WO2021184881A1 - 业务处理方法及装置 - Google Patents

业务处理方法及装置 Download PDF

Info

Publication number
WO2021184881A1
WO2021184881A1 PCT/CN2020/139744 CN2020139744W WO2021184881A1 WO 2021184881 A1 WO2021184881 A1 WO 2021184881A1 CN 2020139744 W CN2020139744 W CN 2020139744W WO 2021184881 A1 WO2021184881 A1 WO 2021184881A1
Authority
WO
WIPO (PCT)
Prior art keywords
user
business
service
dimension
sample
Prior art date
Legal status (The legal status 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 status listed.)
Ceased
Application number
PCT/CN2020/139744
Other languages
English (en)
French (fr)
Inventor
王力
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Alipay Hangzhou Information Technology Co Ltd
Original Assignee
Alipay Hangzhou Information Technology Co Ltd
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 Alipay Hangzhou Information Technology Co Ltd filed Critical Alipay Hangzhou Information Technology Co Ltd
Publication of WO2021184881A1 publication Critical patent/WO2021184881A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
    • G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
    • G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
    • G06F21/6254—Protecting personal data, e.g. for financial or medical purposes by anonymising data, e.g. decorrelating personal data from the owner's identification
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/0241—Advertisements
    • G06Q30/0251—Targeted advertisements
    • G06Q30/0269—Targeted advertisements based on user profile or attribute
    • G06Q30/0271—Personalized advertisement
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Recommending goods or services

Definitions

  • This manual relates to the technical field of business processing, and in particular to business processing methods and devices.
  • Internet technology has become an indispensable part of real life. For example, in public safety incidents, Internet technology is used to track user behavior or user behavior, and to conduct public safety incidents based on user behavior. Emergency treatment, or in various network services emerging on the Internet, use Internet technology to collect and analyze the consumption process, recommend and analyze consumption trends according to the user’s consumption process, but with the improvement of users’ living standards, users are more Security incidents and network service processing efficiency and accuracy requirements are also increasing.
  • this specification provides a business processing method, a business processing device, a computing device, and a computer-readable storage medium.
  • a service processing method including: obtaining the service identification of users in the user group in the service dimension; obtaining the location data and corresponding time data of the user according to the service identification; based on the location Data, the time data, the sample user path network and service parameters of the service dimension, the group time-shift contact calculation is performed on the user in the service dimension; the sample user path network is based on the sample user in the service dimension The user path construction of the user; according to the business contact degree of the user obtained by calculation, the target users that meet the business conditions of the business dimension are screened in the user group.
  • the sample user path network is constructed in the following manner: obtain the business identity of the sample user in the sample user group in the business dimension; perform data desensitization processing on the business identity of the sample user to obtain the user The desensitization mark; obtain the position data and corresponding time data of the sample user based on the desensitization mark; determine the position in time sequence according to the coordinate position corresponding to the position data on the business map of the business dimension The user path of the sample user on the service map; and the sample user path network is constructed based on the service map and the behavior path.
  • the sample user path network is stored in a service database, and correspondingly, the sample user path network and service parameters based on the location data, the time data, the service dimension are stored in the service dimension
  • the method includes: reading a sample user path network matching the service parameter from the service database as the sample user path network of the service dimension.
  • the service processing method further includes: predicting the group contact degree of the target service area in the service map according to the service parameters of the service dimension and the sample user path network; The group contact degree is rendered on the business map, and the business group contact rendering map of the target business area is obtained.
  • the obtaining the service identifier of the user in the user group in the service dimension includes: obtaining the user's identity code in the service dimension; the identity code carries the user in the service dimension Analyze the identity identification code to obtain the user’s business identity in the business dimension.
  • the location data includes at least one of the following: base station location data, GPS location data, mobile communication differential location data; correspondingly, the user’s location data and corresponding
  • the time data step is executed, and the sample user path network and service parameters based on the location data, the time data, and the business dimension are performed
  • the group time-shift contact calculation step is performed on the users in the business dimension Previously, it includes: judging whether at least two of the base station positioning data, GPS positioning data, and mobile communication differential positioning data corresponding to the user's data at the same time correspond to the same coordinate position on the virtual map; if so, the one with the highest position accuracy is determined
  • the location data is used as the location data of the user.
  • the business dimension includes at least one of the following: public safety business dimension, access business dimension, offline store consumption business dimension, and online business dimension.
  • the group time-shift contact calculation includes at least one of the following: calculating the contact degree between users in the user group and sample users in the sample user group using business parameters of the public safety business dimension as constraints
  • the consumption influence of the sample users in the sample user group with respect to the users in the user group is calculated, and the business parameters of the users in the user group relative to the sample users in the sample user group are calculated using the business parameters of the online business dimension as constraints. Probability of participation.
  • the service parameters include at least one of the following: contact time, contact distance, and contact area range.
  • a service processing device which includes: a service identification acquisition module configured to acquire the business identification of users in a user group in the business dimension; and a data acquisition module configured to Identify and obtain the location data of the user and the corresponding time data; the group time-shift contact calculation module is configured to be based on the location data, the time data, the sample user path network and business parameters of the business dimension, in the place
  • the business dimension performs group time-shift contact calculation for the users;
  • the sample user path network is constructed based on the user paths of the sample users in the business dimension;
  • the target user screening module is configured to obtain the group of the users according to calculation
  • the degree of contact is to screen the target users meeting the contact conditions of the business dimension from the user group.
  • a computing device including: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions: acquiring users in a user group Service ID in the service dimension; Obtain the user's location data and corresponding time data according to the service ID; Sample user path network and service parameters based on the location data, the time data, the service dimension, and The business dimension performs group time-shift contact calculation for the user; the sample user path network is constructed based on the user path of the sample user in the business dimension; according to the calculated group contact degree of the user, in the user The target users who meet the contact conditions of the business dimension are selected from the group.
  • a computer-readable storage medium which stores computer instructions that, when executed by a processor, implement the steps of the service processing method.
  • the business processing method is based on the location data and time data of the users in the user group, and the sample user path network constructed based on the user paths of the sample users in the sample user group, and the business parameters of the business dimension are used as constraints, and the users in the user group are The location information and time information are calculated with the sample user path network for group time-shift contact calculation, so as to calculate the business contact degree of users in the user group relative to the sample users in the sample user group in the business dimension, so as to screen the user groups for satisfaction
  • Target users of business needs not only improve the accuracy of target user screening during business processing, but also improve business processing efficiency.
  • Fig. 1 is a processing flowchart of a business processing method provided by an embodiment of this specification
  • FIG. 2 is a processing flowchart of a business processing method applied to an offline store consumption scenario provided by an embodiment of this specification;
  • FIG. 3 is a schematic diagram of a service processing apparatus provided by an embodiment of this specification.
  • Fig. 4 is a structural block diagram of a computing device provided by an embodiment of this specification.
  • first, second, etc. may be used to describe various information in one or more embodiments of this specification, the information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other.
  • the first may also be referred to as the second, and similarly, the second may also be referred to as the first.
  • word “if” as used herein can be interpreted as "when” or “when” or "in response to determination”.
  • An embodiment of this specification provides a service processing method, a service processing apparatus, a computing device, and a computer-readable storage medium. The following is a detailed description one by one in conjunction with the accompanying drawings of the embodiments provided in this specification, and each step of the method is described.
  • FIG. 1 shows a processing flowchart of a business processing method provided in this embodiment.
  • Step S102 Obtain the business identifier of the user in the user group in the business dimension.
  • many business scenarios require corresponding business processing based on the behavior path of the user group.
  • some users in the user group that meet the emergency treatment conditions Carry out emergency treatment; or, in the offline store consumption business scenario, by analyzing the consumer behavior of the user group, find out some users in the user group that meet the offline consumption conditions for consumer business processing; or, in the in and out business scenario, By tracing the user's access path, some users in the user group that meet the requirements are processed for access; or, in an online business scenario, for users who use online services, collect the behavior paths of users in the process of using online services
  • online business recommendation or processing is carried out for some users in the user group who meet the business conditions of the online business.
  • the service processing method provided in this embodiment is based on the sample user path network constructed by the user paths of the sample users in the sample user group, and the service parameters of the service dimension are used as constraints.
  • the sample user path network performs group time-shift contact calculation to calculate the business contact degree of users in the user group relative to the sample users in the sample user group in the business dimension, so as to screen the target users that meet the business needs in the user group , Which not only improves the accuracy of target user screening during business processing, but also improves business processing efficiency.
  • the business dimensions described in this embodiment include public safety business dimensions, access business dimensions, offline store consumption business dimensions, and online business dimensions.
  • the public safety business dimensions refer to business dimensions involving users' public safety business, such as public safety business dimensions such as public health safety, public health safety, public property safety, and public information safety.
  • the business dimension of access and exit refers to the business dimension that involves the user's access and exit business, such as entry and exit traffic processing, public place entry and exit processing, and entry and exit inspection business dimensions involving entry and exit traffic.
  • the offline store consumption business dimension refers to the business dimension of user tracking, analysis, and recommendation services involving offline consumption, such as offline stores conduct consumption analysis on store user groups, and offline shopping malls conduct consumption tracking on mall user groups, etc.
  • the online business dimensions refer to business dimensions involving online bearer services, such as online data services, virtual services, online resource services, and other business dimensions involving online bearer services.
  • the implementation of the business processing method in the public safety business dimension is taken as an example to illustrate the business processing method provided in this embodiment.
  • the specific implementation of the service dimension refer to the specific implementation of the public safety service dimension provided in this embodiment, and this embodiment will not repeat them one by one here.
  • the application of image identification codes represented by two-dimensional codes is very common.
  • the business identity of the user in the user group in the business dimension is obtained based on the identification code. Specifically, the identity of the user in the business dimension is first obtained Identification code; the identification code carries the service feature information and service identification of the user in the service dimension; then the identification code is parsed to obtain the service identification of the user in the service dimension.
  • the identity QR code For example, in the public security business dimension, first collect the identity QR code of each user in the user group, and the identity QR code carries the user’s identity feature information and identity ID, and then analyze the collected identity QR code , And use the parsed identity ID as the user's business identity in the public safety business dimension.
  • Step S104 Acquire location data and corresponding time data of the user according to the service identifier.
  • the position data in this embodiment includes base station positioning data, GPS (Global Positioning System) positioning data, and mobile communication differential positioning data (for example, 5G (5th-Generation) differential positioning data).
  • base station positioning data GPS (Global Positioning System) positioning data
  • mobile communication differential positioning data for example, 5G (5th-Generation) differential positioning data
  • any one or both of the above-mentioned base station positioning data, GPS positioning data, and mobile communication differential positioning data may also be used as the user's position data, which is not limited.
  • the user's base station positioning data, GPS positioning data, mobile communication differential positioning data, and the three corresponding data are obtained from a preset data source.
  • Time data in the process of obtaining the user's location data and corresponding time data, the user's base station positioning data, GPS positioning data, mobile communication differential positioning data, and the three corresponding data are obtained from a preset data source.
  • the data backtracking method is used to trace the base station location data and the corresponding time point from the mobile operator data source, and the GPS location data and the corresponding time are traced back based on the terminal carried by the user. Point, and, through the 5G location-finding technology to trace back the user’s 5G location data and the corresponding time point.
  • the user’s base station positioning data, GPS positioning data, mobile communication differential positioning data and the corresponding time data of the three taking into account the user’s data privacy
  • the user’s base station positioning data, GPS The positioning data, mobile communication differential positioning data, and the time data corresponding to the three are desensitized, such as converting the acquired position data and time data into a hash sequence, thereby protecting the user's data privacy.
  • the three pieces of data are compared by judging whether at least two of the base station positioning data, GPS positioning data, and mobile communication differential positioning data corresponding to the user's data at the same time correspond to the same coordinate position on the virtual map.
  • the location data and time data acquired by the channel are validated. When the location data acquired by at least two of the three data channels are consistent, the location data with the highest location accuracy is used as the user’s location data. This improves the accuracy and accuracy of subsequent business processing based on the location data.
  • Step S106 based on the location data, the time data, the sample user path network of the business dimension, and business parameters, perform group time-shift contact calculation for the user in the business dimension.
  • the location data of the user and the corresponding time data represent the specific location of the user at the historical moment.
  • the sample user path network represents the location of the sample user in the sample user group.
  • time shift refers to moving the time point to the historical time point for calculation; correspondingly, the group time-shift contact calculation refers to the users and sample users in the calculation user group The degree of business contact of the sample users in the group at historical time points.
  • group time-shift contact calculation refers to the calculation of the contact degree between users in the user group and the sample user in the sample user group. If the sample user in the sample user group is a risk sample user, the greater the contact degree High, indicating that the closer the user has contact with the sample user, the lower the user’s safety; conversely, the lower the contact degree, the less the user’s contact with the sample user, and the higher the user’s safety.
  • the group time-shift contact calculation refers to the calculation of the consumption influence of the sample user in the sample user group on the users in the user group. If the sample user in the sample user group is a consumer sample user, the consumption influence is greater High, the higher the probability that the user participates in consumption under the influence of the consumption sample user, the lower the consumption influence, the lower the probability that the user participates in the consumption under the influence of the consumption sample user; on the other hand, if the sample user in the sample user group is For non-consumption sample users, the higher the degree of consumption influence, the lower the probability of users participating in consumption under the influence of non-consumption sample users, and the lower the degree of consumption influence, the lower the probability of users being influenced by non-consumption sample users.
  • the group time-shift contact calculation refers to the calculation of the traffic similarity between the users in the user group and the sample users in the sample user group.
  • the traffic similarity represents the probability of the user and the sample user making the same traffic behavior.
  • the sample user in the sample user group is a forward-passing sample user
  • the sample user in the sample user group is a reverse-traffic sample user
  • the group time-shift contact calculation refers to the calculation of the business participation probability of users in the user group relative to the sample users in the sample user group. If the sample user in the sample user group is a participating sample user, the higher the business participation probability , The higher the probability of a user participating in online business, the lower the probability of participating in the business, and the lower the probability of participating in the online business; on the other hand, if the sample user in the sample user group is a non-participating sample user, the higher the probability of participating in the business , The lower the probability of a user participating in online business, the lower the probability of participating in the business, and the higher the probability of participating in online business.
  • the service parameters described in this embodiment include contact time, contact distance, and contact area range.
  • the contact time is set to 10 minutes, which means that the contact time with the risk sample user in the sample user group is more than 10 minutes;
  • the contact distance is set to 5m, which means the contact time with the risk sample user in the sample user group The contact distance is within 5m;
  • the contact area is 5 square kilometers, which means that calculations are performed within the area of 5 square kilometers.
  • the sample user path network is constructed based on the user paths of sample users in the business dimension.
  • the following method is used to construct The sample user path network:
  • the identity characteristic information of the risk sample users in the sample user group is converted into Hash ID is used as the identification of the user of the risk sample; secondly, based on the hash ID of the user of the risk sample, the base station positioning data and the corresponding time point are traced back from the mobile operator data source, and the GPS positioning data and the corresponding time point are traced back based on the terminal carried by the user.
  • the time sequence of location determines the path of each risk sample user; finally, based on the paths of all risk sample users in the sample user group, a sample user path network of the sample user group on the map is constructed.
  • the location data and corresponding time data of users in the user group obtained in step S104 above, the pre-set service parameters of the business dimension, and the sample user path network are targeted at the user group Of users perform group time-shift contact calculations.
  • graph algorithms or other algorithms can be used to calculate the group time-shift contact, so as to calculate the business contact degree of each user in the user group.
  • model training can also be performed based on training samples and sample user path networks. The training office uses the sample user path network to perform group time-shift contact calculation models for users in the user group. Based on the model obtained by training, it will The location data of the users in the user group and the corresponding time data and business parameters of the business dimension are used as the model input to calculate the group time-shift contact, and output the business contact degree of the users in the user group.
  • the public safety business dimension based on the user's base station positioning data, GPS positioning data, 5G positioning data, and the corresponding time points of the three users in the user group, construct on the map based on the path of all risk sample users in the sample user group
  • the sample user path network of the sample user group uses the contact time, contact distance, and contact area range as parameters, and calculates the business contact degree between users in the user group and risk sample users in the sample user group through a graph algorithm.
  • Step S108 According to the business contact degree of the user obtained by calculation, target users satisfying the business conditions of the business dimension are selected from the user group.
  • users in the user group in the business dimension are selected as target users.
  • the user group whose contact degree is greater than the preset contact degree threshold is screened out.
  • These users are the target users who are in close contact with the risk sample users, and they can be dealt with according to the actual business scenarios of the public safety business dimension.
  • the business contact prediction rendering of the target business area is performed: first, according to the business parameters of the business dimension and the sample user path network, predict the group contact degree of the target business area in the business map , And then render on the business map based on the group contact degree of the target business area to obtain the business group contact rendering map of the target business area.
  • a sample user path network constructed on the map based on the contact time, contact distance, contact area range, and path of all risk sample users in the sample user group, predicts that there are security risks in the designated area of the map. Risk probability, and then predict the risk probability on the map for the risk rendering of the designated area, and generate the business group contact rendering map of the designated area.
  • the business processing method provided in this manual is based on the location data and time data of the users in the user group, and the sample user path network constructed based on the user paths of the sample users in the sample user group.
  • the target users who meet the business needs are screened from the user group, which not only improves the accuracy of target user screening in the business processing process, but also improves the business processing efficiency.
  • the application of the business processing method provided in this embodiment in an offline store consumption scenario is taken as an example to further illustrate the business processing method provided in this embodiment.
  • the business processing method applied to the offline store consumption scenario specifically includes step S202 to step S222.
  • Step S202 Obtain the business identifier of the online store consumption business dimension of the sample user in the sample user group.
  • Step S204 Perform data desensitization processing on the service identification of the sample user to obtain the desensitization identification of the sample user.
  • Step S206 Obtain location data and corresponding time data of the sample user based on the desensitization identification of the sample user.
  • Step S208 Determine the user consumption path of the sample user on the map in chronological order according to the coordinate position corresponding to the position data of the sample user on the map.
  • Step S210 based on the consumption paths of all sample users in the sample user group, construct a sample user consumption path network corresponding to the sample user group on the map.
  • step S212 the constructed sample user consumption path network is stored in the service database.
  • Step S214 Obtain the business identifier of the user's online store consumption business dimension in the user group.
  • Step S216 Obtain the user's location data and corresponding time data according to the user's service identification.
  • Step S218 Read the sample user consumption path network matching the business parameters of the offline store consumption business dimension from the business database.
  • Step S220 Calculate the consumption influence of the sample users in the sample user group on the users in the user group based on the user's location data, time data, the read consumption path network of the sample users and the preset business parameters.
  • Step S222 According to the calculated consumption influence degree of the user, target users that meet the consumption influence degree threshold of the offline store consumption business dimension are selected from the user group.
  • FIG. 3 it shows a schematic diagram of a service processing apparatus provided in this embodiment.
  • the description is relatively simple.
  • the corresponding description of the method embodiment provided above please refer to the corresponding description of the method embodiment provided above.
  • the device embodiments described below are merely illustrative.
  • This specification provides a service processing apparatus, including: a service identification acquisition module 302, configured to acquire the business identification of users in the user group in the business dimension; the data acquisition module 304, configured to acquire the user’s information according to the business identification Location data and corresponding time data; the group time-shift contact calculation module 306 is configured to perform sample user path networks and service parameters based on the location data, the time data, the service dimension, and compare all data in the service dimension The user performs the group time-shift contact calculation; the sample user path network is constructed based on the user path of the sample user in the business dimension; the target user screening module 308 is configured to calculate the group contact degree of the user obtained by calculation, Screening target users meeting the contact conditions of the business dimension from the user group.
  • a service identification acquisition module 302 configured to acquire the business identification of users in the user group in the business dimension
  • the data acquisition module 304 configured to acquire the user’s information according to the business identification Location data and corresponding time data
  • the group time-shift contact calculation module 306 is configured to perform sample user path
  • the sample user path network is constructed in the following manner: obtain the business identity of the sample user in the sample user group in the business dimension; perform data desensitization processing on the business identity of the sample user to obtain the user The desensitization mark; obtain the position data and corresponding time data of the sample user based on the desensitization mark; determine the position in time sequence according to the coordinate position corresponding to the position data on the business map of the business dimension The user path of the sample user on the service map; and the sample user path network is constructed based on the service map and the behavior path.
  • the sample user path network is stored in a business database.
  • the service processing device further includes: a sample user path network reading module configured to read data from the service database and The sample user path network matching the service parameters is used as the sample user path network of the service dimension.
  • the service processing device further includes: a prediction module configured to predict the group contact degree of the target service area in the service map according to the service parameters of the service dimension and the sample user path network; rendering; The module is configured to render on the business map based on the group contact degree of the target business area to obtain a rendering map of the business group contact of the target business area.
  • a prediction module configured to predict the group contact degree of the target service area in the service map according to the service parameters of the service dimension and the sample user path network
  • rendering The module is configured to render on the business map based on the group contact degree of the target business area to obtain a rendering map of the business group contact of the target business area.
  • the obtaining the service identifier of the user in the user group in the service dimension includes: obtaining the user's identity code in the service dimension; the identity code carries the user in the service dimension Analyze the identity identification code to obtain the user’s business identity in the business dimension.
  • the location data includes at least one of the following: base station location data, GPS location data, and mobile communication differential location data; correspondingly, the service processing device further includes: a location data judgment module configured to Determine whether at least two of the base station positioning data, GPS positioning data, and mobile communication differential positioning data corresponding to the user's data at the same time correspond to the same coordinate position on the virtual map; if so, the position data with the highest position accuracy is taken as the location data The user’s location data.
  • a location data judgment module configured to Determine whether at least two of the base station positioning data, GPS positioning data, and mobile communication differential positioning data corresponding to the user's data at the same time correspond to the same coordinate position on the virtual map; if so, the position data with the highest position accuracy is taken as the location data The user’s location data.
  • the business dimension includes at least one of the following: public safety business dimension, access business dimension, offline store consumption business dimension, and online business dimension.
  • the group time-shift contact calculation includes at least one of the following: calculating the contact degree between users in the user group and sample users in the sample user group using business parameters of the public safety business dimension as constraints
  • the consumption influence of the sample users in the sample user group with respect to the users in the user group is calculated, and the business parameters of the users in the user group relative to the sample users in the sample user group are calculated using the business parameters of the online business dimension as constraints. Probability of participation.
  • the service parameters include at least one of the following: contact time, contact distance, and contact area range.
  • FIG. 4 is a structural block diagram of a computing device 400 provided according to an embodiment of this specification.
  • the components of the computing device 400 include but are not limited to a memory 410 and a processor 420.
  • the processor 420 and the memory 410 are connected through a bus 430, and the database 450 is used to store data.
  • the computing device 400 also includes an access device 440 that enables the computing device 400 to communicate via one or more networks 460.
  • networks include a public switched telephone network (PSTN), a local area network (LAN), a wide area network (WAN), a personal area network (PAN), or a combination of communication networks such as the Internet.
  • the access device 440 may include one or more of any type of wired or wireless network interface (for example, a network interface card (NIC)), such as IEEE802.11 wireless local area network (WLAN) wireless interface, global interconnection for microwave access ( Wi-MAX) interface, Ethernet interface, universal serial bus (USB) interface, cellular network interface, Bluetooth interface, near field communication (NFC) interface, etc.
  • NIC network interface card
  • the aforementioned components of the computing device 400 and other components not shown in FIG. 4 may also be connected to each other, for example, via a bus. It should be understood that the structural block diagram of the computing device shown in FIG. 4 is only for the purpose of example, and is not intended to limit the scope of this specification. Those skilled in the art can add or replace other components as needed.
  • the computing device 400 may be any type of stationary or mobile computing device, including a mobile computer or mobile computing device (for example, a tablet computer, a personal digital assistant, a laptop computer, a notebook computer, a netbook, etc.), a mobile phone (for example, a smart phone). ), wearable computing devices (for example, smart watches, smart glasses, etc.) or other types of mobile devices, or stationary computing devices such as desktop computers or PCs.
  • the computing device 400 may also be a mobile or stationary server.
  • This specification provides a computing device that includes a memory 410, a processor 420, and computer instructions stored on the memory and running on the processor.
  • the processor 420 is used to execute the following computer executable instructions: Obtain users in a user group Service ID in the service dimension; Obtain the user's location data and corresponding time data according to the service ID; Sample user path network and service parameters based on the location data, the time data, the service dimension, and The business dimension performs group time-shift contact calculation for the users; the sample user path network is constructed based on the user paths of the sample users in the business dimension; The target users who meet the business conditions of the business dimension are selected from the group.
  • the sample user path network is constructed in the following manner: obtain the business identity of the sample user in the sample user group in the business dimension; perform data desensitization processing on the business identity of the sample user to obtain the user The desensitization mark; obtain the position data and corresponding time data of the sample user based on the desensitization mark; determine the position in time sequence according to the coordinate position corresponding to the position data on the business map of the business dimension The user path of the sample user on the service map; and the sample user path network is constructed based on the service map and the behavior path.
  • the sample user path network is stored in a service database, and correspondingly, the sample user path network and service parameters based on the location data, the time data, the service dimension are stored in the service dimension
  • the processor 420 is further configured to execute the following computer-executable instruction: read a sample user path network matching the business parameter from the business database, as The sample user path network of the business dimension.
  • the processor 420 is further configured to execute the following computer-executable instructions: predict the group contact degree of the target business area in the business map according to the business parameters of the business dimension and the sample user path network;
  • the obtaining the service identifier of the user in the user group in the service dimension includes: obtaining the user's identity code in the service dimension; the identity code carries the user in the service dimension Analyze the identity identification code to obtain the user’s business identity in the business dimension.
  • the location data includes at least one of the following: base station location data, GPS location data, mobile communication differential location data; correspondingly, the user’s location data and corresponding
  • the time data instruction is executed, and the sample user path network and business parameters based on the location data, the time data, and the business dimension are executed, the group time-shift contact calculation instruction is executed for the user in the business dimension
  • the processor 420 was also used to execute the following computer executable instructions: determine whether at least two of the base station positioning data, GPS positioning data, and mobile communication differential positioning data corresponding to the user's data at the same time correspond to a virtual map If it is, the position data with the highest position accuracy is used as the user’s position data.
  • the business dimension includes at least one of the following: public safety business dimension, access business dimension, offline store consumption business dimension, and online business dimension.
  • the group time-shift contact calculation includes at least one of the following: calculating the contact degree between users in the user group and sample users in the sample user group using business parameters of the public safety business dimension as constraints
  • the consumption influence of the sample users in the sample user group with respect to the users in the user group is calculated, and the business parameters of the users in the user group relative to the sample users in the sample user group are calculated using the business parameters of the online business dimension as constraints. Probability of participation.
  • the service parameters include at least one of the following: contact time, contact distance, and contact area range.
  • An example of a computer-readable storage medium provided in this specification is as follows: an embodiment of this specification provides a computer-readable storage medium that stores computer instructions that, when executed by a processor, implement the steps of the business processing method .
  • the computer instructions include computer program codes, and the computer program codes may be in the form of source code, object code, executable files, or some intermediate forms.
  • the computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory) , Random Access Memory (RAM, Random Access Memory), electrical carrier signal, telecommunications signal, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or deleted according to the requirements of the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to the legislation and patent practice, the computer-readable medium Does not include electrical carrier signals and telecommunication signals.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Finance (AREA)
  • Databases & Information Systems (AREA)
  • Accounting & Taxation (AREA)
  • Strategic Management (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Development Economics (AREA)
  • Marketing (AREA)
  • Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Engineering & Computer Science (AREA)
  • General Business, Economics & Management (AREA)
  • Data Mining & Analysis (AREA)
  • Game Theory and Decision Science (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Bioethics (AREA)
  • Software Systems (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Hardware Design (AREA)
  • Medical Informatics (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

业务处理方法及装置，其中，所述业务处理方法包括：获取用户群体中用户在业务维度的业务标识（S102）；根据所述业务标识获取所述用户的位置数据以及对应的时间数据（S104）；基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算（S106）；所述样本用户路径网络基于所述业务维度下样本用户的用户路径构建；根据计算获得的所述用户的业务接触度，在所述用户群体中筛选满足所述业务维度的业务条件的目标用户（S108）。

Description

业务处理方法及装置 技术领域
本说明书涉及业务处理技术领域，特别涉及业务处理方法及装置。
背景技术
随着互联网技术的迅速发展，互联网技术已经成为实际生活中不可或缺的一环，比如在公共安全事件中，利用互联网技术来追踪用户行为或者用户的行为，并且根据用户行为进行公共安全事件的应急处理，或者在网络上涌现的各种网络服务中，利用互联网技术来采集和分析的消费过程，根据用户的消费过程进行推荐和消费趋势分析，但随着用户生活水平的提高，用户对公共安全事件和网络服务处理效率和精度的要求也越来越高。
发明内容
有鉴于此，本说明书提供了一种业务处理方法、一种业务处理装置、一种计算设备以及一种计算机可读存储介质。
本说明书实施例一个方面，提供一种业务处理方法，包括：获取用户群体中用户在业务维度的业务标识；根据所述业务标识获取所述用户的位置数据以及对应的时间数据；基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算；所述样本用户路径网络基于所述业务维度下样本用户的用户路径构建；根据计算获得的所述用户的业务接触度，在所述用户群体中筛选满足所述业务维度的业务条件的目标用户。
可选的，所述样本用户路径网络，采用如下方式构建：获取样本用户群体中样本用户在所述业务维度的业务标识；对所述样本用户的业务标识进行数据脱敏处理，获得所述用户的脱敏标识；基于所述脱敏标识获取所述样本用户的位置数据以及对应的时间数据；根据所述位置数据在所述业务维度的业务地图上对应的坐标位置，按照时间顺序确定所述样本用户在所述业务地图上的用户路径；基于所述业务地图和所述行为路径构建所述样本用户路径网络。
可选的，所述样本用户路径网络存储在业务数据库中，相应的，所述基于所述位 置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算步骤执行之前，包括：从所述业务数据库中读取与所述业务参数匹配的样本用户路径网络，作为所述业务维度的样本用户路径网络。
可选的，所述业务处理方法，还包括：根据所述业务维度的业务参数和所述样本用户路径网络，预测所述业务地图中目标业务区域的群体接触度；基于所述目标业务区域的群体接触度在所述业务地图上进行渲染，获得所述目标业务区域的业务群体接触渲染图。
可选的，所述获取用户群体中用户在业务维度的业务标识，包括：获取所述用户在所述业务维度的身份标识码；所述身份标识码中携带有所述用户在所述业务维度的业务特征信息和业务标识；对所述身份标识码进行解析，获得所述用户在所述业务维度的业务标识。
可选的，所述位置数据，包括下述至少一项：基站定位数据、GPS定位数据、移动通信差分定位数据；相应的，所述根据所述业务标识获取所述用户的位置数据以及对应的时间数据步骤执行之后，且所述基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算步骤执行之前，包括：判断所述用户在同一时间数据对应的基站定位数据、GPS定位数据、移动通信差分定位数据三者中至少二者是否对应虚拟地图上同一坐标位置；若是，将位置精确度最高的位置数据作为所述用户的位置数据。
可选的，所述业务维度，包括下述至少一项：公共安全业务维度、出入通行业务维度、线下门店消费业务维度、线上业务维度。
可选的，所述群体时移接触计算，包括下述至少一项：以所述公共安全业务维度的业务参数作为约束计算所述用户群体中用户与所述样本用户群体中样本用户的接触度，以所述出入通行业务维度的业务参数作为约束计算所述用户群体中用户与所述样本用户群体中样本用户的通行相似度，以所述线下门店消费业务维度的业务参数作为约束计算所述样本用户群体中样本用户针对所述用户群体中用户的消费影响度，以所述线上业务维度的业务参数作为约束计算所述用户群体中用户相对于所述样本用户群体中样本用户的业务参与概率。
可选的，所述业务参数，包括下述至少一项：接触时间、接触距离、接触区域范围。
本说明书实施例第二方面，提供一种一种业务处理装置，包括：业务标识获取模块，被配置为获取用户群体中用户在业务维度的业务标识；数据获取模块，被配置为根据所述业务标识获取所述用户的位置数据以及对应的时间数据；群体时移接触计算模块，被配置为基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算；所述样本用户路径网络基于所述业务维度下样本用户的用户路径构建；目标用户筛选模块，被配置为根据计算获得的所述用户的群体接触度，在所述用户群体中筛选满足所述业务维度的接触条件的目标用户。
本说明书实施例第三方面，提供一种计算设备，包括：存储器和处理器；所述存储器用于存储计算机可执行指令，所述处理器用于执行所述计算机可执行指令：获取用户群体中用户在业务维度的业务标识；根据所述业务标识获取所述用户的位置数据以及对应的时间数据；基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算；所述样本用户路径网络基于所述业务维度下样本用户的用户路径构建；根据计算获得的所述用户的群体接触度，在所述用户群体中筛选满足所述业务维度的接触条件的目标用户。
本说明书实施例第三方面，提供一种计算机可读存储介质，其存储有计算机指令，该指令被处理器执行时实现所述业务处理方法的步骤。
所述业务处理方法，根据用户群体中用户的位置数据和时间数据以及基于样本用户群体中样本用户的用户路径构建的样本用户路径网络，以业务维度的业务参数为约束，对用户群体中用户的位置信息和时间信息与所述样本用户路径网络进行群体时移接触计算，从而来计算业务维度下用户群体中用户相对于样本用户群体中样本用户的业务接触度，以此在用户群体中筛选满足业务需求的目标用户，不仅提升了业务处理过程中目标用户筛选的准确度，同时还提升了业务处理效率。
附图说明
图1是本说明书实施例提供的一种业务处理方法处理流程图；
图2是本说明书实施例提供的一种应用于线下门店消费场景的业务处理方法处理流程图；
图3是本说明书实施例提供的一种业务处理装置的示意图；
图4是本说明书实施例提供的一种计算设备的结构框图。
具体实施方式
在下面的描述中阐述了很多具体细节以便于充分理解本说明书。但是本说明书能够以很多不同于在此描述的其它方式来实施，本领域技术人员可以在不违背本说明书内涵的情况下做类似推广，因此本说明书不受下面公开的具体实施的限制。
在本说明书一个或多个实施例中使用的术语是仅仅出于描述特定实施例的目的，而非旨在限制本说明书一个或多个实施例。在本说明书一个或多个实施例和所附权利要求书中所使用的单数形式的“一种”、“所述”和“该”也旨在包括多数形式，除非上下文清楚地表示其他含义。还应当理解，本说明书一个或多个实施例中使用的术语“和/或”是指并包含一个或多个相关联的列出项目的任何或所有可能组合。
应当理解，尽管在本说明书一个或多个实施例中可能采用术语第一、第二等来描述各种信息，但这些信息不应限于这些术语。这些术语仅用来将同一类型的信息彼此区分开。例如，在不脱离本说明书一个或多个实施例范围的情况下，第一也可以被称为第二，类似地，第二也可以被称为第一。取决于语境，如在此所使用的词语“如果”可以被解释成为“在……时”或“当……时”或“响应于确定”。
本说明书一个实施例提供一种业务处理方法、一种业务处理装置、一种计算设备以及一种计算机可读存储介质。以下分别结合本说明书提供的实施例的附图逐一进行详细说明，并且对方法的各个步骤进行说明。
本说明书提供的一种业务处理方法实施例如下，参照附图1，其示出了本实施例提供的一种业务处理方法处理流程图。
步骤S102，获取用户群体中用户在业务维度的业务标识。
实际应用中，许多业务场景均需从用户群体的行为路径出发进行相应的业务处理，比如在公共安全业务场景中，在掌握用户群体行为的基础上，对用户群体中符合应急处理条件的部分用户进行应急处理；或者，在线下门店消费业务场景中，通过分析用户群体的消费行为，在用户群体中找出满足线下消费条件的部分用户进行消费业务处理；或者，在出入通行业务场景中，通过追踪用户的出入通行路径，对用户群体中符合通行条件的部分用户进行通行处理；或者，在线上业务场景中，针对使用线上业务的用户，在采集用户使用线上业务过程中的行为路径的基础上，对用户群体中符合线上业务的业务 条件的部分用户进行线上业务推荐或者处理。
本实施例提供的业务处理方法，以样本用户群体中样本用户的用户路径构建的样本用户路径网络为基准，以业务维度的业务参数为约束，按照时间信息对用户群体中用户的位置信息与所述样本用户路径网络进行群体时移接触计算，从而来计算所述业务维度下用户群体中用户相对于样本用户群体中样本用户的业务接触度，以此在用户群体中筛选满足业务需求的目标用户，不仅提升了业务处理过程中目标用户筛选的准确度，同时还提升了业务处理效率。
本实施例所述业务维度，包括公共安全业务维度、出入通行业务维度、线下门店消费业务维度、线上业务维度。具体而言，所述公共安全业务维度，是指涉及用户公共安全业务的业务维度，比如公共卫生安全、公共健康安全、公共财产安全、公共信息安全等公共安全的业务维度。所述出入通行业务维度，是指涉及用户出入通行业务的业务维度，比如出入境通行办理、公共场所出入办理和出入检查等涉及出入通行的业务维度。所述线下门店消费业务维度，是指涉及线下消费的用户追踪、分析以及推荐业务的业务维度，比如线下门店对门店用户群体进行消费分析、线下商场对商场用户群体进行消费追踪等涉及线下消费业务的业务维度。所述线上业务维度，是指涉及线上承载业务的业务维度，比如线上数据业务、虚拟业务、线上资源业务等涉及线上承载业务的业务维度。
本实施例以所述业务处理方法在公共安全业务维度的实现为例，对本实施例提供的业务处理方法进行说明，所述业务处理方法在出入通行业务维度、线下门店消费业务维度、线上业务维度的具体实现，参照本实施例提供的公共安全业务维度的具体实现即可，本实施例在此不再一一赘述。
实际应用中，以二维码为代表的图像标识码的应用非常普遍，为了提升本实施例提供的所述业务处理方法在实际应用过程中的应用范围，同时也为了提升用户的业务标识采集的便捷性和通用性，本实施例提供的一种可选实施方式中，基于标识码来获取用户群体中用户在业务维度的业务标识，具体的，首先获取所述用户在所述业务维度的身份标识码；所述身份标识码中携带有所述用户在所述业务维度的业务特征信息和业务标识；然后对所述身份标识码进行解析，获得所述用户在所述业务维度的业务标识。
例如，在公共安全业务维度下，首先采集用户群体中每个用户的身份二维码，身份二维码中携带有用户的身份特征信息和身份ID，然后对采集到的身份二维码进行解析，将解析出的身份ID作为用户在公共安全业务维度下的业务标识。
步骤S104，根据所述业务标识获取所述用户的位置数据以及对应的时间数据。
本实施例所述位置数据，包括基站定位数据、GPS(Global Positioning System)定位数据、移动通信差分定位数据(比如，5G(5th-Generation)差分定位数据)。除此之外，在具体实施时，还可以采用上述基站定位数据、GPS定位数据、移动通信差分定位数据三者中任意一者或者两者作为用户的位置数据，对此不做限定。
本步骤中，在获取所述用户的位置数据以及对应的时间数据的过程中，从预设的数据源获取所述用户的基站定位数据、GPS定位数据、移动通信差分定位数据以及三者对应的时间数据。
例如，在公共安全业务维度下，根据用户的手机号码，采用数据回溯的方式从移动运营商数据源回溯基站定位数据以及对应的时间点，并基于用户携带的终端回溯GPS定位数据以及对应的时间点，以及，通过5G查分定位技术回溯用户的5G定位数据以及对应的时间点。
在获取到所述用户的基站定位数据、GPS定位数据、移动通信差分定位数据以及三者对应的时间数据的基础上，考虑到用户的数据隐私，还可对所述用户的基站定位数据、GPS定位数据、移动通信差分定位数据以及三者对应的时间数据进行脱敏处理，比如将获取到的位置数据和时间数据转化为哈希序列，从而保护用户的数据隐私。
此外，在获取到所述用户的基站定位数据、GPS定位数据、移动通信差分定位数据以及三者对应的时间数据的基础上，为了提升业务处理的精度和准确度，本实施例提供的一种可选实施方式中，通过判断所述用户在同一时间数据对应的基站定位数据、GPS定位数据、移动通信差分定位数据三者中至少二者是否对应虚拟地图上同一坐标位置，来对3个数据通道获取的位置数据和时间数据进行有效性验证，在3个数据通道中至少两个数据通道获取的位置数据一致的情况下，将位置精确度最高的位置数据作为所述用户的位置数据，以此来提升后续在所述位置数据基础上进行的业务处理的精度和准确度。
步骤S106，基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算。
本实施例中，所述用户的位置数据以及对应的时间数据，代表的是所述用户在历史时刻所处的具体位置，同样，所述样本用户路径网络代表的是样本用户群体中样本用户在历史时间时段的行为路径，此处，“时移”是指将时间点移动至历史时间点进行计 算；相应的，所述群体时移接触计算，是指在计算用户群体中的用户与样本用户群体中样本用户在历史时间点的业务接触程度。
具体的，在公共安全业务维度下，群体时移接触计算，是指计算用户群体中用户与样本用户群体中样本用户的接触度，如果样本用户群体中样本用户为风险样本用户，则接触度越高，表明用户与样本用户的接触越密切，用户的安全度越低；反之，接触度越低，表明用户与样本用户的接触越少，用户的安全度越高。
在线下门店消费业务维度下，群体时移接触计算，是指计算样本用户群体中样本用户对用户群体中用户的消费影响度，如果样本用户群体中样本用户为消费样本用户，则消费影响度越高，用户在消费样本用户的影响下参与消费的概率越高，消费影响度越低，用户在消费样本用户的影响下参与消费的概率越低；另一方面，如果样本用户群体中样本用户为非消费样本用户，则消费影响度越高，用户在非消费样本用户的影响下参与消费的概率越低，消费影响度越低，用户被非消费样本用户的影响的概率越低。
在出入通行业务维度下，群体时移接触计算，是指计算用户群体中用户与样本用户群体中样本用户的通行相似度，通行相似度代表的是用户与样本用户做出相同通行行为的概率，在样本用户群体中样本用户为正向通行样本用户的情况下，相似度越高，表明用户正向通行的概率越低，相似度越低，表明用户正向通行的概率越高；另一方面，在样本用户群体中样本用户为反向通行样本用户的情况下，相似度越高，表明用户反向通行的概率越高，相似度越低，表明用户反向通行的概率越低。
在线上业务维度下，群体时移接触计算，是指计算用户群体中用户相对于样本用户群体中样本用户的业务参与概率，如果样本用户群体中样本用户为参与样本用户，则业务参与概率越高，用户参与线上业务的概率越高，业务参与概率越低，用户参与线上业务的概率越低；另一方面，如果样本用户群体中样本用户为非参与样本用户，则业务参与概率越高，用户参与线上业务的概率越低，业务参与概率越低，用户参与线上业务的概率越高。
本实施例所述业务参数，包括接触时间、接触距离、接触区域范围。例如，公共安全业务维度的业务参数中，接触时间设为10min，是指与样本用户群体中风险样本用户接触时间在10min以上；接触距离设为5m，是指与样本用户群体中风险样本用户的接触距离在5m范围内；接触区域范围为5平方千米，是指在5平方千米这一区域范围内进行计算。
本实施例所述样本用户路径网络，所述样本用户路径网络基于所述业务维度下样本用户的用户路径构建，可选的，本实施例提供的一种可选实施方式中，采用如下方式构建所述样本用户路径网络：
1)获取样本用户群体中样本用户在所述业务维度的业务标识；
2)对所述样本用户的业务标识进行数据脱敏处理，获得所述用户的脱敏标识；
3)基于所述脱敏标识获取所述样本用户的位置数据以及对应的时间数据；
4)根据所述位置数据在所述业务维度的业务地图上对应的坐标位置，按照时间顺序确定所述样本用户在所述业务地图上的用户路径；
5)基于所述业务地图和所述行为路径构建所述样本用户路径网络。
例如，在公共安全业务维度下，首先，获取样本用户群体中风险样本用户的身份特征信息，然后对风险样本用户的身份特征进行数据脱敏处理，具体是将风险样本用户的身份特征信息转换为哈希ID，作为风险样本用户的身份标识；其次，基于风险样本用户的哈希ID，从移动运营商数据源回溯基站定位数据以及对应的时间点，并基于用户携带的终端回溯GPS定位数据以及对应的时间点，以及，通过5G查分定位技术回溯用户的5G定位数据以及对应的时间点；再次，确定上述回溯的3种位置数据在地图上对应的坐标位置，并根据风险样本用户经过各个坐标位置的时间顺序确定每个风险样本用户的路径；最后，基于样本用户群体中全部风险样本用户的路径，构建样本用户群体在地图上的样本用户路径网络。
具体实施时，根据上述步骤S104获取的所述用户群体中用户的位置数据及其对应的时间数据、预先设置的所述业务维度的业务参数、所述样本用户路径网络，针对所述用户群体中的用户进行群体时移接触计算。具体计算过程中，可采用图算法或者其他算法进行群体时移接触计算，从而计算出用户群体中每个用户的业务接触度。除此之外，还可以基于训练样本和样本用户路径网络进行模型训练，训练处基于样本用户路径网络对用户群体中用户进行群体时移接触计算的模型，在训练获得的模型的基础上，将用户群体中用户的位置数据及其对应的时间数据、业务维度的业务参数作为模型输入进行群体时移接触计算，输出用户群体中用户的业务接触度。
沿用上例，在公共安全业务维度下，根据用户群体中用户的基站定位数据、GPS定位数据5G定位数据以及三者对应的时间点，基于样本用户群体中全部风险样本用户的路径在地图上构建的样本用户路径网络，以接触时间、接触距离、接触区域范围为参 数，通过图算法计算用户群体中用户与样本用户群体中风险样本用户的业务接触度。
步骤S108，根据计算获得的所述用户的业务接触度，在所述用户群体中筛选满足所述业务维度的业务条件的目标用户。
根据上述步骤S106计算获得的所述用户群体中用户在所述业务维度的业务接触度，筛选所述用户群体中满足所述业务维度的业务条件的用户作为目标用户。
例如，在公共安全业务维度下，根据计算获得的用户群体中每个用户与样本用户群体中风险样本用户的接触度，在用户群体中筛选接触度大于预设接触度阈值的用户，筛选出的这部分用户即为与风险样本用户密切接触的目标用户，可根据公共安全业务维度的实际业务场景对这部分目标用户进行应急处理。
此外，在具体实施时，除上述计算所述用户群体中用户与所述样本用户群体中样本用户的业务接触度之外，本实施例提供的一种可选实施方式中，在上述构建的所述样本用户路径网络的基础上，进行目标业务区域的业务接触预测渲染：首先是根据所述业务维度的业务参数和所述样本用户路径网络，预测所述业务地图中目标业务区域的群体接触度，然后基于所述目标业务区域的群体接触度在所述业务地图上进行渲染，获得所述目标业务区域的业务群体接触渲染图。
例如，在公共安全业务维度下，根据接触时间、接触距离、接触区域范围和基于样本用户群体中全部风险样本用户的路径在地图上构建的样本用户路径网络，预测地图中指定区域存在安全风险的风险概率，然后将预测出风险概率在地图上进行指定区域的风险渲染，生成指定区域的业务群体接触渲染图。
综上所述，本说明书提供的所述业务处理方法，根据用户群体中用户的位置数据和时间数据以及基于样本用户群体中样本用户的用户路径构建的样本用户路径网络，以业务维度的业务参数为约束，对用户群体中用户的位置信息和时间信息与所述样本用户路径网络进行群体时移接触计算，从而来计算业务维度下用户群体中用户相对于样本用户群体中样本用户的业务接触度，以此在用户群体中筛选满足业务需求的目标用户，不仅提升了业务处理过程中目标用户筛选的准确度，同时还提升了业务处理效率。
下述结合附图2，以本实施例提供的业务处理方法在线下门店消费场景中的应用为例，对本实施例提供的业务处理方法进行进一步说明。参照附图2，应用于线下门店消费场景的业务处理方法具体包括步骤S202至步骤S222。
步骤S202，获取样本用户群体中样本用户在线下门店消费业务维度的业务标识。
步骤S204，对样本用户的业务标识进行数据脱敏处理，获得样本用户的脱敏标识。
步骤S206，基于样本用户的脱敏标识获取样本用户的位置数据以及对应的时间数据。
步骤S208，根据样本用户的位置数据在地图上对应的坐标位置，按照时间顺序确定样本用户在地图上的用户消费路径。
步骤S210，基于样本用户群体中全部样本用户消费路径，在地图上构建样本用户群体对应的样本用户消费路径网络。
步骤S212，将构建的样本用户消费路径网络存入业务数据库。
步骤S214，获取用户群体中用户在线下门店消费业务维度的业务标识。
步骤S216，根据用户的业务标识获取用户的位置数据以及对应的时间数据。
步骤S218，从业务数据库中读取与线下门店消费业务维度的业务参数匹配的样本用户消费路径网络。
步骤S220，基于用户的位置数据、时间数据、读取的样本用户消费路径网络和预先设置的业务参数，计算样本用户群体中样本用户针对用户群体中用户的消费影响度。
步骤S222，根据计算获得的用户的消费影响度，在用户群体中筛选满足线下门店消费业务维度的消费影响度阈值的目标用户。
本说明书提供的一种业务处理装置实施例如下：
在上述的实施例中，提供了一种业务处理方法，与之相对应的，还提供了一种业务处理装置，下面结合附图进行说明。
参照附图3，其示出了本实施例提供的一种业务处理装置的示意图。
由于装置实施例对应于方法实施例，所以描述得比较简单，相关的部分请参见上述提供的方法实施例的对应说明即可。下述描述的装置实施例仅仅是示意性的。
本说明书提供一种业务处理装置，包括：业务标识获取模块302，被配置为获取用户群体中用户在业务维度的业务标识；数据获取模块304，被配置为根据所述业务标识获取所述用户的位置数据以及对应的时间数据；群体时移接触计算模块306，被配置为基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算；所述样本用户路径网络基于所述业务 维度下样本用户的用户路径构建；目标用户筛选模块308，被配置为根据计算获得的所述用户的群体接触度，在所述用户群体中筛选满足所述业务维度的接触条件的目标用户。
可选的，所述样本用户路径网络，采用如下方式构建：获取样本用户群体中样本用户在所述业务维度的业务标识；对所述样本用户的业务标识进行数据脱敏处理，获得所述用户的脱敏标识；基于所述脱敏标识获取所述样本用户的位置数据以及对应的时间数据；根据所述位置数据在所述业务维度的业务地图上对应的坐标位置，按照时间顺序确定所述样本用户在所述业务地图上的用户路径；基于所述业务地图和所述行为路径构建所述样本用户路径网络。
可选的，所述样本用户路径网络存储在业务数据库中，相应的，所述业务处理装置，还包括：样本用户路径网络读取模块，被配置为从所述业务数据库中读取与所述业务参数匹配的样本用户路径网络，作为所述业务维度的样本用户路径网络。
可选的，所述业务处理装置，还包括：预测模块，被配置为根据所述业务维度的业务参数和所述样本用户路径网络，预测所述业务地图中目标业务区域的群体接触度；渲染模块，被配置为基于所述目标业务区域的群体接触度在所述业务地图上进行渲染，获得所述目标业务区域的业务群体接触渲染图。
可选的，所述获取用户群体中用户在业务维度的业务标识，包括：获取所述用户在所述业务维度的身份标识码；所述身份标识码中携带有所述用户在所述业务维度的业务特征信息和业务标识；对所述身份标识码进行解析，获得所述用户在所述业务维度的业务标识。
可选的，所述位置数据，包括下述至少一项：基站定位数据、GPS定位数据、移动通信差分定位数据；相应的，所述业务处理装置，还包括：位置数据判断模块，被配置为判断所述用户在同一时间数据对应的基站定位数据、GPS定位数据、移动通信差分定位数据三者中至少二者是否对应虚拟地图上同一坐标位置；若是，将位置精确度最高的位置数据作为所述用户的位置数据。
可选的，所述业务维度，包括下述至少一项：公共安全业务维度、出入通行业务维度、线下门店消费业务维度、线上业务维度。
可选的，所述群体时移接触计算，包括下述至少一项：以所述公共安全业务维度的业务参数作为约束计算所述用户群体中用户与所述样本用户群体中样本用户的接触度，以所述出入通行业务维度的业务参数作为约束计算所述用户群体中用户与所述样本 用户群体中样本用户的通行相似度，以所述线下门店消费业务维度的业务参数作为约束计算所述样本用户群体中样本用户针对所述用户群体中用户的消费影响度，以所述线上业务维度的业务参数作为约束计算所述用户群体中用户相对于所述样本用户群体中样本用户的业务参与概率。
可选的，所述业务参数，包括下述至少一项：接触时间、接触距离、接触区域范围。
本说明书提供的一种计算设备实施例如下，图4是示出了根据本说明书一个实施例提供的计算设备400的结构框图。该计算设备400的部件包括但不限于存储器410和处理器420。处理器420与存储器410通过总线430相连接，数据库450用于保存数据。
计算设备400还包括接入设备440，接入设备440使得计算设备400能够经由一个或多个网络460通信。这些网络的示例包括公用交换电话网(PSTN)、局域网(LAN)、广域网(WAN)、个域网(PAN)或诸如因特网的通信网络的组合。接入设备440可以包括有线或无线的任何类型的网络接口(例如，网络接口卡(NIC))中的一个或多个，诸如IEEE802.11无线局域网(WLAN)无线接口、全球微波互联接入(Wi-MAX)接口、以太网接口、通用串行总线(USB)接口、蜂窝网络接口、蓝牙接口、近场通信(NFC)接口，等等。
在本说明书的一个实施例中，计算设备400的上述部件以及图4中未示出的其他部件也可以彼此相连接，例如通过总线。应当理解，图4所示的计算设备结构框图仅仅是出于示例的目的，而不是对本说明书范围的限制。本领域技术人员可以根据需要，增添或替换其他部件。
计算设备400可以是任何类型的静止或移动计算设备，包括移动计算机或移动计算设备(例如，平板计算机、个人数字助理、膝上型计算机、笔记本计算机、上网本等)、移动电话(例如，智能手机)、可佩戴的计算设备(例如，智能手表、智能眼镜等)或其他类型的移动设备，或者诸如台式计算机或PC的静止计算设备。计算设备400还可以是移动式或静止式的服务器。
本说明书提供一种计算设备，包括存储器410、处理器420及存储在存储器上并可在处理器上运行的计算机指令，所述处理器420用于执行如下计算机可执行指令：获取用户群体中用户在业务维度的业务标识；根据所述业务标识获取所述用户的位置数据以及对应的时间数据；基于所述位置数据、所述时间数据、所述业务维度的样本用户路径 网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算；所述样本用户路径网络基于所述业务维度下样本用户的用户路径构建；根据计算获得的所述用户的业务接触度，在所述用户群体中筛选满足所述业务维度的业务条件的目标用户。
可选的，所述样本用户路径网络，采用如下方式构建：获取样本用户群体中样本用户在所述业务维度的业务标识；对所述样本用户的业务标识进行数据脱敏处理，获得所述用户的脱敏标识；基于所述脱敏标识获取所述样本用户的位置数据以及对应的时间数据；根据所述位置数据在所述业务维度的业务地图上对应的坐标位置，按照时间顺序确定所述样本用户在所述业务地图上的用户路径；基于所述业务地图和所述行为路径构建所述样本用户路径网络。
可选的，所述样本用户路径网络存储在业务数据库中，相应的，所述基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算指令执行之前，所述处理器420还用于执行如下计算机可执行指令：从所述业务数据库中读取与所述业务参数匹配的样本用户路径网络，作为所述业务维度的样本用户路径网络。
可选的，所述处理器420还用于执行如下计算机可执行指令：根据所述业务维度的业务参数和所述样本用户路径网络，预测所述业务地图中目标业务区域的群体接触度；
基于所述目标业务区域的群体接触度在所述业务地图上进行渲染，获得所述目标业务区域的业务群体接触渲染图。
可选的，所述获取用户群体中用户在业务维度的业务标识，包括：获取所述用户在所述业务维度的身份标识码；所述身份标识码中携带有所述用户在所述业务维度的业务特征信息和业务标识；对所述身份标识码进行解析，获得所述用户在所述业务维度的业务标识。
可选的，所述位置数据，包括下述至少一项：基站定位数据、GPS定位数据、移动通信差分定位数据；相应的，所述根据所述业务标识获取所述用户的位置数据以及对应的时间数据指令执行之后，且所述基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算指令执行之前，所述处理器420还用于执行如下计算机可执行指令：判断所述用户在同一时间数据对应的基站定位数据、GPS定位数据、移动通信差分定位数据三者中至少二者是否对应虚拟地图上同一坐标位置；若是，将位置精确度最高的位置数据作为所述用户 的位置数据。
可选的，所述业务维度，包括下述至少一项：公共安全业务维度、出入通行业务维度、线下门店消费业务维度、线上业务维度。
可选的，所述群体时移接触计算，包括下述至少一项：以所述公共安全业务维度的业务参数作为约束计算所述用户群体中用户与所述样本用户群体中样本用户的接触度，以所述出入通行业务维度的业务参数作为约束计算所述用户群体中用户与所述样本用户群体中样本用户的通行相似度，以所述线下门店消费业务维度的业务参数作为约束计算所述样本用户群体中样本用户针对所述用户群体中用户的消费影响度，以所述线上业务维度的业务参数作为约束计算所述用户群体中用户相对于所述样本用户群体中样本用户的业务参与概率。
可选的，所述业务参数，包括下述至少一项：接触时间、接触距离、接触区域范围。
本说明书提供的一种计算机可读存储介质实施例如下：本说明书一个实施例提供一种计算机可读存储介质，其存储有计算机指令，该指令被处理器执行时实现所述业务处理方法的步骤。
上述为本实施例的一种计算机可读存储介质的示意性方案。需要说明的是，该存储介质的技术方案与上述的业务处理方法的技术方案属于同一构思，存储介质的技术方案未详细描述的细节内容，均可以参见上述业务处理方法的技术方案的描述。
上述对本说明书特定实施例进行了描述。其它实施例在所附权利要求书的范围内。在一些情况下，在权利要求书中记载的动作或步骤可以按照不同于实施例中的顺序来执行并且仍然可以实现期望的结果。另外，在附图中描绘的过程不一定要求示出的特定顺序或者连续顺序才能实现期望的结果。在某些实施方式中，多任务处理和并行处理也是可以的或者可能是有利的。
所述计算机指令包括计算机程序代码，所述计算机程序代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机程序代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适 当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括电载波信号和电信信号。
需要说明的是，对于前述的各方法实施例，为了简便描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本说明书实施例并不受所描述的动作顺序的限制，因为依据本说明书实施例，某些步骤可以采用其它顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定都是本说明书实施例所必须的。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其它实施例的相关描述。
以上公开的本说明书优选实施例只是用于帮助阐述本说明书。可选实施例并没有详尽叙述所有的细节，也不限制该发明仅为所述的具体实施方式。显然，根据本说明书实施例的内容，可作很多的修改和变化。本说明书选取并具体描述这些实施例，是为了更好地解释本说明书实施例的原理和实际应用，从而使所属技术领域技术人员能很好地理解和利用本说明书。本说明书仅受权利要求书及其全部范围和等效物的限制。

Claims (12)

  1. 一种业务处理方法，包括：
    获取用户群体中用户在业务维度的业务标识；
    根据所述业务标识获取所述用户的位置数据以及对应的时间数据；
    基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算；所述样本用户路径网络基于所述业务维度下样本用户的用户路径构建；
    根据计算获得的所述用户的业务接触度，在所述用户群体中筛选满足所述业务维度的业务条件的目标用户。
  2. 根据权利要求1所述的业务处理方法，所述样本用户路径网络，采用如下方式构建：
    获取样本用户群体中样本用户在所述业务维度的业务标识；
    对所述样本用户的业务标识进行数据脱敏处理，获得所述用户的脱敏标识；
    基于所述脱敏标识获取所述样本用户的位置数据以及对应的时间数据；
    根据所述位置数据在所述业务维度的业务地图上对应的坐标位置，按照时间顺序确定所述样本用户在所述业务地图上的用户路径；
    基于所述业务地图和所述行为路径构建所述样本用户路径网络。
  3. 根据权利要求2所述的业务处理方法，所述样本用户路径网络存储在业务数据库中，相应的，所述基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算步骤执行之前，包括：
    从所述业务数据库中读取与所述业务参数匹配的样本用户路径网络，作为所述业务维度的样本用户路径网络。
  4. 根据权利要求3所述的业务处理方法，还包括：
    根据所述业务维度的业务参数和所述样本用户路径网络，预测所述业务地图中目标业务区域的群体接触度；
    基于所述目标业务区域的群体接触度在所述业务地图上进行渲染，获得所述目标业务区域的业务群体接触渲染图。
  5. 根据权利要求1所述的业务处理方法，所述获取用户群体中用户在业务维度的业务标识，包括：
    获取所述用户在所述业务维度的身份标识码；所述身份标识码中携带有所述用户在 所述业务维度的业务特征信息和业务标识；
    对所述身份标识码进行解析，获得所述用户在所述业务维度的业务标识。
  6. 根据权利要求1或2所述的业务处理方法，所述位置数据，包括下述至少一项：
    基站定位数据、GPS定位数据、移动通信差分定位数据；
    相应的，所述根据所述业务标识获取所述用户的位置数据以及对应的时间数据步骤执行之后，且所述基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算步骤执行之前，包括：
    判断所述用户在同一时间数据对应的基站定位数据、GPS定位数据、移动通信差分定位数据三者中至少二者是否对应虚拟地图上同一坐标位置；
    若是，将位置精确度最高的位置数据作为所述用户的位置数据。
  7. 根据权利要求1所述的业务处理方法，所述业务维度，包括下述至少一项：
    公共安全业务维度、出入通行业务维度、线下门店消费业务维度、线上业务维度。
  8. 根据权利要求7所述的业务处理方法，所述群体时移接触计算，包括下述至少一项：
    以所述公共安全业务维度的业务参数作为约束计算所述用户群体中用户与所述样本用户群体中样本用户的接触度，以所述出入通行业务维度的业务参数作为约束计算所述用户群体中用户与所述样本用户群体中样本用户的通行相似度，以所述线下门店消费业务维度的业务参数作为约束计算所述样本用户群体中样本用户针对所述用户群体中用户的消费影响度，以所述线上业务维度的业务参数作为约束计算所述用户群体中用户相对于所述样本用户群体中样本用户的业务参与概率。
  9. 根据权利要求1所述的业务处理方法，所述业务参数，包括下述至少一项：
    接触时间、接触距离、接触区域范围。
  10. 一种业务处理装置，包括：
    业务标识获取模块，被配置为获取用户群体中用户在业务维度的业务标识；
    数据获取模块，被配置为根据所述业务标识获取所述用户的位置数据以及对应的时间数据；
    群体时移接触计算模块，被配置为基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算；所述样本用户路径网络基于所述业务维度下样本用户的用户路径构建；
    目标用户筛选模块，被配置为根据计算获得的所述用户的群体接触度，在所述用户群体中筛选满足所述业务维度的接触条件的目标用户。
  11. 一种计算设备，包括：
    存储器和处理器；
    所述存储器用于存储计算机可执行指令，所述处理器用于执行所述计算机可执行指令：
    获取用户群体中用户在业务维度的业务标识；
    根据所述业务标识获取所述用户的位置数据以及对应的时间数据；
    基于所述位置数据、所述时间数据、所述业务维度的样本用户路径网络和业务参数，在所述业务维度对所述用户进行群体时移接触计算；所述样本用户路径网络基于所述业务维度下样本用户的用户路径构建；
    根据计算获得的所述用户的群体接触度，在所述用户群体中筛选满足所述业务维度的接触条件的目标用户。
  12. 一种计算机可读存储介质，其存储有计算机指令，该指令被处理器执行时实现权利要求1至9任意一项所述业务处理方法的步骤。
PCT/CN2020/139744 2020-03-17 2020-12-26 业务处理方法及装置 Ceased WO2021184881A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202010188687.1A CN111414536A (zh) 2020-03-17 2020-03-17 业务处理方法及装置
CN202010188687.1 2020-03-17

Publications (1)

Publication Number Publication Date
WO2021184881A1 true WO2021184881A1 (zh) 2021-09-23

Family

ID=71491316

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/139744 Ceased WO2021184881A1 (zh) 2020-03-17 2020-12-26 业务处理方法及装置

Country Status (2)

Country Link
CN (1) CN111414536A (zh)
WO (1) WO2021184881A1 (zh)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111414536A (zh) * 2020-03-17 2020-07-14 支付宝(杭州)信息技术有限公司 业务处理方法及装置
CN111596673A (zh) * 2020-07-27 2020-08-28 江苏广宇科技产业发展有限公司 一种基于5g的智慧驾驶云辅助系统

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005050279A (ja) * 2003-07-31 2005-02-24 Sato Corp 入場管理システム
CN102263814A (zh) * 2011-06-27 2011-11-30 上海市疾病预防控制中心 应用于智能手机的疾病监控系统
CN102576492A (zh) * 2009-09-04 2012-07-11 三菱电机株式会社 移动路径处理装置及使用该装置的信息提供系统
CN108986921A (zh) * 2018-07-04 2018-12-11 泰康保险集团股份有限公司 疾病预测方法、装置、介质及电子设备
CN111414536A (zh) * 2020-03-17 2020-07-14 支付宝(杭州)信息技术有限公司 业务处理方法及装置

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103617422B (zh) * 2013-10-29 2017-01-11 浙江工业大学 一种基于名片识别的社交关系管理方法
CN104933201A (zh) * 2015-07-15 2015-09-23 蔡宏铭 基于同行信息的内容推荐方法及系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2005050279A (ja) * 2003-07-31 2005-02-24 Sato Corp 入場管理システム
CN102576492A (zh) * 2009-09-04 2012-07-11 三菱电机株式会社 移动路径处理装置及使用该装置的信息提供系统
CN102263814A (zh) * 2011-06-27 2011-11-30 上海市疾病预防控制中心 应用于智能手机的疾病监控系统
CN108986921A (zh) * 2018-07-04 2018-12-11 泰康保险集团股份有限公司 疾病预测方法、装置、介质及电子设备
CN111414536A (zh) * 2020-03-17 2020-07-14 支付宝(杭州)信息技术有限公司 业务处理方法及装置

Also Published As

Publication number Publication date
CN111414536A (zh) 2020-07-14

Similar Documents

Publication Publication Date Title
JP6540314B2 (ja) 施設推定方法、デバイス及びプログラム
CN113127667A (zh) 图像处理方法及装置、图像分类方法及装置
CN109447246B (zh) 用于生成模型的方法和装置
CN116010574A (zh) 智能对话处理的方法、云服务器及可读存储介质
CN110009059B (zh) 用于生成模型的方法和装置
CN104302000A (zh) 基于信号接收强度指示相关性的室内定位方法
CN111915086A (zh) 异常用户预测方法和设备
WO2021184881A1 (zh) 业务处理方法及装置
US20220309405A1 (en) Combined-learning-based internet of things data service method and apparatus, device and medium
CN110555749A (zh) 基于神经网络的信用行为预测方法以及装置
CN110008977A (zh) 聚类模型构建方法以及装置
Gong et al. STTF: A spatiotemporal transformer framework for multi-task mobile network prediction
CN115438249A (zh) QoS预测模型构建方法、装置、设备及可读存储介质
Wang et al. Embracing uncertainty: A diffusion generative model of spectrum efficiency in 5g networks
CN113221989A (zh) 基于分布式的评估模型训练方法、系统以及装置
CN110020728B (zh) 业务模型强化学习方法以及装置
CN112163505A (zh) 生成图像的方法、装置、设备和计算机可读介质
CN114782000B (zh) 家庭关系识别方法、装置、设备、介质及产品
US11575641B2 (en) Estimating device, estimating method, and estimating program
Mobo et al. Using automated contact tracing system app with QR code to monitor and safeguard parishioners against COVID-19 at St. Anthony of Padua Parish, Subic, Zambales
CN109598508B (zh) 一种识别方法和装置、一种计算设备及存储介质
CN118013117B (zh) 对象推荐方法、装置、电子设备及可读存储介质
CN113806517A (zh) 一种基于机器学习模型的外呼方法、装置、设备及介质
WO2021158943A1 (en) Systems configured for cell-based histopathological learning and prediction and methods thereof
HK40033603A (zh) 业务处理方法及装置

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: 20925234

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: 20925234

Country of ref document: EP

Kind code of ref document: A1