测的目的地更贴合用户实际需求。The method for predicting the destination station provided in the embodiment of the application uses the aforementioned target station prediction method to predict the first destination station based on long-term historical ride records, and uses the aforementioned target station prediction method to predict the first destination station based on recent historical ride records. Second destination site, and then determine the final predicted destination site based on the first destination site and the second destination site. In this way, when predicting the destination site, both the long-term riding habits of the user and the recent riding habits of the user are taken into consideration. When the recent riding habits change, the inaccurate prediction caused by the single learning long-term riding habits can be avoided. , Improve the accuracy of the destination site prediction, making the predicted destination more suitable for the actual needs of users.
在一些实施例中,在得到预测的目的站点后,还可以实现到站提醒功能。具体地,请参阅图8,图8示出了本申请又一个实施例提供的目的站点的预测方法的流程示意图。下面将针对图8所示的流程进行详细的阐述,所示目的站点的预测方法具体可以包括以下步骤:In some embodiments, after the predicted destination site is obtained, the arrival reminder function can also be implemented. Specifically, please refer to FIG. 8, which shows a schematic flowchart of a method for predicting a destination site according to another embodiment of the present application. The following will elaborate on the process shown in FIG. 8. The prediction method of the destination site shown may specifically include the following steps:
步骤S410:判断当前站点是否为所述目的站点的相邻站点。若是,则可执行步骤S420;若否,则可执行步骤S430。Step S410: Determine whether the current site is a neighboring site of the destination site. If yes, step S420 can be executed; if not, step S430 can be executed.
步骤S420:触发移动终端输出提示信息,所述提示信息用于提醒用户即将到站。Step S420: Trigger the mobile terminal to output prompt information, which is used to remind the user that the station is about to arrive.
步骤S430:不执行触发移动终端输出提示信息,所述提示信息用于提醒用户即将到站的步骤。Step S430: The step of triggering the mobile terminal to output prompt information is not executed, and the prompt information is used to remind the user that the station is about to arrive.
在本申请实施例中,可以实时定位当前站点,以在当前站点到达目的站点的相邻站点时,触发到站提醒功能。具体地,在获取到当前站点时,可以判断当前站点是否为目的站点的相邻站点。如目的站点的前一站点。在当前站点为目的站点的相邻站点时,可触发移动终端输出提示信息,该提示信息用于提醒用户即将到站。在当前站点不为目的站点的相邻站点时,可不触发移动终端输出提示信息。如图9所示,图9示出了一种应用场景示意图,用户从A进站,预测目的站点E。手机感知到A->B->C->D,并且快到E时提醒用户到站。其中,可以通过GPS、WiFi、蓝牙、基站和IMU等多种方式对当前站点进行定位,此处并不作限定。In the embodiment of the present application, the current site can be located in real time, so as to trigger the arrival reminder function when the current site reaches the neighboring site of the destination site. Specifically, when the current site is acquired, it can be determined whether the current site is a neighboring site of the destination site. Such as the site before the destination site. When the current site is an adjacent site of the destination site, the mobile terminal can be triggered to output prompt information, which is used to remind the user that the station is about to arrive. When the current site is not an adjacent site of the destination site, the mobile terminal may not be triggered to output prompt information. As shown in Figure 9, Figure 9 shows a schematic diagram of an application scenario where a user enters a station from A and predicts a destination site E. The mobile phone perceives A->B->C->D and reminds the user to arrive when it is approaching E. Among them, the current site can be located in multiple ways such as GPS, WiFi, Bluetooth, base station, and IMU, which is not limited here.
在一些实施例中,可以通过基站信息对当前站点进行定位,具体地,请参阅图10,在步骤S410之前,本申请的目的站点的预测方法还可以包括:In some embodiments, the current site can be located based on the base station information. Specifically, referring to FIG. 10, before step S410, the method for predicting the target site of the present application may further include:
步骤S400:获取移动终端当前连接的基站信息。Step S400: Obtain the information of the base station to which the mobile terminal is currently connected.
通常情况下用户手机等移动终端都是插了手机卡,因为手机连接到了移动,联通或者电信运营商的基站,这样手机才能打电话和上网。因此,在一些实施例中,可以获取移动终端当前连接的基站信息,该基站信息包括基站唯一Cell ID号,运营商等信息,以便根据该基站信息查询数据库。Generally, mobile terminals such as mobile phones of users are plugged into mobile phone cards, because the mobile phone is connected to the base station of China Mobile, China Unicom, or telecom operator, so that the mobile phone can make calls and surf the Internet. Therefore, in some embodiments, information of the base station to which the mobile terminal is currently connected can be obtained. The base station information includes the unique Cell ID of the base station, the operator, and other information, so as to query the database based on the base station information.
步骤S401:根据基站数据库中存储的基站与站点的对应关系,获取与所述基站信息对应的目标站点。Step S401: Acquire a target site corresponding to the base station information according to the corresponding relationship between the base station and the site stored in the base station database.
例如,于地铁场景,如图11所示,图11示出了一种基站覆盖示意图。可以看出基站覆盖范围是有限的。通常基站覆盖范围在2公里左右,一般不会超过5公里。考虑到地铁站内信号质量不好,各大运营商会在地铁中部署更多数量的基站,确保地铁上通话质量,上网质量,所以在不同的地铁站,手机会注册到不通的基站上。For example, in a subway scene, as shown in FIG. 11, FIG. 11 shows a schematic diagram of base station coverage. It can be seen that the coverage of the base station is limited. The coverage range of the base station is usually about 2 kilometers, and generally no more than 5 kilometers. Taking into account the poor signal quality in subway stations, major operators will deploy more base stations in the subway to ensure the quality of calls and Internet access on the subway. Therefore, at different subway stations, mobile phones will be registered to unreachable base stations.
在一些实施例中,可以建立一个基站和站点的映射表,存到数据库中,从而得到基站数据库。其中,数据库表格内容可以是通过线下人工去实际站点用手机去扫描周围基站信息得到。从而可根据基站数据库中存储的基站与站点的对应关系,获取与当前移动终端连接的基站信息对应的目标站点。In some embodiments, a base station and site mapping table can be established and stored in the database to obtain the base station database. Among them, the content of the database table can be obtained by manually going offline to the actual site and scanning the surrounding base station information with a mobile phone. Therefore, the target site corresponding to the information of the base station currently connected to the mobile terminal can be obtained according to the corresponding relationship between the base station and the site stored in the base station database.
例如,建立的基站和地铁站点的映射表可以如表2所示。根据移动终端当前连接的基站信息,查询数据库,可以得到该基站所覆盖的地铁站点。从而可以定位到用户的当前站点。For example, the established mapping table between the base station and the subway station may be as shown in Table 2. According to the information of the base station to which the mobile terminal is currently connected, query the database to obtain the subway stations covered by the base station. So you can locate the user's current site.
表2Table 2
基站信息Base station information | 地铁站信息Subway station information |
mcc-460-mnc-00-ci-243174306-pci-303-tac-6227mcc-460-mnc-00-ci-243174306-pci-303-tac-6227 | 上海-7号线-龙华中路Shanghai Line 7-Longhua Middle Road |
mcc-460-mnc-01-ci-11114774-pci-501-tac-6263mcc-460-mnc-01-ci-11114774-pci-501-tac-6263 | 上海-12号线-龙漕路Shanghai Line-12-Longcao Road |
mcc-460-mnc-01-ci-11115275-pci-466-tac-6263mcc-460-mnc-01-ci-11115275-pci-466-tac-6263 | 上海-12号线-龙华中路Shanghai Line-12-Longhua Middle Road |
在一些实施例中,可能会数据采集不全,或者是运营商后续维护时新增了基站,导致在基站数据库查询不到对应的基站信息,从而获取不到目标站点。此时可以其他定位方式对当前站 点进行定位。In some embodiments, data collection may be incomplete, or the operator may add a new base station during subsequent maintenance, resulting in the base station database not being queried for corresponding base station information, and the target site may not be obtained. At this time, other positioning methods can be used to locate the current site.
步骤S402:根据所述目标站点,确定当前站点。Step S402: Determine the current site according to the target site.
在一些实施例中,当获取到与基站信息对应的目标站点时,且对应到的目标站点为一个,可将该目标站点,作为当前站点。其可说明此时用户进入即将到达该站点或者当前处于该站点。In some embodiments, when the target site corresponding to the base station information is obtained, and the corresponding target site is one, the target site may be used as the current site. It can indicate that the user is about to arrive at the site or is currently at the site at this time.
在一些实施例中,当基站覆盖范围比较广时,基站信息可能会覆盖到2个或者更多个站点,此时法确定用户当前在哪个站点。因此,需要借助前一个站点来辅助确定。具体地,目标站点为多个时,步骤S402可以包括:In some embodiments, when the coverage of the base station is relatively wide, the information of the base station may cover 2 or more sites. In this case, it is determined which site the user is currently at. Therefore, the previous site needs to be used to assist in the determination. Specifically, when there are multiple target sites, step S402 may include:
步骤S4021:获取前一站点的相邻站点。Step S4021: Obtain neighboring sites of the previous site.
步骤S4022:获取多个所述目标站点与所述相邻站点中的相同站点,作为当前站点。Step S4022: Obtain multiple identical sites among the target sites and the neighboring sites as the current site.
在一些实施例中,当需要对当前站点进行定位时,可以获取前一个已经确定的站点。从而可以根据官方路线信息,获取前一站的的相邻站点,然后可以获取基站覆盖的多个目标站点与该相邻站点中的相同站点,作为当前站点。示例性地,请参阅图12,图12示出了一种基站定位的整体流程示意图。需要说明的是,在乘坐初始时刻,已确定的站点为进站站点。In some embodiments, when the current site needs to be located, the previously determined site can be acquired. In this way, the neighboring site of the previous station can be obtained according to the official route information, and then multiple target sites covered by the base station and the same site in the neighboring site can be obtained as the current site. Exemplarily, please refer to FIG. 12, which shows a schematic diagram of an overall flow of base station positioning. It should be noted that at the initial moment of the ride, the determined stop is the pit stop.
例如,A-B-C-D-E地铁站,若之前已确定当前站是B(也可以理解为前一站),由于地铁一直在前行,需实时确定当前站,B的相邻站集合(A,C),如果当前连接的基站覆盖了2个地铁站(C,D),这两集合的交集为C,C即为地铁前行后的当前站(即B的后一站)。For example, at the ABCDE subway station, if the current station is determined to be B (also known as the previous station), since the subway is always moving forward, the current station needs to be determined in real time, and the adjacent stations of B are set (A, C), if The currently connected base station covers 2 subway stations (C, D), the intersection of the two sets is C, and C is the current station after the subway travels (that is, the next station after B).
作为一个具体的实施例,于地铁场景,请参阅图13,图13示出了一种目的站点的预测的整体流程示意框图。其中:As a specific embodiment, in the subway scene, please refer to FIG. 13, which shows a schematic block diagram of the overall flow of prediction of a destination site. in:
进站识别模块101:用户进地铁站识别,方法有手机刷码识别,手机NFC识别,进站位置GPS识别,地铁行驶识别,该模块是内嵌到android系统中。当用户使用手机APP刷码进入地铁站,比如上海地铁是metro大都会,深圳有微信小程序深圳地铁,北京用亿通行。刷码进站后,手机上会弹出进站成功和进站站点。进站出站识别模块是定制化系统的一部分,可以识别到手机屏幕上的变化,进站消息和进站站点。Inbound recognition module 101: the user enters the subway station to recognize, the methods include mobile phone swiping recognition, mobile phone NFC recognition, inbound location GPS recognition, subway driving recognition, this module is embedded in the android system. When a user uses a mobile phone APP to swipe the code to enter a subway station, for example, the Shanghai subway is a metropolitan city, Shenzhen has a WeChat applet Shenzhen subway, and Beijing uses Yitong. After swiping the code to enter the station, the successful entry and the entry site will pop up on the phone. The inbound and outbound recognition module is a part of the customized system, which can identify changes on the mobile phone screen, inbound messages and inbound sites.
乘坐地铁习惯数据库模块102:用户乘坐地铁习惯的数据库,该数据库是用户半年或1年的乘坐地铁信息的数据集合。The subway riding habit database module 102: a database of the user's subway riding habit. The database is a data collection of the subway riding information of the user for six months or one year.
48小时内乘坐地铁习惯模块103:用户乘坐地铁习惯的48小时数据库,是用户近期乘坐地铁的数据集合。Module 103 of subway ride habit within 48 hours: A 48-hour database of subway ride habits of users, which is a data collection of users’ recent subway rides.
目的站点预测模块104:在进站的条件下预测用户目的站点,具体预测方法见前述实施例的内容。The destination site prediction module 104: predicts the user's destination site under the condition of entering the site. For the specific prediction method, refer to the content of the foregoing embodiment.
目的站点融合结果模块105:用户长期乘坐地铁习惯预测目的站点和48小时乘坐地铁习惯预测的目的站点进行融合得到最终的目的站预测结果。Target site fusion result module 105: The user's long-term subway ride habit predicts the target site and the 48-hour subway ride habit predicts the target site to merge to obtain the final target station prediction result.
地铁站点识别模块106:地铁模式站点识别的算法,识别当前所在地铁站点的信息,如线路、名字等。Metro station recognition module 106: an algorithm for station recognition in subway mode, which recognizes the information of the current subway station, such as line, name, etc.
目的站点提醒模块107:在地铁模式站点识别的基础上,如果当前站点和目的站点是相邻站,则手机发送通知和振动,提醒用户即将到站准备下车。Destination station reminding module 107: Based on the identification of subway mode stations, if the current station and the destination station are adjacent stations, the mobile phone sends notifications and vibrations to remind the user that they are about to arrive at the station and prepare to get off the train.
本申请实施例提供的目的站点的预测方法,在通过前述的目的站点的预测方法,得到预测的目的站点后,可以实时定位当前站点,以在当前站点为目的站点的相邻站点时,可以触发移动终端输出提示信息,该提示信息用于提醒用户即将到站。从而无需用户参与,为出行用户提供到站提醒、地铁运行情况等人性化服务,解决了用户在出行时的痛点,防止坐过站。且不需要额外增加成本,无需额外部署设备,对外部没有依赖,具备可行性和领先性。The method for predicting the destination site provided by the embodiment of the application, after obtaining the predicted destination site through the aforementioned method for predicting the destination site, the current site can be located in real time, and the current site can be triggered when the current site is the neighboring site of the destination site The mobile terminal outputs prompt information, which is used to remind the user that the station is about to arrive. This eliminates the need for user participation, and provides travel users with humanized services such as station arrival reminders and subway operation status, which solves the pain points of users when traveling and prevents users from passing the station. And there is no need for additional costs, no additional deployment equipment, no dependence on the outside, and feasibility and leadership.
请参阅图14,其示出了本申请实施例提供的一种目的站点的预测装置700的结构框图,该目的站点的预测装置700包括:当前进站获取模710、目标进站获取模块720、第一预测模块730以及第二预测模块740。其中,当前进站获取模块710用于获取当前进站信息,所述当前进站信息包括当前进站时间和当前进站点;目标进站获取模块720用于根据历史乘坐记录,获取与所述当前进站时间匹配的目标历史进站点;第一预测模块730用于当所述当前进站点与所述目标历史进站点相同时,根据所述历史乘坐记录、所述当前进站时间及所述当前进站点预测目的站点;第二预测模块740用于当所述当前进站点与所述目标历史进站点不相同时,根据所述历史乘坐记录及所述当前进站点预测目的站点。Please refer to FIG. 14, which shows a structural block diagram of a destination site prediction device 700 provided by an embodiment of the present application. The destination site prediction device 700 includes: a current inbound acquisition module 710, a target inbound acquisition module 720, The first prediction module 730 and the second prediction module 740. Wherein, the current inbound acquisition module 710 is used to acquire current inbound information, and the current inbound information includes the current inbound time and the current inbound station; the target inbound acquisition module 720 is used to obtain information related to the current inbound station based on historical ride records. The target historical entry point with the pit stop time matching; the first prediction module 730 is used for when the current entry point is the same as the target historical entry point, according to the historical ride records, the current pit stop time and the current The inbound station predicts the destination station; the second prediction module 740 is used to predict the destination station based on the historical ride records and the current inbound station when the current inbound station is different from the target historical inbound station.
在一些实施例中,第一预测模块730可以包括:第一概率获取单元和第一概率预测单元。其中,第一概率获取单元用于根据所述历史乘坐记录,获取所述当前进站点对应的第一概率, 所述第一概率为在所述当前进站时间下,由所述当前进站点前往每个历史出站点的概率;第一概率预测单元用于获取数值最大的所述第一概率对应的历史出站点,作为预测的目的站点。In some embodiments, the first prediction module 730 may include: a first probability acquisition unit and a first probability prediction unit. Wherein, the first probability obtaining unit is configured to obtain a first probability corresponding to the current arrival station according to the historical ride record, and the first probability is that the current arrival station is to go from the current station at the current arrival time. The probability of each historical exit site; the first probability prediction unit is used to obtain the historical exit site corresponding to the first probability with the largest value, as a predicted destination site.
在一些实施例中,第一概率获取单元可以具体用于:根据所述历史乘坐记录,获取与所述当前进站时间匹配的历史进站时间;获取在所述历史进站时间及不同历史出站时间下,由所述当前进站点前往每个历史出站点的转移概率,作为所述当前进站点对应的第一概率。In some embodiments, the first probability obtaining unit may be specifically configured to: obtain a historical inbound time matching the current inbound time according to the historical ride record; obtain the historical inbound time and different historical outbound times. At station time, the transition probability from the current inbound station to each historical outbound station is taken as the first probability corresponding to the current inbound station.
在一些实施例中,目的站点的预测装置700还可以包括:到站时间预测模块和提示触发模块。其中,到站时间预测模块用于获取数值最大的所述第一概率对应的历史出站时间,作为预测的到站时间;提示触发模块用于根据所述到站时间,触发移动终端输出提示信息,所述提示信息用于提醒用户已到站或者即将到站。In some embodiments, the prediction device 700 of the destination site may further include: an arrival time prediction module and a prompt triggering module. Wherein, the arrival time prediction module is used to obtain the historical outbound time corresponding to the first probability with the largest value as the predicted arrival time; the prompt trigger module is used to trigger the mobile terminal to output prompt information according to the arrival time , The prompt information is used to remind the user that he has arrived at the station or is about to arrive at the station.
在一些实施例中,第二预测模块740可以包括:第二概率获取单元、进站时间预测单元、第三概率获取单元及第三概率预测单元。其中,第二概率获取单元用于根据所述历史乘坐记录,获取所述当前进站点对应的第二概率,所述第二概率为在不同历史进站时间下,进入所述当前进站点的概率;进站时间预测单元用于获取数值最大的所述第二概率对应的历史进站时间,作为预测的目标进站时间;第三概率获取单元用于根据所述历史乘坐记录,获取所述当前进站点对应的第三概率,所述第三概率为在所述目标进站时间下,由所述当前进站点前往每个历史出站点的概率;第三概率预测单元用于获取数值最大的所述第三概率对应的历史出站点,作为预测的目的站点。In some embodiments, the second prediction module 740 may include: a second probability acquisition unit, a station time prediction unit, a third probability acquisition unit, and a third probability prediction unit. Wherein, the second probability obtaining unit is configured to obtain a second probability corresponding to the current station entry according to the historical ride record, and the second probability is the probability of entering the current station station at different historical pit stops. The pit stop time prediction unit is used to obtain the historical pit stop time corresponding to the second probability with the largest value as the predicted target pit stop time; the third probability acquisition unit is used to obtain the current pit stop time according to the historical ride record The third probability corresponding to the entry site, the third probability is the probability of going from the current entry site to each historical exit site under the target entry time; the third probability prediction unit is used to obtain the highest numerical value The historical outbound site corresponding to the third probability is used as the predicted destination site.
在一些实施例中,目的站点的预测装置700还可以包括:第一站点获取模块、第二站点获取模块和目的站点预测模块。其中,第一站点获取模块用于获取根据第一历史乘坐记录预测得到的第一目的站点,所述第一历史乘坐记录为从当前日期起追溯至第一日期的历史乘坐记录;第二站点获取模块用于获取根据第二历史乘坐记录预测得到的第二目的站点,所述第二历史乘坐记录为从当前日期起追溯至第二日期的历史乘坐记录,所述第二日期早于所述第一日期;目的站点预测模块用于根据所述第一目的站点和所述第二目的站点,确定预测的目的站点。In some embodiments, the device 700 for predicting a destination site may further include: a first site acquisition module, a second site acquisition module, and a destination site prediction module. Wherein, the first site acquisition module is used to acquire the first destination site predicted based on the first historical ride record, the first historical ride record being the historical ride record dating back to the first date from the current date; the second site acquires The module is used to obtain a second destination station predicted based on a second historical ride record, the second historical ride record being a historical ride record retroactive from the current date to a second date, the second date being earlier than the first A date; the destination site prediction module is used to determine the predicted destination site based on the first destination site and the second destination site.
在一些实施例中,上述目的站点预测模块可以具体用于:分别获取所述第一目的站点对应的第一权重、所述第二目的站点对应的第二权重;获取所述第一目的站点对应的预测概率与所述第一权重的第一乘积;获取所述第二目的站点对应的预测概率与所述第二权重的第二乘积;从所述第一乘积和所述第二乘积中,获取数值最大的乘积所对应的目的站点,作为预测的目的站点。In some embodiments, the aforementioned destination site prediction module may be specifically configured to: obtain the first weight corresponding to the first destination site and the second weight corresponding to the second destination site respectively; and obtain the corresponding first destination site. The first product of the predicted probability of and the first weight; obtain the second product of the predicted probability of the second destination site and the second weight; from the first product and the second product, Obtain the destination site corresponding to the product with the largest value as the predicted destination site.
在一些实施例中,目的站点的预测装置700还可以包括:基站信息获取模块、目标站点获取模块及当前站点定位模块。其中,基站信息获取模块用于获取移动终端当前连接的基站信息;目标站点获取模块用于根据基站数据库中存储的基站与站点的对应关系,获取与所述基站信息对应的目标站点;当前站点定位模块用于根据所述目标站点,确定当前站点。In some embodiments, the device 700 for predicting the destination site may further include: a base station information acquisition module, a target site acquisition module, and a current site positioning module. Among them, the base station information acquisition module is used to acquire the base station information that the mobile terminal is currently connected to; the target site acquisition module is used to acquire the target site corresponding to the base station information according to the corresponding relationship between the base station and the site stored in the base station database; the current site location The module is used to determine the current site according to the target site.
在一些实施例中,上述当前站点定位模块可以具体用于:获取前一站点的相邻站点;获取多个所述目标站点与所述相邻站点中的相同站点,作为当前站点。In some embodiments, the above-mentioned current station positioning module may be specifically used to: obtain a neighboring station of the previous station; and obtain the same station among multiple target stations and the neighboring stations as the current station.
所属领域的技术人员可以清楚地了解到,为描述的方便和简洁,上述描述装置和模块的具体工作过程,可以参考前述方法实施例中的对应过程,在此不再赘述。Those skilled in the art can clearly understand that, for the convenience and conciseness of description, the specific working process of the device and module described above can refer to the corresponding process in the foregoing method embodiment, which will not be repeated here.
在本申请所提供的几个实施例中,模块相互之间的耦合可以是电性,机械或其它形式的耦合。In the several embodiments provided in this application, the coupling between the modules may be electrical, mechanical or other forms of coupling.
另外,在本申请各个实施例中的各功能模块可以集成在一个处理模块中,也可以是各个模块单独物理存在,也可以两个或两个以上模块集成在一个模块中。上述集成的模块既可以采用硬件的形式实现,也可以采用软件功能模块的形式实现。In addition, the functional modules in the various embodiments of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The above-mentioned integrated modules can be implemented in the form of hardware or software functional modules.
综上所述,本申请实施例提供的目的站点的预测装置用于实现前述方法实施例中相应的目的站点的预测方法,并具有相应的方法实施例的有益效果,在此不再赘述。In summary, the prediction device of the destination site provided in the embodiment of the present application is used to implement the prediction method of the corresponding destination site in the foregoing method embodiment, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.
请参考图15,其示出了本申请实施例提供的一种电子设备的结构框图。该电子设备100可以是笔记本电脑、智能手机、智能手表、智能眼镜等能够运行应用程序的移动终端。本申请中的电子设备100可以包括一个或多个如下部件:处理器110、存储器120以及一个或多个应用程序,其中,一个或多个应用程序可以被存储在存储器120中并被配置为由一个或多个处理器110执行,一个或多个应用程序配置用于执行如前述方法实施例所描述的方法。Please refer to FIG. 15, which shows a structural block diagram of an electronic device provided by an embodiment of the present application. The electronic device 100 may be a mobile terminal capable of running application programs, such as a notebook computer, a smart phone, a smart watch, or a smart glasses. The electronic device 100 in this application may include one or more of the following components: a processor 110, a memory 120, and one or more application programs, where one or more application programs may be stored in the memory 120 and configured to be configured by One or more processors 110 execute, and one or more application programs are configured to execute the methods described in the foregoing method embodiments.
处理器110可以包括一个或者多个处理核。处理器110利用各种接口和线路连接整个电子设备100内的各个部分,通过运行或执行存储在存储器120内的指令、程序、代码集或指令集, 以及调用存储在存储器120内的数据,执行电子设备100的各种功能和处理数据。可选地,处理器110可以采用数字信号处理(Digital Signal Processing,DSP)、现场可编程门阵列(Field-Programmable Gate Array,FPGA)、可编程逻辑阵列(Programmable Logic Array,PLA)中的至少一种硬件形式来实现。处理器110可集成中央处理器(Central Processing Unit,CPU)、目的站点的预测器(Graphics Processing Unit,GPU)和调制解调器等中的一种或几种的组合。其中,CPU主要处理操作系统、用户界面和应用程序等;GPU用于负责显示内容的渲染和绘制;调制解调器用于处理无线通信。可以理解的是,上述调制解调器也可以不集成到处理器110中,单独通过一块通信芯片进行实现。The processor 110 may include one or more processing cores. The processor 110 uses various interfaces and lines to connect various parts of the entire electronic device 100, and executes by running or executing instructions, programs, code sets, or instruction sets stored in the memory 120, and calling data stored in the memory 120. Various functions and processing data of the electronic device 100. Optionally, the processor 110 may adopt at least one of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). A kind of hardware form to realize. The processor 110 may integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a predictor (Graphics Processing Unit, GPU) of a destination site, and a modem. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is used for rendering and drawing of display content; the modem is used for processing wireless communication. It can be understood that the above-mentioned modem may not be integrated into the processor 110, but may be implemented by a communication chip alone.
存储器120可以包括随机存储器(Random Access Memory,RAM),也可以包括只读存储器(Read-Only Memory)。存储器120可用于存储指令、程序、代码、代码集或指令集。存储器120可包括存储程序区和存储数据区,其中,存储程序区可存储用于实现操作系统的指令、用于实现至少一个功能的指令(比如触控功能、声音播放功能、图像播放功能等)、用于实现下述各个方法实施例的指令等。存储数据区还可以存储电子设备100在使用中所创建的数据(比如电话本、音视频数据、聊天记录数据)等。The memory 120 may include random access memory (RAM) or read-only memory (Read-Only Memory). The memory 120 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 120 may include a program storage area and a data storage area, where the program storage area may store instructions for implementing the operating system and instructions for implementing at least one function (such as touch function, sound playback function, image playback function, etc.) , Instructions used to implement the following various method embodiments, etc. The storage data area can also store data (such as phone book, audio and video data, chat record data) created by the electronic device 100 during use.
可以理解,图15所示结构仅为示例,电子设备100还可以包括比图15所示更多或更少的组件,或是具有与图15所示完全不同的配置。本申请实施例对此没有限制。It can be understood that the structure shown in FIG. 15 is only an example, and the electronic device 100 may also include more or fewer components than those shown in FIG. 15, or have a completely different configuration from that shown in FIG. 15. The embodiments of the present application have no limitation on this.
请参考图16,其示出了本申请实施例提供的一种计算机可读存储介质的结构框图。该计算机可读介质800中存储有程序代码,所述程序代码可被处理器调用执行上述方法实施例中所描述的方法。Please refer to FIG. 16, which shows a structural block diagram of a computer-readable storage medium provided by an embodiment of the present application. The computer-readable medium 800 stores program code, and the program code can be invoked by a processor to execute the method described in the foregoing method embodiment.
计算机可读存储介质800可以是诸如闪存、EEPROM(电可擦除可编程只读存储器)、EPROM、硬盘或者ROM之类的电子存储器。可选地,计算机可读存储介质800包括非易失性计算机可读介质(non-transitory computer-readable storage medium)。计算机可读存储介质800具有执行上述方法中的任何方法步骤的程序代码810的存储空间。这些程序代码可以从一个或者多个计算机程序产品中读出或者写入到这一个或者多个计算机程序产品中。程序代码810可以例如以适当形式进行压缩。The computer-readable storage medium 800 may be an electronic memory such as flash memory, EEPROM (Electrically Erasable Programmable Read Only Memory), EPROM, hard disk, or ROM. Optionally, the computer-readable storage medium 800 includes a non-transitory computer-readable storage medium. The computer-readable storage medium 800 has storage space for the program code 810 for executing any method steps in the above-mentioned methods. These program codes can be read from or written into one or more computer program products. The program code 810 may be compressed in a suitable form, for example.
最后应说明的是:以上实施例仅用以说明本申请的技术方案,而非对其限制;尽管参照前述实施例对本申请进行了详细的说明,本领域的普通技术人员当理解:其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换;而这些修改或者替换,并不驱使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application, not to limit them; although the application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: The technical solutions recorded in the foregoing embodiments are modified, or some of the technical features thereof are equivalently replaced; these modifications or replacements do not drive the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (20)
- 一种目的站点的预测方法,其特征在于,所述方法包括:A method for predicting a destination site, characterized in that the method includes:获取当前进站信息,所述当前进站信息包括当前进站时间和当前进站点;Acquire current inbound information, where the current inbound information includes the current inbound time and the current inbound station;根据历史乘坐记录,获取与所述当前进站时间匹配的目标历史进站点;According to the historical ride records, obtain the target historical entry site matching the current entry time;当所述当前进站点与所述目标历史进站点相同时,根据所述历史乘坐记录、所述当前进站时间及所述当前进站点预测目的站点;When the current inbound station is the same as the target historical inbound station, predict the destination station based on the historical ride records, the current inbound time, and the current inbound station;当所述当前进站点与所述目标历史进站点不相同时,根据所述历史乘坐记录及所述当前进站点预测目的站点。When the inbound station is different from the target historical inbound station, the destination station is predicted based on the historical ride records and the current inbound station.
- 根据权利要求1所述的方法,其特征在于,所述根据所述历史乘坐记录、所述当前进站时间及所述当前进站点预测目的站点,包括:The method according to claim 1, wherein the predicting a destination station based on the historical ride record, the current arrival time, and the current arrival station comprises:根据所述历史乘坐记录,获取所述当前进站点对应的第一概率,所述第一概率为在所述当前进站时间下,由所述当前进站点前往每个历史出站点的概率;Obtaining, according to the historical ride record, a first probability corresponding to the current inbound station, where the first probability is the probability of going from the current inbound station to each historical outbound station at the current inbound time;获取数值最大的所述第一概率对应的历史出站点,作为预测的目的站点。Obtain the historical outbound site corresponding to the first probability with the largest value as the predicted destination site.
- 根据权利要求2所述的方法,其特征在于,所述根据所述历史乘坐记录,获取所述当前进站点对应的第一概率,包括:The method according to claim 2, wherein the obtaining the first probability corresponding to the current stoppage according to the historical ride record comprises:根据所述历史乘坐记录,获取与所述当前进站时间匹配的历史进站时间;According to the historical ride records, obtain a historical pit stop time that matches the current pit stop time;获取在所述历史进站时间及不同历史出站时间下,由所述当前进站点前往每个历史出站点的转移概率,作为所述当前进站点对应的第一概率。Obtain the transition probability from the current inbound station to each historical outbound station under the historical inbound time and different historical outbound times as the first probability corresponding to the current inbound station.
- 根据权利要求2或3所述的方法,其特征在于,所述方法还包括:The method according to claim 2 or 3, wherein the method further comprises:获取数值最大的所述第一概率对应的历史出站时间,作为预测的到站时间;Acquiring the historical outbound time corresponding to the first probability with the largest value as the predicted arrival time;根据所述到站时间,触发移动终端输出提示信息,所述提示信息用于提醒用户已到站或者即将到站。According to the arrival time, the mobile terminal is triggered to output prompt information, and the prompt information is used to remind the user that the station has arrived or is about to arrive.
- 根据权利要求1-4任一项所述的方法,其特征在于,所述根据所述历史乘坐记录及所述当前进站点预测目的站点,包括:The method according to any one of claims 1 to 4, wherein the predicting a destination station based on the historical ride record and the current arrival station comprises:根据所述历史乘坐记录,获取所述当前进站点对应的第二概率,所述第二概率为在不同历史进站时间下,进入所述当前进站点的概率;According to the historical ride records, obtain a second probability corresponding to the current stop, where the second probability is the probability of entering the current stop at a different historical stop time;获取数值最大的所述第二概率对应的历史进站时间,作为预测的目标进站时间;Acquiring the historical inbound time corresponding to the second probability with the largest value as the predicted target inbound time;根据所述历史乘坐记录,获取所述当前进站点对应的第三概率,所述第三概率为在所述目标进站时间下,由所述当前进站点前往每个历史出站点的概率;According to the historical ride records, obtain a third probability corresponding to the current inbound station, where the third probability is the probability of going from the current inbound station to each historical outbound station at the target inbound time;获取数值最大的所述第三概率对应的历史出站点,作为预测的目的站点。Obtain the historical outbound site corresponding to the third probability with the largest value as the predicted destination site.
- 根据权利要求1-5任一项所述的方法,其特征在于,所述方法还包括:The method according to any one of claims 1-5, wherein the method further comprises:获取根据第一历史乘坐记录预测得到的第一目的站点,所述第一历史乘坐记录为从当前日期起追溯至第一日期的历史乘坐记录;Acquiring a first destination station predicted based on a first historical ride record, the first historical ride record being a historical ride record dating back to the first date from the current date;获取根据第二历史乘坐记录预测得到的第二目的站点,所述第二历史乘坐记录为从当前日期起追溯至第二日期的历史乘坐记录,所述第二日期早于所述第一日期;Acquiring a second destination site predicted based on a second historical ride record, the second historical ride record being a historical ride record retroactive from the current date to a second date, the second date being earlier than the first date;根据所述第一目的站点和所述第二目的站点,确定预测的目的站点。Determine the predicted destination site according to the first destination site and the second destination site.
- 根据权利要6所述的方法,其特征在于,所述根据所述第一目的站点和第二目的站点,确定预测的目的站点,包括:The method according to claim 6, wherein the determining the predicted destination site according to the first destination site and the second destination site comprises:分别获取所述第一目的站点对应的第一权重、所述第二目的站点对应的第二权重;Acquiring the first weight corresponding to the first destination site and the second weight corresponding to the second destination site respectively;获取所述第一目的站点对应的预测概率与所述第一权重的第一乘积;Acquiring a first product of the predicted probability corresponding to the first destination site and the first weight;获取所述第二目的站点对应的预测概率与所述第二权重的第二乘积;Acquiring a second product of the predicted probability corresponding to the second destination site and the second weight;从所述第一乘积和所述第二乘积中,获取数值最大的乘积所对应的目的站点,作为预测的目的站点。From the first product and the second product, obtain the destination site corresponding to the product with the largest value as the predicted destination site.
- 根据权利要求7所述的方法,其特征在于,所述第一权重大于所述第二权重。8. The method of claim 7, wherein the first weight is greater than the second weight.
- 根据权利要求1-8任一项所述的方法,其特征在于,所述根据历史乘坐记录,获取与所述当前进站时间匹配的目标历史进站点,包括:The method according to any one of claims 1-8, wherein the acquiring, based on historical ride records, a target historical entry point matching the current entry time comprises:根据历史乘坐记录,获取与所述当前进站时间匹配的进站时间段,所述进站时间段为对历史乘坐记录中的所有历史进站时间进行分段划分后得到时间段;According to the historical ride records, obtain the inbound time period that matches the current inbound time, where the inbound time period is obtained by dividing all historical inbound times in the historical ride records into sections;根据历史乘坐记录,获取在所述进站时间段下,进入不同历史进站点的进站概率;According to the historical ride records, obtain the entry probability of entering different historical entry stations during the said entry time period;获取数值最大的所述进站概率对应的历史进站点,作为目标历史进站点。Obtain the historical entry site corresponding to the entry probability with the largest value as the target historical entry site.
- 根据权利要求9所述的方法,其特征在于,所述根据历史乘坐记录,获取与所述当前进站时间匹配的进站时间段,包括:The method according to claim 9, characterized in that the obtaining the pit stop time period matching the current pit stop time according to historical ride records comprises:根据历史乘坐记录,获取所有的历史进站时间;Obtain all historical pit stops according to historical ride records;对所述所有的历史进站时间进行聚类分析,得到聚类后的多个时间段;Perform a cluster analysis on all the historical inbound times to obtain multiple time periods after clustering;从所述多个时间段中获取距离所述当前进站时间最近的时间段,作为与所述当前进站时间匹配的进站时间段。The time period closest to the current inbound time is acquired from the multiple time periods as the inbound time period that matches the current inbound time.
- 根据权利要求10所述的方法,其特征在于,所述从所述多个时间段中获取距离所述当前进站时间最近的时间段,作为与所述当前进站时间匹配的进站时间段,包括:The method according to claim 10, wherein the time period closest to the current inbound time is obtained from the multiple time periods as the inbound time period that matches the current inbound time ,include:获取所述多个时间段中每个时间段的先验概率;Acquiring the prior probability of each time period in the multiple time periods;从所述多个时间段中,获取所述先验概率大于指定阈值的目标时间段;From the multiple time periods, obtain a target time period in which the prior probability is greater than a specified threshold;从所述目标时间段中获取距离所述当前进站时间最近的时间段,作为与所述当前进站时间匹配的进站时间段。The time period closest to the current inbound time is acquired from the target time period as the inbound time period that matches the current inbound time.
- 根据权利要求9-11任一项所述的方法,其特征在于,所述根据所述历史乘坐记录、所述当前进站时间及所述当前进站点预测目的站点,包括:The method according to any one of claims 9-11, wherein the predicting a destination station based on the historical ride record, the current arrival time, and the current arrival station comprises:根据所述历史乘坐记录,获取在所述进站时间段及不同出站时间段下,由所述当前进站点前往每个历史出站点的转移概率,作为所述当前进站点对应的第一概率,其中,所述出站时间段为对历史乘坐记录中的所有历史出站时间进行分段划分后得到时间段;According to the historical ride records, obtain the transition probability from the current inbound station to each historical outbound station in the inbound time period and different outbound time periods as the first probability corresponding to the current inbound station , Wherein, the outbound time period is a time period obtained by dividing all historical outbound times in the historical ride records into sections;获取数值最大的所述第一概率对应的历史出站点,作为预测的目的站点。Obtain the historical outbound site corresponding to the first probability with the largest value as the predicted destination site.
- 根据权利要求12所述的方法,其特征在于,所述方法还包括:The method according to claim 12, wherein the method further comprises:根据历史乘坐记录,获取所有的历史出站时间;Obtain all historical departure times according to historical ride records;对所述所有的历史出站时间进行聚类分析,得到聚类后的多个出站时间段。Perform cluster analysis on all the historical outbound times to obtain multiple outbound time periods after clustering.
- 根据权利要求1-13任一项所述的方法,其特征在于,所述方法还包括:The method according to any one of claims 1-13, wherein the method further comprises:在当前站点为所述目的站点的相邻站点时,触发移动终端输出提示信息,所述提示信息用于提醒用户即将到站。When the current site is an adjacent site of the destination site, the mobile terminal is triggered to output prompt information, and the prompt information is used to remind the user that the station is about to arrive.
- 根据权利要求14所述的方法,其特征在于,在所述在当前站点为所述目的站点的相邻站点时,触发移动终端输出提示信息之前,所述方法还包括:The method according to claim 14, characterized in that, before triggering the mobile terminal to output prompt information when the current site is a neighboring site of the destination site, the method further comprises:获取移动终端当前连接的基站信息;Acquiring information about the base station to which the mobile terminal is currently connected;根据基站数据库中存储的基站与站点的对应关系,获取与所述基站信息对应的目标站点;Acquiring the target site corresponding to the base station information according to the corresponding relationship between the base station and the site stored in the base station database;根据所述目标站点,确定当前站点。According to the target site, the current site is determined.
- 根据权利要求15所述的方法,其特征在于,所述目标站点为多个,所述根据所述目标站点,确定当前站点,包括:The method according to claim 15, wherein there are multiple target sites, and the determining the current site according to the target site comprises:获取前一站点的相邻站点;Get the neighboring site of the previous site;获取多个所述目标站点与所述相邻站点中的相同站点,作为当前站点。Acquire multiple identical sites among the target site and the neighboring sites as the current site.
- 根据权利要求1-16任一项所述的方法,其特征在于,所述历史乘坐记录包括历史进站信息以及历史出站信息,其中,所述历史进站信息包括历史进站点以及历史进站时间,所述历史出站信息包括历史出站点以及历史出站时间。The method according to any one of claims 1-16, wherein the historical ride record includes historical inbound information and historical outbound information, wherein the historical inbound information includes historical inbound station and historical inbound station Time, the historical outbound information includes historical outbound site and historical outbound time.
- 一种目的站点的预测装置,其特征在于,所述装置包括:A prediction device for a destination site, characterized in that the device includes:当前进站获取模块,用于获取当前进站信息,所述当前进站信息包括当前进站时间和当前进站点;The current inbound acquisition module is used to obtain current inbound information, and the current inbound information includes the current inbound time and the current inbound station;目标进站获取模块,用于根据历史乘坐记录,获取与所述当前进站时间匹配的目标历史进站点;The target inbound acquisition module is used to obtain the target historical inbound stop matching the current inbound time according to the historical ride records;第一预测模块,用于当所述当前进站点与所述目标历史进站点相同时,根据所述历史乘坐记录、所述当前进站时间及所述当前进站点预测目的站点;The first prediction module is configured to predict the destination station based on the historical ride record, the current pit stop time, and the current pit stop when the current inbound station is the same as the target historical inbound station;第二预测模块,用于当所述当前进站点与所述目标历史进站点不相同时,根据所述历史乘坐记录及所述当前进站点预测目的站点。The second prediction module is used to predict the destination station based on the historical ride records and the current arrival station when the current inbound station is different from the target historical inbound station.
- 一种电子设备,其特征在于,包括:An electronic device, characterized in that it comprises:一个或多个处理器;One or more processors;存储器;Memory一个或多个应用程序,其中所述一个或多个应用程序被存储在所述存储器中并被配置为由 所述一个或多个处理器执行,所述一个或多个程序配置用于执行如权利要求1-17任一项所述的方法。One or more application programs, wherein the one or more application programs are stored in the memory and configured to be executed by the one or more processors, and the one or more programs are configured to execute such as The method of any one of claims 1-17.
- 一种计算机可读取存储介质,其特征在于,所述计算机可读取存储介质中存储有程序代码,所述程序代码可被处理器调用执行如权利要求1-17任一项所述的方法。A computer-readable storage medium, wherein the computer-readable storage medium stores program code, and the program code can be called by a processor to execute the method according to any one of claims 1-17 .
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