WO2022148059A1 - 用户匹配方法、装置、电子设备及介质 - Google Patents

用户匹配方法、装置、电子设备及介质 Download PDF

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WO2022148059A1
WO2022148059A1 PCT/CN2021/119366 CN2021119366W WO2022148059A1 WO 2022148059 A1 WO2022148059 A1 WO 2022148059A1 CN 2021119366 W CN2021119366 W CN 2021119366W WO 2022148059 A1 WO2022148059 A1 WO 2022148059A1
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user
close
users
matching
contact
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PCT/CN2021/119366
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English (en)
French (fr)
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宋轩
谢洪彬
张浩然
云沐晟
陈宇
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南方科技大学
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Publication of WO2022148059A1 publication Critical patent/WO2022148059A1/zh

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    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/30—Interconnection arrangements between game servers and game devices; Interconnection arrangements between game devices; Interconnection arrangements between game servers
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F13/00—Video games, i.e. games using an electronically generated display having two or more dimensions
    • A63F13/70—Game security or game management aspects
    • A63F13/79—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories
    • A63F13/795—Game security or game management aspects involving player-related data, e.g. identities, accounts, preferences or play histories for finding other players; for building a team; for providing a buddy list
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/23—Clustering techniques
    • G06F18/232—Non-hierarchical techniques
    • G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
    • G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/40—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterised by details of platform network
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/50—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by details of game servers
    • A63F2300/55—Details of game data or player data management
    • A63F2300/5546—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history
    • A63F2300/5566—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history by matching opponents or finding partners to build a team, e.g. by skill level, geographical area, background, play style
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63F—CARD, BOARD, OR ROULETTE GAMES; INDOOR GAMES USING SMALL MOVING PLAYING BODIES; VIDEO GAMES; GAMES NOT OTHERWISE PROVIDED FOR
    • A63F2300/00—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game
    • A63F2300/50—Features of games using an electronically generated display having two or more dimensions, e.g. on a television screen, showing representations related to the game characterized by details of game servers
    • A63F2300/55—Details of game data or player data management
    • A63F2300/5546—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history
    • A63F2300/5573—Details of game data or player data management using player registration data, e.g. identification, account, preferences, game history player location
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • Embodiments of the present invention relate to the field of short-range communications, and in particular, to a user matching method, apparatus, electronic device, and medium.
  • the embodiments of the present invention provide a user matching method, device, electronic device and medium, so as to achieve the technical effect of constructing a user matching relationship of offline interactive activities and enhancing the realistic interactivity of the activities.
  • an embodiment of the present invention provides a user matching method, including:
  • the close contact information includes the close contact position data and/or the close contact distance data
  • a matching relationship between the close contact user and the other close contact users is constructed, so that the close contact user and the other close contact users jointly participate in the activity.
  • an embodiment of the present invention further provides a user matching device, the device comprising:
  • an information acquisition module used for acquiring the close contact information of the close contact user based on the short-range communication technology; wherein the close contact information includes the close contact position data and/or the close contact distance data;
  • the matching pool determination module is used to determine the user matching pool to which the contact user belongs according to the contact information of the contact user;
  • a user search module configured to search for other close users matching the user level of the close users from the user matching pool to which the close users belong;
  • a relationship building module configured to build a matching relationship between the close user and the other close users, for the close user and the other close users to participate in activities together.
  • an embodiment of the present invention also provides an electronic device, including:
  • processors one or more processors
  • a storage device for storing one or more programs
  • the one or more programs are executed by the one or more processors, such that the one or more processors implement the user matching method as provided in any of the embodiments of the present invention.
  • an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, implements the user matching method provided in any embodiment of the present invention.
  • the embodiment of the present invention provides a user matching method, based on the short-range communication technology, to obtain the close contact information of the close contact user; wherein the close contact information includes the close contact position data and/or the close contact distance data;
  • FIG. 1 is a flowchart of a user matching method provided in Embodiment 1 of the present application.
  • FIG. 2 is a flowchart of another user matching method provided in Embodiment 2 of the present application.
  • FIG. 3 is a design diagram of a close-contact game for players based on short-range communication provided by an embodiment of the present application
  • FIG. 4 is a schematic structural diagram of a user matching device provided in Embodiment 3 of the present application.
  • FIG. 5 is a schematic structural diagram of an electronic device according to Embodiment 4 of the present application.
  • FIG. 1 is a flowchart of a user matching method provided in Embodiment 1 of the present invention.
  • the method can be applied to the situation of constructing a user matching relationship under the condition of short-range communication.
  • the method can be executed by a user matching device, and the device can be implemented by software And/or hardware implementation, and can be integrated into electronic equipment.
  • the user matching method in this embodiment includes the following steps:
  • the mobile terminal obtains the close contact information of the close contact user based on the short distance communication technology
  • the mobile terminal is an electronic device that can be equipped with the short distance communication technology
  • the short distance communication technology may be the Bluetooth technology.
  • the mobile phone Bluetooth connection technology players and users who use a game APP at the same time generate a close connection during the normal login process, and the collected close connection information is stored in the background, so that the subsequent game opponent matching mechanism can be completed. Interactive game opponent matching.
  • the contact information includes contact location data and/or contact distance data, wherein the contact information may also include the player's user id, the time stamp generated during the contact process, and the player's GPS positioning data, etc.
  • the privacy data of players and users can be protected.
  • the close contact information of the close contact user is obtained based on the short-range communication technology, so as to achieve the technical effect of constructing an offline close contact interaction activity.
  • S120 Determine the user matching pool to which the close contact user belongs according to the close contact information of the close contact user.
  • the mobile terminal determines the user matching pool to which the close user belongs according to the obtained close contact information.
  • the user matching pool is a clustering space after the users are clustered for the first time. Considering that each player user has close contact with other player users There is a limited space range, and it is necessary to match opponents for players within a certain range. Therefore, the user matching pool can be determined according to the close contact position data and/or close contact distance data of the matching user.
  • determining the user matching pool to which the close contact user belongs according to the close contact information of the close contact user may include the following steps A1-A2:
  • Step A1 Determine preset effective space range information for close interaction and information about the number of people who are effective in close interaction required for building a matching relationship between close users.
  • Step A2 Based on the preset effective space range information for close interaction and the effective number of people for close interaction, and according to the collected close contact information of each close contact user, a preset density clustering algorithm is used to calculate the user matching pool corresponding to each close contact user. ; There are at least two close users in the same user matching pool.
  • Bluetooth has a certain signal scanning range, that is to say, game players have a certain range of close contact
  • the matching result of user matching in interactive activities is at least two-person interactive activities, at least two game players are required to participate in game interaction, so set the same user
  • the matching pool includes at least two close users.
  • the collected close contact information of each close contact user may be the user's Bluetooth signal strength, GPS positioning result, etc.
  • the preset density clustering algorithm may be the DBSCAN algorithm, which is a density-based clustering algorithm, and the cluster is defined as The largest set of density-connected points that can divide regions of sufficiently high density into clusters and find clusters of arbitrary shapes in noisy spatial databases.
  • Eps and the number of points in the neighborhood Minpts all high-density data sets are identified, that is, better clustering results can be obtained.
  • the user matching pool is divided by a clustering algorithm, and a preliminary user clustering result is obtained, which achieves the technical effect of effectively improving the user matching efficiency of offline close interaction activities.
  • the application program of the mobile terminal searches for other close users matching the user level of the close user from the user matching pool described by the close user, and the user level can be based on the player's point value, experience value or ability value in the game activity.
  • the user level can be based on the player's point value, experience value or ability value in the game activity.
  • grades can be divided according to other conditions, for example, it can be bronze, diamond, king and other grades.
  • a matching relationship between the close user and other close users is constructed, and the matching results of at least two close users are completed, so that the close user and other close users can participate in activities together.
  • the activities can be games under close interaction conditions. , competition, etc.
  • the close contact information of the close contact user is obtained based on the short-range communication technology; wherein the close contact information includes the close contact position data and/or the close contact distance data; according to the close contact information of the close contact user, the user matching to which the close contact user belongs is determined. pool; from the user matching pool to which the close user belongs, find other close users that match the user level of the close user; build a matching relationship between the close user and the other close users for all close users
  • the close contact user and the other close contact users participate in the activity together, which can achieve the technical effect of enhancing the display interactivity of the close contact interaction activity and improving the matching efficiency of the close contact interaction activity on the premise of ensuring the fairness of the activity.
  • FIG. 2 is a flowchart of another user matching method provided in Embodiment 2 of the present application.
  • the embodiments of the present invention further optimize the foregoing embodiments on the basis of the foregoing embodiments, and the embodiments of the present invention may be combined with each optional solution in one or more of the foregoing embodiments.
  • the user matching method provided in the embodiment of the present invention may include the following steps:
  • S210 based on the short-range communication technology, obtain the close contact information of the close contact user; wherein the close contact information includes the close contact position data and/or the close contact distance data.
  • S220 Determine the user matching pool to which the close contact user belongs according to the close contact information of the close contact user.
  • the preset number of clusters in each user matching pool may be 2, 3 or 4, etc., which is determined by the distribution of user points and/or experience values.
  • the preset number of clusters in the user matching pool is generally set to 6.
  • the K-means algorithm is an iterative clustering analysis algorithm. The steps are to divide the data into K groups in advance, and then randomly select K objects as the initial clustering center.
  • the initial clustering center refers to the clustering process.
  • the center object selected in calculate the distance between each object and each cluster center based on it, assign each object to the cluster center closest to it, the cluster center and the objects assigned to them represent a clustering.
  • the categories of user ratings can be, for example, bronze, silver, gold, platinum, diamond, and king.
  • the technical solution of this embodiment further divides the levels and user categories of users in the matching pool through the K-means algorithm, thereby achieving the technical effect of improving the efficiency of user matching in close interaction activities.
  • the user level division of the close users included in the user matching pool may include the following steps B1-B3:
  • Step B1 Calculate the weighted Euclidean distance between the remaining close users in the user matching pool and the initial cluster center according to the degree of influence of different dimensional attributes of close users in the same user matching pool on the clustering; Dimension attributes include ability value, experience value, and points.
  • Step B2 Iterate the initial cluster center according to the weighted Euclidean distance to obtain a new initial cluster center.
  • Step B3 by calculating the membership matrix of each close user in the same user matching pool, update the new initial cluster center to obtain the target clustering result; the membership matrix is used to indicate the degree to which the close user belongs to a class ;
  • the target clustering result includes the grading result for each close user.
  • the weighted Euclidean distance between the remaining close users in the user matching pool and the initial cluster center is calculated according to the influence of different dimensional attributes of the close users in the same user matching pool on the clustering.
  • the feature weight parameter is introduced into the original cluster center.
  • a new weighted Euclidean distance calculation formula is generated, in which the feature weight parameters can include the feature weight vector of the data object and the attribute dimension of the data object, and then the weighted Euclidean distance calculation formula is added to solve the clustering results.
  • the clustering results can be obtained through the objective function.
  • the K-means algorithm is used to select the clustering centers, the classification results of the clustering centers are used, and the degree of influence of different dimensional attributes of the close users in the same user matching pool on the clustering is calculated.
  • the weighted Euclidean distance between the remaining close users and the initial cluster center, and the initial cluster center is updated according to the weighted Euclidean distance, so as to reduce the number of iterations of the user clustering algorithm and improve the accuracy and stability of the user clustering algorithm. .
  • building the matching relationship between the close user and the other close users, for the close user and the other close users to participate in activities together can include the following steps C1-C2:
  • Step C1 Using the ELO algorithm, calculate the expected winning probability of the close user and other searched close users.
  • Step C2 according to the expected winning probability of the close user and other searched close users, form a team of close users whose winning probability difference between close users is within the preset difference range, and construct the close connection according to the team formation result.
  • the ELO algorithm is used to calculate the expected winning probability of the close user and the searched other close users.
  • the ELO algorithm is an evaluation method created by Hungarian-American physicist Arpad Elo to measure the level of various game activities. , can also be used as a method to evaluate the winning rate of the two players in the game process. According to the ELO algorithm, when the scores of the players are the same, the expected winning rate of the game is 50%. , the expected winning rate of low-scoring players will be less than 10%. Therefore, when matching the two sides, try to minimize the difference, so that each user's winning rate is close to 50%.
  • forming a team of close users whose winning probability difference between close users is within a preset difference range includes: different matching time periods adopt different preset difference ranges, and the matching time The higher the segment, the smaller the preset difference range.
  • Step 1 Within 0-N1 seconds, only match opponents whose score difference is within X1;
  • Step 2 If no opponent is found within N1 seconds, the matching conditions will be relaxed, and in the next N1 to N2 seconds, only opponents whose score difference is within X2 (X2>X1) will be matched; and so on, until the final opponent is matched.
  • constructing a matching relationship between the close user and the other close users according to the team formation result includes: calculating the player's points by using an improved ELO algorithm that introduces a streak and a losing streak threshold.
  • the improved algorithm is as follows:
  • R n R o +R(D)+R(N)
  • R n is the player's new ranking score after the game
  • R o is the player's ranking score before the game
  • R(D) is the reward or punishment score
  • K is a bonus coefficient, determined by the player's current score The value level is determined (the higher the score, the smaller the K)
  • P(D) is the expected winning rate
  • D is the difference between the strength scores of the two sides.
  • ELO algorithm since the true strength of a new player cannot be correctly assessed, players are required to keep playing games to slowly converge the points to their true level. The speed of convergence can be accelerated by winning streak rewards and losing streak penalties.
  • R(N) indicates the point reward for winning streak or the point penalty for losing streak
  • N indicates the number of consecutive wins or losses
  • L indicates the point coefficient
  • T indicates the direction coefficient, 1 in case of winning, -1 in case of loss .
  • FIG. 3 is a design diagram of a close-range communication-based player contact game provided by an embodiment of the present application.
  • a close-range communication-based player close game design diagram is composed of a data collection scheme, It consists of matching pool division, user level division, opponent matching, game PK mechanism, and game reward mechanism.
  • the game PK mechanism refers to the user matching result of two users, which is a 1v1 opponent mode, and the game reward mechanism can be a winning streak reward mechanism.
  • the final user matching is carried out by using the ELO algorithm that improves the reward for winning streak and the penalty for losing streak and designing the matching score difference change strategy, which ensures the fairness of the close interaction activities and the efficiency of user matching, which speeds up the ELO.
  • the convergence speed of the algorithm enables the system to more accurately identify the real strength of the user, thereby further ensuring the fairness and attractiveness of the close interaction activities.
  • FIG. 4 is a schematic structural diagram of a user matching apparatus provided in Embodiment 3 of the present invention.
  • the apparatus can be applied to the situation of constructing a user matching relationship under the condition of short-range communication, and the apparatus can be realized by software and/or hardware, and integrated in an electronic device.
  • the apparatus is used to implement the user matching method provided in the above embodiment.
  • the user matching device provided in this embodiment includes:
  • an information acquisition module 410 configured to acquire the close contact information of the close contact user based on the short-range communication technology; wherein the close contact information includes the close contact position data and/or the close contact distance data;
  • a matching pool determination module 420 configured to determine the user matching pool to which the close contact user belongs according to the close contact information of the close contact user;
  • a user search module 430 configured to search for other close contacts matching the user level of the close contact users from the user matching pool to which the close contact users belong;
  • the relationship building module 440 is configured to build a matching relationship between the close user and the other close users, so that the close user and the other close users can jointly participate in activities.
  • the matching pool determination module 420 is further configured to:
  • a preset density clustering algorithm is used to calculate the user matching pool corresponding to each close contact user;
  • a user matching pool includes at least two close users.
  • the user search module 430 is further configured to:
  • K-means algorithm is used to select close users with a preset number of clusters from the same user matching pool as the initial cluster center under the user matching pool;
  • the user search module 430 includes a grade division unit for:
  • the initial cluster center under the user matching pool perform user level division on the close users included in the user matching pool, and obtain a user level division result
  • the user search module 430 is further configured to:
  • the user search module 430 is further configured to:
  • the different dimensional attributes of close users include: Ability value, experience value, points;
  • the new initial cluster center is updated to obtain the target clustering result; the membership matrix is used to indicate the degree to which the close user belongs to a class; the The target clustering result includes the ranking result for each close user.
  • the relationship building module 440 is further configured to:
  • the close users whose winning probability difference between close users is within the preset difference range are formed into teams, and the close users and all close users are constructed according to the team formation results. Describe the matching relationship between other close users.
  • the relationship building module 440 is further configured to:
  • Different matching time periods adopt different preset difference value ranges, and the earlier the matching time period is, the smaller the preset difference value range is.
  • FIG. 5 is a schematic structural diagram of an electronic device according to Embodiment 4 of the present application.
  • the embodiment of the present application provides an electronic device, in which the user matching apparatus provided by the embodiment of the present application can be integrated.
  • this embodiment provides an electronic device 500, which includes: one or more processors 520; and a storage device 510 for storing one or more programs, when the one or more programs are The one or more processors 520 execute, so that the one or more processors 520 implement the user matching method provided by the embodiments of the present application, and the method includes:
  • the close contact information includes the close contact position data and/or the close contact distance data
  • a matching relationship between the close contact user and the other close contact users is constructed, so that the close contact user and the other close contact users jointly participate in the activity.
  • processor 520 also implements the technical solution of the user matching method provided by any embodiment of the present application.
  • the electronic device 500 shown in FIG. 5 is only an example, and should not impose any limitations on the functions and scope of use of the embodiments of the present application.
  • the electronic device 500 includes a processor 520 , a storage device 510 , an input device 530 and an output device 540 ; the number of processors 520 in the electronic device may be one or more, and one processor 520 is used in FIG. 5 .
  • the processor 520 , the storage device 510 , the input device 530 and the output device 540 in the electronic device may be connected by a bus or other means, and the connection by the bus 550 is taken as an example in FIG. 5 .
  • the storage device 510 may be used to store software programs, computer-executable programs, and module units, such as program instructions corresponding to the user matching method in the embodiments of the present application.
  • the storage device 510 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Additionally, storage device 510 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage device. In some examples, storage device 510 may further include memory located remotely from processor 520, which may be connected through a network. Examples of such networks include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.
  • the input device 530 may be used to receive input numbers, character information or voice information, and generate key signal input related to user settings and function control of the electronic device.
  • the output device 540 may include electronic devices such as a display screen, a speaker, and the like.
  • the electronic device provided by the embodiment of the present application can achieve the technical effects of enhancing the display interactivity of the close interaction activity and improving the matching efficiency of the close interaction activity on the premise of ensuring the fairness of the activity.
  • Embodiment 5 of the present invention provides a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, is used to execute a user matching method, and the method includes:
  • the close contact information includes the close contact position data and/or the close contact distance data
  • a matching relationship between the close contact user and the other close contact users is constructed, so that the close contact user and the other close contact users jointly participate in the activity.
  • the computer storage medium in the embodiments of the present invention may adopt any combination of one or more computer-readable media.
  • the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • the computer readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or a combination of any of the above.
  • Computer readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read only memory (Read Only Memory, ROM), Erasable Programmable Read Only Memory (EPROM), flash memory, optical fiber, portable CD-ROM, optical storage device, magnetic storage device, or any suitable combination of the above .
  • a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus, or device.
  • a computer-readable signal medium may include a propagated data signal in baseband or as part of a carrier wave, with computer-readable program code embodied thereon. Such propagated data signals may take a variety of forms including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the foregoing.
  • a computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device .
  • Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber cable, radio frequency (RF), etc., or any suitable combination of the foregoing.
  • suitable medium including but not limited to: wireless, wire, optical fiber cable, radio frequency (RF), etc., or any suitable combination of the foregoing.
  • Computer program code for carrying out operations of the present invention may be written in one or more programming languages, including object-oriented programming languages—such as Java, Smalltalk, C++, but also conventional Procedural programming language - such as the "C" language or similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server.
  • the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (eg, using an Internet service provider to via Internet connection).
  • LAN local area network
  • WAN wide area network

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Abstract

本发明实施例公开了一种用户匹配方法、装置、电子设备及介质。该方法包括：基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据；依据密接用户的密接信息确定密接用户所属的用户匹配池；从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户；构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动。通过执行本技术方案，实现近距离密接用户根据用户等级匹配用户来达到构建线下互动活动，增强活动的现实互动性的效果。

Description

用户匹配方法、装置、电子设备及介质 技术领域
本发明实施例涉及近距离通信领域，尤其涉及一种用户匹配方法、装置、电子设备及介质。
背景技术
在游戏互动的过程中，社交是玩家的刚性需求，而目前社交互动性也是网络游戏的独特优势，是与单机游戏最大的区别，在近年来桌游兴起，线下的社交互动性游戏也因此不断出现，但是目前的线下游戏活动都需要人为地去指定规则选择对手。因此，如何为近距离交互的游戏用户准确、快速、公平地匹配对手成为目前要解决的难题。
技术问题
本发明实施例中提供了一种用户匹配方法、装置、电子设备及介质，以达到构建线下互动活动用户匹配关系，增强活动的现实互动性的技术效果。
技术解决方案
第一方面，本发明实施例中提供了一种用户匹配方法，包括：
基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据；
依据密接用户的密接信息确定密接用户所属的用户匹配池；
从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户；
构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动。
第二方面，本发明实施例中还提供了一种用户匹配装置，所述装置包括：
信息获取模块，用于基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据；
匹配池确定模块，用于依据密接用户的密接信息确定密接用户所属的用户匹配池；
用户查找模块，用于从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户；
关系构建模块，用于构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动。
第三方面，本发明实施例中还提供了一种电子设备，包括：
一个或多个处理器；
存储装置，用于存储一个或多个程序；
所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现如本发明任意实施例中提供的用户匹配方法。
第四方面，本发明实施例中还提供了一种计算机可读存储介质，其上存储有计算机程序，该程序被处理器执行时实现如本发明任意实施例中提供的用户匹配方法。
有益效果
本发明实施例中提供了一种用户匹配方法，基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据；
依据密接用户的密接信息确定密接用户所属的用户匹配池；从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户；构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动，能够实现近距离密接用户根据用户等级匹配用户来达到构建线下互动活动用户匹配关系，增强活动的现实互动性的效果。
采用本申请技术方案，能够在近距离通信技术的基础上实现密接用户匹配池的划分，并且根据用户等级来匹配用户，达到构建线下互动活动用户匹配关系，增强活动的现实互动性的技术效果。
上述发明内容仅是本发明技术方案的概述，为了能够更清楚了解本发明的技术手段，而可依照说明书的内容予以实施，并且为了让本发明的上述和其它目的、特征和优点能够更明显易懂，以下特举本发明的具体实施方式。
附图说明
通过阅读参照以下附图所作的对非限制性实施例所作的详细描述，本发明的其它特征、目的和优点将会变得更明显。附图仅用于示出优选实施方式的目的，而并不认为是对本发明的限制。而且在整个附图中，用相同的参考符号表示相同的部件。在附图中：
图1是本申请实施例一提供的一种用户匹配方法的流程图；
图2是本申请实施例二提供的另一种用户匹配方法的流程图；
图3是本申请实施例提供的一种基于近距离通信的玩家密接游戏设计图；
图4是本申请实施例三提供的用户匹配装置的结构示意图；
图5是本申请实施例四提供的一种电子设备的结构示意图。
本发明的实施方式
下面结合附图和实施例对本发明作进一步的详细说明。可以理解的是，此处所描述的具体实施例仅仅用于解释本发明，而非对本发明的限定。另外还需要说明的是，为了便于描述，附图中仅示出了与本发明相关的部分而非全部结构。
在更加详细地讨论示例性实施例之前应当提到的是，一些示例性实施例被描述成作为流程图描绘的处理或方法。虽然流程图将各项操作(或步骤)描述成顺序的处理，但是其中的许多操作可以被并行地、并发地或者同时实施。此外，各项操作的顺序可以被重新安排。当其操作完成时所述处理可以被终止，但是还可以具有未包括在附图中的附加步骤。所述处理可以对应于方法、函数、规程、子例程、子程序等等。
实施例一
图1是本发明实施例一中提供的一种用户匹配方法的流程图，该方法可适用于构建近距离通信条件下用户匹配关系的情况，该方法可由用户匹配装置来执行，该装置可由软件和/或硬件实现，并可集成于电子设备中。如图1所示，本实施例中的用户匹配方法，包括以下步骤：
S110、基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据。
其中，移动终端基于近距离通信技术，获取到密接用户的密接信息，移动终端是可以搭载近距离通信技术的电子设备，例如近距离通信技术可以是蓝牙技术。示例性地，通过手机蓝牙连接技术，使得同时使用一游戏APP的玩家用户在正常登录过程中产生密接连接，并将采集到的密接信息进行后台存储，使得能够完成后续的游戏对手匹配机制，完成交互游戏对手的匹配。
其中，所述密接信息包括密接位置数据和/或密接距离数据，其中密接信息还可包括玩家的用户id、密接过程中生成的时间戳以及玩家的GPS定位数据等，在收集玩家用户的密接信 息同时可对玩家用户的隐私数据进行保护。
采用上述技术方案，通过基于近距离通信技术来获取密接用户的密接信息，达到了构建线下密接交互活动的技术效果。
S120、依据密接用户的密接信息确定密接用户所属的用户匹配池。
其中，移动终端根据获取到的密接用户的密接信息来确定密接用户所属的用户匹配池，用户匹配池是用户初次聚类之后的一个聚类空间，考虑到每个玩家用户与其他玩家用户发生密接是有空间范围限定，也就需要在一定的范围内为玩家进行匹配对手，因此用户匹配池可以是根据匹配用户的密接位置数据和/或密接距离数据来确定。
在本实施例的一种可选方案中，本实施例可以与上述一个或者多个实施例中各个可选方案结合。其中，依据密接用户的密接信息确定密接用户所属的用户匹配池，可包括以下步骤A1-A2：
步骤A1、确定构建密接用户之间的匹配关系所需的预设密接交互有效空间范围信息和密接交互有效人数信息。
步骤A2、基于所述预设密接交互有效空间范围信息和密接交互有效人数信息，依据已采集的各个密接用户的密接信息，采用预设的密度聚类算法计算得到各个密接用户对应的用户匹配池；其中同一个用户匹配池中至少包括两个密接用户。
其中，确定构建密接用户之间的匹配关系所需的预设密接交互有效空间范围信息和密接交互有效人数信息时，由于在采用近距离通信技术进行游戏玩家之间的密接时，示例性地，蓝牙有一定的信号扫描范围，也就是说游戏玩家有一定的密接有效范围，并且由于交互活动用户匹配的匹配结果至少是双人交互活动，至少需要两个游戏玩家参与游戏交互，因此设置同一个用户匹配池中至少包括两个密接用户。
其中，已采集的各个密接用户的密接信息可以是用户的蓝牙信号强度，GPS定位结果等，预设的密度聚类算法可以是DBSCAN算法，其是一个基于密度的聚类算法，将簇定义为密度相连的点的最大集合，能够把具有足够高密度的区域划分为簇，并可在噪声的空间数据库中发现任意形状的聚类。通过设置邻域半径Eps和邻域内点数Minpts,将所有的高密度数据集都识别出来，即能得到较好地聚类结果。
采用上述技术方案，通过聚类算法对用户匹配池进行划分，得到初步的一个用户聚类结果，达到了有效提高线下密接交互活动用户匹配效率的技术效果。
S130、从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户。
其中，移动终端的应用程序从密接用户所述的用户匹配池中，查找与密接用户的用户等级相匹配的其他密接用户，用户等级可以是游戏活动中根据玩家的积分值、经验值或者能力值等情况来进行划分的多个等级，示例性的，可以是青铜、钻石、王者等级别。
S140、构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动。
其中，构建密接用户和其他密接用户之间的匹配关系，完成至少2个密接用户的匹配结果，用于密接用户与其他密接用户共同参与活动，示例性地，活动可以是密接交互条件下的游戏、竞赛等。
本实施例的技术方案，通过基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据；依据密接用户的密接信息确定密接用户所属的用户匹配池；从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户；构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动，能够达到增强密接交互活动显示互动性和在保证活动公平性的前提下提高密接交互活动匹配效率的技术效果。
实施例二
图2是本申请实施例二提供的另一种用户匹配方法的流程图。本发明实施例在上述实施例的基础上对前述实施例进行进一步优化，本发明实施例可以与上述一个或者多个实施例中各个可选方案结合。如图2所示，本发明实施例中提供的用户匹配方法，可包括以下步骤：
S210、基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据。
S220、依据密接用户的密接信息确定密接用户所属的用户匹配池。
S230、基于每一个用户匹配池的预设聚类个数，采用K-means算法从同一用户匹配池中选择预设聚类个数的密接用户，作为该户匹配池下的初始聚类中心。
S240、依据该户匹配池下的初始聚类中心，对该用户匹配池中包括的密接用户进行用户等级划分，得到用户等级划分结果。
S250、依据所述用户等级划分结果，从所述用户匹配池中查找与所述密接用户的用户等级相匹配的其他密接用户。
其中，每一个用户匹配池的预设聚类个数可以是2个、3个或4个等，是由用户积分值和/或经验值等的分布来决定的，一般为了保证公平性，在用户人数较多的情况下，用户匹配池的预设聚类个数一般设置为6个。其中K-means算法是一种迭代求解的聚类分析算法，其步骤是预先将数据分为K组，则随机选取K个对象作为初始的聚类中心，初始聚类中心指的是聚类过程中选取的中心对象，以它为基准计算每个对象与各个聚类中心之间的距离，把每个对象分配给距离它最近的聚类中心，聚类中心以及分配给它们的对象就代表一个聚类。用户等级划分的类别，示例性地，可以是青铜、白银、黄金、铂金、钻石、王者。
本实施例的技术方案，通过K-means算法进一步划分匹配池中用户的等级和用户类别，达到了提升密接交互活动用户匹配效率的技术效果。
S260、构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动。
在本实施例的一种可选方案中，本实施例可以与上述一个或者多个实施例中各个可选方案结合。其中，依据所述用户匹配池下的初始聚类中心，对所述用户匹配池中包括的密接用户进行用户等级划分，可包括以下步骤B1-B3：
步骤B1、依据同一用户匹配池中密接用户的不同维度属性对聚类的影响程度，计算所述用户匹配池中剩余密接用户与所述初始聚类中心之间的权重欧式距离；密接用户的不同维度属性包括能力值、经验值、积分。
步骤B2、依据所述权重欧式距离对所述初始聚类中心进行迭代，得到新的初始聚类中心。步骤B3、通过计算同一用户匹配池中各个密接用户的隶属度矩阵，对新的初始聚类中心进行更新，得到目标聚类结果；所述隶属度矩阵用于指示密接用户隶属于一个类的程度；所述目标聚类结果包括对各个密接用户的等级划分结果。
其中，依据同一用户匹配池中密接用户的不同维度属性对聚类的影响程度，计算用户匹配池中剩余密接用户与初始聚类中心之间的权重欧式距离，具体是通过将特征权重参数引入原有的欧式距离公式中，产生新的权重欧氏距离计算公式，其中特征权重参数可以包括数据对象的特征权重矢量和数据对象的属性维度，再将权重欧氏距离计算公式加进求解聚类结果的目标函数中，通过目标函数就可以得到聚类结果。
采用上述技术方案，通过使用K-means算法选择聚类中心，根据聚类中心划分等级结果，依据同一用户匹配池中密接用户的不同维度属性对聚类的影响程度，计算所述用户匹配池中剩余密接用户与所述初始聚类中心之间的权重欧式距离，根据权重欧式距离更新初始聚类中心，达到了减少用户聚类算法迭代次数，提高用户聚类算法的精度和稳定性的技术效果。
在本实施例的一种可选方案中，本实施例可以与上述一个或者多个实施例中各个可选方案结合。其中，构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所 述其他密接用户共同参与活动，可包括以下步骤C1-C2：
步骤C1、采用ELO算法，计算所述密接用户和查找的其他密接用户的预期获胜概率。
步骤C2、依据所述密接用户和查找的其他密接用户的预期获胜概率，将密接用户间获胜概率差值在预设差值范围内的密接用户进行组队，并依据组队结果构建所述密接用户与所述其他密接用户之间的匹配关系。
其中，采用ELO算法，计算所述密接用户和查找的其他密接用户的预期获胜概率，ELO算法是是匈牙利裔美国物理学家阿帕德·埃洛创建的一个衡量各类对弈活动水平的评价方法，也可作为评估双方博弈过程中胜率的方法，根据ELO算法可知家分数相同时，对战预期胜率为50％，分差越大，玩家之间的胜率差距也就越大，当分差大于400时，低分玩家的预期胜率将不足10％，因此，匹配对战双方的时候尽量使得分差最小，使得每个用户的胜率接近于50％。
在上述实施例中，可选的，将密接用户间获胜概率差值在预设差值范围内的密接用户进行组队，包括：不同匹配时间段采用的预设差值范围不同，且匹配时间段越靠前，预设差值范围越小。
其中，通常情况下，以最快速度为玩家匹配实力相近的对手是最理想的结果，但如果在偏僻的密接范围内，此时玩家数量相对较少，会出现无法快速为玩家匹配到实力相近的对手的情况，从而会让玩家一直在线等待系统的匹配，导致用户体验非常差。为了兼顾密接游戏的公平性和用户体验，基于1V1竞赛模式基础上，可以设计如下对手匹配策略：第1步：在0～N1秒内，仅匹配分差在X1以内的对手；第2步：若N1秒内未找到满足条件对手，则放宽匹配条件，在接下来的N1～N2秒内，仅匹配分差在X2(X2＞X1)以内的对手；以此类推，直到匹配到最终对手。
在上述实施例中，可选的，依据组队结果构建所述密接用户与所述其他密接用户之间的匹配关系，包括：采用引入连胜与连败阈值的改进ELO算法来计算玩家的积分奖励，改进后的算法如下：
R n＝R o+R(D)+R(N)
R(D)＝K*(W-P(D))
R(N)＝L*T*(N-1)
其中，R n是玩家比赛结束后的新的排位分值，R o是比赛前玩家的排位分，R(D)是奖励或惩罚的分数，K是一个加成系数，由玩家当前分值水平决定(分值越高K越小)，W是玩家实际对局结果得分(赢＝1，输＝0)，P(D)是预期胜率，D为竞赛双方的实力分数之差。在传统ELO算法中，由于无法正确的评估一个新玩家的真实实力，需要玩家通过不断的进行比赛让积分慢慢收敛到其真实水平，可以通过连胜奖励和连败惩罚来加快收敛的速度，R(N)表示连胜的积分奖励或连败的积分惩罚，N表示连胜或连败的场次，L表示积分系数，T表示方向系数，赢的情况下为1，输了则为-1。假设玩家A连胜了3场，取L＝10，代入R(N)公式，则可得连胜奖励R(N)＝20。相反，假设玩家A连败了3场，则会得到连败惩罚R(N)＝-20。
示例性地，图3是本申请实施例提供的一种基于近距离通信的玩家密接游戏设计图，如图3所示，一种基于近距离通信的玩家密接游戏设计图，由数据采集方案、匹配池划分、用户等级划分、对手匹配、游戏PK机制、游戏奖励机制组成，游戏PK机制指的是用户匹配结果是两个用户，为1v1对手模式，游戏奖励机制可以是连胜奖励机制。
采用上述技术方案，通过使用改进连胜奖励连败惩罚的ELO算法和设计匹配分差改变策略进行最终的用户匹配，保证了密接交互活动公平性的同时也保证了用户匹配的效率，加快了ELO算法的收敛速度，以使得系统更加准确的标识用户的真实实力，从而进一步保证了密接 交互活动的公平性和吸引力。
实施例三
图4是本发明实施例三中提供的一种用户匹配装置的结构示意图。该装置可适用于构建近距离通信条件下用户匹配关系的情况，该装置可由软件和/或硬件实现，并集成在电子设备中。该装置用于实现上述实施例提供的用户匹配方法。如图4所示，本实施例中提供的用户匹配装置，包括：
信息获取模块410，用于基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据；
匹配池确定模块420，用于依据密接用户的密接信息确定密接用户所属的用户匹配池；
用户查找模块430，用于从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户；
关系构建模块440，用于构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动。
在上述实施例的基础上，可选地，匹配池确定模块420，还用于：
确定构建密接用户之间的匹配关系所需的预设密接交互有效空间范围信息和密接交互有效人数信息；
基于所述预设密接交互有效空间范围信息和密接交互有效人数信息，依据已采集的各个密接用户的密接信息，采用预设的密度聚类算法计算得到各个密接用户对应的用户匹配池；其中同一个用户匹配池中至少包括两个密接用户。
在上述实施例的基础上，可选地，用户查找模块430，还用于：
基于每一个用户匹配池的预设聚类个数，采用K-means算法从同一用户匹配池中选择预设聚类个数的密接用户，作为所述用户匹配池下的初始聚类中心；
在上述实施例的基础上，可选地，用户查找模块430包括等级划分单元，用于：
依据所述用户匹配池下的初始聚类中心，对所述用户匹配池中包括的密接用户进行用户等级划分，得到用户等级划分结果；
在上述实施例的基础上，可选地，用户查找模块430，还用于：
依据所述用户等级划分结果，从所述用户匹配池中查找与所述密接用户的用户等级相匹配的其他密接用户。
在上述实施例的基础上，可选地，用户查找模块430，还用于：
依据同一用户匹配池中密接用户的不同维度属性对聚类的影响程度，计算所述用户匹配池中剩余密接用户与所述初始聚类中心之间的权重欧式距离；密接用户的不同维度属性包括能力值、经验值、积分；
依据所述权重欧式距离对所述初始聚类中心进行迭代，得到新的初始聚类中心；
通过计算同一用户匹配池中各个密接用户的隶属度矩阵，对新的初始聚类中心进行更新，得到目标聚类结果；所述隶属度矩阵用于指示密接用户隶属于一个类的程度；所述目标聚类结果包括对各个密接用户的等级划分结果。
在上述实施例的基础上，可选地，关系构建模块440，还用于：
采用ELO算法，计算所述密接用户和查找的其他密接用户的预期获胜概率；
依据所述密接用户和查找的其他密接用户的预期获胜概率，将密接用户间获胜概率差值在预设差值范围内的密接用户进行组队，并依据组队结果构建所述密接用户与所述其他密接用户之间的匹配关系。
在上述实施例的基础上，可选地，关系构建模块440，还用于：
不同匹配时间段采用的预设差值范围不同，且匹配时间段越靠前，预设差值范围越小。
实施例四
图5是本申请实施例四提供的一种电子设备的结构示意图。本申请实施例提供了一种电子设备，该电子设备中可集成本申请实施例提供的用户匹配装置。如图5所示，本实施例提供了一种电子设备500，其包括：一个或多个处理器520；存储装置510，用于存储一个或多个程序，当所述一个或多个程序被所述一个或多个处理器520执行，使得所述一个或多个处理器520实现本申请实施例所提供的用户匹配方法，该方法包括：
基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据；
依据密接用户的密接信息确定密接用户所属的用户匹配池；
从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户；
构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动。
当然，本领域技术人员可以理解，处理器520还实现本申请任意实施例所提供的用户匹配方法的技术方案。
图5显示的电子设备500仅仅是一个示例，不应对本申请实施例的功能和使用范围带来任何限制。
如图5所示，该电子设备500包括处理器520、存储装置510、输入装置530和输出装置540；电子设备中处理器520的数量可以是一个或多个，图5中以一个处理器520为例；电子设备中的处理器520、存储装置510、输入装置530和输出装置540可以通过总线或其他方式连接，图5中以通过总线550连接为例。
存储装置510作为一种计算机可读存储介质，可用于存储软件程序、计算机可执行程序以及模块单元，如本申请实施例中的用户匹配方法对应的程序指令。
存储装置510可主要包括存储程序区和存储数据区，其中，存储程序区可存储操作系统、至少一个功能所需的应用程序；存储数据区可存储根据终端的使用所创建的数据等。此外，存储装置510可以包括高速随机存取存储器，还可以包括非易失性存储器，例如至少一个磁盘存储器件、闪存器件、或其他非易失性固态存储器件。在一些实例中，存储装置510可进一步包括相对于处理器520远程设置的存储器，这些远程存储器可以通过网络连接。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
输入装置530可用于接收输入的数字、字符信息或语音信息，以及产生与电子设备的用户设置以及功能控制有关的键信号输入。输出装置540可包括显示屏、扬声器等电子设备。
本申请实施例提供的电子设备，可以达到增强密接交互活动显示互动性和在保证活动公平性的前提下提高密接交互活动匹配效率的技术效果。
实施例五
本发明实施例五中提供了一种计算机可读介质，其上存储有计算机程序，该程序被处理器执行时用于执行用户匹配方法，该方法包括：
基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据；
依据密接用户的密接信息确定密接用户所属的用户匹配池；
从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户；
构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动。
可选的，该程序被处理器执行时还可以用于执行本发明任意实施例中所提供的用户匹配方法。本发明实施例的计算机存储介质，可以采用一个或多个计算机可读的介质的任意组合。计算 机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(Random Access Memory，RAM)、只读存储器(Read Only Memory，ROM)、可擦式可编程只读存储器(Erasable Programmable Read Only Memory，EPROM)、闪存、光纤、便携式CD-ROM、光存储器件、磁存储器件、或者上述的任意合适的组合。计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括但不限于：电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括但不限于：无线、电线、光缆、无线电频率(RadioFrequency，RF)等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本发明操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)——连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
在本说明书的描述中，参考术语“一个实施例”、“一些实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本发明的至少一个实施例或示例中。在本说明书中，对上述术语的示意性表述不一定指的是相同的实施例或示例。而且，描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
注意，上述仅为本发明的较佳实施例及所运用技术原理。本领域技术人员会理解，本发明不限于这里所述的特定实施例，对本领域技术人员来说能够进行各种明显的变化、重新调整和替代而不会脱离本发明的保护范围。因此，虽然通过以上实施例对本发明进行了较为详细的说明，但是本发明不仅仅限于以上实施例，在不脱离本发明构思的情况下，还可以包括更多其他等效实施例，而本发明的范围由所附的权利要求范围决定。

Claims (10)

  1. 一种用户匹配方法，其特征在于，包括：
    基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据；
    依据密接用户的密接信息确定密接用户所属的用户匹配池；
    从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户；
    构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动。
  2. 根据权利要求1所述的方法，其特征在于，依据密接用户的密接信息确定密接用户所属的用户匹配池，包括：
    确定构建密接用户之间的匹配关系所需的预设密接交互有效空间范围信息和密接交互有效人数信息；
    基于所述预设密接交互有效空间范围信息和密接交互有效人数信息，依据已采集的各个密接用户的密接信息，采用预设的密度聚类算法计算得到各个密接用户对应的用户匹配池；其中同一个用户匹配池中至少包括两个密接用户。
  3. 根据权利要求1所述的方法，其特征在于，从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户，包括：
    基于每一个用户匹配池的预设聚类个数，采用K-means算法从同一用户匹配池中选择预设聚类个数的密接用户，作为所述用户匹配池下的 初始聚类中心；
    依据所述用户匹配池下的初始聚类中心，对所述用户匹配池中包括的密接用户进行用户等级划分，得到用户等级划分结果；
    依据所述用户等级划分结果，从所述用户匹配池中查找与所述密接用户的用户等级相匹配的其他密接用户。
  4. 根据权利要求3所述的方法，其特征在于，依据所述用户匹配池下的初始聚类中心，对所述用户匹配池中包括的密接用户进行用户等级划分，包括：
    依据同一用户匹配池中密接用户的不同维度属性对聚类的影响程度，计算所述用户匹配池中剩余密接用户与所述初始聚类中心之间的权重欧式距离；密接用户的不同维度属性包括能力值、经验值、积分；依据所述权重欧式距离对所述初始聚类中心进行迭代，得到新的初始聚类中心；
    通过计算同一用户匹配池中各个密接用户的隶属度矩阵，对新的初始聚类中心进行更新，得到目标聚类结果；所述隶属度矩阵用于指示密接用户隶属于一个类的程度；所述目标聚类结果包括对各个密接用户的等级划分结果。
  5. 根据权利要求1所述的方法，其特征在于，构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动，包括：
    采用ELO算法，计算所述密接用户和查找的其他密接用户的预期获胜概率；
    依据所述密接用户和查找的其他密接用户的预期获胜概率，将密接用户间获胜概率差值在预设差值范围内的密接用户进行组队，并依据组队结果构建所述密接用户与所述其他密接用户之间的匹配关系。
  6. 根据权利要求5所述的方法，其特征在于，不同匹配时间段采用的预设差值范围不同，且匹配时间段越靠前，预设差值范围越小。
  7. 一种用户匹配装置，其特征在于，所述装置包括：
    信息获取模块，用于基于近距离通信技术，获取密接用户的密接信息；其中所述密接信息包括密接位置数据和/或密接距离数据；
    匹配池确定模块，用于依据密接用户的密接信息确定密接用户所属的用户匹配池；
    用户查找模块，用于从所述密接用户所属的用户匹配池中，查找与所述密接用户的用户等级相匹配的其他密接用户；
    关系构建模块，用于构建所述密接用户与所述其他密接用户之间的匹配关系，用于所述密接用户与所述其他密接用户共同参与活动。
  8. 根据权利要求7所述的装置，其特征在于，所述用户查找模块包括：
    等级划分单元，用于依据所述用户匹配池下的初始聚类中心，对所述用户匹配池中包括的密接用户进行用户等级划分，得到用户等级划分结果。
  9. 一种电子设备，其特征在于，包括：
    一个或多个处理器；
    存储装置，用于存储一个或多个程序；
    当所述一个或多个程序被所述一个或多个处理器执行，使得所述一个或多个处理器实现权利要求1-6中任一所述的用户匹配方法。
  10. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，该程序被处理器执行时实现权利要求1-6中任一所述的用户匹配方法。
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