CN111222076B - Topic pushing method, system, readable storage medium and computer equipment - Google Patents

Topic pushing method, system, readable storage medium and computer equipment Download PDF

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CN111222076B
CN111222076B CN202010298208.1A CN202010298208A CN111222076B CN 111222076 B CN111222076 B CN 111222076B CN 202010298208 A CN202010298208 A CN 202010298208A CN 111222076 B CN111222076 B CN 111222076B
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赵聪
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Abstract

The invention discloses a topic pushing method, a system, a readable storage medium and computer equipment, wherein the method comprises the following steps: obtaining a region diffusion chain for pre-pushing the titles, wherein the region diffusion chain comprises diffusion level integer values and similarity corresponding to each region in a title source database; calculating a vector correlation value between the target region and the region where the pre-pushed question is located according to the diffusion level integer value and the similarity; calculating a correlation degree value of the pre-push questions and the target area according to the vector correlation value and the distance between the target area and the area where the pre-push questions are located; if the correlation degree value is larger than the threshold value, adding the pre-pushed topics into a topic pushing set of the target area, and pushing the topics in the topic pushing set of the target area when receiving a topic pushing request from the target area. The invention can automatically push the subject contents adaptive to the local education and teaching examination and solve the problems of low manual labeling efficiency and large workload.

Description

Topic pushing method, system, readable storage medium and computer equipment
Technical Field
The invention relates to the technical field of education systems, in particular to a topic pushing method, a topic pushing system, a readable storage medium and computer equipment.
Background
The education of primary and secondary schools in China has obvious regionalized characteristics, and the teaching plan, the operation subject, the examination content and the subject of a teacher have different degrees of difference due to the reasons of region difference, teaching material version difference, cultural difference, education teaching level progress and the like among different cities of different provinces.
With the promotion of education informatization, the generation and arrangement of homework/examination papers by using a commercial question bank system are becoming more and more popular, however, in the using process, the requirement of teachers is that the question bank system can push question contents adaptive to local education and teaching examinations urgently. At present, regional labeling can only be carried out in a manual mode, but the problems on the Internet are many, more than 1000 ten thousand problems are needed for single junior high school mathematics, the manual labeling mode is low in efficiency, the workload is large, and the actual requirements are difficult to meet.
Disclosure of Invention
Therefore, an object of the present invention is to provide a topic pushing method to automatically push topic contents suitable for a local education and teaching test, so as to solve the problems of low efficiency and large workload of manual annotation.
A title pushing method comprises the following steps:
obtaining a region diffusion chain for pre-pushing topics, wherein the region diffusion chain comprises diffusion level integer values and similarity corresponding to each region in a topic source database;
calculating a vector correlation value between a target region and the region where the pre-pushed question is located according to the diffusion level integer value and the similarity;
calculating a correlation degree value of the pre-pushed question and the target region according to the vector correlation value and the distance between the target region and the region where the pre-pushed question is located;
if the correlation degree value is larger than the threshold value, adding the pre-pushed titles into the title pushing set of the target area, and pushing the titles in the title pushing set of the target area when receiving a title pushing request from the target area.
According to the title pushing method provided by the invention, firstly, a region diffusion chain for pre-pushing titles is obtained, the region diffusion chain comprises diffusion level integral values and similarities corresponding to all regions in a title source database, then, a vector association value between a target region and the region where the pre-pushing title is located is calculated according to the diffusion level integral values and the similarities, regional vector topology analysis of title contents is realized, then, a correlation degree value between the pre-pushing title and the target region is calculated according to the vector association value and the distance between the target region and the region where the pre-pushing title is located, applicability dynamic scores of each title in different regions can be formed, if the correlation degree value is greater than a threshold value, the pre-pushing title is added into a title pushing set of the target region for subsequent pushing, and automatic regional labeling effects are sequentially realized, the method has the advantages that the method does not need to be labeled, and can help teachers quickly screen out titles in proper locations.
In addition, according to the title push method of the present invention, the following additional technical features may be further provided:
further, the target region and the region where the pre-pushed topic is located are calculated according to the diffusion level integer value and the similarityIn the step of vector correlation value between, the vector correlation value is calculated according to the following formula
Figure 522591DEST_PATH_IMAGE001
：
Figure 309282DEST_PATH_IMAGE002
Wherein the content of the first and second substances,
Figure 29106DEST_PATH_IMAGE003
question pre-pushedTBelonging to a set of topics
Figure 686483DEST_PATH_IMAGE004
A is the target area, X is the area where the pre-push topic is located,Yis an integer value of the diffusion level or levels,Sis prepared by reacting withTThe similarity of (c).
Further, in the step of calculating the degree of correlation value between the pre-push topic and the target region according to the vector correlation value and the distance between the target region and the region where the pre-push topic is located, the degree of correlation value R (T, a) is calculated according to the following formula:
Figure 714351DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 722758DEST_PATH_IMAGE006
。
further, the threshold value is 0.5.
Further, the method further comprises:
calculating a second vector correlation value between the target region and each region except the target region in the topic source database;
adding the titles in the region corresponding to the second vector correlation value larger than the correlation threshold value into the title push set of the target region, and pushing the titles in the title push set of the target region when receiving a title push request from the target region.
Another objective of the present invention is to provide a topic push system to automatically push topic contents suitable for local education and teaching examinations, so as to solve the problems of low efficiency and heavy workload of manual annotation.
A topic push system, comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring a region diffusion chain for pre-pushing titles, and the region diffusion chain comprises diffusion level integer values and similarity corresponding to each region in a title source database;
the first calculation module is used for calculating a vector association value between a target region and a region where the pre-pushed question is located according to the diffusion level integer value and the similarity;
the second calculation module is used for calculating the degree of correlation value of the pre-pushed question and the target region according to the vector correlation value and the distance between the target region and the region where the pre-pushed question is located;
and the first pushing module is used for adding the pre-pushed questions into the question pushing set of the target area if the correlation degree value is greater than the threshold value, and pushing the questions in the question pushing set of the target area when receiving a question pushing request from the target area.
According to the title pushing system provided by the invention, firstly, a region diffusion chain for pre-pushing titles is obtained, the region diffusion chain comprises diffusion level integral values and similarities corresponding to all regions in a title source database, then, a vector association value between a target region and the region where the pre-pushing title is located is calculated according to the diffusion level integral values and the similarities, regional vector topology analysis of title contents is realized, then, a correlation degree value between the pre-pushing title and the target region is calculated according to the vector association value and the distance between the target region and the region where the pre-pushing title is located, applicability dynamic scores of each title in different regions can be formed, if the correlation degree value is greater than a threshold value, the pre-pushing title is added into a title pushing set of the target region for subsequent pushing, and automatic regional labeling effects are sequentially realized, the method has the advantages that the method does not need to be labeled, and can help teachers quickly screen out titles in proper locations.
In addition, the topic pushing system according to the present invention may further have the following additional technical features:
further, the first calculating module is configured to calculate the vector correlation value according to the following formula
Figure 963115DEST_PATH_IMAGE007
：
Figure 271737DEST_PATH_IMAGE008
Wherein the content of the first and second substances,
Figure 690080DEST_PATH_IMAGE009
question pre-pushedTBelonging to a set of topics
Figure 435051DEST_PATH_IMAGE004
A is the target area, X is the area where the pre-push topic is located,Yis an integer value of the diffusion level or levels,Sis prepared by reacting withTThe similarity of (c).
Further, the second calculating module is configured to calculate the correlation degree value R (T, a) according to the following formula:
Figure 495411DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 642227DEST_PATH_IMAGE006
。
further, the threshold value is 0.5.
Further, the system further comprises:
a third calculating module, configured to calculate a second vector correlation value between the target region and each region in the topic source database except the target region;
and the second pushing module is used for adding the titles in the region corresponding to the second vector correlation value larger than the correlation threshold value into the title pushing set of the target region, and pushing the titles in the title pushing set of the target region when receiving a title pushing request from the target region.
The invention also proposes a readable storable medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method.
The invention also proposes a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the above method when executing the program.
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The above and/or additional aspects and advantages of embodiments of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flowchart of a title pushing method according to a first embodiment of the present invention;
FIG. 2 is a schematic view of a geographical dispersion chain for a topic;
FIG. 3 is a block diagram showing a structure of a title pushing system according to a second embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a title push method according to a first embodiment of the present invention includes steps S101 to S104.
S101, obtaining a region diffusion chain for pre-pushing topics, wherein the region diffusion chain comprises diffusion level integer values and similarity corresponding to each region in a topic source database.
Before acquiring the region diffusion chain, a topic source database needs to be established, which specifically includes establishing a national city/district/county and school information base thereof, establishing information tables and longitude and latitude information of 1800 cities/districts/counties across the country, and establishing an attribution relationship of 24 thousands of schools across the country. The internet provides a large amount of test paper contents through crawling the public test paper document information, and the main information of the contents comprises the test paper name and the test paper contents (usually word, pdf, picture is a carrier). These contents can be automatically crawled and saved through crawler technology. In addition, the information of the teacher organizing the homework/examination questions in the question bank itself is also recorded, and the recorded information includes key information such as the school where the teacher is located, the relevant city/district/county regional information, the question content, and the question organizing time.
By the means, a constantly updated topic source database can be established, and the key information of the database comprises: (1) topic content, (2) topic sources (e.g., the XX middle school monthly entrance in the XX city of 2019-2020, or the XX middle school mathematics assignment in the XX city of 2019-12-30), and (3) subject.
As the originality proportion of most school homework and examination questions is generally lower, the originality of the questions of national famous schools (generally referred to as 100-strength schools) is higher, taking physics as an example, the originality questions of the national famous schools account for 50 percent in the test paper, the originality questions of general schools are almost 0, and partial content editing is usually carried out on the existing questions. Finally, a region diffusion chain of the topics can be obtained through the comparison of the similarity of the contents of the topics.
It should be noted that the method of this embodiment is for a certain subject, and for a certain subject (e.g. physics), its topic set is
Figure 497051DEST_PATH_IMAGE010
To a certain subjectT x In other words, the region spreading chain is:
Figure 11209DEST_PATH_IMAGE011
whereinL x Is a region;Y x is an integer value of a diffusion level (as shown in fig. 2, the balance water of Hebei river is 0, the Hebei stone house and the Hebei corridor are 1, the Hebei Zhengding, the Shandong Ziziqiang, the Jiangxi Nanchang are 2, the Shandong Qingdao is 3, and the Shandong Weifang is 4);S x subject and of corresponding regionT x Is a value between 0 and 1.
And S102, calculating a vector association value between a target region and the region where the pre-pushed title is located according to the diffusion level integer value and the similarity.
Wherein, given two areas A and B, the vector correlation value between the areas A and B can be calculated through the topic area diffusion chain
Figure 593369DEST_PATH_IMAGE012
And
Figure 79845DEST_PATH_IMAGE013
. It should be noted that the association between a and B is unidirectional, for example, the title of the north river water balance is applicable to the north river corridor, but does not mean that the title of the north river corridor is applicable to the north river water balance.
Figure 354837DEST_PATH_IMAGE014
Wherein the content of the first and second substances,
Figure 825133DEST_PATH_IMAGE015
. In that
Figure 227295DEST_PATH_IMAGE016
The number of occurrences of region A means that if a topic is derived from A, it is counted once.
Through the above-mentioned calculations, it is possible to,
Figure 83125DEST_PATH_IMAGE012
the larger the value of (A), the more suitable the topic of region B is for region A. On the contrary, the method can be used for carrying out the following steps,
Figure 545330DEST_PATH_IMAGE012
when the value of (A) is 0 or negative, it indicates that the title of the region B is not applicable to the region A.
Based on the above, the vector association value between the target region and the region where the pre-pushed topic is located can be calculated according to the diffusion level integer value and the similarity
Figure 492470DEST_PATH_IMAGE017
：
Figure 432744DEST_PATH_IMAGE018
Wherein the content of the first and second substances,
Figure 143080DEST_PATH_IMAGE019
question pre-pushedTBelonging to a set of topics
Figure 41766DEST_PATH_IMAGE010
A is the target area, X is the area where the pre-push topic is located,Yis an integer value of the diffusion level or levels,Sis prepared by reacting withTThe similarity of (c).
S103, calculating a correlation degree value of the pre-pushed topic and the target region according to the vector correlation value and the distance between the target region and the region where the pre-pushed topic is located.
Wherein, the correlation degree value R (T, A) is calculated according to the following formula:
Figure 955495DEST_PATH_IMAGE020
wherein the content of the first and second substances,
Figure 948728DEST_PATH_IMAGE021
the distance between the region X and the region A is the distance between the two regions pre-stored in the computerAnd means the distance between two regions, which may be in kilometers. The distance is determined according to the actual distance between two geographical regions, and is a defined value, for example, the distance between the two regions of Hebei Shizhu can be defined as 150km, it should be noted that in the above formula, the value of the distance between the two regions is simply taken, i.e. 150 is taken, and is not counted in units, and 10 in the formula is an empirical value.
S104, if the correlation degree value is larger than the threshold value, adding the pre-pushed titles into the title pushing set of the target area, and pushing the titles in the title pushing set of the target area when receiving a title pushing request from the target area.
In this embodiment, the threshold value is 0.5 according to the empirical value, and if the calculated correlation degree value is greater than 0.5, it indicates that the pre-push topic is suitable for the target area a, the pre-push topic is added to the topic push set of the target area, and the topics suitable for the target area can be gradually accumulated through continuous update. When a topic pushing request from a target area is received, topics in a topic pushing set of the target area are pushed, and the pre-pushed topics can be pushed to a user because the pre-pushed topics are in the topic pushing set of the target area. In specific implementation, a user can select to push all the titles in the title push set of the target area, or can push only a preset number of titles.
It can be understood that if the calculated correlation degree value is less than or equal to 0.5, it indicates that the pre-push topic is not suitable for the target area a, the pre-push topic is not added to the topic push set of the target area, and when a topic push request from the target area is received, the pre-push topic is not pushed.
Further, as a specific example, the method further includes:
calculating a second vector correlation value between the target region and each region except the target region in the topic source database;
adding the titles in the region corresponding to the second vector correlation value larger than the correlation threshold value into the title push set of the target region, and pushing the titles in the title push set of the target region when receiving a title push request from the target region.
If, for the set of topics
Figure 795461DEST_PATH_IMAGE022
The locale in the source database for the topic has a locale B, C, D, E in addition to the target locale a. Thus, according to the formula in step S102, the second vector associated values can be calculated respectively
Figure 333890DEST_PATH_IMAGE023
The correlation threshold is, for example, 0 if
Figure 515341DEST_PATH_IMAGE024
Are all greater than 0, and
Figure 797418DEST_PATH_IMAGE025
both are smaller than 0, which indicates that the topics in the regions C and D are also suitable for the target region A, the topics in the regions C and D are added into the topic push set of the target region A, that is, the topics are added into the subset
Figure 764237DEST_PATH_IMAGE026
Subset of
Figure 722834DEST_PATH_IMAGE027
The h-channel questions are labeled with the target area A, so that the question push set of the target area A can be further expanded.
According to the title pushing method provided by the embodiment, a region diffusion chain for pre-pushing titles is firstly obtained, the region diffusion chain comprises diffusion level integer values and similarities corresponding to all regions in a title source database, then a vector association value between a target region and the region where the pre-pushing title is located is calculated according to the diffusion level integer values and the similarities, regional vector topology analysis of title contents is realized, then a correlation degree value between the pre-pushing title and the target region is calculated according to the vector association value and the distance between the target region and the region where the pre-pushing title is located, applicability dynamic scores of each title in different regions can be formed, if the correlation degree value is greater than a threshold value, the pre-pushing title is added into a title pushing set of the target region for subsequent pushing, and automatic regional labeling effects are sequentially realized, the method has the advantages that the method does not need to be labeled, and can help teachers quickly screen out titles in proper locations.
Referring to fig. 3, based on the same inventive concept, a topic push system according to a second embodiment of the present invention includes:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring a region diffusion chain for pre-pushing titles, and the region diffusion chain comprises diffusion level integer values and similarity corresponding to each region in a title source database;
the first calculation module is used for calculating a vector association value between a target region and a region where the pre-pushed question is located according to the diffusion level integer value and the similarity;
the second calculation module is used for calculating the degree of correlation value of the pre-pushed question and the target region according to the vector correlation value and the distance between the target region and the region where the pre-pushed question is located;
and the first pushing module is used for adding the pre-pushed questions into the question pushing set of the target area if the correlation degree value is greater than the threshold value, and pushing the questions in the question pushing set of the target area when receiving a question pushing request from the target area.
In this embodiment, the first calculating module is configured to calculate the vector correlation value according to the following formula
Figure 876735DEST_PATH_IMAGE007
：
Figure 211771DEST_PATH_IMAGE008
Wherein，
Figure 767517DEST_PATH_IMAGE009
Question pre-pushedTBelonging to a set of topics
Figure 178907DEST_PATH_IMAGE004
A is the target area, X is the area where the pre-push topic is located,Yis an integer value of the diffusion level or levels,Sis prepared by reacting withTThe similarity of (c).
Further, the second calculating module is configured to calculate the correlation degree value R (T, a) according to the following formula:
Figure 69371DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 958830DEST_PATH_IMAGE006
。
further, the threshold value is 0.5.
In this embodiment, the system further includes:
a third calculating module, configured to calculate a second vector correlation value between the target region and each region in the topic source database except the target region;
and the second pushing module is used for adding the titles in the region corresponding to the second vector correlation value larger than the correlation threshold value into the title pushing set of the target region, and pushing the titles in the title pushing set of the target region when receiving a title pushing request from the target region.
According to the topic push system provided by this embodiment, a region diffusion chain for pre-pushing topics is first obtained, where the region diffusion chain includes diffusion level integer values and similarities corresponding to regions in a topic source database, and then a vector association value between a target region and a region where the pre-pushing topic is located is calculated according to the diffusion level integer values and the similarities, so as to implement regional vector topology analysis of topic contents, and then a correlation degree value between the pre-pushing topic and the target region is calculated according to the vector association value and a distance between the target region and the region where the pre-pushing topic is located, so as to form dynamic applicability scores of each topic in different regions, and if the correlation degree value is greater than a threshold, the pre-pushing topic is added to a topic push set in the target region for subsequent push, so as to sequentially implement an automatic regional labeling effect, the method has the advantages that the method does not need to be labeled, and can help teachers quickly screen out titles in proper locations.
Furthermore, an embodiment of the present invention also proposes a readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method described in the first embodiment.
Furthermore, an embodiment of the present invention also provides a computer device, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor implements the steps of the method in the first embodiment when executing the program.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit of a logic gate circuit specifically used for realizing a logic function for a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (10)

1. A title pushing method is characterized by comprising the following steps:
obtaining a region diffusion chain for pre-pushing topics, wherein the region diffusion chain comprises diffusion level integer values and similarities corresponding to regions in a topic source database, and the similarities represent content similarities of the topics;
calculating a vector correlation value between a target region and the region where the pre-pushed question is located according to the diffusion level integer value and the similarity;
calculating a correlation degree value of the pre-pushed question and the target region according to the vector correlation value and the distance between the target region and the region where the pre-pushed question is located;
if the correlation degree value is larger than the threshold value, adding the pre-pushed titles into the title pushing set of the target area, and pushing the titles in the title pushing set of the target area when receiving a title pushing request from the target area.
2. The topic push method according to claim 1, wherein in the step of calculating the vector correlation value between the target region and the region where the pre-pushed topic is located according to the diffusion-level integer value and the similarity, the vector correlation value is calculated according to the following formula
Figure 366022DEST_PATH_IMAGE001
：
Figure 956404DEST_PATH_IMAGE002
Wherein the content of the first and second substances,
Figure 778735DEST_PATH_IMAGE003
question pre-pushedTBelonging to a set of topics
Figure 872593DEST_PATH_IMAGE004
A is the target area, X is the area where the pre-push topic is located,Yis a diffusion levelThe integer value of (a) is,Sto be the subject ofTChanging questions and in different regionsTWherein the item setT 1,T 2,…T nThe number of occurrences of the title in region A indicates: if a topic has a from its source, it is counted once.
3. The title pushing method according to claim 2, wherein in the step of calculating the degree of correlation value between the pre-pushed title and the target region according to the vector correlation value and the distance between the target region and the region where the pre-pushed title is located, the degree of correlation value R (T, a) is calculated according to the following formula:
Figure 872910DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 934276DEST_PATH_IMAGE006
。
4. the title pushing method according to claim 3, wherein the threshold is 0.5.
5. The title pushing method according to claim 1, further comprising:
calculating a second vector correlation value between the target region and each region except the target region in the topic source database;
adding the titles in the region corresponding to the second vector correlation value larger than the correlation threshold value into the title push set of the target region, and pushing the titles in the title push set of the target region when receiving a title push request from the target region.
6. A topic push system, comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring a region diffusion chain for pre-pushing topics, the region diffusion chain comprises diffusion level integer values and similarities corresponding to regions in a topic source database, and the similarities represent content similarities of the topics;
the first calculation module is used for calculating a vector association value between a target region and a region where the pre-pushed question is located according to the diffusion level integer value and the similarity;
the second calculation module is used for calculating the degree of correlation value of the pre-pushed question and the target region according to the vector correlation value and the distance between the target region and the region where the pre-pushed question is located;
and the first pushing module is used for adding the pre-pushed questions into the question pushing set of the target area if the correlation degree value is greater than the threshold value, and pushing the questions in the question pushing set of the target area when receiving a question pushing request from the target area.
7. The title-pushing system of claim 6, wherein the first calculating module is configured to calculate the vector correlation value according to the following formula
Figure 311030DEST_PATH_IMAGE007
：
Figure 524974DEST_PATH_IMAGE008
Wherein the content of the first and second substances,
Figure 211039DEST_PATH_IMAGE003
question pre-pushedTBelonging to a set of topics
Figure 510433DEST_PATH_IMAGE004
A is the target area, X is the area where the pre-push topic is located,Yis an integer value of the diffusion level or levels,Sto be the subject ofTChanging questions and in different regionsTWherein the item setT 1,T 2,…T nThe number of occurrences of the title in region A indicates: if a topic has a from its source, it is counted once.
8. The topic push system of claim 7, wherein the second computing module is configured to compute the degree of correlation value R (T, A) according to the following formula:
Figure 425300DEST_PATH_IMAGE005
wherein the content of the first and second substances,
Figure 8597DEST_PATH_IMAGE006
。
9. a readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-5.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 5 when executing the program.
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