A multi-class matter data fusion method and device

Abstract

The present application relates to the field of big data, and provides a multi-class matter data fusion method and device. The method comprises the following steps: obtaining first-class matter data to be analyzed; extracting keyword information from the first-class matter data to be analyzed; calculating the hit quantity of the keyword information and each fusion-class matter feature word information in a feature library, the feature library recording the association relationship between second-class matters and the fusion-class matter feature word information; for the hit quantity of the keyword information and the feature word information of each fusion-class matter, determining whether the hit quantity meets a preset condition; if yes, associating the second-class matter associated with the feature word information of the fusion-class matter as a second-class matter to be associated; and generating fusion suggestion information according to the matter to be analyzed and the second-class matter to be associated. The present application can break away from the constraints of artificial experience, quickly mine the second-class matters potentially fused with the first-class matters from a large amount of second-class matters through data analysis, and improve the fusion efficiency and accuracy.

Description

Technical Field

[0001] This article relates to the field of big data, and in particular to a method and apparatus for data fusion of multiple types of data. Background Technology

[0002] In existing technologies, there are interrelated relationships between various types of matters, that is, the handling of one type of matter often involves the handling of another type of matter. For example, when handling public service matters (including but not limited to government service matters, electricity service matters, etc.), financial service matters are often accompanied by the handling of financial service matters. In existing technologies, the public service matter handling system and the financial service matter handling system are independent of each other. Users need to go to the public service matter handling agency to handle public service matters and to the financial institution to handle financial service matters. The existing handling methods for such public service matters and financial service matters have the drawbacks of wasting manpower and low processing efficiency.

[0003] To address the aforementioned issues, existing technologies have introduced solutions for integrating public services and financial services. This involves adding an interface for financial services to the process of handling public services. However, these existing solutions primarily rely on human experience for confirmation. Because human experience takes precedence, some public services with potential for integration with financial services are easily overlooked, making it difficult to uncover more integration solutions that align with the actual situation. Summary of the Invention

[0004] This paper addresses the shortcomings of existing methods that rely on human experience to determine the integration of different types of matters, such as wasting manpower and failing to identify matters with potential for integration.

[0005] To address the aforementioned technical issues, the first aspect of this paper provides a method for fusing data from multiple categories of items, including:

[0006] Obtain data on the first type of items to be analyzed, wherein the data on the first type of items to be analyzed includes at least service guide information;

[0007] Extract keyword information from the data of the first category of items to be analyzed;

[0008] Calculate the hit rate between the keyword information and the feature word information of each fusion category item in the feature library, wherein the feature library records the association relationship between the feature word information of the second category item and the fusion category item, and the fusion category item includes all the first category items associated with the second category item;

[0009] The following judgment is made on the hit count of the keyword information and the feature word information of each fusion category item: it is determined whether the hit count meets the preset conditions. If so, the second category item associated with the feature word information of the fusion category item is taken as the second category item to be associated.

[0010] Based on the first category of items to be analyzed and the second category of items to be associated, fusion suggestion information is generated.

[0011] As a further embodiment of this paper, the feature library establishment process includes:

[0012] Obtain user information for Category II matters, Category I user information, and service guide information for Category I matters within the designated time period and area;

[0013] Match user information for the second category of items with user information for the first category of items;

[0014] Based on the matching results between the user information of the second category of items and the user information of the first category of items, determine the fusion category items associated with each second category of items;

[0015] Extract the feature word information of each integrated item from the service guide information of each integrated item associated with each second category item;

[0016] A feature database is established based on the feature word information of the second category of items and the integration category of items.

[0017] As a further embodiment of this paper, based on the matching results between the user information of the second type of items and the user information of the first type of items, the associated fusion items of each second type of item are determined, including:

[0018] From the matching results of each user information of each second category item and the user information of each first category item, filter out the first category items that are successfully matched, and use the filtered first category items as a group of items associated with the second category item.

[0019] The number of users who processed each Category II item and each combination of related items in Category II items was counted.

[0020] Based on the user volume of each item combination associated with each second category item, determine the fusion category items associated with each second category item.

[0021] As a further embodiment of this paper, based on the user volume of each item combination associated with each second type of item, the associated fusion item is determined, including:

[0022] From the combination of items associated with each second category of items, select the combination of items with a user volume greater than the predetermined user volume as the fusion category of items associated with that second category of items.

[0023] As a further embodiment of this article, the service guide information for the first type of matter includes multiple element parameters;

[0024] From the service guide information of the integrated items associated with each Category II item, extract the feature word information of each integrated item, including:

[0025] The parameters of each element in the service guide information of each category II related integrated item are processed by word segmentation;

[0026] The word segmentation results of all element parameters of the service guide information associated with each second-category item are processed as follows:

[0027] From the word segmentation results of each element parameter, select the word segments with a frequency greater than a predetermined number of times as the feature words of each element parameter;

[0028] The feature words of this fusion category are composed of the feature words of each element parameter.

[0029] As a further embodiment of this paper, the word segmentation results of all element parameters of the service guide information of each second type of matter associated with the fusion type of matter are processed as follows, including:

[0030] Determine the related words of the feature words for each element parameter;

[0031] The feature word information of this fusion category, which is composed of the feature words of each element parameter, is further defined as: the feature word information of this fusion category, which is composed of the feature words of each element parameter and their related words.

[0032] As a further embodiment of this article, the element parameters include multiple elements from the following: item name, acceptance conditions, application materials, application form, processing procedure, service recipient, and approval result identifier.

[0033] As a further embodiment of this paper, after performing word segmentation on the element parameters in the service guide information of the integrated items associated with each second type of item, the method further includes:

[0034] The word segmentation results of the element parameters are filtered using a preset blacklist;

[0035] The preset blacklist contains words unrelated to the first category of matters.

[0036] As a further embodiment of this paper, extracting the feature word information of each integrated item from the service guide information of each integrated item associated with each second type of item also includes:

[0037] The names of each item in the second category are segmented into words;

[0038] Using the word segmentation results of the names of each second category of items, the feature word information of each fusion category of items is filtered and processed.

[0039] In a further embodiment of this paper, the keyword information includes keywords of multiple element parameters, and the feature word information of the fusion category includes feature words of multiple element parameters.

[0040] The hit count includes the hit count of each element parameter;

[0041] The preset conditions include: the hit rate of at least N feature parameters is greater than the predetermined number of parameters, where N is a positive integer.

[0042] As a further embodiment of this paper, based on the first type of matter to be analyzed and the second type of matter to be associated, fusion suggestion information is generated, including:

[0043] The first category of items to be analyzed and the second category of items to be associated are loaded into the suggestion template to obtain fusion suggestion information.

[0044] As a further embodiment of this article, after extracting keyword information from the service guide information of the first type of matter to be analyzed, the method further includes:

[0045] The keyword information is filtered using a preset blacklist;

[0046] The preset blacklist contains words unrelated to the first category of matters.

[0047] The second aspect of this paper provides a multi-category data fusion device, including:

[0048] The acquisition unit is used to acquire data on the first type of matter to be analyzed, wherein the data on the first type of matter to be analyzed includes at least service guide information;

[0049] The extraction unit is used to extract keyword information from the first type of item data to be analyzed;

[0050] The calculation unit is used to calculate the hit rate between the keyword information and the feature word information of each fusion category item in the feature library, wherein the feature library records the association relationship between the feature word information of the second category item and the fusion category item, and the fusion category item includes all the first category items associated with the second category item;

[0051] The filtering unit is used to perform the following judgment on the hit count of the keyword information and the feature word information of each fusion category item: determine whether the hit count meets the preset conditions; if so, the second category item associated with the feature word information of the fusion category item is taken as the second category item to be associated.

[0052] The suggestion unit is used to generate fusion suggestion information based on the first type of matter to be analyzed and the second type of matter to be associated.

[0053] A third aspect of this document provides a computer device including a memory, a processor, and a computer program stored on the memory, the computer program being executed by the processor to perform instructions of the method according to any of the foregoing embodiments.

[0054] A fourth aspect of this document provides a computer storage medium having a computer program stored thereon, which, when executed by a processor of a computer device, performs instructions for the method according to any of the foregoing embodiments.

[0055] A fifth aspect of this document provides a computer-readable program that, when executed by a processor in a computer, performs instructions of the method according to any of the foregoing embodiments.

[0056] The multi-category data fusion method and apparatus described in this paper constructs a feature library in advance, based on historical user information of the second category of items, user information of the first category of items, and service guide information of the first category of items. This library records the association relationships between the feature words of the second category of items and the fusion category of items. Using this feature library, the method identifies the second category of items to be associated with the first category of items to be analyzed. This approach eliminates the constraints of manual experience and uses data analysis to discover first category of items with fusion potential for second category of items, as well as to uncover potential second category of items for fusion from a massive amount of second category of items, thus improving fusion efficiency and accuracy. Furthermore, once the system for fusion of first and second category of items is online, it will improve the efficiency of users handling both categories of items and reduce the need for users to travel to multiple locations.

[0057] To make the above and other objects, features and advantages of this document more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0058] To more clearly illustrate the technical solutions in the embodiments or prior art described herein, the accompanying drawings used in the description of the embodiments or prior art will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this article. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0059] Figure 1 The diagram shows the structure of the multi-item data fusion system as described in this embodiment.

[0060] Figure 2 A flowchart illustrating the feature library establishment process in the embodiments of this paper is shown;

[0061] Figure 3 A flowchart illustrating the process for determining fusion-type items in the embodiments of this article is shown;

[0062] Figure 4This paper presents a first flowchart of the feature word information extraction process in an embodiment of the invention.

[0063] Figure 5 The second flowchart of the feature word information extraction process in the embodiments of this paper is shown;

[0064] Figure 6 The flowchart of the data fusion method for multiple types of items in the embodiments of this article is shown;

[0065] Figure 7 The diagram shows the structure of the data fusion device for multiple items as described in this embodiment;

[0066] Figure 8 The flowchart of the data fusion method for multiple types of items in the embodiments of this article is shown;

[0067] Figure 9 A structural diagram of the computer device described in this embodiment is shown.

[0068] Explanation of symbols in the attached drawings:

[0069] 110. Database;

[0070] 120. Modeling server;

[0071] 130. Converged Server;

[0072] 710, Acquisition Unit;

[0073] 720, Extraction Unit;

[0074] 730, computing unit;

[0075] 740, Filtering unit;

[0076] 750, recommended unit;

[0077] 902. Computer equipment;

[0078] 904, Processor;

[0079] 906. Memory;

[0080] 908. Drive mechanism;

[0081] 910. Input / Output Module;

[0082] 912. Input devices;

[0083] 914. Output devices;

[0084] 916. Presentation equipment;

[0085] 918. Graphical User Interface;

[0086] 920. Network interface;

[0087] 922. Communication link;

[0088] 924. Communication bus. Detailed Implementation

[0089] The technical solutions in the embodiments described below will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments described herein, and not all of the embodiments. Based on the embodiments described herein, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this document.

[0090] It should be noted that the terms "first," "second," etc., used in the specification, claims, and accompanying drawings herein are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, apparatus, product, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0091] This specification provides the operational steps of the methods described in the embodiments or flowcharts, but based on conventional or non-inventive labor, more or fewer operational steps may be included. The order of steps listed in the embodiments is merely one possible execution order among many and does not represent the only possible execution order. In actual system or device products, the methods shown in the embodiments or drawings can be executed sequentially or in parallel.

[0092] It should be noted that the multi-category data fusion method and apparatus described in this paper can be used to fuse any two categories of public service items and financial service items. Public service items include, but are not limited to, government services, electricity services, and telecommunications services. This paper does not limit the application scope of the multi-category data fusion method and apparatus. The following example illustrates the specific implementation process using a first category of public service items (government services, electricity services, telecommunications services, etc.) and a second category of financial service items. For ease of description, in subsequent embodiments, unless otherwise specified, financial services refer to financial service items, and items refer to public service items. The financial services described in this paper are services provided by financial institutions, such as bank card account opening.

[0093] It should be noted that the user information regarding historical financial services and historical transactions involved in this application (including but not limited to user identification and transaction identification) is all data authorized by the users or fully authorized by all parties. The acquisition, storage, use, and processing of all information comply with relevant national laws and regulations.

[0094] The user information for historical financial services discussed in this paper comes from financial institution systems (such as bank servers, securities company servers, etc.), while the user information for public service matters comes from service agency systems (such as government system servers, power grid branch servers, etc.). The data fusion method and device for multiple types of matters described in this paper can run on third-party systems with independent data sources, smart terminals, including smartphones, tablets, desktop computers, etc., and can also be standalone applications, mini-programs embedded in other programs, or web pages, etc. This paper does not limit the specific implementation method.

[0095] In existing technologies, the confirmation of financial services associated with public service items mainly relies on human experience. This method is wasteful of manpower and lacks standardized criteria, thus failing to guarantee the practicality of the association between financial services and public service items. Furthermore, relying on human experience to determine the associated financial services for public service items cannot uncover public service items with potential for financial service integration or confirm the financial services associated with public service items. Therefore, in one embodiment of this paper, a multi-item data fusion system is provided, such as... Figure 1 As shown, the multi-item data fusion system includes: database 110, modeling server 120, and fusion server 130.

[0096] Database 110 is used to store user information for financial services, user information for public services, and service guides for public services.

[0097] User information for financial services can be obtained from financial institution systems, while user information and service guides for public services can be obtained from public service agency systems. The specific time and geographical scope of this information can be specified according to user needs; this document does not impose any limitations on this.

[0098] User information for financial services identifies users who have used financial services. User information for public services refers to users who have used public services. User identity includes one or more of the following: ID number, telephone number, address, username, company name, and organization code. Service guide information is related to the information published for each public service, and includes, for example: service identifier, acceptance conditions, required materials, application form, processing procedure, service recipients, and approval results. The service identifier, such as the service name, is used to uniquely identify the service. Acceptance conditions include the conditions required for the service agency to process the service. Required materials include the materials required for the service. Application forms are the forms that users need to fill out. The processing procedure indicates the departments involved and their order of processing. Service recipients specify the scope of use of the service. Approval results are the documents generated during the service process.

[0099] Modeling server 120 connects to database 110 to retrieve user information for financial services, public services, and service guides for public services within a predetermined time period and region. It then matches the user information for financial services with that for public services. Based on the matching results, it determines the associated integrated services for each financial service. From the service guides for these integrated services, it extracts relevant feature words for each financial service. Finally, it builds a feature library based on these feature words and sends it to database 110 for storage. The predetermined time period and region can be set according to actual conditions; this document does not impose any limitations on this. For example, the predetermined time period could be 12 months.

[0100] The fusion server 130 connects to the database 110, and the user obtains information on public service items to be analyzed and their service guides. Keyword information is extracted from the service guide information of the public service items to be analyzed. The hit rate between the keyword information and the feature words of each fusion category item in the feature library is calculated. The feature library is constructed based on user information of historical financial services, user information of public service items, and service guide information of public service items. The feature library records the association relationship between financial services and feature words of fusion category items. Fusion category items are public service items associated with financial services. The hit rate between the keyword information and the feature words of each fusion category item is judged as follows: whether the hit rate meets the preset conditions. If so, the financial service associated with the feature words of the fusion category item is taken as the financial service to be associated. Fusion suggestion information is generated based on the public service items to be analyzed and the financial services to be associated.

[0101] The public service items to be analyzed can be specified through a client, such as items newly added by any public service agency, or items not analyzed when the feature database was established. In some embodiments of this specification, the client can be a desktop computer, tablet computer, laptop computer, smartphone, digital assistant, smart wearable device, etc. Smart wearable devices can include smart bracelets, smartwatches, smart glasses, smart helmets, etc. Of course, the client is not limited to the aforementioned physical electronic devices; it can also be software running on the aforementioned electronic devices.

[0102] This embodiment can break free from the constraints of human experience, using data analysis to identify public service items with potential for financial service integration, and to uncover potential financial services that could be integrated with public service items from a vast pool of financial services, thereby improving the efficiency and accuracy of integration. Furthermore, once the public service and financial service integration system is online, it will improve the efficiency for users in handling public service and financial service matters, reducing the need for users to physically visit government offices.

[0103] In one embodiment of this paper, a method for establishing a feature library is provided, such as... Figure 2 As shown, the feature library creation process includes:

[0104] Step 201: Obtain user information for financial services, public services, and service guides for public services within the predetermined time period and area;

[0105] Step 202: Calculate the matching degree between user information for financial services and user information for public services;

[0106] Step 203: Based on the matching degree between user information of financial services and user information of public services, determine the public services associated with each financial service, screen the public services associated with financial services, and take the screened public services as integrated items.

[0107] Step 204: Extract feature word information related to each financial service from the service guide information of the integrated matters related to each financial service;

[0108] Step 205: Establish a feature database based on the feature word information related to each financial service.

[0109] This embodiment has the advantages of in-depth information mining and comprehensive analysis. It can mine integrated items (i.e. public service items associated with financial services) based on user information of historical financial services and user information of historical public service items. By analyzing the service guide information of integrated items, it can extract feature words that can represent the commonalities of integrated items. Based on the feature words and financial services, a feature database can be established, which can provide a theoretical basis for analyzing whether newly added public service items can be integrated with financial services and with which financial services.

[0110] In practice, the predetermined time period and predetermined area in step 201 can be specified by the user.

[0111] Step 202 calculates the matching degree between user information for financial services and user information for public services, i.e., determining whether the user information for financial services and the user information for public services belong to the same person (or organization). If they belong to the same person (or organization), the matching degree is 100%; if they do not belong to the same person (or organization), the matching degree is 0%. In practice, a user information database can be established in advance, recording all user parameter information, including but not limited to username, ID number, contact information, contact address, organization name, and organization code. When the user information for financial services and the user information for public services are different, the matching degree between the user information for financial services and the user information for public services will be confirmed using the user information database.

[0112] When implementing step 203, if Figure 3 As shown, based on the matching degree between user information of the financial services and user information of public services, the integrated items associated with each financial service are determined, including:

[0113] Step 301: For each financial service user information, filter out public service items with a 100% matching degree, and use the filtered items as a group of items associated with the financial service;

[0114] Step 302: Count the number of users who have processed each financial service and the combination of each financial service related to each other;

[0115] Step 303: Determine the integrated items associated with each financial service based on the user volume of each item combination associated with each financial service.

[0116] Specifically, in step 301, for example, if the user information A1 of financial service A matches the user information of public service items 1, 2, 3 with a 100% match rate, then public service items 1, 2, 3 are the selected items, and items 1, 2, 3 are a combination of items associated with financial service A.

[0117] The number of users in step 302 refers to the number of users who simultaneously handle financial services and combinations of related financial services. Continuing with the previous example, for instance, the number of users who simultaneously handle financial service A and public service items 1, 2, and 3 is counted.

[0118] Step 303 involves integrating at least one item into the financial service-related category. Taking the financial service "corporate account opening" as an example, related integrated items include government service items such as "company establishment" and "small and micro enterprise subsidy application." Step 303 specifically involves: selecting item combinations with a user base greater than a predetermined user base from all item combinations associated with each financial service as the integrated items associated with that financial service. For example, assuming a predetermined user base of 100, the item combinations associated with financial service A include item combination 1 {items 11, 12, 13}, item combination 2 {items 21, 22, 23}, and item combination 3 {items 31, 32, 33}. Through the statistics obtained in step 302, the user base for item combination 1 is 20, for item combination 2 is 5, and for item combination 3 is 150. Step 303 determines that item combination 3 contains items that are integrated items associated with financial service A.

[0119] Each financial service in step 204 has feature word information. To improve the efficiency of feature word extraction, the feature word information can be extracted according to the element parameters in the service guide information. These element parameters include several of the following: service name, acceptance conditions, application materials, application form, processing procedure, service recipients, and approval result identifier.

[0120] Specifically, such as Figure 4 As shown, step 204 extracts feature word information related to each financial service from the service guide information of integrated matters related to each financial service, including:

[0121] Step 401: Perform word segmentation on the parameters of each element in the service guide information of the integrated matters related to various financial services;

[0122] Step 402: Perform the following processing on the word segmentation results of all element parameters of the service guide information for each integrated service-related financial service:

[0123] Step 4021: Select word segments with a frequency greater than a predetermined number of times from the word segmentation results of each element parameter as feature words of each element parameter;

[0124] Step 4022: The feature words of each element parameter constitute the feature word information related to the financial service.

[0125] In step 401, the element parameters in the service guide information of the integrated items refer to the sum of the element parameters of all items in the integrated items. For example, if integrated item A includes {item 11, item 12, item 13}, then the acceptance name of integrated item A refers to {acceptance name of item 11, acceptance name of item 12, acceptance name of item 13}.

[0126] The number of times the reservation is made in step 4021 can be adjusted according to actual needs, and this article does not specify its value.

[0127] Furthermore, in order to improve the accuracy of subsequent feature library matching, such as Figure 5 As shown, step 402 further includes: step 4023, determining the related words of the feature words of each element parameter, wherein the related words include, but are not limited to, synonyms, abbreviations, etc. The corresponding step 4022 is replaced by step 4022' (the feature word information related to the financial service is composed of the feature words of each element parameter and their related words).

[0128] Furthermore, to improve the efficiency of feature database establishment and avoid interference from words unrelated to public service matters, after word segmentation in step 401, the method also includes: using a preset blacklist to filter the word segmentation results of the element parameters. The preset blacklist contains words unrelated to public service matters, which can be obtained through manual analysis.

[0129] The feature words obtained from each element parameter can be represented as shown in Table 1:

[0130] Table 1

[0131]

[0132]

[0133] Furthermore, considering that the feature words analyzed by the above method have poor relevance to financial services, step 204 extracts feature word information related to each financial service from the service guide information of the integrated matters associated with each financial service. This also includes: performing word segmentation on the names of each financial service; and using the word segmentation results of the names of each financial service to filter the feature word information related to each financial service, that is, filtering out feature words with low relevance to financial services.

[0134] Based on the established feature database, it is possible to analyze whether newly added items can be integrated with financial services, and which financial services they can be integrated with. Based on this, this paper provides a method for data fusion of multiple types of items, such as... Figure 6 As shown, the methods for data fusion across multiple categories include:

[0135] Step 601: Obtain information on the public service items to be analyzed and their service guidelines;

[0136] Step 602: Extract keyword information from the service guide information of the public service items to be analyzed;

[0137] Step 603: Calculate the hit rate between keyword information and feature word information of each fusion category in the feature library. The process of establishing the feature library is described in the above embodiment and will not be detailed here.

[0138] Step 604: The hit count of keyword information and feature word information of each fusion category is processed as follows: determine whether the hit count meets the preset conditions. If so, the financial service associated with the feature word information of the fusion category is taken as the financial service to be associated.

[0139] Step 605: Generate integration suggestion information based on the public service items to be analyzed and the financial services to be associated.

[0140] When implementing step 601, the public service items to be analyzed can be obtained from the public service agency system.

[0141] The keyword information obtained in step 602 includes keywords from multiple element parameters. In practice, the service guide information for the public service items to be analyzed is first segmented into words, and keyword information is extracted from the segmentation results. In some implementations, to ensure a strong correlation between keywords and financial services, after extracting the keyword information in step 602, a preset blacklist is used to filter the keyword information. This blacklist contains words unrelated to the service items.

[0142] In step 603, the feature word information of the fusion category items in the feature library includes feature words of multiple element parameters. Similarly, the hit rate of the keyword information obtained in step 603 and the feature word information of each fusion category item includes the hit rate of each element parameter.

[0143] The preset conditions for step 604 include: the hit rate of at least N element parameters is greater than the predetermined number of parameters, where N is a positive integer and N is less than the total number of element parameters. The preset conditions can be set according to actual circumstances; this document does not limit N or the predetermined number of parameters in the preset conditions. In some implementations, it is assumed that the element parameters in the service guide information include the item name, acceptance conditions, application materials, application form, processing procedure, service recipient, and approval result identifier, and the preset condition is that the hit rate of at least 4 element parameters is greater than 2.

[0144] When implementing step 605, the public service items to be analyzed and the financial services to be associated can be loaded into the suggestion template to obtain fusion suggestion information. The suggestion template records the item and financial service fields, and may also include suggestion fields, such as adding a financial service process to the service flow of the item (e.g., adding self-service account opening). In specific implementation, after obtaining the fusion suggestion information, the suggestion information can also be sent to financial institutions or the service handling agency so that the financial institution can provide financial services for the public service items to be analyzed, or the service handling agency can adjust the service guide information or the service system to access the corresponding financial services of the financial institution.

[0145] This embodiment can break free from the constraints of human experience, using data analysis to identify public service items with potential for financial service integration, and to uncover potential financial services that could be integrated with public service items from a vast amount of financial services, thereby improving the efficiency and accuracy of integration. Furthermore, once the system integrating public service items and financial services is online, it will improve the efficiency of users in handling transactions and accessing financial services, reducing the need for users to physically visit government offices.

[0146] Based on the same inventive concept, this paper also provides a multi-item data fusion device, as described in the following embodiments. Since the principle of the multi-item data fusion device in solving the problem is similar to that of the multi-item data fusion method, the implementation of the multi-item data fusion device can refer to the multi-item data fusion method, and the repeated parts will not be described again.

[0147] Specifically, such as Figure 7 As shown, the multi-item data fusion device includes:

[0148] Acquisition unit 710 is used to acquire information on public service items to be analyzed and their service guidelines;

[0149] Extraction unit 720 is used to extract keyword information from the service guide information of the public service analysis items;

[0150] The calculation unit 730 is used to calculate the hit rate between the keyword information and the feature word information of each fusion type item in the feature library. The feature library is constructed based on the user information of historical financial services, the user information of the items, and the service guide information of the items. The feature library records the association relationship between the feature word information of financial services and fusion type items. Fusion type items include all items associated with financial services.

[0151] The filtering unit 740 is used to determine whether the hit count of the keyword information and the feature word information of each fusion category meets the preset conditions. If so, the financial service associated with the feature word information of the fusion category is taken as the financial service to be associated.

[0152] The suggestion unit 750 is used to generate fusion suggestion information based on the public service items to be analyzed and the financial services to be associated.

[0153] This embodiment can break free from the constraints of human experience and quickly analyze massive amounts of public service data to identify items with potential for integration with financial services. This enables rapid analysis and mining of items that integrate public services and financial services, saving manpower and resources and improving work efficiency.

[0154] To more clearly illustrate the technical solution presented in this paper, the following section uses government service items as an example to explain the process of mining and analyzing government-bank integration items. Specifically, for example... Figure 8 As shown, the data fusion method for various government service items includes: Database establishment:

[0155] Step 811: Obtain user information for financial services, government services, and service guides for government services within the past year and the designated area.

[0156] Step 812: Calculate the matching degree between user information for financial services and user information for government services. The matching degree includes 100% matching and 0 matching. If there are fundamental differences in the information, the matching degree is 0.

[0157] Step 813: Based on the matching degree between user information of financial services and user information of government service items, determine the integrated items associated with each financial service, where the integrated items associated with financial services include multiple government service items;

[0158] Step 814: Extract the feature words and related words of each financial service from the service guide information of the integrated matters related to each financial service;

[0159] Step 815: Establish a feature database based on the feature words and related words of each financial service.

[0160] Integration of financial services and government services:

[0161] Step 821: Obtain the government service items to be analyzed and their service guide information, wherein the service guide information contains multiple element parameters;

[0162] Step 822: Extract keyword information of each element parameter from the service guide information of the government service items to be analyzed;

[0163] Step 823: Filter the keyword information obtained in step 822 using a preset blacklist;

[0164] Step 824: Calculate the hit rate of keyword information of each element parameter with feature word information of element parameters of each fusion category in the feature library;

[0165] Step 825: For the number of hits between keyword information and feature word information of each fusion category, perform the following judgment: determine whether the number of hits meets the preset conditions. If so, the financial service associated with the feature word information of the fusion category is taken as the financial service to be associated.

[0166] Step 826: Generate integration suggestion information based on the government service items to be analyzed and the financial services to be associated.

[0167] This paper utilizes a feature database to identify the associated financial services for the government service items being analyzed. This approach breaks free from the constraints of manual experience and uses data analysis to uncover government service items with potential for financial service integration, as well as to extract potential financial services for government service items from a vast pool of financial services, thereby improving the efficiency and accuracy of integration. Furthermore, once the system integrating government service items and financial services is launched, it will improve the efficiency of users in handling government service transactions and financial services, reducing the need for users to travel to government offices.

[0168] In one embodiment of this document, a computer device is also provided for implementing the methods described in any of the above embodiments, specifically, as follows: Figure 9 As shown, computer device 902 may include one or more processors 904, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. Computer device 902 may also include any memory 906 for storing information of any kind, such as code, settings, data, etc. Non-limitingly, for example, memory 906 may include any type of RAM, any type of ROM, flash memory, hard disk, optical disk, etc. More generally, any memory can use any technology to store information. Furthermore, any memory may provide volatile or non-volatile retention of information. Furthermore, any memory may represent a fixed or removable component of computer device 902. In one case, when processor 904 executes associated instructions stored in any memory or combination of memories, computer device 902 may perform any operation of the associated instructions. Computer device 902 also includes one or more drive mechanisms 908 for interacting with any memory, such as hard disk drive mechanisms, optical disk drive mechanisms, etc.

[0169] Computer device 902 may also include an input / output module 910 (I / O) for receiving various inputs (via input device 912) and providing various outputs (via output device 914). A specific output mechanism may include a presentation device 916 and an associated graphical user interface 918 (GUI). In other embodiments, the input / output module 910 (I / O), input device 912, and output device 914 may be omitted, and the device may function solely as a computer device within a network. Computer device 902 may also include one or more network interfaces 920 for exchanging data with other devices via one or more communication links 922. One or more communication buses 924 couple the components described above together.

[0170] Communication link 922 can be implemented in any way, such as via a local area network (LAN), a wide area network (WAN) (e.g., the Internet), a point-to-point connection, or any combination thereof. Communication link 922 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0171] Corresponding to Figures 2-6 In addition to the methods described above, this embodiment also provides a computer-readable storage medium storing a computer program that, when executed by a processor, performs the steps of the above-described methods.

[0172] This embodiment also provides a computer-readable instruction, wherein when a processor executes the instruction, the program therein causes the processor to perform the following: Figures 2 to 6 The method shown.

[0173] It should be understood that in the various embodiments of this document, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this document.

[0174] It should also be understood that, in the embodiments herein, the term "and / or" is merely a description of the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following associated objects have an "or" relationship.

[0175] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this document.

[0176] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0177] In the embodiments provided herein, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, or they may be electrical, mechanical, or other forms of connection.

[0178] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of the embodiments described herein, depending on actual needs.

[0179] Furthermore, the functional units in the various embodiments of this document can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0180] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this paper, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this paper. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0181] This document uses specific embodiments to illustrate the principles and implementation methods of this document. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this document. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this document. Therefore, the content of this specification should not be construed as a limitation of this document.

Claims

1. A method for fusing data on multiple types of items, characterized in that, include: Acquire data on the first type of items to be analyzed, wherein the data on the first type of items to be analyzed includes at least service guide information; Extract keyword information from the data of the first category of items to be analyzed; Calculate the hit rate between the keyword information and the feature word information of each fusion category item in the feature library, wherein the feature library records the association relationship between the feature word information of the second category item and the fusion category item, and the fusion category item includes all the first category items associated with the second category item; The following judgment is made on the hit count of the keyword information and the feature word information of each fusion category: it is determined whether the hit count meets the preset conditions. If so, the second category of items associated with the feature word information of the fusion category is taken as the second category of items to be associated. Based on the first category of items to be analyzed and the second category of items to be associated, fusion suggestion information is generated; The first category consists of public service items, and the second category consists of financial service items. The feature library establishment process includes: Obtain user information for Category II matters, Category I matters, and service guide information for Category I matters within the designated time period and area; Match user information for the second type of item with user information for the first type of item; when user information for the second type of item is different from user information for the first type of item, use a pre-established user information database to confirm the matching degree between user information for the second type of item and user information for the first type of item; the user information database records all parameter information of the user; Based on the matching results between the user information of the second category of items and the user information of the first category of items, determine the fusion category items associated with each second category of items; From the service guide information of the integrated items associated with each Category II item, extract the feature word information of the integrated items according to the element parameters; A feature database is established based on the keyword information of the second category of items and the integrated category of items; Specifically, based on the matching results between user information of the second category of items and user information of the first category of items, the associated fusion items for each second category of items are determined, including: From the matching results of each user information of each second category item and the user information of each first category item, filter out the first category items that are successfully matched, and use the filtered first category items as a group of items associated with the second category item. The number of users who processed each Category II item and each combination of related items in Category II items was counted. From all the item combinations associated with each second category item, select the item combinations with a user volume greater than the predetermined user volume as the fusion category items associated with that second category item.

2. The method as described in claim 1, characterized in that, The service guide information for the first type of matter includes multiple element parameters; From the service guide information of the integrated items associated with each Category II item, extract the feature word information of each integrated item, including: The parameters of each element in the service guide information of each category II related integrated item are processed by word segmentation; The word segmentation results of all element parameters of the service guide information associated with each second-category item are processed as follows: From the word segmentation results of each element parameter, select the word segments with a frequency greater than a predetermined number of times as the feature words of each element parameter; The feature words of this fusion category are composed of the feature words of each element parameter.

3. The method as described in claim 2, characterized in that, The word segmentation results of all element parameters of the service guide information associated with each second category of items are processed as follows, including: Determine the related words of the feature words for each element parameter; The feature word information of this fusion category, which is composed of the feature words of each element parameter, is further defined as: the feature word information of this fusion category, which is composed of the feature words of each element parameter and their related words.

4. The method as described in claim 2, characterized in that, The element parameters include multiple elements from the following: item name, acceptance conditions, application materials, application form, processing procedure, service recipient, and approval result identifier.

5. The method as described in claim 2, characterized in that, After word segmentation of the parameters of each element in the service guide information of the integrated items associated with each Category II item, it also includes: The word segmentation results of the element parameters are filtered using a preset blacklist; The preset blacklist contains words unrelated to the first category of matters.

6. The method as described in claim 2, characterized in that, From the service guide information of the integrated items associated with each Category II item, extract the feature word information of each integrated item, including: The names of each item in the second category are segmented into words; Using the word segmentation results of the names of each second category of items, the feature word information of each fusion category of items is filtered and processed.

7. The method as described in claim 1, characterized in that, The keyword information includes keywords of multiple element parameters, and the feature word information of the fusion category includes feature words of multiple element parameters; The hit count includes the hit count of each element parameter; The preset conditions include: the hit rate of at least N feature parameters is greater than the predetermined number of parameters, where N is a positive integer.

8. The method as described in claim 1, characterized in that, Based on the first category of items to be analyzed and the second category of items to be associated, fusion suggestion information is generated, including: The first category of items to be analyzed and the second category of items to be associated are loaded into the suggestion template to obtain fusion suggestion information.

9. The method as described in claim 1, characterized in that, After extracting keyword information from the service guide information of the first type of matter to be analyzed, the following is also included: The keyword information is filtered using a preset blacklist; The preset blacklist contains words unrelated to the first category of matters.

10. A data fusion device for multiple types of items, characterized in that, include: The acquisition unit is used to acquire data on the first type of items to be analyzed, wherein the data on the first type of items to be analyzed includes at least service guide information; The extraction unit is used to extract keyword information from the first type of item data to be analyzed; The calculation unit is used to calculate the hit rate between the keyword information and the feature word information of each fusion category item in the feature library, wherein the feature library records the association relationship between the feature word information of the second category item and the fusion category item, and the fusion category item includes all the first category items associated with the second category item; The filtering unit is used to perform the following judgment on the hit count of the keyword information and the feature word information of each fusion category item: determine whether the hit count meets the preset conditions; if so, then the second category item associated with the feature word information of the fusion category item is taken as the second category item to be associated. The suggestion unit is used to generate fusion suggestion information based on the first type of matter to be analyzed and the second type of matter to be associated; The first category consists of public service items, and the second category consists of financial service items. The feature library establishment process includes: Obtain user information for Category II matters, Category I matters, and service guide information for Category I matters within the designated time period and area; Match user information for the second type of item with user information for the first type of item; when user information for the second type of item is different from user information for the first type of item, use a pre-established user information database to confirm the matching degree between user information for the second type of item and user information for the first type of item; the user information database records all parameter information of the user; Based on the matching results between the user information of the second category of items and the user information of the first category of items, determine the fusion category items associated with each second category of items; From the service guide information of the integrated items associated with each Category II item, extract the feature word information of the integrated items according to the element parameters; A feature database is established based on the keyword information of the second category of items and the integrated category of items; Specifically, based on the matching results between user information of the second category of items and user information of the first category of items, the associated fusion items for each second category of items are determined, including: From the matching results of each user information of each second category item and the user information of each first category item, filter out the first category items that are successfully matched, and use the filtered first category items as a group of items associated with the second category item. The number of users who processed each Category II item and each combination of related items in Category II items was counted. From all the item combinations associated with each second category item, select the item combinations with a user volume greater than the predetermined user volume as the fusion category items associated with that second category item.

11. A computer device comprising a memory, a processor, and a computer program stored in the memory, characterized in that, When the computer program is run by the processor, it executes the instructions of the method according to any one of claims 1-9.

12. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor of the computer device, it executes the instructions of the method according to any one of claims 1-9.

13. A computer-readable program product, characterized in that, When the processor in the computer executes the program, the program executes the instructions of the method according to any one of claims 1 to 9.

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