CN114219601A - Information processing method, device, equipment and storage medium - Google Patents

Information processing method, device, equipment and storage medium Download PDF

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CN114219601A
CN114219601A CN202111558895.7A CN202111558895A CN114219601A CN 114219601 A CN114219601 A CN 114219601A CN 202111558895 A CN202111558895 A CN 202111558895A CN 114219601 A CN114219601 A CN 114219601A
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target event
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王伟
肖雷
张磊
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China Construction Bank Corp
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Abstract

The present disclosure provides an information processing method, which can be applied to the financial field and the computer technical field. The information processing method includes: acquiring reporting information of a plurality of target events, wherein the reporting information of each target event comprises structured element information and unstructured description information; processing the description information of the report information aiming at each target event, and outputting at least one keyword set corresponding to the target event, wherein each keyword set comprises at least one keyword, and each keyword set corresponds to one keyword attribute; processing at least one keyword set corresponding to the target event, and outputting characteristic information corresponding to each keyword attribute; and generating a data analysis report corresponding to the target event according to the element information and the characteristic information in the report information of each target event. The present disclosure also provides an information processing apparatus, a device, a storage medium, and a program product.

Description

Information processing method, device, equipment and storage medium
Technical Field
The present disclosure relates to the field of finance and computer technology, and more particularly, to an information processing method, apparatus, device, medium, and program product.
Background
The current economic environment is complex and changeable, the safety prevention and control foundation of financial institutions is weak, and the target events causing the invasion risk to financial assets are endless. In order to enhance the protection work of financial security, generally, a sharing mechanism is used to collect target events occurring at each branch, share the target events to other branches, and warn other branches of the occurrence of related events.
In carrying out the inventive concept of the present disclosure, the inventors found that at least the following problems exist in the related art: the processing efficiency of the information related to the target event is low, and the quality of the output analysis result is poor.
Disclosure of Invention
In view of the above, the present disclosure provides an information processing method, apparatus, device, medium, and program product.
According to a first aspect of the present disclosure, there is provided an information processing method including:
acquiring reporting information of a plurality of target events, wherein the reporting information of each target event comprises structured element information and unstructured description information;
processing the description information of the report information aiming at each target event, and outputting at least one keyword set corresponding to the target event, wherein each keyword set comprises at least one keyword, and each keyword set corresponds to one keyword attribute;
processing at least one keyword set corresponding to the target event, and outputting feature information corresponding to each keyword attribute;
and generating a data analysis report corresponding to the target event according to the element information and the feature information in the report information of each target event.
According to an embodiment of the present disclosure, the processing at least one keyword set corresponding to the target event and outputting feature information corresponding to each keyword attribute includes:
determining keyword feature information, description parameter information and a target feature analysis model corresponding to each keyword set;
inputting each keyword in each keyword set, the keyword feature information and the description parameter information into the target feature analysis model, and outputting the feature information corresponding to the keyword set.
According to an embodiment of the present disclosure, the determining the keyword feature information, the description parameter information, and the target feature analysis model corresponding to the keyword set includes:
determining the keyword feature information according to each keyword in the keyword set; and
and determining the description parameter information and the target characteristic analysis model according to the keyword attributes corresponding to the keyword set.
According to an embodiment of the present disclosure, the keyword feature information includes at least one of: a keyword frequency change characteristic and a keyword appearance frequency sorting characteristic.
According to an embodiment of the present disclosure, the above-mentioned element information includes at least one of: time information, location information, and resource value information.
According to an embodiment of the present disclosure, the generating a data analysis report corresponding to the target event according to the element information and the feature information in the report information of each target event includes:
processing the element information and outputting basic description information corresponding to the target event;
and inputting the basic description information and the characteristic information into a first preset template to generate the data processing result, wherein the first preset template comprises a first filling area corresponding to the basic description information and a second filling area corresponding to the characteristic information.
According to an embodiment of the present disclosure, the processing the element information and outputting the basic description information corresponding to the target event includes:
and filling each element information into a second preset template through a preset target tool to generate the basic description information.
According to an embodiment of the present disclosure, the processing the description information of the report information and outputting at least one keyword set corresponding to the target event includes:
extracting keywords from the description information by natural language processing technology, and outputting a plurality of keywords;
and processing the plurality of keywords to generate the at least one keyword set.
According to an embodiment of the present disclosure, the processing the plurality of keywords and generating the at least one keyword set includes:
determining the keyword attribute of each keyword based on the description information;
and generating the at least one keyword set according to the keyword attribute of each keyword.
A second aspect of the present disclosure provides an information processing apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring the reporting information of a plurality of target events, and the reporting information of each target event comprises structured element information and unstructured description information;
a first processing module, configured to process, for each target event, description information of the report information, and output at least one keyword set corresponding to the target event, where each keyword set includes at least one keyword, and each keyword set corresponds to a keyword attribute;
a second processing module, configured to process at least one keyword set corresponding to the target event, and output feature information corresponding to each keyword attribute;
and a generation module, configured to generate a data analysis report corresponding to the target event according to the element information and the feature information in the report information of each target event.
A third aspect of the present disclosure provides an electronic device, comprising: one or more processors; a memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the above-described information processing method.
The fourth aspect of the present disclosure also provides a computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to perform the above-mentioned information processing method.
The fifth aspect of the present disclosure also provides a computer program product comprising a computer program which, when executed by a processor, implements the above-described information processing method.
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The foregoing and other objects, features and advantages of the disclosure will be apparent from the following description of embodiments of the disclosure, which proceeds with reference to the accompanying drawings, in which:
fig. 1 schematically illustrates an application scenario diagram of an information processing method, apparatus, device, medium, and program product according to embodiments of the present disclosure;
FIG. 2 schematically shows a flow chart of an information processing method according to an embodiment of the present disclosure;
fig. 3 schematically shows an application scenario diagram of an information processing method according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a flow diagram for generating a data analysis report corresponding to a target event, in accordance with an embodiment of the present disclosure;
fig. 5 schematically shows another application scenario diagram of the information processing method according to an embodiment of the present disclosure;
fig. 6 schematically shows a block diagram of the structure of an information processing apparatus according to an embodiment of the present disclosure;
fig. 7 schematically shows a block diagram of an electronic device adapted to implement an information processing method according to an embodiment of the present disclosure.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
The embodiment of the disclosure provides an information processing method, which includes: acquiring reporting information of a plurality of target events, wherein the reporting information of each target event comprises structured element information and unstructured description information; processing the description information of the report information aiming at each target event, and outputting at least one keyword set corresponding to the target event, wherein each keyword set comprises at least one keyword, and each keyword set corresponds to one keyword attribute; processing at least one keyword set corresponding to the target event, and outputting characteristic information corresponding to each keyword attribute; and generating a data analysis report corresponding to the target event according to the element information and the characteristic information in the report information of each target event.
According to the embodiment of the disclosure, by processing the description information of the report information and outputting at least one keyword set corresponding to the target event, keywords in the unstructured description information can be extracted to avoid information loss. Processing at least one keyword set corresponding to the target event, outputting the feature information corresponding to each keyword attribute, extracting the feature information from the unstructured description information, generating a data analysis report corresponding to the target event according to the element information and the feature information, and automatically generating the data analysis report on the basis of extracting the feature information in the description information, so that human resources for analyzing the description information can be saved, and the efficiency and accuracy of information processing are improved.
Since the data processing result is automatically generated according to the delivery information, the processing efficiency of the target event can be improved, and the feature information can be generated according to the keyword set, so that the data quality of the data processing result is improved.
The information processing method and apparatus of the present disclosure may be used in the financial field, for example, for information processing of information about financial illegal events, and may also be used in any fields other than the financial field.
In the technical scheme of the disclosure, the acquisition, storage, application and the like of the personal information of the related user all accord with the regulations of related laws and regulations, necessary security measures are taken, and the customs of the public order is not violated.
Fig. 1 schematically shows an application scenario diagram of an information processing method, apparatus, device, medium, and program product according to an embodiment of the present disclosure.
As shown in fig. 1, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background management server (for example only) providing support for websites browsed by users using the terminal devices 101, 102, 103. The background management server may analyze and perform other processing on the received data such as the user request, and feed back a processing result (e.g., a webpage, information, or data obtained or generated according to the user request) to the terminal device.
It should be noted that the information processing method provided by the embodiment of the present disclosure may be generally executed by the server 105. Accordingly, the information processing apparatus provided by the embodiment of the present disclosure may be generally provided in the server 105. The information processing method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster that is different from the server 105 and is capable of communicating with the terminal devices 101, 102, 103 and/or the server 105. Accordingly, the information processing apparatus provided in the embodiment of the present disclosure may also be provided in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and/or the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The information processing method of the disclosed embodiment will be described in detail below with fig. 2 to 6 based on the scenario described in fig. 1.
Fig. 2 schematically shows a flow chart of an information processing method according to an embodiment of the present disclosure.
As shown in fig. 2, the information processing method of this embodiment includes operations S210 to S240.
In operation S210, submission information of a plurality of target events is acquired, wherein the submission information of each target event includes structured element information and unstructured description information.
According to an embodiment of the present disclosure, the target event may include an event that can generate an invasion risk to the relevant person, for example, the risk of causing an invasion to property, physical health, or personal information of the relevant person. The submission information for the target event may include textual information describing the target event.
According to an embodiment of the present disclosure, the structured element information may include information that can be structurally stored in a database. The unstructured description information may include information that cannot be stored in a database in a structured manner, and may be, for example, text information describing a target event. It should be understood that the unstructured description information may be stored in a text-type unstructured file, such as a text-type unstructured file of word, Excel, and the like.
In operation S220, for each target event, the description information of the report information is processed, and at least one keyword set corresponding to the target event is output, where each keyword set includes at least one keyword, and each keyword set corresponds to one keyword attribute.
According to an embodiment of the disclosure, the keywords may include words capable of characterizing the descriptive information, such as words related to information such as time, place, person, or number in the descriptive information.
According to an embodiment of the disclosure, the keyword attribute may include an attribute that the keyword has in the description information of the submission information of the target event, and the keyword attribute may, for example, characterize a risk victim subject identity, a risk applying subject identity, or a risk manufacturing tool of the target event.
According to the embodiment of the disclosure, the keyword set corresponds to the keyword attributes, so that the keywords can be classified according to the corresponding keyword attributes, and a foundation is laid for subsequently extracting the feature information.
In operation S230, at least one keyword set corresponding to the target event is processed, and feature information corresponding to each keyword attribute is output.
In operation S240, a data analysis report corresponding to the target event is generated according to the element information and the feature information in the report information of each target event.
According to the embodiment of the disclosure, by processing the description information of the report information and outputting at least one keyword set corresponding to the target event, keywords in the unstructured description information can be extracted to avoid information loss. Processing at least one keyword set corresponding to the target event, outputting the feature information corresponding to each keyword attribute, extracting the feature information from the unstructured description information, generating a data analysis report corresponding to the target event according to the element information and the feature information, and automatically generating the data analysis report on the basis of extracting the feature information in the description information, so that human resources for analyzing the description information can be saved, and the efficiency and accuracy of information processing are improved.
According to an embodiment of the present disclosure, the factor information includes at least one of: time information, location information, and resource value information.
According to an embodiment of the present disclosure, the time information may include information of a time period, a time point, and the like for the target event. The location information may include geographic location information for the target event. The asset value information may include information relating to a financial amount involved in the target event, such as an amount at which there is a risk of loss, etc.
According to an embodiment of the present disclosure, the processing the description information of the posting information and outputting the at least one keyword set corresponding to the target event in operation S220 may include:
extracting keywords from the description information by a natural language processing technology, and outputting a plurality of keywords; and processing the plurality of keywords to generate at least one keyword set.
It should be noted that the generated keyword set may include at least one keyword, and the keyword may be a vector that can be recognized by a computer. In the case where the keyword set contains a plurality of keywords, the keyword set may be a set containing a plurality of vectors.
According to the embodiment of the disclosure, keyword extraction can be performed on the description information based on a TF-IDF (Term-IDF) algorithm, for example, to obtain keywords for representing the characteristics of the description information, so that unstructured description information can be represented by structured keywords, and the efficiency of information processing is improved.
According to an embodiment of the present disclosure, processing a plurality of keywords, generating at least one keyword set includes: determining a keyword attribute of each keyword based on the description information; and generating at least one keyword set according to the keyword attribute of each keyword.
According to embodiments of the present disclosure, the keyword attributes may include a risk victim identity, a risk impostor identity, and a risk manufacturing tool for the target event. The risk applying principal identity may include an identity that the principal of the manufacturing target event lies in the descriptive information. The risk manufacturing tool may include methods, mediums, etc. employed to manufacture the risk in the target event.
According to the embodiment of the disclosure, for the submission information of each target event, the keyword attributes are the identity of the risk victim subject, the identity of the risk applying subject and the risk manufacturing tool, and a keyword set J corresponding to the identity of the risk victim subject can be generated1Set of keywords J corresponding to risk applying subject identity2Set of keywords J corresponding to risk manufacturing tool3。
It should be understood that the reported information for m target events can be generated based on the same method, and the keyword set J corresponding to the identity of the risk victim subject1i, set of keywords J corresponding to risk applying principal identities2i, set of keywords J corresponding to risk manufacturing tool3iAnd i can represent the number of target events, where 1 ≦ i ≦ m.
According to an embodiment of the present disclosure, the operation S230 of processing at least one keyword set corresponding to the target event and outputting the feature information corresponding to each keyword attribute may include the following operations.
Determining keyword feature information, description parameter information and a target feature analysis model corresponding to each keyword set; and inputting each keyword, the keyword characteristic information and the description characteristic information in each keyword set into a target characteristic analysis model, and outputting the characteristic information corresponding to the keyword set.
According to the embodiment of the disclosure, the keyword feature information can be determined according to the frequency of occurrence of the keyword in the description information, so that the keyword feature information can represent the variation trend of the keyword.
According to the embodiment of the disclosure, the description parameter information may include parameter information representing the risk degree of the description information, and the description parameter information may be determined by a model constructed based on a neural network or may be determined by manual review.
According to the embodiment of the disclosure, each keyword, the keyword feature information and the description feature information in each keyword set are processed by using the target feature analysis model, and the feature information corresponding to each keyword attribute is generated, so that the labor cost generated by analyzing unstructured description information by using manpower can be saved, and the generation efficiency of the feature information is improved.
According to an embodiment of the present disclosure, the keyword feature information includes at least one of: a keyword frequency change characteristic and a keyword appearance frequency sorting characteristic.
According to the embodiment of the disclosure, the keyword frequency change feature may represent a change trend of the occurrence frequency of the keyword, and the change trend may include ascending, descending, newly increasing, or the like. The ranking characteristics of the occurrence frequency of the keywords can represent the ranking position of the occurrence frequency of each keyword in a plurality of keywords of the keywords. Through the sorting characteristic of the occurrence frequency of the keywords, related personnel can be prompted to take related measures in a targeted manner.
According to an embodiment of the present disclosure, determining keyword feature information, description information, and a target feature analysis model corresponding to a set of keywords may include:
determining keyword characteristic information according to each keyword in the keyword set; and determining the description information and the target characteristic analysis model according to the keyword attributes corresponding to the keyword set.
According to the embodiment of the disclosure, each keyword set corresponds to one keyword attribute, so that the corresponding target feature analysis model is determined according to the keyword attributes, and the keyword sets can be processed in a targeted manner based on the keyword attributes corresponding to the keyword sets to generate feature information corresponding to the keyword sets.
Fig. 3 schematically shows an application scenario diagram of an information processing method according to an embodiment of the present disclosure.
As shown in FIG. 3, the set of keywords corresponding to the keyword attribute characterization risk victim subject identity is J31The set of keywords corresponding to the identity of the keyword attribute characterization risk applying subject is J32The set of keywords corresponding to the keyword attribute characterization risk manufacturing tool is J33. Note that the keyword set J31、J32、J32Each keyword set in (a) contains at least one keyword.
According to the set of keywords as J31The description parameter information T can be determined31And keyword feature information P31And a target feature analysis model 341. Key pointsWord feature information P31 may be a set of keywords J31The frequency change characteristics of the keywords in each keyword are used for representing the keyword set J31The change trend of the occurrence frequency of each keyword.
Set keywords as J31Each keyword in (1), description parameter information T31And keyword feature information P31Inputting the target feature analysis model 341 to obtain feature information F corresponding to the identity of the keyword attribute representation risk victim351。
In this embodiment, the target feature analysis model 341 may be constructed based on equation (1),
F351＝T31×P31×J31 (1)
in the present embodiment, the keyword set J31Keywords "elderly", "financial product customers" may be included. Wherein the frequent change characteristics of the keywords 'old people' are increased, the frequent change characteristics of the keywords 'financial staff' are newly increased, and the characteristic information F51Can be as follows: "from the view point of the identity of the risk victims, the occupation ratio of the old people tends to increase, and the phenomenon that financial product customers suffer from risk infringement occurs, and the attention to the customers and the promotion of financial risk policies should be increased. "
According to the set of keywords as J32The description parameter information T can be determined32And keyword feature information P32And a target feature analysis model 342. Keyword feature information P32May be a set of keywords J32The ranking characteristics of the occurrence frequency of the medium keywords to represent the keyword set J32The rank position of the occurrence frequency of each keyword in the list.
Set keywords as J32Each keyword in (1), description parameter information T32And keyword feature information P32Inputting the target feature analysis model 342, feature information F corresponding to the identity of the key word attribute characterization risk applying subject can be obtained352。
In this embodiment, the target feature analysis model 342 may be constructed based on equation (2),
F352＝T32×P32×J32 (2)
in the present embodiment, the keyword set J32Keywords "masquerading as customer relatives", "masquerading as bank workers" may be included. The ranking characteristic of the occurrence frequency of the keywords masquerading as the relatives of the customers is that the ranking position is first, the ranking characteristic of the occurrence frequency of the keywords masquerading as the staff of the bank is that the ranking position is second, and the characteristic information F352Can be as follows: "from the view of the identity of the risk applying subject, the risk applying subject mostly gives priority to impersonating the family of the client and the staff of the bank, and the attention and the understanding of the details of the business handling on the basis of the family of the client and the staff of the bank should be enhanced. "
According to the set of keywords as J33The description parameter information T can be determined33And keyword feature information P33And a target feature analysis model 343. Keyword feature information P33May be a set of keywords J33The ranking characteristics of the occurrence frequency of the medium keywords to represent the keyword set J33The rank position of the occurrence frequency of each keyword in the list.
Set keywords as J33Each keyword in (1), description parameter information T33And keyword feature information P33Inputting the target feature analysis model 343, the feature information F corresponding to the keyword attribute characterization risk manufacturing tool can be obtained353。
In this embodiment, the target feature analysis model 343 may be constructed based on equation (3),
F353＝T33×P33×J33 (3)
in the present embodiment, the keyword set J33The keywords "network loan", "Trojan message", "ETC", "winning" may be included. The ranking characteristics of the occurrence frequency of the keywords can be expressed as the top three positions of the occurrence frequency ranking positions of the keywords of 'network loan', 'winning prize', 'Trojan short message'. Frequency change characteristics of keywords of keyword' ETCSymbolized as new addition. Characteristic information F353Can be as follows: from the perspective of risk manufacturing tools, network loan, winning prize and Trojan short message are taken as main points, wherein the network loan is taken as a name, the fraud is in an increasing trend, and a false ETC novel fraud method is appeared, and the vigilance is improved aiming at the situation. "
The feature information F is351、F352、F353The characteristic information can be used as a whole for generating a data analysis report corresponding to the target event.
FIG. 4 schematically illustrates a flow diagram for generating a data analysis report corresponding to a target event according to an embodiment of the disclosure.
As shown in fig. 4, generating a data analysis report corresponding to the target event according to the element information and the feature information in the report information of each target event in operation S240 may include operations S410 to S420.
In operation S410, the factor information is processed, and basic description information corresponding to the target event is output.
In operation S420, basic description information and feature information are input into a first preset template, and a data processing result is generated, wherein the first preset template includes a first filling area corresponding to the basic description information and a second filling area corresponding to the feature information.
According to an embodiment of the present disclosure, the basic description information may include information reflecting a summary situation or a change trend of the element information, for example, in a case where the element information includes resource value information, the basic description information may be summary situation information reflecting the resource value information, such as a total amount of money or the like, or may also be information reflecting a change trend of the resource value information, such as an increase amount of money or the like. It should be noted that the basic description information may include text information, chart information, table information, and the like.
According to an embodiment of the present disclosure, the data processing result may include data information describing and analyzing the posting information of one or more target events, and the data processing result may include text information, chart information, table information, and the like.
It should be understood that the data processing results may be stored as a file for convenient processing by the relevant personnel. For example, the file may be stored as a word format file, but is not limited thereto, and may also be a file in another format.
According to the embodiment of the disclosure, the data processing result may include basic description information corresponding to the structured element information and feature information corresponding to the unstructured description information, so that the data processing result may include implicit feature information of a keyword in the submission information of the target event, and thus, the analysis dimension of the data processing result for the target event may be extended, the automatically generated data processing result may deeply analyze the submission information of the target event, the human cost of a relevant professional analyzing the submission information of the target event is saved, and the report generation speed is increased.
According to an embodiment of the present disclosure, processing the factor information, and outputting the basic description information corresponding to the target event may include:
and filling each element information into a second preset template through a preset target tool to generate basic description information.
According to an embodiment of the present disclosure, the second preset template may include a template for describing the element information, and may include, for example, a text template, a table template, a chart template, and the like.
According to an embodiment of the present disclosure, the preset targeting tool may include a tool for generating documents based on templates and information, such as, but not limited to, a Freemaker template engine, and may also include other tools for generating documents.
According to the embodiment of the disclosure, the basic description information may include text information describing element information, for example, the text information may include "20 xx year," the number of target events is avoided to be 100, and economic loss is avoided to be 2000 ten thousand yuan. "
Fig. 5 schematically shows another application scenario diagram of the information processing method according to the embodiment of the present disclosure.
As shown in fig. 5, the structured element information 510 can be filled into a second preset template 520 through a preset target tool to generate basic description information F510. The second default template 520 may be "20 xx years, avoiding the number of target events being $ data. '-', to avoid economic loss of $ data. Basic description information F510It can be "20 xx years, the number of target events is avoided being 100, and the economic loss is avoided being 2000 ten thousand yuan".
Characteristic information F521May be the characteristic information corresponding to the identity of the key word attribute representation risk victim subject, the characteristic information F522May be feature information corresponding to the identity of the keyword attribute characterization risk applying agent, feature information F523May be feature information corresponding to the keyword attribute characterization risk manufacturing tool.
The first preset template 530 may include a first filling area 531 and a second filling area 532. Will basically describe the information F510Inputting the first filling area 531, and inputting the feature information F521And characteristic information F522And characteristic information F523The second filled area 532 is input and a data processing result may be generated.
The data processing result can comprise basic description information corresponding to the structured element information and characteristic information corresponding to the unstructured description information, so that the data processing result can contain implicit characteristic information of keywords in the submission information of the target event, the analysis dimensionality of the data processing result for the target event can be expanded, the submission information of the target event can be deeply analyzed by the automatically generated data processing result, the labor cost of relevant professionals for analyzing the submission information of the target event is saved, and the generation speed of the report is increased.
The element information and the feature information in fig. 5 are merely exemplary. Any number of element information and feature information may be provided as required for implementation.
Based on the information processing method, the disclosure also provides an information processing device. The apparatus will be described in detail below with reference to fig. 6.
Fig. 6 schematically shows a block diagram of the structure of an information processing apparatus according to an embodiment of the present disclosure.
As shown in fig. 6, the information processing apparatus 600 of this embodiment includes an acquisition module 610, a first processing module 620, a second processing module 630, and a generation module 640.
An obtaining module 610, configured to obtain delivery information of a plurality of target events, where the delivery information of each target event includes structured element information and unstructured description information;
a first processing module 620, configured to process description information of the report information for each target event, and output at least one keyword set corresponding to the target event, where each keyword set includes at least one keyword, and each keyword set corresponds to one keyword attribute;
a second processing module 630, configured to process at least one keyword set corresponding to the target event, and output feature information corresponding to each keyword attribute;
and a generating module 640, configured to generate a data analysis report corresponding to the target event according to the element information and the feature information in the report information of each target event.
According to an embodiment of the present disclosure, the second processing module may include: a first determination unit and a feature information output unit.
And the first determining unit is used for determining the keyword characteristic information, the description parameter information and the target characteristic analysis model corresponding to the keyword set aiming at each keyword set.
And the characteristic information output unit is used for inputting each keyword, the keyword characteristic information and the description parameter information in each keyword set into the target characteristic analysis model and outputting the characteristic information corresponding to the keyword set.
According to an embodiment of the present disclosure, the first determining unit may include: a first determining subunit and a second determining subunit.
The first determining subunit is used for determining the keyword characteristic information according to each keyword in the keyword set.
And the second determining subunit is used for determining the description parameter information and the target characteristic analysis model according to the keyword attributes corresponding to the keyword set.
According to an embodiment of the present disclosure, the keyword feature information includes at least one of: a keyword frequency change characteristic and a keyword appearance frequency sorting characteristic.
According to an embodiment of the present disclosure, the factor information includes at least one of: time information, location information, and resource value information.
According to an embodiment of the present disclosure, the generating module may include: a basic description information generating unit and a data processing result generating unit.
And the basic description information generating unit is used for processing the element information and outputting the basic description information corresponding to the target event.
And the data processing result generating unit is used for inputting the basic description information and the characteristic information into a first preset template and generating a data processing result, wherein the first preset template comprises a first filling area corresponding to the basic description information and a second filling area corresponding to the characteristic information.
According to an embodiment of the present disclosure, the basic description information generating unit may include: and a basic description information generation subunit.
And the basic description information generating subunit is used for filling each element information into a second preset template through a preset target tool to generate basic description information.
According to an embodiment of the present disclosure, the first processing module may include: a keyword extraction unit and a keyword generation unit.
And the keyword extraction unit is used for extracting keywords from the description information through a natural language processing technology and outputting a plurality of keywords.
And the keyword generating unit is used for processing the plurality of keywords and generating at least one keyword set.
According to an embodiment of the present disclosure, the keyword generation unit may include: a keyword attribute determining subunit and a keyword set determining subunit.
A keyword attribute determining subunit, configured to determine a keyword attribute of each keyword based on the description information.
And the keyword set determining subunit is used for generating at least one keyword set according to the keyword attribute of each keyword.
According to the embodiment of the present disclosure, any plurality of the obtaining module 610, the first processing module 620, the second processing module 630, and the generating module 640 may be combined and implemented in one module, or any one of the modules may be split into a plurality of modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of the other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the obtaining module 610, the first processing module 620, the second processing module 630, and the generating module 640 may be implemented at least partially as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or any suitable combination of any of them. Alternatively, at least one of the obtaining module 610, the first processing module 620, the second processing module 630 and the generating module 640 may be at least partially implemented as a computer program module, which when executed may perform a corresponding function.
Fig. 7 schematically shows a block diagram of an electronic device adapted to implement an information processing method according to an embodiment of the present disclosure.
As shown in fig. 7, an electronic device 700 according to an embodiment of the present disclosure includes a processor 701, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)702 or a program loaded from a storage section 708 into a Random Access Memory (RAM) 703. The processor 701 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 701 may also include on-board memory for caching purposes. The processor 701 may comprise a single processing unit or a plurality of processing units for performing the different actions of the method flows according to embodiments of the present disclosure.
In the RAM 703, various programs and data necessary for the operation of the electronic apparatus 700 are stored. The processor 701, the ROM 702, and the RAM 703 are connected to each other by a bus 704. The processor 701 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM 702 and/or the RAM 703. Note that the programs may also be stored in one or more memories other than the ROM 702 and RAM 703. The processor 701 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
Electronic device 700 may also include input/output (I/O) interface 705, which input/output (I/O) interface 705 is also connected to bus 704, according to an embodiment of the present disclosure. The electronic device 700 may also include one or more of the following components connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, and the like; an output section 707 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 708 including a hard disk and the like; and a communication section 709 including a network interface card such as a LAN card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet. A drive 710 is also connected to the I/O interface 705 as needed. A removable medium 711 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 710 as necessary, so that a computer program read out therefrom is mounted into the storage section 708 as necessary.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to embodiments of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example but is not limited to: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM 702 and/or the RAM 703 and/or one or more memories other than the ROM 702 and the RAM 703 described above.
Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flow chart. When the computer program product runs in a computer system, the program code is used for causing the computer system to realize the information processing method provided by the embodiment of the disclosure.
The computer program performs the above-described functions defined in the system/apparatus of the embodiments of the present disclosure when executed by the processor 701. The systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In one embodiment, the computer program may be hosted on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted in the form of a signal on a network medium, distributed, downloaded and installed via the communication section 709, and/or installed from the removable medium 711. The computer program containing program code may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.
In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 709, and/or installed from the removable medium 711. The computer program, when executed by the processor 701, performs the above-described functions defined in the system of the embodiment of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
In accordance with embodiments of the present disclosure, program code for executing computer programs provided by embodiments of the present disclosure may be written in any combination of one or more programming languages, and in particular, these computer programs may be implemented using high level procedural and/or object oriented programming languages, and/or assembly/machine languages. The programming language includes, but is not limited to, programming languages such as Java, C + +, python, the "C" language, or the like. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
The embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments are described separately above, this does not mean that the measures in the embodiments cannot be used in advantageous combination. The scope of the disclosure is defined by the appended claims and equivalents thereof. Various alternatives and modifications can be devised by those skilled in the art without departing from the scope of the present disclosure, and such alternatives and modifications are intended to be within the scope of the present disclosure.

Claims (13)

1. An information processing method comprising:
acquiring reporting information of a plurality of target events, wherein the reporting information of each target event comprises structured element information and unstructured description information;
processing the description information of the report information aiming at each target event, and outputting at least one keyword set corresponding to the target event, wherein each keyword set comprises at least one keyword, and each keyword set corresponds to one keyword attribute;
processing at least one keyword set corresponding to the target event, and outputting feature information corresponding to each keyword attribute;
and generating a data analysis report corresponding to the target event according to the element information and the feature information in the report information of each target event.
2. The method of claim 1, wherein said processing at least one of said keyword sets corresponding to said target event, and outputting feature information corresponding to each of said keyword attributes comprises
For each keyword set, determining keyword feature information, description parameter information and a target feature analysis model corresponding to the keyword set;
and inputting each keyword, the keyword characteristic information and the description parameter information in each keyword set into the target characteristic analysis model, and outputting the characteristic information corresponding to the keyword set.
3. The method of claim 2, wherein the determining keyword feature information, descriptive parameter information, and a target feature analysis model corresponding to the set of keywords comprises:
determining the keyword characteristic information according to each keyword in the keyword set; and
and determining the description parameter information and the target characteristic analysis model according to the keyword attributes corresponding to the keyword set.
4. The method of claim 2, wherein the keyword feature information comprises at least one of: a keyword frequency change characteristic and a keyword appearance frequency sorting characteristic.
5. The method of claim 1, wherein the factor information comprises at least one of: time information, location information, and resource value information.
6. The method of claim 1, wherein the generating a data analysis report corresponding to the target event according to the element information and the feature information in the report information of each target event comprises:
processing the element information and outputting basic description information corresponding to the target event;
and inputting the basic description information and the feature information into a first preset template to generate the data processing result, wherein the first preset template comprises a first filling area corresponding to the basic description information and a second filling area corresponding to the feature information.
7. The method of claim 6, wherein the processing the element information and outputting basic description information corresponding to the target event comprises:
and filling each element information into a second preset template through a preset target tool to generate the basic description information.
8. The method of claim 1, wherein the processing the description information of the submission information and outputting at least one keyword set corresponding to the target event comprises:
extracting keywords from the description information by a natural language processing technology, and outputting a plurality of keywords;
and processing the keywords to generate the at least one keyword set.
9. The method of claim 8, wherein the processing the plurality of keywords to generate the at least one keyword set comprises:
determining the keyword attribute of each keyword based on the description information;
and generating the at least one keyword set according to the keyword attribute of each keyword.
10. An information processing apparatus comprising:
the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring the submission information of a plurality of target events, and the submission information of each target event comprises structured element information and unstructured description information;
the first processing module is used for processing the description information of the report information aiming at each target event and outputting at least one keyword set corresponding to the target event, wherein each keyword set comprises at least one keyword, and each keyword set corresponds to one keyword attribute;
the second processing module is used for processing at least one keyword set corresponding to the target event and outputting feature information corresponding to each keyword attribute;
and the generating module is used for generating a data analysis report corresponding to the target event according to the element information and the characteristic information in the report information of each target event.
11. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to perform the method of any of claims 1-9.
12. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to perform the method of any one of claims 1 to 9.
13. A computer program product comprising a computer program which, when executed by a processor, implements a method according to any one of claims 1 to 9.
CN202111558895.7A 2021-12-17 2021-12-17 Information processing method, device, equipment and storage medium Pending CN114219601A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116304117A (en) * 2023-05-15 2023-06-23 北京睿企信息科技有限公司 Data processing method, system and storage medium for acquiring text information

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116304117A (en) * 2023-05-15 2023-06-23 北京睿企信息科技有限公司 Data processing method, system and storage medium for acquiring text information
CN116304117B (en) * 2023-05-15 2023-09-08 北京睿企信息科技有限公司 Data processing method, system and storage medium for acquiring text information

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