WO2021232962A1 - Procédé et appareil de prédiction de site cible, ainsi que dispositif électronique et support de stockage - Google Patents

Procédé et appareil de prédiction de site cible, ainsi que dispositif électronique et support de stockage Download PDF

Info

Publication number
WO2021232962A1
WO2021232962A1 PCT/CN2021/085231 CN2021085231W WO2021232962A1 WO 2021232962 A1 WO2021232962 A1 WO 2021232962A1 CN 2021085231 W CN2021085231 W CN 2021085231W WO 2021232962 A1 WO2021232962 A1 WO 2021232962A1
Authority
WO
WIPO (PCT)
Prior art keywords
historical
current
station
inbound
site
Prior art date
Application number
PCT/CN2021/085231
Other languages
English (en)
Chinese (zh)
Inventor
蒋燚
Original Assignee
Oppo广东移动通信有限公司
上海瑾盛通信科技有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Oppo广东移动通信有限公司, 上海瑾盛通信科技有限公司 filed Critical Oppo广东移动通信有限公司
Publication of WO2021232962A1 publication Critical patent/WO2021232962A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

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.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Development Economics (AREA)
  • Economics (AREA)
  • General Physics & Mathematics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Human Resources & Organizations (AREA)
  • Finance (AREA)
  • Accounting & Taxation (AREA)
  • Marketing (AREA)
  • General Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Databases & Information Systems (AREA)
  • Tourism & Hospitality (AREA)
  • Game Theory and Decision Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Remote Sensing (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)
  • Navigation (AREA)
  • Telephonic Communication Services (AREA)

Abstract

La présente invention se rapporte au domaine technique des dispositifs électroniques et porte sur un procédé et un appareil de prédiction de site cible, ainsi que sur un dispositif électronique et sur un support de stockage. Le procédé de prédiction de site cible consiste : à obtenir des informations d'arrivée actuelles, les informations d'arrivée actuelles comprenant une heure d'arrivée actuelle et un point d'arrivée actuel ; à obtenir, selon un enregistrement de conduite historique, un point d'arrivée historique cible correspondant à l'heure d'arrivée actuelle ; lorsque le point d'arrivée actuel est le même que le point d'arrivée historique cible, à prédire un site cible selon l'enregistrement de conduite historique, l'heure d'arrivée actuelle et le point d'arrivée actuel ; et lorsque le point d'arrivée actuel est différent du point d'arrivée historique cible, à prédire un site cible selon l'enregistrement de conduite historique et le point d'arrivée actuel. Le procédé peut améliorer la précision de prédiction du site de destination.
PCT/CN2021/085231 2020-05-21 2021-04-02 Procédé et appareil de prédiction de site cible, ainsi que dispositif électronique et support de stockage WO2021232962A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010437224.4A CN111582605B (zh) 2020-05-21 2020-05-21 目的站点的预测方法、装置、电子设备及存储介质
CN202010437224.4 2020-05-21

Publications (1)

Publication Number Publication Date
WO2021232962A1 true WO2021232962A1 (fr) 2021-11-25

Family

ID=72125182

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2021/085231 WO2021232962A1 (fr) 2020-05-21 2021-04-02 Procédé et appareil de prédiction de site cible, ainsi que dispositif électronique et support de stockage

Country Status (2)

Country Link
CN (1) CN111582605B (fr)
WO (1) WO2021232962A1 (fr)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114358428A (zh) * 2022-01-06 2022-04-15 深圳大学 一种基于时空特征的公交站间行程时间预测方法
CN117974128A (zh) * 2024-03-29 2024-05-03 成都智元汇信息技术股份有限公司 一种基于地铁主动补登的对账方法

Families Citing this family (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111582605B (zh) * 2020-05-21 2023-09-12 Oppo广东移动通信有限公司 目的站点的预测方法、装置、电子设备及存储介质
CN113393029B (zh) * 2021-06-11 2023-11-17 上海逸迅信息科技有限公司 一种轨交客流量的预测方法和设备
CN115033309B (zh) * 2021-12-01 2023-04-07 荣耀终端有限公司 地铁出行检测方法及电子设备
CN114550275B (zh) * 2022-04-22 2022-08-02 北京城建设计发展集团股份有限公司 一种多信号融合人脸图片识别方法、系统及电子设备
CN115294785B (zh) * 2022-06-30 2024-03-29 通号城市轨道交通技术有限公司 基于手机信号的地铁动态运行调整方法及装置
CN117252650B (zh) * 2023-11-17 2024-03-15 滕州市中等职业教育中心学校 一种商品信息推送服务系统

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110874668A (zh) * 2018-09-03 2020-03-10 深圳先进技术研究院 一种轨道交通od客流预测方法、系统及电子设备
CN111046288A (zh) * 2019-12-11 2020-04-21 Oppo广东移动通信有限公司 内容推荐方法、装置、终端及存储介质
CN111582605A (zh) * 2020-05-21 2020-08-25 Oppo广东移动通信有限公司 目的站点的预测方法、装置、电子设备及存储介质

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103310287B (zh) * 2013-07-02 2016-07-06 北京航空航天大学 基于svm预测乘客出行概率的轨道交通客流预测方法
CN106919996A (zh) * 2015-12-25 2017-07-04 滴滴（中国）科技有限公司 一种目的地预测方法及装置
CN110020216A (zh) * 2017-07-20 2019-07-16 北京嘀嘀无限科技发展有限公司 目的地推送方法和装置
CN109425351B (zh) * 2017-08-24 2022-02-15 北京嘀嘀无限科技发展有限公司 出行处理方法及装置
CN108093131A (zh) * 2017-12-13 2018-05-29 广东欧珀移动通信有限公司 到站提醒方法、装置、存储介质及移动终端
CN108197739B (zh) * 2017-12-29 2021-03-16 中车工业研究院有限公司 一种城市轨道交通乘客流量预测方法

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110874668A (zh) * 2018-09-03 2020-03-10 深圳先进技术研究院 一种轨道交通od客流预测方法、系统及电子设备
CN111046288A (zh) * 2019-12-11 2020-04-21 Oppo广东移动通信有限公司 内容推荐方法、装置、终端及存储介质
CN111582605A (zh) * 2020-05-21 2020-08-25 Oppo广东移动通信有限公司 目的站点的预测方法、装置、电子设备及存储介质

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114358428A (zh) * 2022-01-06 2022-04-15 深圳大学 一种基于时空特征的公交站间行程时间预测方法
CN117974128A (zh) * 2024-03-29 2024-05-03 成都智元汇信息技术股份有限公司 一种基于地铁主动补登的对账方法

Also Published As

Publication number Publication date
CN111582605A (zh) 2020-08-25
CN111582605B (zh) 2023-09-12

Similar Documents

Publication Publication Date Title
WO2021232962A1 (fr) Procédé et appareil de prédiction de site cible, ainsi que dispositif électronique et support de stockage
CN106875066B (zh) 用车出行行为的预测方法、装置、服务器以及存储介质
US10037503B2 (en) System and method for managing supply of service
US11182871B2 (en) System and apparatus for ridesharing
CN111405478A (zh) 服务提供方法、装置、终端及存储介质
CN111401328B (zh) 数据采集方法、装置、终端及存储介质
US20160357804A1 (en) Determining location of a calendar event
CN112380448B (zh) 车辆数据处理方法、装置、计算机设备和存储介质
CN107615345A (zh) 移动辅助装置、移动辅助用服务器及移动辅助系统
CN108171965B (zh) 到站提醒方法、装置、存储介质及移动终端
WO2021190145A1 (fr) Procédé et dispositif d'identification de station, terminal et support de stockage
CN112488388B (zh) 基于概率分布的出站客流预测方法及装置
WO2021115232A1 (fr) Procédé et dispositif de rappel d'arrivée, terminal et support d'informations
CN111858806A (zh) 乘客出行轨迹检测方法、装置、设备及存储介质
CN111091222A (zh) 人流量预测方法、装置及系统
CN111046288B (zh) 内容推荐方法、装置、终端及存储介质
CN109299403A (zh) 信息推送方法及相关装置
CN105608921B (zh) 在电子装置中提示公共交通线路的方法和设备
CN111882912A (zh) 乘车信息处理方法、装置及系统
US20220261942A1 (en) Method, information processing device, and system
CN109979190B (zh) 一种道路交通状态的预测方法及装置
CN111460373A (zh) 一种到站提醒方法、装置、存储介质和终端设备
CN117874372B (zh) 公交语音播报方法、装置、设备及介质
CN114038225B (zh) 一种公交线路的数据处理方法、装置与电子设备
CN111343726B (zh) 用户调度方法及装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21808429

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 21808429

Country of ref document: EP

Kind code of ref document: A1