CN109670817B - Data processing method and device - Google Patents
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Abstract
一种数据处理方法，包括：获取第一对象的地理位置信息；确定与地理位置信息匹配的至少一个第二对象；计算第二对象与第一对象之间的关联度；根据关联度，从至少一个第二对象中选取目标对象，并将目标对象的信息推荐给第一对象。如此，提高用户体验。
A data processing method, including: obtaining the geographical location information of a first object; determining at least one second object that matches the geographical location information; calculating the correlation between the second object and the first object; based on the correlation, from at least one Select a target object from a second object and recommend the target object's information to the first object. In this way, the user experience is improved.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a data processing method and apparatus.
Background
In the current scenario of using various APP (Application) to realize offline payment, in order to obtain information of a payee, the most used schemes are scanning two-dimensional code payment, acoustic payment, etc., however, these schemes have certain inconveniences. For example, the payment scheme of scanning two-dimensional code mainly obtains payee information through scanning printed two-dimensional code, but in many scenes, the printed two-dimensional code is smaller, and the user probably needs to find the printed two-dimensional code everywhere to sweep the code, so that the payment scheme is very inconvenient, and in addition, under the scene that light is dim, the recognition accuracy of the two-dimensional code can be influenced, so that the user experience is influenced. Compared with the payment by scanning the two-dimensional code, the sound wave pair has larger influence on the environment and is more inconvenient to use. In addition, the two-dimensional code scanning interfaces of various vending machines and ticket extractors are easy to fail, and the manual input mode is inconvenient and can influence the user experience.
Disclosure of Invention
The following is a summary of the subject matter described in detail herein. This summary is not intended to limit the scope of the claims.
The embodiment of the application provides a data processing method and device, which can improve user experience.
In a first aspect, an embodiment of the present application provides a data processing method, including:
obtaining geographic position information of a first object;
determining at least one second object matching the geographic location information;
calculating the association degree between the second object and the first object;
and selecting a target object from the at least one second object according to the association degree, and recommending the information of the target object to the first object.
In an exemplary embodiment, the geographic location information may include at least one of: global positioning system information, base station information, wireless network information.
In an exemplary embodiment, the determining at least one second object that matches the geographic location information may include:
determining a first grid to which the first object belongs according to the geographic position information of the first object;
determining a second grid meeting a set condition between the first grid and the first grid;
and determining a second object in the second grid as a second object matched with the geographic position information of the first object.
In an exemplary embodiment, the calculating the association degree between the second object and the first object may include:
acquiring a feature vector of the second object corresponding to the first object, wherein the feature vector at least reflects the association relationship between the second object and the first object;
and processing the feature vector by adopting a machine learning classification model, and calculating to obtain the association degree between the second object and the first object.
In an exemplary embodiment, the feature vector may include at least one of the following information: the comparison information between the geographic position information of the first object and the second object, the interaction information between the first object and the second object and the attribute information of the second object.
In a second aspect, an embodiment of the present application provides a data processing method, including:
after receiving an instruction of a target operation or detecting a triggering condition for starting the target operation, displaying information of a recommended object corresponding to the target operation on a display interface.
In an exemplary embodiment, before the information of the recommended object corresponding to the target operation is displayed on the display interface, the method may further include: and receiving information of a recommended object corresponding to the target operation, which is sent by the server.
In an exemplary embodiment, the recommended object may include a computing device.
In an exemplary embodiment, the information of the recommended objects is displayed in a list form on the display interface.
In a third aspect, an embodiment of the present application provides a data processing method, including:
obtaining geographic location information of a first computing device;
determining a second computing device that matches the geographic location information;
determining that the second computing device is an associated device of the first computing device;
information of the second computing device is sent to the first computing device.
In an exemplary embodiment, after the determining that the second computing device is the associated device of the first computing device, the method may further include: calculating a degree of association between the second computing device and the first computing device;
the sending the information of the second computing device to the first computing device may include:
and sending the information of the second computing equipment with the association degree meeting the set association condition to the first computing equipment.
In a fourth aspect, an embodiment of the present application provides an apparatus, including: a memory and a processor, the memory storing a data processing program which, when read for execution by the processor, performs the following operations: obtaining geographic position information of a first object; determining at least one second object matching the geographic location information; calculating the association degree between the second object and the first object; and selecting a target object from the at least one second object according to the association degree, and recommending the information of the target object to the first object.
In a fifth aspect, an embodiment of the present application provides an apparatus, including: a memory and a processor, the memory storing a data processing program which, when read for execution by the processor, performs the following operations: after receiving an instruction of a target operation or detecting a triggering condition for starting the target operation, displaying information of a recommended object corresponding to the target operation on a display interface.
In a sixth aspect, an embodiment of the present application provides an apparatus, including: a memory and a processor, the memory storing a data processing program which, when read for execution by the processor, performs the following operations: obtaining geographic location information of a first computing device; determining a second computing device that matches the geographic location information; determining that the second computing device is an associated device of the first computing device; information of the second computing device is sent to the first computing device.
In a seventh aspect, an embodiment of the present application provides a computer readable medium storing a data processing program, where the data processing program when read and executed by a processor implements the steps of the data processing method according to any one of the first aspect, the second aspect, and the third aspect.
In the embodiment of the application, the geographic position information of the first object is obtained, at least one second object matched with the geographic position information of the first object is determined, the association degree between the second object and the first object is calculated, the target object is selected from the at least one second object according to the association degree, and the information of the target object is recommended to the first object. In the embodiment of the application, the information of the target object is automatically recommended to the first object, so that the user experience is improved. For example, the information of the payee is automatically recommended to the user when the user pays, so that the trouble that the user searches for the two-dimensional code everywhere to scan and the inconvenience of using sound wave payment when paying are avoided; when the user takes a ticket through the ticket taking machine, the currently available ticket taking machine is automatically recommended to the user, so that the code scanning operation and complicated manual input of the user are avoided.
Of course, it is not necessary for any one product to practice the application to achieve all of the above advantages simultaneously.
Drawings
FIG. 1 is a flow chart of a data processing method according to an embodiment of the present application;
FIG. 2 is a system architecture diagram of a data processing method according to an embodiment of the present application;
FIG. 3 is a diagram illustrating an exemplary system architecture for applying a data processing method according to an embodiment of the present application;
FIG. 4 is a flow chart of an example one of an embodiment of the present application;
FIG. 5 is a flow chart of an example two of an embodiment of the present application;
FIG. 6 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;
fig. 7 is a flowchart of another data processing method according to an embodiment of the present application.
Detailed Description
The following detailed description of embodiments of the application is provided in connection with the accompanying drawings, and it is to be understood that the embodiments described below are merely illustrative and explanatory of the application, and are not restrictive of the application.
It should be noted that, if not conflicting, the embodiments of the present application and the features of the embodiments may be combined with each other, which are all within the protection scope of the present application. In addition, while a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in a different order than is shown.
In some implementations, a computing device performing the data processing method may include one or more processors (CPUs, central Processing Unit), input/output interfaces, network interfaces, and memory (memory).
The memory may include forms of non-volatile memory, random Access Memory (RAM), and/or nonvolatile memory in a computer-readable medium, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media. The memory may include module 1, module 2, … …, module N (N is an integer greater than 2).
Computer readable media include both non-transitory and non-transitory, removable and non-removable storage media. The storage medium may implement information storage by any method or technique. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, read only optical disk read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer readable media, as defined herein, does not include non-transitory computer readable media (transmission media), such as modulated data signals and carrier waves.
Fig. 1 is a flowchart of a data processing method according to an embodiment of the present application. As shown in fig. 1, the data processing method provided in this embodiment includes the following steps:
s101, acquiring geographic position information of a first object;
s102, determining at least one second object matched with the geographic position information;
s103, calculating the association degree between the second object and the first object;
s104, selecting a target object from at least one second object according to the association degree, and recommending the information of the target object to the first object.
The data processing method provided by the embodiment can be used for recommending the information of the payee to the payer or recommending the information of the available vending machine, ticket taker and the like to the user. For example, the first object may be a payer, the second object may be a payee, or the first object may be a computing device such as a smart phone, and the second object may be a computing device such as a vending machine or ticket machine. However, the application is not limited in this regard.
In this embodiment, the geographic location information may include at least one of: global positioning system (GPS, global Positioning System) information, base station information, wireless network information (e.g., WIFI information). Wherein the GPS information may include three aspects of longitude, latitude, and altitude. However, the application is not limited in this regard.
In this embodiment, the association degree between the second object and the first object may be calculated by using a machine learning classification model. The machine learning classification model is not limited in the present application, for example, the classification algorithm adopted by the machine learning classification model may be an SVM (Support Vector Machine ) algorithm, a naive bayes algorithm, a decision tree algorithm, or a KNN (k-Nearest Neighbor) algorithm.
Fig. 2 is a system architecture diagram of a data processing method according to an embodiment of the present application. As shown in fig. 2, a client computing device 202 connects to a server computing device 204 (e.g., a server) over a network.
Among other things, client computing devices 202 may include mobile terminals such as smartphones, tablets, notebooks, palmtops, personal digital assistants (Personal Digital Assistant, PDA), portable media players (Portable Media Player, PMP), wearable devices, and stationary terminals such as desktop computers. However, the application is not limited in this regard.
In an exemplary implementation, the data processing method provided in this embodiment may be performed by the server computing device 204. After the client computing device 202 receives the operation instruction of the user or detects that the trigger condition of the operation is met (for example, the current time meets the time range of the ticket taking operation), an operation request is sent to the server computing device 204, where the operation request may carry the current geographical location information and the current time information of the client computing device 202, after the server computing device 204 receives the operation request, the data processing method provided in this embodiment is executed (that is, the steps S101 to S104 are executed), after the server computing device 204 obtains the information of the target object, the information of the target object is sent to the client computing device 202, and then the information of the target object is displayed to the user by the client computing device 202, so that the user selects one target object from among them to perform the operation, for example, pay for the target object, or take a ticket using the target object.
In another exemplary implementation, the data processing method provided by the present embodiment may be performed by the client computing device 202. After receiving the operation instruction of the user or when detecting that the triggering condition of the operation is met, the client computing device 202 requests the server computing device 204 for information (for example, including geographic location information, interaction information with the first object, attribute information, and the like) of the second object stored in the server computing device 204, after obtaining the information of the second object, the client computing device 202 determines the second object matching the geographic location information of the first object, then calculates a degree of association between the first object and any one of the screened second objects matching the geographic location information of the first object, then screens one or more target objects from the second objects matching the geographic location information of the first object according to the degree of association, and displays the information of the target objects to the user on a display interface so that the user can select one target object for operation. However, the application is not limited in this regard. In other implementations, the data processing method provided in this embodiment may be executed by the client computing device 202 and the server computing device 204, for example, the server computing device 204 may be responsible for screening out a second object that matches with the geographic location information of the first object, and sending the information of the screened second object to the client computing device 202, and then, the client computing device 202 screens out the target object recommended to the user by calculating the association degree between the first object and any of the screened second objects.
The data processing method of the present embodiment is described below with reference to fig. 3 by taking the server computing device 204 as an example. In this example, the server computing device 204 may include a communication unit 301, an information processing unit 302, a database 303, and an SVM classifier 304. Wherein the database 303 is adapted to store information (e.g. geographical location information, attribute information, behavior information, etc.) of the second object in the network wide area.
The communication unit 301 of the server computing device 204 is adapted to receive an operation request from the client computing device 202 over the network; the information processing unit 302 is adapted to parse the received operation request to obtain the current geographic position information of the first object (e.g. the user terminal), query the database 303 for second objects matching the current geographic position information of the first object, obtain the information of the second objects from the database 303 after querying to obtain the second objects matching the geographic position information, and determine feature vectors capable of representing the association relationship between the first object and the second object, the information processing unit 302 inputs the feature vectors into the SVM classifier 304, the association degree between the first object and the second object is calculated by the SVM classifier 304, the information processing unit 302 screens out target objects from the second objects screened out according to the geographic position information according to the calculated association degree, for example, screens out a set number of second objects as target objects in order of higher association degree; the communication unit 301 may then transmit the information of the screened target object to the client computing device 202 via the network. After receiving the information of the target object, the client computing device 202 displays a list of target objects on the display interface for selection by the user.
The application of the data processing method provided in the present embodiment in different scenarios is described below by taking the execution of the data processing method of the present embodiment by the server computing device as an example.
Fig. 4 is a flowchart of an example one of a data processing method according to an embodiment of the present application. In this example, the client computing device is exemplified by a mobile terminal such as a smart phone, and the server computing device is exemplified by a server. In this example, after the user clicks the payment button on the operation interface of the mobile terminal, the information of the recommended payee is automatically popped up on the display interface of the mobile terminal, and then the user can perform the payment operation by clicking the recommended payee.
As shown in fig. 4, the present example includes the steps of:
s401, the mobile terminal sends a payment operation request to a server;
in this step, after the user clicks the payment button on the operation interface of the mobile terminal, the mobile terminal automatically acquires current geographical location information, and combines the current geographical location information and time information in the payment operation request and sends the combined information to the server. In other words, the mobile terminal obtains authorization for information acquisition and transmission after detecting the operation instruction of the user.
The geographic location information may include the following dimensions: gps_x (GPS coordinate 1), gps_y (GPS coordinate 2), gps_z (GPS coordinate 3), base station number (base), WIFI ADDRESS (wifi_address). Wherein gps_ X, GPS _ Y, GPS _z may represent longitude, latitude, and altitude, respectively. However, the application is not limited in this regard.
It should be noted that, in other implementations, the geographic location information may include one or more of GPS information, base station information, and WIFI information, so long as the current location of the user can be determined based on the geographic location information.
S402, after receiving the payment operation request, the server acquires the geographical location information of the user, and then determines the payee matched with the geographical location information of the user according to the geographical location information of the payee (such as a seller and a vending machine) stored in the database.
In addition to the geographical location information of the payee, transaction information (such as the number of payouts, the time of payouts, etc.), attribute information (such as the type of payee), etc. of the payee may be stored in the database of the server. The server may periodically update the data within the database. However, the application is not limited in this regard.
In this step, the server may perform gridding division on the geographic location, and may determine, according to the geographic location information of the current user, a first grid to which the user currently belongs, and then screen out a second grid that meets a set condition with the first grid, for example, a grid that is the same as the first grid or a grid that is adjacent to the first grid; taking the first grid as the second grid meeting the set condition, all the payees in the first grid can be screened. The setting of the setting condition may be determined according to the actual scene, and the present application is not limited thereto.
S403, aiming at the selected payee matched with the geographical position information of the current user, the server analyzes the geographical position information and the historical payment information of the user, and extracts the feature vector of each payee corresponding to the user from a plurality of dimensions.
The feature vector may represent a relationship of the payee with respect to the current user, so as to describe an association relationship between the payee and the user.
In this example, the feature vector of each payee for the user may include at least one of the following information: the comparison information between the current user and the geographical location information of the payee, the interaction information between the current user and the payee (e.g., payment time, payment number, etc.), the attribute information of the payee (e.g., type, liveness, etc.).
For example, the feature vector for each payee for the user may include the following dimensions: gps_x_distance (GPS coordinate 1 DISTANCE value), gps_y_distance (GPS coordinate 2 DISTANCE value), gps_z_distance (GPS coordinate 3 DISTANCE value), the base station number of the payee (or information indicating whether the payee matches the base station number of the current user), the WIFI address of the payee (or information indicating whether the payee matches the WIFI address of the current user), the number of times the current user pays the Payee (PAYCOUNT), the payment request time of the current user (payime), the type of payee (SELLERTYPE), the activity of the payee (SELLERACTIVEDEGREE).
Wherein gps_x_distance, gps_y_distance, and gps_z_distance represent DISTANCE values of GPS coordinates between the current user and the payee, respectively. The payment request time of the current user may be current time information carried by the payment operation request of the current user. The type of payee may include one of: convenience stores, supermarkets, restaurants, individual vendors, and vending machines. The activity of a payee may be determined based on a total number of payments received by the payee over a period of time, wherein the total number of payments is obtained for all payors of the payee over the period of time.
It should be noted that the above feature vectors are only examples, and in practical application, different dimension combinations may be selected as the feature vectors according to practical situations.
S404, after the feature vector of each selected payee corresponding to the current user is processed by the server by using the SVM classifier, classifying the payee, namely, calculating the association degree between each payee and the current user by using the SVM classifier, and screening the payee recommended to the user according to the association degree.
For example, in this example, the value of the degree of association may be between 0 and 1, and the larger the value of the degree of association, the higher the association between the payee and the current user. The server may select one or more payees in order of the association from high to low and transmit information of the selected payees (e.g., the payees' account information of the payees, the machine type and number information of the vending machine) to the mobile terminal, which displays recommended information of the payees to the user.
In this example, the likelihood prediction is performed on the payee using an SVM classifier, however, the application is not limited in this regard. In other implementations, other machine learning classification algorithms may also be employed for likelihood prediction.
And S405, after receiving the recommended payee information, the mobile terminal can display the recommended payee information in a list mode so that a user selects one payee in the list to pay, namely, the user can click the displayed payee to pay.
In this example, since the SVM classifier needs to be continuously trained to optimize, after the user selects the final payee, the mobile terminal may send user-confirmed payee information to the server for optimization of the SVM classifier.
In the example, geographic position information and user behavior are used as guidance, a machine learning algorithm is utilized to intelligently predict a payee, a plurality of options with highest possibility are given, and then the options are automatically recommended to a user, so that the trouble that the user searches for a two-dimensional code everywhere to scan and the inconvenience of using a sound wave pair are avoided, and the user experience is improved.
Fig. 5 is a flowchart of an example two of a data processing method according to an embodiment of the present application. In this example, the client computing device is exemplified by a mobile terminal such as a smart phone, and the server computing device is exemplified by a server. In this example, when the mobile terminal detects that the triggering condition of the ticket taking operation is met according to the stored time information of the ticket order (such as the time of opening a movie), the information of the recommended ticket taking machine is automatically displayed, and then the user selects the ticket taking machine to perform the ticket taking operation.
As shown in fig. 5, the present example includes the steps of:
s501, the mobile terminal sends a ticket taking operation request to a server;
in this step, the mobile terminal may determine whether the current time meets the triggering condition of the ticket taking operation according to the stored time information of the ticket taking order, for example, if the time information of the ticket taking order is the time of the movie, the mobile terminal may set to take a ticket within half an hour before the movie is launched. When the mobile terminal detects that the current moment meets the triggering condition of the ticket taking operation, the mobile terminal automatically acquires the current geographic position information, combines the current geographic position information and the time information in a ticket taking operation request and sends the combined geographic position information and the time information to the server. In other words, the mobile terminal is automatically authorized to acquire and send information when the mobile terminal detects that the triggering condition of the ticket taking operation is met.
The geographic location information may include the following dimensions: gps_x (GPS coordinate 1), gps_y (GPS coordinate 2), gps_z (GPS coordinate 3), base station number (base), WIFI ADDRESS (wifi_address). Wherein gps_ X, GPS _ Y, GPS _z may represent longitude, latitude, and altitude, respectively. However, the application is not limited in this regard.
It should be noted that, in other implementations, the geographic location information may include one or more of GPS information, base station information, and WIFI information, so long as the current location of the user can be determined based on the geographic location information.
S502, after receiving the ticket taking operation request, the server acquires the geographic position information of the user, and then determines the ticket taking machine matched with the geographic position information of the user according to the geographic position information of the ticket taking machine stored in the database.
In addition to the geographic location information of the ticket machine, the database of the server may store transaction information (such as number of ticket draws, time of drawing, etc.), attribute information (such as machine model number, machine number, etc.) of the ticket machine. The server may periodically update the data within the database. However, the application is not limited in this regard.
In this step, the server may perform gridding division on the geographic location, and may determine, according to the geographic location information of the current user, a first grid to which the user currently belongs, and then screen out a second grid that meets a set condition with the first grid, for example, a grid that is the same as the first grid or a grid that is adjacent to the first grid; taking the first grid as the second grid meeting the set condition, the ticket collector in the first grid may be screened. The setting of the setting condition may be determined according to the actual scene, and the present application is not limited thereto.
S503, aiming at the selected ticket extractor matched with the geographical position information of the current user, the server analyzes the geographical position information and the historical ticket extracting information of the user, and extracts the feature vector of each ticket extractor corresponding to the user from multiple dimensions.
The feature vector may represent a relationship of a ticket machine relative to a current user, so as to describe an association relationship between the ticket machine and the user.
For example, the feature vector for each ticket machine for that user may include the following dimensions: GPS_X_DISTANCE (GPS coordinate 1 DISTANCE value), GPS_Y_DISTANCE (GPS coordinate 2 DISTANCE value), GPS_Z_DISTANCE (GPS coordinate 3 DISTANCE value), a base station label of the ticket machine (or information indicating whether the ticket machine is consistent with a base station label of a current user), a WIFI address of the ticket machine (or information indicating whether the ticket machine is consistent with the WIFI address of the current user), the number of times the current user takes a ticket at the ticket machine, the ticket taking request time of the current user, the type of the ticket machine and the activity degree of the ticket machine.
Wherein gps_x_distance, gps_y_distance, and gps_z_distance represent DISTANCE values of GPS coordinates between the current user and the ticket machine, respectively. The ticket taking request time of the current user can be the current time information carried by the ticket taking operation request of the current user. The type of ticket machine may include one of the following: movie ticket taker, car ticket taker, train ticket taker, and airplane ticket taker. The liveness of the ticket machine can be determined according to the total ticket number of the ticket machine in a period of time.
It should be noted that the above feature vectors are only examples, and in practical application, different dimension combinations may be selected as the feature vectors according to practical situations.
S504, after the feature vectors of the current users corresponding to each screened ticket machine are processed by the server through the SVM classifier, classifying the ticket machines, namely calculating the association degree between each ticket machine and the current users through the SVM classifier, and screening out the ticket machines recommended to the users according to the association degree.
For example, in this example, the value of the association degree may be between 0 and 1, and the greater the value of the association degree, the higher the association between the ticket machine and the current user. The server may select one or more ticket extractors according to the order of the association degree from high to low, and send the information of the selected ticket extractors (for example, the machine type of the ticket extractor, the machine number information) to the mobile terminal, and the mobile terminal displays the information of the recommended ticket extractor to the user.
In this example, the likelihood prediction is performed on the ticket machine using an SVM classifier, however, the application is not limited in this regard. In other implementations, other machine learning classification algorithms may also be employed for likelihood prediction.
And S505, after receiving the recommended ticket machine information, the mobile terminal can display the recommended ticket machine information in a list mode so that a user can select one ticket machine in the list to fetch a ticket.
In this example, after the user selects the ticket machine on the mobile terminal, the server may transmit ticket order information for the user to the ticket machine for the user to perform a ticket operation on the ticket machine.
In this example, since the SVM classifier needs to be continuously trained to optimize, after the user selects the ticket machine, the mobile terminal transmits the ticket machine information confirmed by the user to the server for optimizing the SVM classifier.
In the example, the user operation can be realized only by connecting the ticket machine with the network, the user does not need to scan codes or manually input by using the ticket machine, and the use process of the user is not influenced by surrounding environment and hardware equipment, so that the user experience is improved.
It should be noted that, this example is applicable to other scenes as well, for example, after receiving the logistics notification and knowing that the package arrives at the storage bin, after applying the scheme of this embodiment, after the user approaches the storage bin, the user terminal (such as a mobile phone) can automatically pop up the bin recommendation page, and the user can directly select the corresponding bin to perform the pickup operation, thereby improving user experience.
Fig. 6 is a schematic diagram of a data processing apparatus according to an embodiment of the present application. As shown in fig. 6, the data processing apparatus provided in this embodiment includes:
an obtaining unit 601, adapted to obtain geographical location information of a first object;
a location matching unit 602 adapted to determine at least one second object matching the geographical location information;
a computing unit 603 adapted to compute a degree of association between the second object and the first object;
the processing unit 604 is adapted to select a target object from the at least one second object according to the degree of association and recommend information of the target object to the first object.
The description of the data processing apparatus provided in this embodiment may refer to the description of the method embodiment, so that the description is omitted here.
The embodiment of the application also provides a data processing method, which comprises the following steps:
after receiving an instruction of the target operation or detecting a triggering condition for starting the target operation, displaying information of a recommended object corresponding to the target operation on a display interface.
For example, before displaying the information of the recommended object corresponding to the target operation on the display interface, the method of this embodiment may further include: and receiving information of a recommended object corresponding to the target operation sent by the server.
Illustratively, the recommended object may include a computing device. That is, information of the computing device corresponding to the target operation may be displayed on the display interface.
Illustratively, the information of the recommended objects is displayed in list form on the display interface.
The data processing method provided by the embodiment can be applied to the client computing device. The method provided in this embodiment may refer to the related descriptions on the mobile terminal side in the above-mentioned example one and example two, so that the descriptions are not repeated here.
Fig. 7 is a flowchart of another data processing method according to an embodiment of the present application. As shown in fig. 7, the data processing method provided in this embodiment includes:
s701, obtaining geographic position information of a first computing device;
s702, determining a second computing device matched with the geographic position information;
s703, determining that the second computing device is an associated device of the first computing device;
s704, transmitting the information of the second computing device to the first computing device.
In this embodiment, the first computing device may include a mobile terminal such as a smart phone, tablet, notebook, palm top, personal digital assistant (Personal Digital Assistant, PDA), portable media player (Portable Media Player, PMP), wearable device, and a stationary terminal such as a desktop computer. However, the application is not limited in this regard.
In this embodiment, the second computing device may include a mobile terminal, a vending machine, a ticket machine, and the like. However, the application is not limited in this regard.
The data processing method provided in this embodiment may be performed by a server computing device (e.g., a server).
Illustratively, after S703, the method of the present embodiment may further include:
calculating a degree of association between the second computing device and the first computing device;
s704 may include: and sending the information of the second computing device with the association degree meeting the set association condition to the first computing device.
The degree of association between the first computing device and the second computing device may be calculated using a machine learning classification model. The machine learning classification model is not limited in the present application, for example, the classification algorithm adopted by the machine learning classification model may be an SVM algorithm, a naive bayes algorithm, a decision tree algorithm, or a KNN algorithm.
Wherein the second computing device whose association degree satisfies the set association condition may include: and the second computing device is more than or equal to a preset value in association with the first computing device. However, the application is not limited in this regard.
The method provided in this embodiment may refer to the descriptions related to the first and second examples, and thus will not be repeated here.
The embodiment of the application also provides equipment, which comprises: the system comprises a memory and a processor, wherein the memory stores a data processing program which, when read and executed by the processor, performs the steps of the data processing method shown in fig. 1.
The embodiment of the application also provides equipment, which comprises: a memory and a processor, the memory storing a data processing program which, when read by the processor for execution, performs the following operations:
after receiving an instruction of the target operation or detecting a triggering condition for starting the target operation, displaying information of a recommended object corresponding to the target operation on a display interface.
The embodiment of the application also provides equipment, which comprises: the apparatus includes a memory and a processor, the memory storing a data processing program which, when read by the processor for execution, performs the steps of the data processing method shown in fig. 7.
In addition, an embodiment of the present application further provides a computer readable medium storing a data processing program that, when read and executed by a processor, implements the steps of the data processing method of any one of the above aspects.
Those of ordinary skill in the art will appreciate that all or some of the steps, systems, functional modules or units in the apparatus, or methods disclosed above, may be implemented as software, firmware, hardware, or any suitable combination thereof. In a hardware implementation, the division between functional modules or units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed cooperatively by several physical components. Some or all of the components may be implemented as software executed by a processor, such as a digital signal processor or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as known to those skilled in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer. Furthermore, as is well known to those of ordinary skill in the art, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media.
The foregoing has shown and described the basic principles and main features of the present application and the advantages of the present application. The present application is not limited to the above-described embodiments, and the above-described embodiments and descriptions are merely illustrative of the principles of the present application, and various changes and modifications may be made therein without departing from the spirit and scope of the application, which is defined in the appended claims.
Claims (13)
1. A method of data processing, comprising:
obtaining geographic position information of a first object;
determining at least one second object matching the geographic location information;
extracting a feature vector of each second object corresponding to the first object from a plurality of dimensions, wherein the feature vector can embody an association relationship between the first object and the second object; calculating the association degree between each second object and each first object through a classifier;
and selecting a target object from the at least one second object according to the association degree, and recommending the information of the target object to the first object.
2. The method of claim 1, wherein the geographic location information comprises at least one of: global positioning system information, base station information, wireless network information.
3. The method of claim 1, wherein said determining at least one second object that matches the geographic location information comprises:
determining a first grid to which the first object belongs according to the geographic position information of the first object; determining a second grid meeting a set condition between the first grid and the first grid; and determining a second object in the second grid as a second object matched with the geographic position information of the first object.
4. The method of claim 1, wherein the calculating the degree of association between each of the second objects and the first object, respectively, comprises:
respectively obtaining a characteristic vector of each second object corresponding to the first object, wherein the characteristic vector at least reflects the association relation between the second object and the first object; and processing the feature vectors by adopting a machine learning classification model, and respectively calculating the association degree between each second object and each first object.
5. The method of claim 4, wherein the feature vector comprises at least one of the following information: the comparison information between the geographic position information of the first object and the second object, the interaction information between the first object and the second object and the attribute information of the second object.
6. A method of data processing, comprising: after receiving an instruction of a target operation or detecting a triggering condition for starting the target operation, displaying information of a recommended object corresponding to the target operation on a display interface; wherein the recommended object information is information of a target object to be recommended to the first object, which is obtained according to the data processing method of any one of claims 1 to 5.
7. The method of claim 6, wherein before displaying the information of the recommended object corresponding to the target operation on the display interface, the method further comprises: and receiving information of a recommended object corresponding to the target operation, which is sent by the server.
8. The method of claim 6 or 7, wherein the recommended object comprises a computing device.
9. The method of claim 6, wherein the information of the recommended objects is displayed in a list form on the display interface.
10. A method of data processing, comprising:
obtaining geographic location information of a first computing device;
determining a second computing device that matches the geographic location information;
determining that the second computing device is an associated device of the first computing device; extracting feature vectors of each of the second computing devices corresponding to the first computing device from a plurality of dimensions, wherein the feature vectors can embody an association relationship between the first computing device and the second computing device; calculating the association degree between each second computing device and the first computing device through a classifier;
and sending the information of the second computing equipment with the association degree meeting the set association condition to the first computing equipment.
11. An apparatus, comprising: a memory and a processor, the memory storing a data processing program which, when read for execution by the processor, performs the following operations: obtaining geographic position information of a first object; determining at least one second object matching the geographic location information; extracting a feature vector of each second object corresponding to the first object from a plurality of dimensions, wherein the feature vector can embody an association relationship between the first object and the second object; calculating the association degree between each second object and each first object through a classifier; and selecting a target object from the at least one second object according to the association degree, and recommending the information of the target object to the first object.
12. An apparatus, comprising: a memory and a processor, the memory storing a data processing program which, when read for execution by the processor, performs the following operations: after receiving an instruction of a target operation or detecting a triggering condition for starting the target operation, displaying information of a recommended object corresponding to the target operation on a display interface; wherein the recommended object information is information of a target object to be recommended to the first object, which is obtained according to the data processing method of any one of claims 1 to 5.
13. A computer readable medium, characterized in that a data processing program is stored, which, when read by a processor for execution, implements the steps of the data processing method according to any of claims 1-5.
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