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WO2020147597A1 - Procédé d'optimisation de covoiturage basé sur des mégadonnées sociales, structure d'indice et système - Google Patents

Procédé d'optimisation de covoiturage basé sur des mégadonnées sociales, structure d'indice et système Download PDF

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Publication number
WO2020147597A1
WO2020147597A1 PCT/CN2020/070369 CN2020070369W WO2020147597A1 WO 2020147597 A1 WO2020147597 A1 WO 2020147597A1 CN 2020070369 W CN2020070369 W CN 2020070369W WO 2020147597 A1 WO2020147597 A1 WO 2020147597A1
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carpool
candidate
social
commuting
big data
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PCT/CN2020/070369
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English (en)
Chinese (zh)
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夏吉喆
李清泉
乐阳
朱家松
涂伟
周宝定
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深圳大学
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry

Definitions

  • the invention relates to the technical field of intelligent transportation, in particular to a carpool optimization method, index structure and system based on social big data.
  • Carpooling has become a popular and convenient transportation service, and it is also an effective solution to traffic congestion, which can reduce travel costs and reduce related traffic pollution and energy consumption problems.
  • the existing carpooling methods are only aimed at optimizing the cost of carpooling for two persons, which cannot meet the carpooling needs of a large number of passengers, and has low intelligence and efficiency.
  • the technical problem to be solved by the present invention is to provide a carpool optimization method, index structure and system based on social big data in view of the above-mentioned defects of the prior art, aiming to optimize the carpool model based on social big data and road network at the same time Carpool team trust level and commuting cost, in order to obtain the maximum trust and minimum commuting cost carpooling plan, reduce complexity, reduce road congestion, reduce travel costs, and facilitate users.
  • a carpool optimization method based on social big data includes the following steps:
  • the association between the social relationship and the trust rating index is established in advance, and different trust ratings corresponding to different levels are set and stored at the same time.
  • the obtaining all carpool candidates satisfying the social relationship according to the social relationship of the driver specifically includes:
  • the social relationships include direct friends, mutual friends, and strangers;
  • the carpooling optimization method based on social big data, wherein, according to the trust level index between the driver and each carpooling candidate, the driver and several carpooling candidates are combined for carpooling, and several carpooling candidates are established
  • the team includes:
  • the driver will be combined with several carpool candidates to establish several candidate carpool team groups;
  • the trust model of the candidate carpool team is constructed, and the overall trust of each candidate carpool team is calculated.
  • the method for carpooling optimization based on social big data wherein, the commuting information of each carpool candidate and driver in each candidate carpooling team group is obtained, and the commuting route and commuting cost of each candidate carpooling team group are determined.
  • the first commute information includes travel time, departure place and destination, and the second commute The information includes departure time, departure place and end of stop;
  • the first commuting information and the second commuting information determine the commuting route network of each candidate carpool team group
  • a commuter cost model of the candidate carpool team is constructed.
  • the carpool optimization method based on social big data wherein the construction of a carpool model based on social big data and road network, according to the trust level optimization and commuting cost optimization of each candidate carpool team group, matching to obtain maximum trust
  • the best carpool team group with the least commuting cost includes:
  • the objective function of the trust model is processed to maximize the degree of trust under the condition of satisfying the trust constraint, so that the value of the objective function of the trust model is maximized, and at the same time, the objective function of the commuting cost model is minimized under the condition of satisfying the commuting cost constraint Processing to minimize the value of the objective function of the commuting cost model;
  • the value of the trust weight variable is continuously changed and substituted into the objective function of the carpool model to maximize the value of the objective function of the carpool model, thereby obtaining the best carpool team group with the largest trust value and the smallest commuting cost.
  • the method for carpooling optimization based on social big data further includes: establishing a spatiotemporal carpool matching model, and optimizing the time cost under the time constraint condition, for constructing the carpooling model based on social big data and road network.
  • the factors affecting commuting cost include one or more of commuting route distance, driving time, congestion factor, oil cost, toll fee, and cost factors related to commuting.
  • the present invention also provides a car-pooling optimization system based on social big data.
  • the car-pooling optimization system based on social big data includes a number of terminals, a social network center connected to the terminal, a transportation network center, and a social network center.
  • the back-end servers respectively connected to the transportation network center, when the social big data-based carpool optimization system is executed, the aforementioned social big data-based carpool optimization method is implemented.
  • the present invention also provides a carpool optimization index structure based on social big data.
  • the carpool optimization index structure includes a memory connected to a processor and the processor, and the memory stores a carpool optimization program based on social big data.
  • the big data carpool optimization program is executed by the processor, it is used to realize the aforementioned social big data-based carpool optimization method.
  • the present invention discloses a carpooling optimization method, index structure and system based on social big data.
  • the carpooling optimization method based on social big data includes: obtaining all carpool candidates satisfying the social relationship according to the social relationship of the driver ;According to the trust level index between the driver and each carpool candidate, the driver and several carpool candidates are combined to form a number of candidate carpool team groups; each carpool candidate in each candidate carpool team group is obtained
  • the commuting information of people and drivers determines the commuting route and commuting cost of each candidate carpool team; builds a carpool model based on social big data and road network, optimizes the trust level and commute cost of each candidate carpool team, Match the best carpool team group that maximizes trust and minimizes commuting costs.
  • the present invention optimizes the carpool team trust level and commuting cost through the carpool model based on social big data and road network, so as to obtain the carpool scheme with the greatest trust degree and the least commuting cost, reduce complexity, reduce road congestion, and reduce travel cost and traffic Control costs and facilitate users.
  • Fig. 1 is a flowchart of a first preferred embodiment of a carpool optimization method based on social big data of the present invention.
  • Fig. 2 is a schematic diagram of carpool activities in the carpool optimization method based on social big data of the present invention.
  • Fig. 3 is a structural block diagram of the carpool optimization system based on social big data of the present invention.
  • Fig. 4 is a structural block diagram of an optimized index structure for carpooling based on social big data in the present invention.
  • FIG. 1 is a flowchart of a first preferred embodiment of a carpool optimization method based on social big data of the present invention. As shown in Figure 1, the carpool optimization method based on social big data includes the following steps:
  • Step S100 According to the social relationship of the driver, all carpooling candidates satisfying the social relationship are obtained.
  • the association between the social relationship and the trust level index is established in advance, and different levels are set to correspond to different trust level indexes, and stored at the same time.
  • the social relationship includes direct friend relationship, mutual friend relationship and stranger relationship, as shown in Table 1 below. In Table 1, it is assumed that a person has 100 direct friends.
  • the division of the trust level index is obtained according to the six-degree separation theory. In the embodiment of the present invention, based on the scale of the social network in the car-pooling activity, potential carpool candidates are screened according to the six-degree separation theory.
  • the direct friend relationship means that the two in Figure 2 are directly connected, which is defined as 1 degree of separation;
  • the common friend relationship means that there is at least one friend in common between the two in Figure 2, and is indirectly connected. It is defined as a 2-degree separation;
  • a strange friend relationship means that there is no mutual friend between the two in Figure 2, that is, a stranger, which is defined as a 3-degree separation;
  • the absence of any chain friendship relationship represents the marginal blank area in Figure 2.
  • Arbitrary carpool arrangements are made according to the degree of separation corresponding to the trust level index to form an arbitrary carpool team. Figure 2 only illustrates one of the carpool arrangements.
  • step S100 includes:
  • Step S101 Obtain social relationships of all drivers based on the social network; wherein, the social relationships include direct friend relationships, mutual friend relationships, and stranger relationships;
  • Step S102 Search and obtain carpool candidates that satisfy all the social relationships of each driver in the carpool candidate database.
  • step S200 according to the trust level index between the driver and each carpool candidate, the driver is combined with several carpool candidates to establish several candidate carpool team groups.
  • step S200 specifically includes:
  • Step S201 obtaining the association between the social relationship and the trust level index
  • Step S202 Determine the trust level index between the driver and each carpool candidate according to the association and the social relationship between the driver and each carpool candidate;
  • Step S203 According to the capacity of the vehicle, the driver and several carpool candidates are combined to form a carpool group to establish a number of candidate carpool teams;
  • Step S204 Construct a trust model of the candidate carpool team based on the trust level index between the driver and each carpool candidate in each candidate carpool team group, and calculate the overall trust of each candidate carpool team group.
  • the objective function of the trust model of the candidate carpool team is as shown in formula (1):
  • the objective function (1) aims to maximize the trust between passengers and drivers in the entire carpool, and the trust constraint condition (2) requires a given number of passengers to be allocated to the carpool team; c represents the number of carpool candidates ; CP represents the carpool capacity, N ⁇ CP and N is a natural number; Tc represents the trust level index between the driver and the carpool candidate; Xc represents 1 when the carpool candidate is selected and combined into a candidate carpool team, no If selected, it is 0.
  • carpooling candidates are 10 people (c1, c2,...,c10), carpooling candidates are c2, c4, c5, c6, and the vehicle capacity CP is 4
  • Tc1 represents the trust rating index of the carpool members of the carpool candidate c1.
  • the objective function (1) of the trust model of the candidate carpool team is selected to maximize the value of the trust level index.
  • the value of the objective function (1) is the largest, the trust among the corresponding carpool candidate teams is the highest. In this way, the entire team of carpooling candidates is more stable, with a stronger experience, and the highest natural travel efficiency.
  • Step S300 Obtain commuting information of each carpool candidate and driver in each candidate carpool team group, and determine the commuting route and commuting cost of each candidate carpool team group.
  • step S300 specifically includes:
  • Step S301 Obtain the first commute information of each carpool candidate in each candidate carpool team and the second commute information of the driver;
  • Step S302 according to the first commuting information and the second commuting information, determine the commuting route network of each candidate carpool team group;
  • Step S303 at the same time, construct a commuting cost model of the candidate carpool team according to the commuter route network.
  • the first commute information includes the travel time, departure place and destination of the carpool candidate (passenger), and the second commute information includes the departure time of the driver, the departure place and the end of the stop
  • the starting point of the vehicle refers to the location of the driver's residence
  • the end point of the parking refers to the location of the driver's workplace.
  • the commuting cost in the embodiment of the present invention is defined as the cost incurred in the process of picking up and dropping carpooling candidates and not only refers to the additional cost of driving alone.
  • the route between the residence location and the workplace location is defined as a commuter route, and the residence location and the workplace location are positioned as nodes on the commuter route. Therefore, starting from the current actual residence location, the driver has several different routes to receive each team member, so that different emergence routes are established, thus forming a commuting route network.
  • a complete commuter route means that the driver starts from the current actual residence location, receives all other team members in a predetermined order, puts all team members to their destination node, and finally arrives at the driver’s workplace node.
  • CP represents the size of the carpool team, that is, the total number of people in the carpool team
  • VP represents the capacity of the vehicle
  • s represents the driver’s residence
  • t represents the driver’s workplace
  • t represents the driver’s workplace
  • i and j are the index of the node (house or workplace)
  • d ij is the impedance i and node j of the edge between nodes; d ij represents any cost function suitable for the driver to commute.
  • the factors affecting commuting cost include commuting route distance, travel time, congestion factor, oil cost, toll And one or more of the cost factors related to commuting;
  • the above objective function (3) aims to minimize the commuting cost of the entire carpool route.
  • Commuter constraints (4) and (5) require that the carpool route must start from the driver's residence and end with the driver's workplace.
  • Commuter constraints (6) and (7) are used to indicate that the driver picks up a given number of passengers (CP at the residence) and drops a given number of passengers (CP at the workplace).
  • the commuting constraint (8) is used to indicate that the driver will not throw passengers off at will to form a logical commuting path.
  • the commuting constraint (9) is used to indicate that only one edge can be selected for any step of the carpool route.
  • the commuting constraint (10) and the commuting constraint (11) ensure that only one edge enters or exits the selected node.
  • the commuting constraint (12) ensures the topology of the commuter route network, and the commuting constraint (13) is used to indicate that the size of the carpool team is smaller than the capacity of the vehicle.
  • the aforementioned impedance associated with the edge represents a cost function, that is, it can be any combination of factors that affect commuting costs.
  • the commuting cost of the objective function (3) must be minimized in the end, that is, the commuting cost should be minimized.
  • Step S400 Construct a carpool model based on social big data and road network, optimize the trust level and commute cost of each candidate carpool team group, and match the best carpool team group that maximizes trust and minimizes commuting cost.
  • step S400 specifically includes:
  • Step S401 set a trust weight variable (W);
  • Step S402 construct a carpool model based on social big data and road network according to the obtained trust model and commuting cost model of the candidate carpool team and trust weight variables;
  • step S403 the objective function of the trust model is processed to maximize the degree of trust under the condition of satisfying the trust constraint, so that the value of the objective function of the trust model is maximized, and the objective function of the commuting cost model is performed under the condition of satisfying the commuting cost constraint.
  • step S404 the value of the trust weight variable is continuously changed at the same time, and substituted into the objective function of the carpool model respectively, so that the value of the objective function of the carpool model is maximized, thereby obtaining the best carpool with the largest trust value and the smallest commuting cost Team group.
  • W represents the trust weight variable, which is used to balance the trust of the carpool team and the commuting cost.
  • the value of W ranges from [0,1]. As the value increases, W continuously enhances social comfort; Trust normalized represents the entire carpool The normalized trust value of the team; Cost normalized represents the normalized cost value of the carpool commuting route, 0 ⁇ Cost normalized ⁇ 1, Trust team and Cost carpool correspond to formulas (1) and (3), Cost original represents commuting without passengers cost.
  • the value of the objective function (14) is made the highest.
  • the best carpool team group with the largest trust value and the smallest commuting cost can be obtained.
  • the carpool team with high trust and the lowest commuting cost is obtained, which reduces complexity, reduces road congestion, reduces travel costs, and facilitates users.
  • FIG. 3 illustrates a structural block diagram of the carpool optimization system based on social big data.
  • the carpool optimization system based on social big data
  • the optimization system includes several terminals 100, a social network center 201 connected to the terminal 100, a transportation network center 202, and a backend server 301 and a backend server 302 respectively connected to the social network center 201 and the transportation network center 202.
  • the terminal 100 may be a mobile phone, a computer, a tablet, or the like.
  • the terminal 100 includes a memory 20 connected to the processor 10 and the processor 10.
  • the memory 20 stores a carpool optimization program based on social big data.
  • the processor 10 may be a central processing unit (CPU), microprocessor or other data processing chip, which is used to run the program code or process data stored in the memory 20, For example, execute a carpool optimization program based on social big data, etc.
  • CPU central processing unit
  • microprocessor or other data processing chip, which is used to run the program code or process data stored in the memory 20, For example, execute a carpool optimization program based on social big data, etc.
  • the system for carpooling optimization based on social big data when executed, it is used to realize the steps of the method for carpooling optimization based on social big data, as described in the first embodiment.
  • the present invention also provides a carpool optimization index structure based on social big data.
  • the carpool optimization index structure includes a first memory 12 connected to a first processor 11 and a first processor 11.
  • a memory 12 stores a carpool optimization program based on social big data, and the first processor 11 executes the above-mentioned carpool optimization method based on social big data; the specifics are as described above.
  • the present invention discloses a carpool optimization method, index structure, and system based on social big data.
  • the social big data-based carpool optimization method includes: obtaining, according to the social relationship of the driver, satisfying the social relationship All carpooling candidates in the company; according to the trust level index between the driver and each carpooling candidate, the driver and several carpooling candidates are combined to form a number of candidate carpooling team groups; each candidate carpooling team group is obtained To determine the commuting route and commuting cost of each candidate carpooling team in the commute information of each carpool candidate and driver; build a carpool model based on social big data and road network, and optimize according to the trust level of each candidate carpool team group Optimized with commuting cost, matching the best carpool team group that maximizes trust and minimizes commuting cost.
  • the present invention optimizes the carpool team trust level and commuting cost through the carpool model based on social big data and road network, so as to obtain the carpool scheme with the greatest trust degree and the least commuting cost, reduce complexity, reduce road congestion, and reduce travel cost and traffic Control costs and facilitate users.

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Abstract

L'invention se rapporte à un procédé d'optimisation de covoiturage basé sur des mégadonnées sociales, ainsi qu'à une structure d'indice et à un système, le procédé comprenant les étapes consistant à : selon la relation sociale d'un conducteur, acquérir tous les candidats de covoiturage répondant à la relation sociale décrite (S100) ; réaliser une combinaison de covoiturage pour le conducteur et de multiples candidats de covoiturage en fonction de l'indice de niveau de confiance entre le conducteur et chaque candidat de covoiturage, et établir de multiples groupes d'équipe de covoiturage candidats (S200) ; acquérir les informations de déplacement domicile-travail de chaque groupe de covoiturage candidat dans chaque groupe d'équipe de covoiturage candidat et du conducteur, et déterminer l'itinéraire de déplacement domicile-travail et les coûts de déplacement domicile-travail des groupes d'équipe de covoiturage candidats (S300) ; construire un modèle de covoiturage, et selon l'optimisation de niveau de confiance et l'optimisation de coût de déplacement domicile-travail de chaque groupe d'équipe de covoiturage candidat, mettre en correspondance pour obtenir le meilleur groupe d'équipe de covoiturage ayant le niveau de confiance maximal et les coûts de déplacement domicile-travail minimaux (S400). Au moyen d'un modèle de covoiturage basé sur des mégadonnées sociales et un réseau routier, et en optimisant simultanément le niveau de confiance d'équipes de covoiturage et les coûts de déplacement domicile-travail au moyen du procédé décrit, une solution de covoiturage ayant le niveau de confiance maximal et les coûts de déplacement domicile-travail minimaux est obtenue, ce qui est pratique pour les utilisateurs.
PCT/CN2020/070369 2019-01-15 2020-01-06 Procédé d'optimisation de covoiturage basé sur des mégadonnées sociales, structure d'indice et système WO2020147597A1 (fr)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112561639A (zh) * 2020-12-15 2021-03-26 北京嘀嘀无限科技发展有限公司 拼车服务处理方法、装置、设备及存储介质
CN114067597A (zh) * 2021-11-17 2022-02-18 哈尔滨工业大学 一种基于强化学习的不同合乘意愿下出租车调度方法
CN114792187A (zh) * 2022-01-12 2022-07-26 山东师范大学 基于意愿和信任双重约束的群智感知团队招募方法及系统

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109919354B (zh) * 2019-01-15 2022-04-22 深圳大学 一种基于社交大数据的拼车优化方法、索引结构及系统
CN110992122B (zh) * 2019-10-22 2024-05-14 北京交通大学 人到车的网约顺风车匹配方法

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106251024A (zh) * 2016-08-11 2016-12-21 淮阴工学院 一种基于数据挖掘的城市上下班拼车线路优化拟合方法
CN106651027A (zh) * 2016-12-21 2017-05-10 北京航空航天大学 一种基于社交网络的互联网班车线路优化方法
US20170243172A1 (en) * 2016-02-23 2017-08-24 International Business Machines Corporation Cognitive optimal and compatible grouping of users for carpooling
CN108304946A (zh) * 2018-01-08 2018-07-20 南京理工大学 一种基于社交的智能拼车方法
CN109919354A (zh) * 2019-01-15 2019-06-21 深圳大学 一种基于社交大数据的拼车优化方法、索引结构及系统

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9305284B2 (en) * 2010-05-23 2016-04-05 Technion Research & Development Foundation Limited Methods and systems for managing a multi participant event
GR20130100414A (el) * 2013-07-12 2015-02-20 Ανδρεας-Λεωνιδας Κυπριανου Προδρομιδης Μεθοδος και συστημα για τη μεταφορα αντικειμενων μεσω δικτυων εμπιστοσυνης
EP3189479A1 (fr) * 2014-09-02 2017-07-12 Telecom Italia S.p.A. Procédé et système de fourniture de service de covoiturage dynamique
US10197410B2 (en) * 2014-11-18 2019-02-05 International Business Machines Corporation Dynamic real-time carpool matching
CN105489002B (zh) * 2016-01-05 2017-12-26 深圳大学 一种基于智能匹配和路径优化的拼车方法及系统

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170243172A1 (en) * 2016-02-23 2017-08-24 International Business Machines Corporation Cognitive optimal and compatible grouping of users for carpooling
CN106251024A (zh) * 2016-08-11 2016-12-21 淮阴工学院 一种基于数据挖掘的城市上下班拼车线路优化拟合方法
CN106651027A (zh) * 2016-12-21 2017-05-10 北京航空航天大学 一种基于社交网络的互联网班车线路优化方法
CN108304946A (zh) * 2018-01-08 2018-07-20 南京理工大学 一种基于社交的智能拼车方法
CN109919354A (zh) * 2019-01-15 2019-06-21 深圳大学 一种基于社交大数据的拼车优化方法、索引结构及系统

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112561639A (zh) * 2020-12-15 2021-03-26 北京嘀嘀无限科技发展有限公司 拼车服务处理方法、装置、设备及存储介质
CN114067597A (zh) * 2021-11-17 2022-02-18 哈尔滨工业大学 一种基于强化学习的不同合乘意愿下出租车调度方法
CN114792187A (zh) * 2022-01-12 2022-07-26 山东师范大学 基于意愿和信任双重约束的群智感知团队招募方法及系统

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