CN106161553B - Community application information pushing method and system - Google Patents
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Abstract
The invention relates to a community application information pushing method and a system, which are used for acquiring and analyzing the actual geographic positions of community members, performing aggregation analysis according to the geographic positions of all the community members, counting the aggregation conditions of the community members, extracting the aggregated geographic positions of the community members as target positions, and then pushing information related to the target positions, so that the pushed information is matched with the main concentration areas of the community members, more accurate and suitable information can be pushed, redundant and useless information pushing is avoided, the occupation of network bandwidth is reduced, and the attention capacity of pushed information acquiring users is enhanced.
Description
Technical Field
The invention relates to the technical field of communication, in particular to a method and a system for pushing community application information.
Background
With the development of science and the progress of society, instant messaging tools based on products such as computers, smart phones and tablet computers occupy more and more important positions in daily life of people.
Most of the existing instant messaging tools have functions similar to groups, application information can be pushed in the groups, and users in the groups can conveniently obtain various application information, such as weather forecast, traffic information and the like. The conventional application information pushing method uses the geographical location set by the group owner as the centralized area of the group members, and pushes the application information related to the geographical location set by the group owner to the group members. When the majority of the group members are not in the area, the probability that the application information will be focused on by the user is greatly reduced. Therefore, the conventional application information pushing method has a disadvantage of low pushing accuracy.
Disclosure of Invention
Based on this, there is a need to provide a method and a system for pushing community application information, which can improve the pushing accuracy.
A community application information pushing method comprises the following steps:
acquiring the geographical position of each community member in the community;
performing aggregation analysis according to the geographical positions of all community members, and taking the geographical positions aggregated by the community members in the community as target positions;
pushing information related to the target location.
A community application information push system comprising:
the acquisition module is used for acquiring the geographical position of each community member in the community;
the processing module is used for carrying out aggregation analysis according to the geographical positions of all community members and taking the geographical positions aggregated by the community members in the community as target positions;
and the pushing module is used for pushing the information related to the target position.
According to the community application information pushing method and system, the actual geographic positions of community members are obtained and analyzed, aggregation analysis is carried out according to the geographic positions of the community members, the aggregation conditions of the community members are counted, the aggregated geographic positions of the community members are extracted to serve as target positions, then information related to the target positions is pushed, the pushed information is guaranteed to be matched with the main concentration areas of the community members, more accurate and suitable information can be pushed, redundant and useless information pushing is avoided, occupation of network bandwidth is reduced, and the attention capacity of pushed information obtaining users is enhanced.
Drawings
FIG. 1 is a flow diagram of a method for pushing community application information in one embodiment;
FIG. 2 is a flowchart illustrating an aggregation analysis performed according to geographic locations of community members in an embodiment, where the aggregated geographic locations of the community members are used as target locations;
FIG. 3 is a flow diagram that illustrates the calculation of a member concentration for each geographic location, according to one embodiment;
FIG. 4 is a flowchart illustrating an aggregated analysis performed according to the geographical locations of the community members in another embodiment, wherein the aggregated geographical locations of the community members in the community are used as target locations;
FIG. 5 is a flow diagram that illustrates computing a degree of aggregation for a community based on geographic location, under an embodiment;
FIG. 6 is a block diagram of a community application pushing system in one embodiment;
FIG. 7 is a block diagram of a processing module in one embodiment;
FIG. 8 is a block diagram of a member concentration calculation unit in one embodiment;
FIG. 9 is a block diagram of a processing module in another embodiment;
FIG. 10 is a block diagram of a community aggregation level calculation unit in an embodiment;
FIG. 11 is a block diagram of a computer system in one embodiment.
Detailed Description
A method for pushing community application information, as shown in fig. 1, includes the following steps:
step S100: and acquiring the geographical position of each community member in the community. The community member refers to a user in the community, the community may specifically be a social group in the instant messaging tool, and the community member may be a member of a certain edition of a website forum, a member in a certain chat room, or the like, besides the member of the social group in the instant messaging tool. The geographical position can be obtained by inquiring the account login place of the user, and the login place can be obtained through information such as GPS equipment, an IP address, a mobile base station and the like. For example, community members include member A, member B, and member C. The geographical positions of the member A and the member B are 'Shanghai' and the geographical position of the member C is 'Beijing' by acquiring the geographical positions of the community members.
In one embodiment, step S100 includes step 110, step 120, or step 130.
Step 110: and acquiring the login location of the community member in a preset time period to obtain the geographical position of the community member. The preset time period can be adjusted according to actual requirements, the number of the obtained login places may be one or multiple, and the login places of the community members in one week are obtained in the embodiment. Taking the acquisition of a plurality of login places as an example, the login places in a preset time period can be screened, and the place with the largest login times is extracted as the geographic position of the community member. For example, the login location of Member A within the week is acquired, including Shanghai, Guangzhou, Shenzhen, and Huizhou. And screening the acquired login places of the member A, and selecting the city Shanghai with the largest login times as the geographical position of the member A. It can be understood that when only one login place within the preset time period is obtained, the login place can be directly used as the geographic position of the community member.
In other embodiments, after obtaining a plurality of login locations within a preset time period, the location that has been most recently logged in may also be used as the geographical location of the community member, or the location with the longest login time may be used as the geographical location of the community member, and the specific processing manner is not unique and may be adjusted according to actual situations.
Step 120: and taking the login place with the largest login times as the geographical position of the community member. And obtaining all login places of community users for screening, and selecting the place with the most login times as the geographical position of the community member.
Step 130: and taking the position where the community member logs in recently as the geographic position of the community member. And similarly, all login places of the community members are obtained for screening, and the geographical position used by the community member for logging in recently is selected as the geographical position of the community member.
Alternatively, the geographic location with the longest duration of use within the preset time period of the community member may also be used as the geographic location of the community member in step S100.
Step S200: and performing aggregation analysis according to the geographical positions of all community members, and taking the geographical positions aggregated by the community members in the community as target positions. And counting the aggregation conditions of the community members according to the geographic positions, and extracting the aggregated geographic positions of the community members as target positions. The geographical location of the community member aggregation refers to a location in a relatively concentrated community member, for example, a location where the number of community members is greater than a threshold value may be defined as the geographical location of the community member aggregation.
In one embodiment, as shown in fig. 2, step S200 includes step S210 and step S220.
Step S210: the member concentration for each geographic location is calculated. The member concentration rate represents the concentration degree of community members in each geographic position.
Step S220: and extracting the geographical position of which the member concentration ratio is greater than or equal to a preset threshold value to obtain the target position. The preset threshold value can be adjusted according to actual conditions, the member concentration ratios of the geographic positions are compared with the preset threshold value respectively, whether the member concentration ratios are larger than or equal to the preset threshold value or not is judged, if yes, the community members of the geographic position are considered to be concentrated, and the geographic position can be used as a target position. It is understood that there may be only one target position or a plurality of target positions according to the difference of the preset threshold.
In one embodiment, as shown in fig. 3, step S210 includes steps S212 to S216.
Step S212: and calculating the distance between each community member according to the geographic position. Because the geographic position of each community member is known, the distance between every two community members can be directly calculated according to the geographic position. For example, the distance between the member A and the member B is calculated to be SabThe distance between the member A and the member C is SacThe distance between the member B and the member C is Sbc。
Step S214: and respectively calculating the aggregation scores among the community members according to the distances among the community members. The method specifically comprises the following steps:
wherein f is an aggregation score, s represents the distance between community members, A is a preset total score, and a is a distance threshold. In this embodiment, the total score a is 100, and the distance threshold a is 100 kilometers, and in other embodiments, specific values of the total score a and the distance threshold a may be adjusted according to actual requirements.
Since both member A and member B are in the sea, the distance S between themabIs 0, the aggregation fraction f between the member A and the member BABIs 100. The distance between Shanghai and Beijing is more than 100 km, and the aggregation fraction f between the member A and the member CACAnd an aggregation score f between member B and member CBCAre all 0. In this example, the city is simplified to illustrate, and in actual operation, a more accurate position such as a GPS coordinate can be used for accurate calculation.
Step S216: and calculating the polymerization degree of the corresponding geographic position according to the polymerization score to serve as the member concentration degree. According to the aggregation scores related to the geographic positions, the aggregation degree of each geographic position can be calculated. In this embodiment, the step S216 of calculating the degree of polymerization of the corresponding geographic location according to the aggregation score specifically includes:
wherein f isiThe aggregation scores are aggregation scores related to the geographic positions, m represents the number of the aggregation scores related to the geographic positions, and F represents the aggregation degrees of the corresponding geographic positions, represents the average value of the aggregation scores related to the geographic positions and can represent the member concentration degree of the geographic positions. An aggregate score associated with a geographic location refers to an aggregate score calculated from members of the geographic location.
For example, in calculating the degree of aggregation for the geographic location "Shanghai," since member A and member B are located in Shanghai, the aggregation score associated with "Shanghai" includes the aggregation score fABPolymerization fraction fACAnd a polymerization fraction fBCAnd summing the three polymerization scores and calculating an average value to obtain the polymerization degree of the geographical position Shanghai of 33.33. In calculating the degree of polymerization for the geographic location "Beijing", member C is located in Beijing, and the aggregation score associated with "Beijing" includes the aggregation score fACAnd a polymerization fraction fBCAnd summing the two aggregation scores and calculating an average value to obtain the aggregation degree of the geographic position 'Beijing' as 0.
In this embodiment, after the distance between every two community members is converted into the corresponding aggregation score, for each geographic location, the aggregation score related to the geographic location is extracted, the aggregation degree obtained by calculating the average value after summing can represent the aggregation degree of the members of the geographic location, and the subsequent steps can be used for judging whether to push the information related to the geographic location. By calculating the aggregation degree of the geographic position, more accurate and suitable information can be pushed, redundant and useless information pushing is avoided, and the occupation of network bandwidth is reduced. It is understood that after the aggregation scores related to the geographic locations are calculated and summed, the summed value may be directly used as the aggregation degree of the geographic locations.
In another embodiment, step S210 is to use the number of community members located in each geographic location as the member concentration of the corresponding geographic location. For example, member a and member B are located in "shanghai", the member concentration of "shanghai" is 2, and member C is located in "beijing", the member concentration of "beijing" is 1.
The number of community members located in each geographic position is counted respectively, the counted number is directly used as the member concentration degree of the corresponding geographic position and used as a follow-up step for judging whether information related to the geographic position is pushed, and the operation is simple, convenient and quick. It is understood that in other embodiments, the ratio of the community members in the geographic location to the total number of community members may be used as the member concentration ratio of the geographic location.
In another embodiment, as shown in fig. 4, step S200 includes step S230 and step S240.
Step S230: and calculating the polymerization degree of the community according to the geographic position. The degree of aggregation of a community characterizes the concentration of members throughout the community.
Step S240: and when the polymerization degree of the community is greater than or equal to a preset threshold value, acquiring the geographic position of which the number of community members is greater than or equal to a preset numerical value to obtain the target position. The preset threshold value and the preset numerical value can be adjusted according to actual conditions, and if the polymerization degree of the community is smaller than the preset threshold value, the situation that members in the community are discrete is proved, and information pushing based on the geographic position is not suitable for being carried out. If the degree of polymerization of the community is larger than or equal to a preset threshold value, the member concentration degree in the community is high, and the geographic position of which the number of community members is larger than or equal to a preset numerical value is extracted as the target position.
In one embodiment, as shown in fig. 5, step S230 includes steps S232 to S236.
Step S232: and calculating the distance between each community member according to the geographic position. Since the geographic location of each community member is known, the distance between every two community members can be directly calculated according to the geographic location, which is similar to step S212 and will not be described herein again.
Step S234: and respectively calculating the aggregation scores among the community members according to the distances among the community members. The method specifically comprises the following steps:
wherein f is an aggregation score, s represents the distance between community members, A is a preset total score, and a is a distance threshold. In this embodiment, the total score a is 100, and the distance threshold a is 100 kilometers, and in other embodiments, specific values of the total score a and the distance threshold a may be adjusted according to actual requirements. Specifically, similar to step S214, it is not described herein again.
Step S236: and calculating the polymerization degree of the community according to the polymerization fraction. And calculating the polymerization degree of the community according to all the polymerization scores. In this embodiment, step S236 specifically includes:
N＝(n-1)*n/2
and F is the polymerization degree of the community, represents the average value of all polymerization scores, and can represent the polymerization degree of the members of the community. f. ofiN is the total number of community members for the aggregate score between each two geographic locations.
For example, the aggregation fraction f between member A and member BABAn aggregation score f between member A and member C of 100ACAnd an aggregation score f between member B and member CBCAll are 0, and if the total number N of community members is 3, N is 3. The polymerization fraction fABPolymerization fraction fACAnd a polymerization fraction fBCThe sum is divided by 3 to give a degree of aggregation for the community of 33.33.
In this embodiment, after the distance between every two community members is converted into the corresponding aggregation score, all aggregation scores are summed and divided by the total number N of aggregation score calculations, and the obtained aggregation degree can represent the aggregation degree of the members in the whole community, so that the aggregation degree can be used in the subsequent steps to judge whether to push information, and the real situation of most members in the community is met. It is understood that after all aggregation scores are summed, the summed value can be directly used as the aggregation degree of the community.
Step S300: pushing information related to the target location. The information related to the target position may include text, picture, sound or other types of information, and specifically may include advertisement or application message related to the target position. For example, the target location is shanghai, advertisement information related to shanghai is pushed, and application information related to shanghai, such as news, traffic, weather, and the like, related to shanghai is pushed. Since the geographic location of the community member is more highly correlated with Shanghai overall, the pushed information can more match the actual needs of the community member.
In the above embodiments, the example of the city as the geographic location is used to explain the above community application information pushing method, and it can be understood that the city as the geographic location is not the only application scenario of the above community application information pushing method. For example, a pre-divided area range, such as the jingzhi area, the changjiang delta, and the zhujiang delta, may be used as the geographic location of the community members. The method comprises the steps of obtaining the geographic position of each community member in a community, namely obtaining the area range of the community member, and if the community member is located in two or more area ranges, considering that the corresponding area ranges all contain the community member during aggregation analysis. And finally, taking the area range aggregated by the community members as a target position, and pushing information related to the target position. In addition, when the geographic positions of the community members are different street areas in the same city, the community application information pushing method is also applicable, and the specific mode is similar to the geographic position area range and is not repeated.
It should be noted that, three different situations of geographic locations are listed above, which are merely used to explain an application scenario of the above community application information pushing method, and are not intended to limit the community application information pushing method.
According to the community application information pushing method, the actual geographic positions of community members are obtained and analyzed, aggregation analysis is carried out according to the geographic positions of the community members, the aggregation condition of the community members is counted, the aggregated geographic positions of the community members are extracted to serve as target positions, then information related to the target positions is pushed, the pushed information is guaranteed to be matched with the main concentration areas of the community members, more accurate and suitable information can be pushed, redundant and useless information pushing is avoided, occupation of network bandwidth is reduced, and the ability of the pushed information obtaining users to pay attention to is enhanced.
A community application information pushing system, as shown in FIG. 6, includes an obtaining module 100, a processing module 200, and a pushing module 300.
The obtaining module 100 is configured to obtain a geographic location of each community member in the community. The community member refers to a user in the community, the community may specifically be a social group in the instant messaging tool, and the community member may be a member of a certain edition of a website forum, a member in a certain chat room, or the like, besides the member of the social group in the instant messaging tool. The geographical position can be obtained by inquiring the account login place of the user, and the login place can be obtained through information such as GPS equipment, an IP address, a mobile base station and the like.
In one embodiment, the obtaining module 100 includes a first obtaining unit, a second obtaining unit, or a third obtaining unit.
The first acquisition unit is used for acquiring login places of community members in a preset time period to obtain geographic positions of the community members. The preset time period can be adjusted according to actual requirements, the number of the obtained login places may be one or multiple, and the login places of the community members in one week are obtained in the embodiment. Taking the acquisition of a plurality of login places as an example, the login places in a preset time period can be screened, and the place with the largest login times is extracted as the geographic position of the community member. It can be understood that when only one login place within the preset time period is obtained, the login place can be directly used as the geographic position of the community member.
In other embodiments, after the first obtaining unit obtains the multiple login locations within the preset time period, the location that is most recently logged in may be used as the geographical location of the community member, or the location with the longest login time may be used as the geographical location of the community member.
The second acquisition unit is used for taking the login place with the largest login times as the geographic position of the community member. And obtaining all login places of community users for screening, and selecting the place with the most login times as the geographical position of the community member.
The third acquisition unit is used for taking the position where the community member logs in recently as the geographic position of the community member. And similarly, all login places of the community members are obtained for screening, and the geographical position used by the community member for logging in recently is selected as the geographical position of the community member.
Alternatively, the obtaining module 100 may further use a geographical location with a longest duration of use within a preset time period of the community member as the geographical location of the community member.
The processing module 200 is configured to perform aggregation analysis according to the geographic location of each community member, and use the aggregated geographic location of the community members in the community as a target location. And counting the aggregation conditions of the community members according to the geographic positions, and extracting the aggregated geographic positions of the community members as target positions. The geographical location of the community member aggregation refers to a location in a relatively concentrated community member, for example, a location where the number of community members is greater than a threshold value may be defined as the geographical location of the community member aggregation.
In one embodiment, as shown in fig. 7, the processing module 200 includes a member concentration calculation unit 210 and a target position acquisition unit 220.
The member concentration calculation unit 210 is configured to calculate the member concentration for each geographic location. The member concentration rate represents the concentration degree of community members in each geographic position.
The target position obtaining unit 220 is configured to extract geographic positions with a member concentration ratio greater than or equal to a preset threshold, so as to obtain target positions. The preset threshold value can be adjusted according to actual conditions, the member concentration ratios of the geographic positions are compared with the preset threshold value respectively, whether the member concentration ratios are larger than or equal to the preset threshold value or not is judged, if yes, the community members of the geographic position are considered to be concentrated, and the geographic position can be used as a target position. It is understood that there may be only one target position or a plurality of target positions according to the difference of the preset threshold.
In one embodiment, as shown in fig. 8, the member concentration ratio calculation unit 210 includes a first calculation unit 212, a second calculation unit 214, and a third calculation unit 216.
The first calculating unit 212 is used for calculating the distance between each community member according to the geographic position. Because the geographic position of each community member is known, the distance between every two community members can be directly calculated according to the geographic position.
The second calculating unit 214 is configured to calculate an aggregation score between the community members according to the distances between the community members. In particular to
Wherein f is an aggregation score, s represents the distance between community members, A is a preset total score, and a is a distance threshold. In this embodiment, the total score a is 100, and the distance threshold a is 100 kilometers, and in other embodiments, specific values of the total score a and the distance threshold a may be adjusted according to actual requirements.
The third calculating unit 216 is configured to calculate a degree of aggregation of the corresponding geographic location as the member concentration ratio according to the aggregation score. According to the aggregation scores related to the geographic positions, the aggregation degree of each geographic position can be calculated. In this embodiment, the third calculating unit 216 calculates a polymerization degree of the corresponding geographic location according to the polymerization score, specifically, calculates a polymerization degree of the corresponding geographic location according to the polymerization score
Wherein f isiIs the aggregation score related to the geographic position, m represents the number of the aggregation scores related to the geographic position, F is the corresponding geographic positionThe degree of aggregation of a place, which characterizes the average of the aggregation scores associated with a geographic location, may reflect the degree of membership concentration of the geographic location. An aggregate score associated with a geographic location refers to an aggregate score calculated from members of the geographic location.
In this embodiment, after the distance between every two community members is converted into the corresponding aggregation score, for each geographic location, the aggregation score related to the geographic location is extracted, the aggregation degree obtained by calculating the average value after summing can represent the aggregation degree of the members of the geographic location, and the subsequent steps can be used for judging whether to push the information related to the geographic location. By calculating the aggregation degree of the geographic position, more accurate and suitable information can be pushed, redundant and useless information pushing is avoided, and the occupation of network bandwidth is reduced. It is understood that after the aggregation scores related to the geographic locations are calculated and summed, the summed value may be directly used as the aggregation degree of the geographic locations.
In another embodiment, the member concentration calculation unit 210 calculates the member concentration for each geographic location such that the number of community members located at each geographic location is taken as the member concentration for the corresponding geographic location.
The number of community members located in each geographic position is counted respectively, the counted number is directly used as the member concentration degree of the corresponding geographic position and used as a follow-up step for judging whether information related to the geographic position is pushed, and the operation is simple, convenient and quick. It is understood that in other embodiments, the ratio of the community members in the geographic location to the total number of community members may be used as the member concentration ratio of the geographic location.
In another embodiment, as shown in fig. 9, the processing module 200 includes a community aggregation degree calculation unit 230 and a target location extraction unit 240.
The community aggregation calculation unit 230 is configured to calculate an aggregation of the communities according to the geographic locations. The degree of aggregation of a community characterizes the concentration of members throughout the community.
The target location extracting unit 240 is configured to, when the degree of polymerization of the community is greater than or equal to a preset threshold, obtain geographic locations where the number of community members is greater than or equal to a preset numerical value, so as to obtain a target location. The preset threshold value and the preset numerical value can be adjusted according to actual conditions, and if the polymerization degree of the community is smaller than the preset threshold value, the situation that members in the community are discrete is proved, and information pushing based on the geographic position is not suitable for being carried out. If the degree of polymerization of the community is larger than or equal to a preset threshold value, the member concentration degree in the community is high, and the geographic position of which the number of community members is larger than or equal to a preset numerical value is extracted as the target position.
In one embodiment, as shown in fig. 10, the community polymerization degree calculating unit 230 includes a fourth calculating unit 232, a fifth calculating unit 234, and a sixth calculating unit 236.
The fourth calculating unit 232 is configured to calculate distances between community members according to the geographic locations. Since the geographic location of each community member is known, the distance between every two community members can be directly calculated according to the geographic location, which is similar to the first calculating unit 212 and is not described herein again.
The fifth calculating unit 234 is configured to calculate an aggregation score between the community members according to the distances between the community members. In particular to
Wherein f is an aggregation score, s represents the distance between community members, A is a preset total score, and a is a distance threshold. In this embodiment, the total score a is 100, and the distance threshold a is 100 kilometers, and in other embodiments, specific values of the total score a and the distance threshold a may be adjusted according to actual requirements. The details are similar to the second calculating unit 214, and are not described herein again.
The sixth calculating unit 236 is configured to calculate the aggregation degree of the community according to the aggregation score. And calculating the polymerization degree of the community according to all the polymerization scores. In this embodiment, the sixth calculating unit 236 calculates the degree of polymerization of the community according to the polymerization score, specifically:
N＝(n-1)*n/2
and F is the polymerization degree of the community, represents the average value of all polymerization scores, and can represent the polymerization degree of the members of the community. f. ofiN is the total number of community members for the aggregate score between each two geographic locations.
In this embodiment, after the distance between every two community members is converted into the corresponding aggregation score, all aggregation scores are summed and divided by the total number N of aggregation score calculations, and the obtained aggregation degree can represent the aggregation degree of the members in the whole community, so that the aggregation degree can be used in the subsequent steps to judge whether to push information, and the real situation of most members in the community is met. It is understood that after all aggregation scores are summed, the summed value can be directly used as the aggregation degree of the community.
The pushing module 300 is used for pushing information related to the target location. The information related to the target position may include text, picture, sound or other types of information, and specifically may include advertisement or application message related to the target position. For example, the target location is shanghai, advertisement information related to shanghai is pushed, and application information related to shanghai, such as news, traffic, weather, and the like, related to shanghai is pushed. Since the geographic location of the community member is more highly correlated with Shanghai overall, the pushed information can more match the actual needs of the community member.
Above-mentioned community application information push system, acquire the actual geographical position of community member and carry out the analysis, carry out the aggregate analysis according to each community member's geographical position, make statistics of the aggregate condition of community member, the geographical position that draws community member aggregate is as the target location, then the information relevant with the target location of propelling movement, ensure the information of propelling movement and the main regional phase-match of concentrating of member in the community, can the propelling movement more accurate, the information that is fit for, avoid the push of redundant useless information, reduce the occupation to the network bandwidth, the ability that the information acquisition user that the reinforcing pushed paid attention to.
FIG. 11 is a block diagram of a computer system 1000 upon which embodiments of the present invention may be implemented. The computer system 1000 is only one example of a suitable computing environment for the invention and is not intended to suggest any limitation as to the scope of use of the invention. Neither should the computer system 1000 be interpreted as having a dependency or requirement relating to a combination of one or more components of the exemplary computer system 1000 illustrated.
The computer system 1000 shown in FIG. 11 is one example of a computer system suitable for use with the invention. Other architectures with different subsystem configurations may also be used. Such as well known desktop, notebook, personal digital assistant, smart phone, tablet, portable media player, and the like, may be suitable for use with some embodiments of the present invention. But are not limited to, the devices listed above.
As shown in fig. 11, the computer system 1000 includes a processor 1010, a memory 1020, and a system bus 1022. Various system components including the memory 1020 and the processor 1010 are connected to the system bus 1022. The processor 1010 is hardware for executing computer program instructions through basic arithmetic and logical operations in a computer system. Memory 1020 is a physical device used for temporarily or permanently storing computing programs or data (e.g., program state information). The system bus 1020 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus. The processor 1010 and the memory 1020 may be in data communication via a system bus 1022. Wherein memory 1020 includes Read Only Memory (ROM) or flash memory (neither shown), and Random Access Memory (RAM), which typically refers to main memory loaded with an operating system and application programs.
The computer system 1000 also includes a display interface 1030 (e.g., a graphics processing unit), a display device 1040 (e.g., a liquid crystal display), an audio interface 1050 (e.g., a sound card), and an audio device 1060 (e.g., speakers). Display device 1040 and audio device 1060 are media devices for experiencing multimedia content.
It should be noted that other computer systems, including more or less subsystems than computer system 1000, can also be suitable for use with the invention. For example, the computer system 1000 may include a bluetooth unit capable of exchanging data over short distances, an image sensor for taking pictures, and an accelerometer for measuring acceleration.
As described in detail above, the computer system 1000 adapted to the present invention can perform the specified operations of the community application information push method. The computer system 1000 performs these operations in the form of software instructions executed by the processor 1010 in a computer-readable medium. These software instructions may be read into memory 1020 from storage device 1070 or from another device via local network interface 1100. The software instructions stored in the memory 1020 cause the processor 1010 to perform the community application information push method described above. Furthermore, the present invention can be implemented by hardware circuits or by a combination of hardware circuits and software instructions. Thus, implementations of the invention are not limited to any specific combination of hardware circuitry and software.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present invention, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the inventive concept, which falls within the scope of the present invention. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (16)
1. A community application information pushing method is characterized by comprising the following steps:
acquiring the geographical position of each community member in the community;
performing aggregation analysis according to the geographical positions of community members, taking the geographical positions aggregated by the community members in the community as target positions, wherein the target positions are geographical positions with a member concentration ratio larger than or equal to a preset threshold value, or geographical positions with a community member number larger than or equal to a preset numerical value in the community with a community polymerization ratio larger than or equal to the preset threshold value, and the community polymerization ratio and the member concentration ratio are determined according to aggregation scores among the community members;
pushing information related to the target location.
2. The community application information pushing method according to claim 1, wherein the step of obtaining the geographical location of each community member in the community comprises the following steps;
acquiring a login place of a community member in a preset time period to obtain the geographical position of the community member; or
Taking the login place with the most login times as the geographic position of the community member; or
And taking the position where the community member logs in recently as the geographic position of the community member.
3. The community application information pushing method according to claim 1, wherein the step of performing aggregation analysis according to the geographical location of each community member and using the geographical location aggregated by the community members in the community as a target location comprises the steps of:
calculating a member concentration ratio of each geographic location;
and extracting the geographical position of which the member concentration ratio is greater than or equal to a preset threshold value to obtain the target position.
4. The community application information pushing method according to claim 3, wherein the step of calculating the member concentration ratio of each geographical location comprises the steps of:
calculating the distance between each community member according to the geographic position;
respectively calculating the aggregation scores among the community members according to the distances among the community members, specifically to calculate the aggregation scores among the community members
Wherein f is an aggregation score, s represents the distance between community members, A is a preset total score, and a is a distance threshold;
and calculating the polymerization degree of the corresponding geographic position according to the polymerization score to serve as the member concentration degree.
5. The community application information pushing method according to claim 4, wherein the aggregation degree of the corresponding geographic location is calculated according to the aggregation score, specifically, the aggregation degree is calculated according to the aggregation score
Wherein f isiM represents the number of aggregation scores associated with a geographical location, and F represents the aggregation degree of the corresponding geographical location.
6. The community application information pushing method according to claim 1, wherein the step of performing aggregation analysis according to the geographical location of each community member and using the geographical location aggregated by the community members in the community as a target location comprises the steps of:
calculating the degree of polymerization of the community according to the geographic position;
and when the polymerization degree of the community is greater than or equal to a preset threshold value, acquiring the geographic position of which the number of community members is greater than or equal to a preset numerical value to obtain the target position.
7. The community application information pushing method according to claim 6, wherein the step of calculating the degree of polymerization of the community according to the geographical location comprises the steps of:
calculating the distance between each community member according to the geographic position;
respectively calculating the aggregation scores among the community members according to the distances among the community members, specifically to calculate the aggregation scores among the community members
Wherein f is an aggregation score, s represents the distance between community members, A is a preset total score, and a is a distance threshold;
and calculating the polymerization degree of the community according to the polymerization score.
8. The community application information pushing method according to claim 7, wherein the degree of polymerization of the community is calculated according to the degree of polymerization, specifically, the degree of polymerization is calculated according to the degree of polymerization
N＝(n-1)*n/2
Wherein F is the degree of polymerization of the community, FiN is the total number of community members for the aggregate score between each two geographic locations.
9. A community application information push system, comprising:
the acquisition module is used for acquiring the geographical position of each community member in the community;
the processing module is used for carrying out aggregation analysis according to the geographical positions of all community members, taking the geographical positions aggregated by the community members in the community as target positions, wherein the target positions are the geographical positions with the member concentration degree larger than or equal to a preset threshold value, or the geographical positions with the community polymerization degree larger than or equal to the preset threshold value and the number of the community members in the community larger than or equal to a preset numerical value, and the community polymerization degree and the member concentration degree are determined according to the aggregation scores among all the community members;
and the pushing module is used for pushing the information related to the target position.
10. The community application information push system according to claim 9, wherein the obtaining module comprises:
the first acquisition unit is used for acquiring the login location of the community member in a preset time period to obtain the geographical position of the community member; or
The second acquisition unit is used for taking the login place with the largest login times as the geographical position of the community member; or
And the third acquisition unit is used for taking the position where the community member logs in recently as the geographical position of the community member.
11. The community application information push system according to claim 9, wherein the processing module comprises:
the member concentration ratio calculating unit is used for calculating the member concentration ratio of each geographic position;
and the target position acquisition unit is used for extracting the geographical position of which the member concentration ratio is greater than or equal to a preset threshold value to obtain the target position.
12. The community application information push system according to claim 11, wherein the member concentration calculation unit includes:
the first calculation unit is used for calculating the distance between community members according to the geographic position;
a second calculating unit, configured to calculate aggregation scores between community members according to distances between the community members, specifically, the aggregation scores are calculated
Wherein f is an aggregation score, s represents the distance between community members, A is a preset total score, and a is a distance threshold;
and the third calculating unit is used for calculating the polymerization degree of the corresponding geographic position according to the polymerization fraction as the member concentration degree.
13. The community application information pushing system according to claim 12, wherein the third calculating unit calculates a degree of aggregation of the corresponding geographic location according to the aggregation score, specifically, calculates a degree of aggregation of the corresponding geographic location according to the degree of aggregation
Wherein f isiM represents the number of aggregation scores associated with a geographical location, and F represents the aggregation degree of the corresponding geographical location.
14. The community application information push system according to claim 9, wherein the processing module comprises:
the community polymerization degree calculation unit is used for calculating the polymerization degree of the community according to the geographic position;
and the target position extraction unit is used for acquiring the geographic positions of which the number of community members is greater than or equal to a preset numerical value when the polymerization degree of the community is greater than or equal to a preset threshold value, so as to obtain the target positions.
15. The community application information pushing system according to claim 14, wherein the community aggregation degree calculating unit includes:
the fourth calculating unit is used for calculating the distance between each community member according to the geographic position;
a fifth calculating unit, configured to calculate aggregation scores between the community members according to distances between the community members, specifically, the fifth calculating unit is configured to calculate aggregation scores between the community members
Wherein f is an aggregation score, s represents the distance between community members, A is a preset total score, and a is a distance threshold;
and the sixth calculating unit is used for calculating the polymerization degree of the community according to the polymerization fraction.
16. The community application information pushing system according to claim 15, wherein the sixth calculating unit calculates a degree of aggregation of the community, specifically, a degree of aggregation according to the aggregation score
N＝(n-1)*n/2
Wherein F is the degree of polymerization of the community, FiN is the total number of community members for the aggregate score between each two geographic locations.
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| CN107689991B (en) * | 2017-08-24 | 2020-11-20 | 创新先进技术有限公司 | Information pushing method and device and server |
| CN109241441B (en) * | 2018-09-30 | 2021-09-17 | 北京达佳互联信息技术有限公司 | Content recommendation method and device, electronic equipment and storage medium |
| CN110659320B (en) * | 2019-09-02 | 2022-08-09 | 恩亿科(北京)数据科技有限公司 | Analysis method and analysis device for occupational distribution and readable storage medium |
| CN111131393B (en) * | 2019-11-25 | 2022-08-16 | 深圳壹账通智能科技有限公司 | User activity data statistical method, electronic device and storage medium |
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| FR2825226B1 (en) * | 2001-05-25 | 2008-02-01 | Fabien Beckers | METHOD AND SYSTEM FOR PROVIDING INFORMATION RELATING TO THE POSITION OCCUPIED BY A USER IN A SITE |
| US7474959B2 (en) * | 2004-10-08 | 2009-01-06 | Scenera Technologies, Llc | Method for providing recommendations using image, location data, and annotations |
| US8577405B2 (en) * | 2009-06-12 | 2013-11-05 | Qualcomm Incorporated | Systems, methods, and machine-readable media providing location-enabled group management |
| CN102034186A (en) * | 2009-09-29 | 2011-04-27 | 中国移动通信集团四川有限公司 | Device and method for determining object user in mobile communication system |
| US9488488B2 (en) * | 2010-02-12 | 2016-11-08 | Apple Inc. | Augmented reality maps |
| CN103902636B (en) * | 2012-12-30 | 2018-04-27 | 腾讯科技（深圳）有限公司 | Method and server based on filter clusters method pushed information |
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