CN111179101A - Internet insurance marketing data processing system based on shared network - Google Patents
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Abstract
The invention discloses an internet insurance marketing data processing system based on a shared network, which belongs to the field of data processing. The central processing server is arranged as an intermediary in the whole sharing network, so that data sharing can be realized among all sub-networks according to a protocol, the safety of each sub-network can be well guaranteed, data sharing can be better realized, user data can be uniformly analyzed and classified, visiting users can be classified, forward demand decomposition and synonymy matching can be carried out according to the categories and user sequence music, comprehensive analysis and positioning are carried out on demand data before and after the time sequence and the demand of the user demand, more accurate sales for the user can be realized, the sales data is more useful, and the demands of the user can be better met.
Description
Technical Field
The invention relates to the field of computer data processing, in particular to an internet insurance marketing data processing system based on a shared network.
Background
The sharing network is a social organization facing the public by taking a computer and other terminal equipment as a carrier and utilizing the internet to carry out information communication and resource sharing, and allows other people to share the labor fruits of the people. With the development of society, data becomes an important resource, so that network data is shared in relevant regulations, which is beneficial to business development among enterprises and cost saving, and therefore, a shared network will become a direction for future development.
Insurance marketing takes insurance as an object, takes the requirement of a consumer on the characteristic commodity as guidance, takes the requirement of the consumer on the risk as a center, and transfers the insurance commodity to the consumer by various marketing means so as to realize a series of activities of long-term operation targets of insurance companies. The marketing cost of the traditional offline insurance marketing mode is high, so that the traditional insurance enterprises and the Internet are arranged with internet insurance at a great dispute with the continuous development of the Internet technology, but the success rate of insurance in the Internet marketing is low due to the low trust between people, and the repeated marketing of the same insurance product of the same user can exist, so that not only is unnecessary resource waste caused, the marketing cost is increased, but also the counter feeling of network consumers can be caused, and the success rate of the Internet insurance marketing is low.
Therefore, accurate insurance marketing needs to be performed on the customers according to the user shared data, waste of resources is reduced, and the marketing success rate is improved.
Disclosure of Invention
The invention aims to provide an internet insurance marketing data processing system based on a shared network, which solves the technical problems of repeated promotion, resource waste and low success rate of the existing insurance business.
An internet insurance marketing data processing system based on a shared network, the method comprising the steps of:
step 1: establishing a sharing network, adding sharing users, setting data authority of the sharing users, and classifying the users;
step 2: the user collects user behavior access data according to the self authority;
and step 3: classifying the user behavior access data by using a semantic analysis method;
and 4, step 4: constructing a user group according to the user behavior access classification data;
and 5: carrying out user division on a user group to obtain sub-users;
step 6: collecting insurance requirements of each sub-user, and decomposing the insurance requirements according to the family condition of the user to obtain independent insurance requirement elements to obtain an initial requirement data set;
and 7: summarizing insurance demand elements of all the sub-users, and combining the insurance demand elements according to the similarity and complementation principle of the demand elements to obtain a sub-user and insurance demand element model;
and 8: taking an insurance demand element as a retrieval domain and a user group as a physical domain, mapping sub-users and the demand element one by one to obtain data corresponding to the users and the demands, and summarizing according to a mapping relation to obtain a data model;
and step 9: and generating a relation table of the user and the insurance demand according to the data model, and printing the relation table to carry out accurate user pushing.
Further, the specific process in step 1 is as follows:
the sharing network consists of a central processing server and a plurality of sharing sub-networks, wherein the sharing sub-networks are connected with the central processing server, each sharing sub-network needs to sign a sharing protocol when joining the sharing network, the sharing protocol limits the central processing server to access the authority content of each sharing sub-network, and data in the authority of the sharing protocol in each sharing sub-network in the sharing network can be shared.
Further, the specific process of step 2 is as follows: when each sharing sub-network needs to call sharing data of other networks, the demand sharing sub-network sends a demand instruction to the central processing server, after the central processing server receives the instruction, the central processing server decomposes which sharing sub-network or which sharing sub-networks need to be searched in the instruction, then the central processing server converts the instruction and adds an access head, wherein the access head is a key for the central processing server to access each sharing sub-network, the central processing server sends the instruction to access the sharing sub-networks to obtain data, and then the data are sent to the demand sharing sub-networks.
Further, the specific process of step 3 is as follows:
step 3.1: generating a semantic table of user demand data;
step 3.2: generating user requirement corpus augmentation data;
step 3.3: inputting the augmentation data into a neural network model for training to generate a related demand semantic sentence vector model and a demand classification model;
step 3.4: each piece of access data of the user is decomposed, and fixed-length characters in the single piece of access data are extracted according to the synonym table;
step 3.5: a fixed-length character input sentence vector model and a demand classification model obtain semantic sentence vectors and demand classification categories which represent the singleton access data;
step 3.6: and comparing the generated access semantic sentence vectors with the sentence vector set S through a local sensitive hash function or vector included angle cosine measurement to obtain a demand set R with the distance less than d.
Further, the specific process of step 3.1 is as follows:
step 3.1.1: obtaining a basic text corpus D of public user demand behavior classification through the Internet;
step 3.1.2: using a word segmentation tool to segment words of the corpus D, wherein the full-use size is 4, and the step length is 2 windows to obtain binary linguistic training data;
step 3.1.3: carrying out Word2Vec model training on binary linguistic training data to obtain Word vector representation;
step 3.1.4: calculating the residual value of an included angle between every two word vectors vi and vj to serve as the similarity of two words, and obtaining a similarity measurement matrix;
step 3.1.5: and obtaining 3 words which are most adjacent to the word vi through measurement, namely 3 synonyms of the word vi to obtain a synonym table of the demand behavior.
Further, the specific process of step 3.2 is as follows:
step 3.2.1: inputting an access data corpus, and judging whether the corpus number n is more than or equal to 1 ten thousand;
step 3.2.2: if n is less than 1 ten thousand, directly sampling and outputting the corpus, and if n is more than or equal to 1 ten thousand, executing the next step;
step 3.2.3: performing word segmentation on the input corpus to obtain a word segmentation table of the corpus situation;
step 3.2.4: generating a random variable N in [ a, b, c, d, e ] according to equal probability, and if N is equal to a, generating a new corpus by adopting a synonym replacement method for 3 words in a participle table of the corpus situation; if N ═ b finds a random synonym of the random word in the sentence, insert the synonym into the random position in the sentence and generate the new corpus; if N ═ c, two word exchange positions in the participle table are randomly selected to generate a new corpus; if N ═ d, randomly deleting 1 word in the word segmentation table to generate a new corpus; and if N is equal to e, directly outputting the corpus.
Further, the process of constructing the user group in step 4 is as follows:
analyzing and summarizing the hierarchical user group of the users according to the age, the place, the access time and the past consumption condition of the sharing users, matching the conventional insurance demand data of the user group of the existing users with the user group according to the user group, and analyzing the conventional insurance demand data of the user group as the access insurance demand data of the users.
Further, the specific process of step 5 is as follows:
and merging users with the same user insurance requirements in the users at the same level into one sub-user to obtain independent sub-users.
Further, the specific process of step 7 is as follows:
merging the user insurance demand elements of all users to obtain a total demand model consisting of independent insurance demand elements and a user and insurance demand model, and merging by adopting the same and complementary principles, wherein the same merging principle refers to the merging of demands with the same and similar semantics; the complementary merge principle refers to the merging of insurance requirements that just meet each other.
By adopting the technical scheme, the invention has the following technical effects:
the invention can realize data sharing among all sub-networks according to a protocol by arranging the central processing server as an intermediary in the whole sharing network, can well ensure the safety of each sub-network, better realizes data sharing, simultaneously carries out uniform semantic analysis and classification on user data, simultaneously classifies visiting users, carries out forward demand decomposition synonymy matching according to the categories of user groups and user sequence music, and simultaneously carries out bidirectional matching on the users and the demands, so that the real demands of the users are better mined, the demand data before and after the demands and the time sequence of the demands of the users are comprehensively analyzed and positioned, the demands are more accurately promoted for the users, the promoted data are more useful, and the demands of the users are better met.
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FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, preferred embodiments are given and the present invention is described in further detail. It should be noted, however, that the numerous details set forth in the description are merely for the purpose of providing the reader with a thorough understanding of one or more aspects of the present invention, which may be practiced without these specific details.
As shown in fig. 1, the internet insurance marketing data processing system based on the shared network of the present invention includes the following steps:
step 1: establishing a sharing network, adding sharing users, setting data authority of the sharing users, and classifying the users. The sharing network consists of a central processing server and a plurality of sharing sub-networks, wherein the sharing sub-networks are connected with the central processing server, each sharing sub-network needs to sign a sharing protocol when joining the sharing network, the sharing protocol limits the central processing server to access the authority content of each sharing sub-network, and data in the authority of the sharing protocol in each sharing sub-network in the sharing network can be shared.
The shared sub-network is then the system or network of each user's own. The central processing server may be a cloud server.
Step 2: and the user collects user behavior access data according to the self authority. When each sharing sub-network needs to call sharing data of other networks, the demand sharing sub-network sends a demand instruction to the central processing server, after the central processing server receives the instruction, the central processing server decomposes which sharing sub-network or which sharing sub-networks need to be searched in the instruction, then the central processing server converts the instruction and adds an access head, wherein the access head is a key for the central processing server to access each sharing sub-network, the central processing server sends the instruction to access the sharing sub-networks to obtain data, and then the data are sent to the demand sharing sub-networks. The access head is when the agreement is signed, and each sharing sub-network opens the port for the inquiry when recognizing the access head. The access header is encrypted using MD 5.
And step 3: and classifying the user behavior access data by using a semantic analysis method.
Step 3.1: and generating a semantic table of the user requirement data.
Step 3.1.1: and acquiring a basic text corpus D of the public user demand behavior classification through the Internet.
Step 3.1.2: and (3) performing word segmentation on the corpus D by using a word segmentation tool, wherein the full-use size is 4, and the step length is 2 windows to obtain binary linguistic training data.
Step 3.1.3: and (3) carrying out Word2Vec model training on the binary linguistic training data to obtain Word vector representation.
Step 3.1.4: and calculating the residual value of an included angle between every two word vectors vi and vj to serve as the similarity of the two words, and obtaining a similarity measurement matrix.
Step 3.1.5: and obtaining 3 words which are most adjacent to the word vi through measurement, namely 3 synonyms of the word vi to obtain a synonym table of the demand behavior.
Step 3.2: and generating user demand corpus augmentation data.
Step 3.2.1: inputting an access data corpus, and judging whether the corpus number n is more than or equal to 1 ten thousand;
step 3.2.2: if n is less than 1 ten thousand, directly sampling and outputting the corpus, and if n is more than or equal to 1 ten thousand, executing the next step;
step 3.2.3: performing word segmentation on the input corpus to obtain a word segmentation table of the corpus situation;
step 3.2.4: generating a random variable N in [ a, b, c, d, e ] according to equal probability, and if N is equal to a, generating a new corpus by adopting a synonym replacement method for 3 words in a participle table of the corpus situation; if N ═ b finds a random synonym of the random word in the sentence, insert the synonym into the random position in the sentence and generate the new corpus; if N ═ c, two word exchange positions in the participle table are randomly selected to generate a new corpus; if N ═ d, randomly deleting 1 word in the word segmentation table to generate a new corpus; and if N is equal to e, directly outputting the corpus.
Step 3.3: and inputting the augmentation data into a neural network model for training to generate a related demand semantic sentence vector model and a demand classification model.
Step 3.4: and decomposing each piece of access data of the user, and extracting fixed-length characters in the single piece of access data according to the synonym table.
Step 3.5: and acquiring a semantic sentence vector and a requirement classification category which represent the singleton access data by using a fixed-length character input sentence vector model and a requirement classification model.
Step 3.6: and comparing the generated access semantic sentence vectors with the sentence vector set S through a local sensitive hash function or vector included angle cosine measurement to obtain a demand set R with the distance less than d.
And 4, step 4: and constructing a user group according to the user behavior access classification data. Analyzing and summarizing the hierarchical user group of the users according to the age, the place, the access time and the past consumption condition of the sharing users, matching the conventional insurance demand data of the user group of the existing users with the user group according to the user group, and analyzing the conventional insurance demand data of the user group as the access insurance demand data of the users.
And 5: and carrying out user division on the user group to obtain sub-users. And merging users with the same user insurance requirements in the users at the same level into one sub-user to obtain independent sub-users.
Step 6: and collecting the insurance requirements of each sub-user, and decomposing the insurance requirements according to the family condition of the user to obtain independent insurance requirement elements to obtain an initial requirement data set.
And 7: and summarizing the insurance requirement elements of all the sub-users, and combining the insurance requirement elements according to the similarity and complementation principle of the requirement elements to obtain a sub-user and insurance requirement element model. Merging the user insurance demand elements of all users to obtain a total demand model consisting of independent insurance demand elements and a user and insurance demand model, and merging by adopting the same and complementary principles, wherein the same merging principle refers to the merging of demands with the same and similar semantics; the complementary merge principle refers to the merging of insurance requirements that just meet each other.
And 8: and taking the insurance demand elements as a retrieval domain and the user group as a physical domain, mapping the sub-users and the demand elements one by one to obtain data corresponding to the users and the demands, and summarizing according to a mapping relation to obtain a data model.
And step 9: and generating a relation table of the user and the insurance demand according to the data model, and printing the relation table to carry out accurate user pushing. The relation table is the basic information of the user, and then maps out the insurance types needed by the family or family members and the indication map of the insurance amount according to the daily income or consumption.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be construed as the protection scope of the present invention.
Claims (9)
1. An internet insurance marketing data processing system based on a shared network is characterized in that: the method comprises the following steps:
step 1: establishing a sharing network, adding sharing users, setting data authority of the sharing users, and classifying the users;
step 2: the user collects user behavior access data according to the self authority;
and step 3: classifying the user behavior access data by using a semantic analysis method;
and 4, step 4: constructing a user group according to the user behavior access classification data;
and 5: carrying out user division on a user group to obtain sub-users;
step 6: collecting insurance requirements of each sub-user, and decomposing the insurance requirements according to the family condition of the user to obtain independent insurance requirement elements to obtain an initial requirement data set;
and 7: summarizing insurance demand elements of all the sub-users, and combining the insurance demand elements according to the similarity and complementation principle of the demand elements to obtain a sub-user and insurance demand element model;
and 8: taking an insurance demand element as a retrieval domain and a user group as a physical domain, mapping sub-users and the demand element one by one to obtain data corresponding to the users and the demands, and summarizing according to a mapping relation to obtain a data model;
and step 9: and generating a relation table of the user and the insurance demand according to the data model, and printing the relation table to carry out accurate user pushing.
2. The shared network-based internet insurance marketing data processing system of claim 1, wherein: the specific process in the step 1 is as follows:
the sharing network consists of a central processing server and a plurality of sharing sub-networks, wherein the sharing sub-networks are connected with the central processing server, each sharing sub-network needs to sign a sharing protocol when joining the sharing network, the sharing protocol limits the central processing server to access the authority content of each sharing sub-network, and data in the authority of the sharing protocol in each sharing sub-network in the sharing network can be shared.
3. The shared network-based internet insurance marketing data processing system of claim 1, wherein: the specific process of the step 2 is as follows: when each sharing sub-network needs to call sharing data of other networks, the demand sharing sub-network sends a demand instruction to the central processing server, after the central processing server receives the instruction, the central processing server decomposes which sharing sub-network or which sharing sub-networks need to be searched in the instruction, then the central processing server converts the instruction and adds an access head, wherein the access head is a key for the central processing server to access each sharing sub-network, the central processing server sends the instruction to access the sharing sub-networks to obtain data, and then the data are sent to the demand sharing sub-networks.
4. The shared network-based internet insurance marketing data processing system of claim 1, wherein: the specific process of the step 3 is as follows:
step 3.1: generating a semantic table of user demand data;
step 3.2: generating user requirement corpus augmentation data;
step 3.3: inputting the augmentation data into a neural network model for training to generate a related demand semantic sentence vector model and a demand classification model;
step 3.4: each piece of access data of the user is decomposed, and fixed-length characters in the single piece of access data are extracted according to the synonym table;
step 3.5: a fixed-length character input sentence vector model and a demand classification model obtain semantic sentence vectors and demand classification categories which represent the singleton access data;
step 3.6: and comparing the generated access semantic sentence vectors with the sentence vector set S through a local sensitive hash function or vector included angle cosine measurement to obtain a demand set R with the distance less than d.
5. The shared network-based internet insurance marketing data processing system of claim 4, wherein: the specific process of the step 3.1 is as follows:
step 3.1.1: obtaining a basic text corpus D of public user demand behavior classification through the Internet;
step 3.1.2: using a word segmentation tool to segment words of the corpus D, wherein the full-use size is 4, and the step length is 2 windows to obtain binary linguistic training data;
step 3.1.3: carrying out Word2Vec model training on binary linguistic training data to obtain Word vector representation;
step 3.1.4: calculating the residual value of an included angle between every two word vectors vi and vj to serve as the similarity of two words, and obtaining a similarity measurement matrix;
step 3.1.5: and obtaining 3 words which are most adjacent to the word vi through measurement, namely 3 synonyms of the word vi to obtain a synonym table of the demand behavior.
6. The shared network-based internet insurance marketing data processing system of claim 4, wherein: the specific process of the step 3.2 is as follows:
step 3.2.1: inputting an access data corpus, and judging whether the corpus number n is more than or equal to 1 ten thousand;
step 3.2.2: if n is less than 1 ten thousand, directly sampling and outputting the corpus, and if n is more than or equal to 1 ten thousand, executing the next step;
step 3.2.3: performing word segmentation on the input corpus to obtain a word segmentation table of the corpus situation;
step 3.2.4: generating a random variable N in [ a, b, c, d, e ] according to equal probability, and if N is equal to a, generating a new corpus by adopting a synonym replacement method for 3 words in a participle table of the corpus situation; if N ═ b finds a random synonym of the random word in the sentence, insert the synonym into the random position in the sentence and generate the new corpus; if N ═ c, two word exchange positions in the participle table are randomly selected to generate a new corpus; if N ═ d, randomly deleting 1 word in the word segmentation table to generate a new corpus; and if N is equal to e, directly outputting the corpus.
7. The shared network-based internet insurance marketing data processing system of claim 1, wherein: the process of constructing the user group in the step 4 is as follows:
analyzing and summarizing the hierarchical user group of the users according to the age, the place, the access time and the past consumption condition of the sharing users, matching the conventional insurance demand data of the user group of the existing users with the user group according to the user group, and analyzing the conventional insurance demand data of the user group as the access insurance demand data of the users.
8. The shared network-based internet insurance marketing data processing system of claim 1, wherein: the specific process of the step 5 is as follows:
and merging users with the same user insurance requirements in the users at the same level into one sub-user to obtain independent sub-users.
9. The shared network-based internet insurance marketing data processing system of claim 1, wherein: the specific process of the step 7 is as follows:
merging the user insurance demand elements of all users to obtain a total demand model consisting of independent insurance demand elements and a user and insurance demand model, and merging by adopting the same and complementary principles, wherein the same merging principle refers to the merging of demands with the same and similar semantics; the complementary merge principle refers to the merging of insurance requirements that just meet each other.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112883684A (en) * | 2021-01-15 | 2021-06-01 | 王艺茹 | Information processing method for multipurpose visual transmission design |
| CN114844961A (en) * | 2022-04-22 | 2022-08-02 | 苏州浪潮智能科技有限公司 | Method, device, equipment and storage medium for protocol intercommunication of distributed system |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105989004A (en) * | 2015-01-27 | 2016-10-05 | 阿里巴巴集团控股有限公司 | Information releasing pretreatment method and device |
| CN108376371A (en) * | 2018-02-02 | 2018-08-07 | 众安信息技术服务有限公司 | A kind of internet insurance marketing method and system based on social networks |
| CN109491641A (en) * | 2018-10-25 | 2019-03-19 | 广西大学 | A kind of construction method of product operational management data model |
| CN110196977A (en) * | 2019-05-31 | 2019-09-03 | 广西南宁市博睿通软件技术有限公司 | A kind of intelligence alert inspection processing system and method |
| CN110930189A (en) * | 2019-11-20 | 2020-03-27 | 深圳市豪斯莱科技有限公司 | Personalized marketing method based on user behaviors |
| CN113935812A (en) * | 2021-10-29 | 2022-01-14 | 广西职业技术学院 | Intelligent recommendation method and system for E-commerce platform products |
-
2019
- 2019-12-13 CN CN201911278416.9A patent/CN111179101A/en active Pending
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105989004A (en) * | 2015-01-27 | 2016-10-05 | 阿里巴巴集团控股有限公司 | Information releasing pretreatment method and device |
| CN108376371A (en) * | 2018-02-02 | 2018-08-07 | 众安信息技术服务有限公司 | A kind of internet insurance marketing method and system based on social networks |
| WO2019149268A1 (en) * | 2018-02-02 | 2019-08-08 | 众安信息技术服务有限公司 | Method and system for marketing internet-based insurance products |
| CN109491641A (en) * | 2018-10-25 | 2019-03-19 | 广西大学 | A kind of construction method of product operational management data model |
| CN110196977A (en) * | 2019-05-31 | 2019-09-03 | 广西南宁市博睿通软件技术有限公司 | A kind of intelligence alert inspection processing system and method |
| CN110930189A (en) * | 2019-11-20 | 2020-03-27 | 深圳市豪斯莱科技有限公司 | Personalized marketing method based on user behaviors |
| CN113935812A (en) * | 2021-10-29 | 2022-01-14 | 广西职业技术学院 | Intelligent recommendation method and system for E-commerce platform products |
Non-Patent Citations (1)
| Title |
|---|
| 王浩宇;孙启明;胡凯;: "信令大数据技术在精准营销中的应用" * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112883684A (en) * | 2021-01-15 | 2021-06-01 | 王艺茹 | Information processing method for multipurpose visual transmission design |
| CN112883684B (en) * | 2021-01-15 | 2023-07-07 | 王艺茹 | Information processing method of multipurpose visual transmission design |
| CN114844961A (en) * | 2022-04-22 | 2022-08-02 | 苏州浪潮智能科技有限公司 | Method, device, equipment and storage medium for protocol intercommunication of distributed system |
| CN114844961B (en) * | 2022-04-22 | 2023-08-11 | 苏州浪潮智能科技有限公司 | Distributed system protocol intercommunication method, device, equipment and storage medium |
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