CN114820205A - Timeline security asset management system based on artificial intelligence - Google Patents
Timeline security asset management system based on artificial intelligence Download PDFInfo
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Abstract
The invention discloses a time line security asset management system based on artificial intelligence, and relates to the technical field of security management. The timeline security asset management system based on artificial intelligence comprises a target object management module, a storage module and an intelligent analysis module, wherein the target object management module is used for classifying, collecting and proving information of different storage subject objects, the storage module is used for storing time parameters related to a plurality of subject objects and at least one other metadata value, and the intelligent analysis module is used for searching related storage information in the storage module through the target object management module based on customer requirements. According to the time line security asset management system based on artificial intelligence, the computing unit is arranged in the intelligent analysis module and used for extracting relevant information to evaluate, the occupation ratios of different main objects to customer selection are integrated to obtain a computing result, occupation ratio division is carried out according to the priority degree of time, the occupation ratio of time-priority data can be larger, and the timeliness of the data is exerted.
Description
Technical Field
The invention relates to the technical field of security management, in particular to a timeline security asset management system based on artificial intelligence.
Background
Many types of products and systems for security analysis exist in domestic and foreign markets, but some problems exist, for example, the security analysis mainly evaluates the credit and qualification of the security, and reasonable suggestions are provided for clients without combining the information of the clients; secondly, the division of the clients is only carried out on the basis of simple attributes, and the fine division of different clients cannot be realized, so that proper suggestions cannot be provided for the clients, and the trust of the clients on the provided information is not high; finally, the analysis and management of the certificate data and the client are carried out by adopting a single means, the data is not deeply mined, and the conditions that the certificate is not accurately analyzed by the client, the trust degree of the client on the analysis result is low and the like are caused.
Chinese patent CN114331728A discloses a stock analysis management system, which comprises a stock data storage module for detecting the collected stock data and storing the collected stock data in a corresponding storage area; the client information storage module is used for acquiring the information of the client for analysis and classifying and storing the client information; the security analysis module is used for extracting relevant information from the security data storage module and the customer information storage module for evaluation based on customer requirements to obtain an evaluation result; the information recommendation module is used for generating recommendation information for the client based on the evaluation result; the invention realizes effective management of data and improves the analysis capability of securities, thereby providing accurate recommendation information for customers and improving the trust of the customers on the security analysis management system.
Although this application solves the problems in the background art to some extent, the following problems exist in this application: 1. the timeliness of the obtained various information of the client is not examined because of different obtained channels and different time, and the reference value of the information which is over-aged is not large; 2. after various information is integrated, the prior timeliness of the information cannot be distinguished, and further errors are prone to occur in the judgment of the information.
Disclosure of Invention
The invention aims to provide an artificial intelligence-based timeline security asset management system which is provided with a target object management module, a storage module and an intelligent analysis module, wherein the target object management module, the storage module and the intelligent analysis module are used for respectively finishing the classified collection, classified storage and intelligent matching of data, a security asset management scheme with high matching degree is obtained based on the existing artificial intelligence analysis through data support, a calculation unit is arranged in the intelligent analysis module and used for extracting relevant information for evaluation, the proportion of different main body objects to customer selection is integrated to obtain a calculation result, wherein in the aspect of proportion control, proportion division is carried out according to the priority degree of time, the proportion of time-preferred data can be larger, the timeliness of the data is exerted, and the problems in the background technology are solved.
In order to achieve the purpose, the invention provides the following technical scheme: the time line security asset management system based on artificial intelligence comprises a target object management module, a data acquisition unit and an information evidence obtaining unit, wherein the target object management module is used for classifying, collecting and proving information of different storage main body objects, the target object management module comprises an object dividing unit, an information acquisition unit and an information evidence obtaining unit, the object dividing unit is used for dividing main body objects corresponding to different information, the objects are mainly divided into security markets, market controlled factors, clients and client controlled factors, the information acquisition unit is used for reversely searching information corresponding to different objects after the objects are classified, the information evidence obtaining unit is used for receiving internet first data information acquired by the information acquisition unit, and screening the first data to obtain second data information based on a source end of the first data information;
the storage module is used for storing time parameters associated with a plurality of main objects and at least one other metadata value, and comprises a classification storage unit, a timeline extraction unit, an integration unit and a modification unit, wherein the classification storage unit is used for classifying and storing information of different classification objects, the timeline extraction unit is used for extracting a timeline of second data information, the integration unit is used for organizing all types of information according to the sequence of the timeline, so that the information of the same object takes the timeline as an axis and is stored and displayed according to the chronological order, and the modification unit is used for modifying the same time point conflicting events or the same event conflicting time points in the same object after the integration unit is organized;
the intelligent analysis module is used for searching related storage information in the storage module through the target object management module based on customer requirements, extracting the related information for evaluation, obtaining an evaluation result and generating recommendation information for the customer based on the evaluation result, and comprises an information retrieval unit, a calculation unit, a matching unit and a pushing unit, wherein the information retrieval unit is used for retrieving a corresponding target object, the calculation unit is used for extracting the related information for evaluation and integrating the proportions of different main body objects selected by the customer to obtain a calculation result, the matching unit is used for matching stock products required by the customer according to the calculation result of the calculation unit, suggestions are given to stock products existing by the customer, and the pushing unit accurately delivers the products.
Preferably, the information corresponding to the object mainly includes stock market real-time update data, variable factors of various stock assets, customer self-information and customer associated information, wherein the variable factors of various stock assets at least include income and standard deviation of historical index of target stock futures products, exchange rate between currencies of countries within a period of time and economic macro-factor of target countries within a period of time, the customer self-information includes basic information, credit information, historical position holding condition, risk bearing capacity and fund management condition, and the customer associated information includes family condition related to the customer and self-information condition of family members.
Preferably, the classification storage unit includes a stock market information database, a market controlled factor database, a customer information database, and a customer controlled factor database, and different databases store information of different subject objects.
Preferably, the working process of the timeline extracting unit comprises the following steps:
s11: acquiring the release time of the second data information, and recording the release time as first time;
s12: extracting keywords in the second data information, identifying the time of the second data information, recording as a second time, entering S14, and if the second data information cannot be identified, entering S13;
s13: extracting keywords in the second data information, calculating the occurrence time of the second data information by combining with current politics and news reports, recording the occurrence time as a third time, entering S14, calculating the occurrence time as a fourth time when the calculation cannot be obtained, and entering S15;
s14: judging whether the first time is consistent with the second time or the third time, if so, determining that the first time is the real time of the second data, otherwise, determining that the second time or the third time is the real time of the second data, and marking the real time of the data;
s15: marking the second data information as pending timeline data.
Preferably, the working process of the integration unit comprises the following steps:
s21: extracting real time of different data of the same object, and sequencing according to the time sequence to form a development time line of the main object;
s22: different data correspond to the time point corresponding to the real time, and form the storage data of the object;
s23: when new data is added, extracting the real time of the data, inserting the real time into the development time line of the main object, and correspondingly storing the data content corresponding to the data and the real time.
Preferably, the working process of the modification unit includes the following steps:
s31: checking whether a development time line in the integration unit has a coincidence time point, judging whether data on the coincidence time point conforms to a set rule, if so, not modifying the data, and if not, extracting the time point and the data corresponding to the time point;
s32: judging the extracted data by using a learning model, judging the data to be correct, returning the extracted data without modification or judgment error, sending the data to a worker, manually carrying out secondary judgment, returning the error data after modification, and manually selecting and deleting the data which cannot be judged;
s33: receiving undetermined time line data, judging the authenticity of the data by a learning model, deleting the data with a judgment result of data false or error, judging the data with a judgment result of true or correct data, reserving the data, searching through the whole network, giving the true time of the undetermined time line data, and returning the data.
Preferably, the working process of the computing unit includes the following steps:
s41: setting a corresponding total proportion factor for the main object, wherein the sum of all the total proportion factors is 1;
s42: setting a time-interval ratio factor according to corresponding data information in different main objects;
s43: calculating the risk bearing capacity and credit worthiness of the client according to the retrieved related storage information to obtain the investment capacity value of the client;
s44: for security products already held by a customer that do not meet the customer investment ability value, the calculation unit gives relevant suggestions.
Preferably, the time-interval ratio factor is defined as: and segmenting the time line, wherein each segment of time line is provided with a corresponding proportion factor.
Preferably, the S42 specifically includes the following steps:
s421: extracting time lines of different subject objects, and taking nodes on different time lines to divide time periods;
s422: setting time period priority levels according to the distance from the current time, wherein the time period closest to the current time has the highest priority level;
s423: and sequentially setting the ratio according to the priority level, wherein the time period with the highest priority level has the highest ratio.
Preferably, the node is a turning point of data or an event, or the node is a segmentation point of equal time period.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention provides an artificial intelligence-based timeline security asset management system which is provided with a target object management module, a storage module and an intelligent analysis module, wherein the target object management module, the storage module and the intelligent analysis module are used for respectively finishing the work of classified collection, classified storage and intelligent matching of data;
2. the invention provides an artificial intelligence-based timeline security asset management system, wherein in the aspect of data storage, a storage module comprises a classification storage unit, a timeline extraction unit, an integration unit and a modification unit, the timeline extraction unit is used for extracting the timeline of second data information, the integration unit is used for organizing various information according to the sequence of the timeline, so that the information of the same object is stored and displayed in a time-based mode according to the year with consistent timeline, and the modification unit is used for modifying the conflicted events at the same time point or the conflicted time points of the same event in the same object after integration, storing the data in the timeline mode, so that the thought is clear and convenient to view;
3. the time line security asset management system based on artificial intelligence is characterized in that a computing unit is arranged in an intelligent analysis module and used for extracting relevant information to evaluate, and integrating the proportion of different subject objects to customer selection to obtain a computing result, wherein the proportion is divided according to the priority degree of time in the aspect of proportion control, the proportion of time-priority data can be larger, and the timeliness of the data is exerted.
Drawings
FIG. 1 is a block diagram of a system of the present invention;
FIG. 2 is a diagram of the working steps of the timeline extraction unit of the present invention;
FIG. 3 is a timeline extraction unit workflow diagram of the present invention;
FIG. 4 is a diagram of the working steps of the integration unit of the present invention;
FIG. 5 is a diagram of the steps of the modification unit of the present invention;
FIG. 6 is a flow chart of a modification unit operation of the present invention;
FIG. 7 is a diagram of the operational steps of the computing unit of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the artificial intelligence based timeline security asset management system includes a target object management module for classifying, collecting and proving information of different storage subject objects, the target object management module includes an object classifying unit for classifying subject objects corresponding to different information, the objects are mainly classified into security markets, market controlled factors, customers and customer controlled factors, an information collecting unit for reversely searching information corresponding to different objects after object classification, the information corresponding to the objects mainly includes real-time updated data of the security markets, variable factors of various security assets, information of the customers and associated information of the customers, wherein the variable factors of various security assets at least include profits and standard deviations of historical indexes of target security future products, and the like, The system comprises an information evidence obtaining unit, an information acquiring unit, an information obtaining unit and an information obtaining unit, wherein the information obtaining unit is used for obtaining the information of the first data of the internet collected by the information collecting unit, and screening the first data to obtain the second data information based on the source end of the first data information;
the storage module is used for storing time parameters and at least one other metadata value which are associated with a plurality of main objects, and comprises a classification storage unit, a timeline extraction unit, an integration unit and a modification unit, wherein the classification storage unit is used for classifying and storing information of different classification objects, the classification storage unit comprises a stock market information database, a market controlled factor database, a customer information database and a customer controlled factor database, the different databases store information of different main objects, the classification storage is convenient to search when called, the timeline extraction unit is used for extracting a timeline of second data information, and the working process of the timeline extraction unit comprises the following steps:
s11: acquiring the release time of the second data information, and recording the release time as first time;
s12: extracting keywords in the second data information, identifying the time of the second data information, recording as a second time, entering S14, and if the second data information cannot be identified, entering S13;
s13: extracting keywords in the second data information, calculating the occurrence time of the second data information by combining with current politics and news reports, recording the occurrence time as a third time, entering S14, calculating the occurrence time as a fourth time when the calculation cannot be obtained, and entering S15;
s14: judging whether the first time is consistent with the second time or the third time, if so, determining that the first time is the real time of the second data, otherwise, determining that the second time or the third time is the real time of the second data, and marking the real time of the data;
s15: marking the second data information as pending timeline data.
The integration unit is used for organizing various information according to the sequence of the time line, so that the same object information is stored and displayed according to the chronological order by taking the time line as an axis, and the working process of the integration unit comprises the following steps:
s21: extracting real time of different data of the same object, and sequencing according to the time sequence to form a development time line of the main object;
s22: different data correspond to the time point corresponding to the real time and form the storage data of the object;
s23: when new data is added, extracting the real time of the data, inserting the real time into the development time line of the main object, and correspondingly storing the data content corresponding to the data and the real time.
The modification unit is used for modifying the same time point conflicting events or the same event conflicting time points in the same object after the integration unit is structured, and the working process of the modification unit comprises the following steps:
s31: checking whether a development time line in the integration unit has a coincidence time point, judging whether data on the coincidence time point conforms to a set rule, if so, not modifying the data, and if not, extracting the time point and the data corresponding to the time point;
s32: judging the extracted data by using a learning model, judging the data to be correct, returning the extracted data without modification or judgment error, sending the data to a worker, manually carrying out secondary judgment, returning the error data after modification, and manually selecting and deleting the data which cannot be judged;
s33: receiving undetermined time line data, judging the authenticity of the data by a learning model, deleting the data with a judgment result of data false or error, judging the data with a judgment result of true or correct data, reserving the data, searching through the whole network, giving the true time of the undetermined time line data, and returning the data.
The intelligent analysis module is used for searching related storage information in the storage module through the target object management module based on customer requirements, extracting the related information for evaluation, obtaining an evaluation result and generating recommendation information for customers based on the evaluation result, and comprises an information retrieval unit, a calculation unit, a matching unit and a pushing unit, wherein the information retrieval unit is used for retrieving corresponding target objects, the calculation unit is used for extracting the related information for evaluation, integrating the proportions of different main body objects selected by the customers to obtain the calculation result, the matching unit is used for matching stock products required by the customers according to the calculation result of the calculation unit and giving suggestions to the stock products existing in the customers, the pushing unit accurately delivers the products, and the working process of the calculation unit comprises the following steps:
s41: setting a corresponding total proportion factor for the main object, wherein the sum of all the total proportion factors is 1;
s42: setting a time-interval-based ratio factor according to corresponding data information in different subject objects, wherein the time-interval-based ratio factor is defined as: segmenting a time line, wherein each segment of time line is provided with a corresponding proportion factor, and the method specifically comprises the following steps:
s421: extracting timelines of different main objects, taking nodes on different timelines, wherein the nodes are turning points of data or events, or the nodes are segmentation points of equal time periods, and dividing the time periods, the turning points of the events, such as time points of national economic policy change, time points of a large number of assets entering of customers, time points of family accidents of the customers and the like, the segmentation points of the equal time periods, namely under the condition that no large accidents occur, dividing according to the equal time periods, and the end points of the time periods are marked as the segmentation points;
s422: setting time period priority levels according to the distance from the current time, wherein the time period closest to the current time has the highest priority level;
s423: setting the ratio values in sequence according to the priority levels, wherein the time period with the highest priority level has the highest ratio value;
s43: calculating the risk bearing capacity and credit worthiness of the client according to the retrieved related storage information to obtain the investment capacity value of the client;
s44: for security products already held by customers that do not meet the customer investment ability value, the calculation unit gives relevant recommendations to assist the customer in reallocating assets.
The working process is as follows: the information acquisition unit collects data information at different ports, the information evidence obtaining unit is used for receiving first data information of the internet acquired by the information acquisition unit, screening the first data based on the source end of the first data information to obtain second data information, and storing the second data information corresponding to corresponding storage databases in the storage module according to the classification of the object dividing unit, the time line extraction unit, the integration unit and the modification unit extract time lines from data in different databases, and arrange various information according to the sequence of the time lines, so that the same object information is stored and displayed in a chronological time-based mode with consistent time lines, and error data is eliminated or modified, when in use, based on customer requirements, related storage information in the storage module is searched through the target object management module, and related information is extracted for evaluation, and during evaluation, a time priority principle is adopted to obtain an evaluation result, and recommendation information for the client is generated based on the evaluation result.
In summary, the following steps: the time line security asset management system based on artificial intelligence is provided with a target object management module, a storage module and an intelligent analysis module, which are used for respectively finishing the work of classified collection, classified storage and intelligent matching of data, and obtaining a security asset management scheme with high matching degree based on the current artificial intelligence analysis through data support; in the aspect of data storage, the storage module comprises a classification storage unit, a timeline extraction unit, an integration unit and a modification unit, wherein the timeline extraction unit is used for extracting the timeline of the second data information, the integration unit is used for organizing various information according to the sequence of the timeline, so that the information of the same object is stored and displayed in a chronological time-based mode with consistent timelines, and the modification unit is used for modifying the events conflicting at the same time point or the time points conflicting with the same event in the same object after integration, so that the data is stored in the timeline mode, the thought is clear, and the data is convenient to view; and a calculation unit is arranged in the intelligent analysis module and used for extracting relevant information for evaluation, integrating the proportion of different subject objects to customer selection and obtaining a calculation result, wherein in the aspect of proportion control, proportion division is carried out according to the priority degree of time, the proportion of time-preferred data can be larger, and the timeliness of the data is exerted.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be able to cover the technical solutions and the inventive concepts of the present invention within the technical scope of the present invention.
Claims (10)
1. The time line security asset management system based on artificial intelligence is characterized by comprising a target object management module, an information acquisition unit and an information evidence finding unit, wherein the target object management module is used for classifying, collecting and proving information of different storage main body objects, the target object management module comprises an object dividing unit, an information acquisition unit and an information evidence finding unit, the object dividing unit is used for dividing main body objects corresponding to different information, the objects are mainly divided into security markets, market controlled factors, clients and client controlled factors, the information acquisition unit is used for reversely searching the information corresponding to the different objects after the objects are classified, the information evidence finding unit is used for receiving internet first data information acquired by the information acquisition unit, and screening the first data to obtain second data information based on the source end of the first data information;
the storage module is used for storing time parameters associated with a plurality of main objects and at least one other metadata value, and comprises a classification storage unit, a timeline extraction unit, an integration unit and a modification unit, wherein the classification storage unit is used for classifying and storing information of different classification objects, the timeline extraction unit is used for extracting a timeline of second data information, the integration unit is used for organizing all types of information according to the sequence of the timeline, so that the information of the same object takes the timeline as an axis and is stored and displayed according to the chronological order, and the modification unit is used for modifying the same time point conflicting events or the same event conflicting time points in the same object after the integration unit is organized;
the intelligent analysis module is used for searching related storage information in the storage module through the target object management module based on customer requirements, extracting the related information for evaluation, obtaining an evaluation result and generating recommendation information for the customer based on the evaluation result, and comprises an information retrieval unit, a calculation unit, a matching unit and a pushing unit, wherein the information retrieval unit is used for retrieving a corresponding target object, the calculation unit is used for extracting the related information for evaluation, integrating the proportions of different main body objects selected by the customer to obtain a calculation result, the matching unit is used for matching stock products required by the customer according to the calculation result of the calculation unit, suggestions are given to stock products existing by the customer, and the pushing unit accurately delivers the products.
2. The artificial intelligence based timeline security asset management system of claim 1, wherein: the information corresponding to the object mainly comprises real-time updating data of a stock market, variable factors of various stock assets, client self information and client associated information, wherein the variable factors of various stock assets at least comprise income and standard deviation of historical indexes of target stock futures products, exchange rates among currencies of countries within a period of time and economic macroscopic factors of the target countries within a period of time, the client self information comprises basic information, credit information, historical position holding conditions, risk bearing capacity and fund management conditions, and the client associated information comprises client-related family conditions and family member self information conditions.
3. The artificial intelligence based timeline security asset management system of claim 1, wherein: the classified storage unit comprises a stock market information database, a market controlled factor database, a customer information database and a customer controlled factor database, and different databases store information of different subject objects.
4. The artificial intelligence based timeline security asset management system of claim 1, wherein: the working process of the time line extraction unit comprises the following steps:
s11: acquiring the release time of the second data information, and recording the release time as first time;
s12: extracting keywords in the second data information, identifying the time of the second data information, recording as a second time, entering S14, and if the second data information cannot be identified, entering S13;
s13: extracting keywords in the second data information, calculating the occurrence time of the second data information by combining with current politics and news reports, recording the occurrence time as a third time, entering S14, recording the occurrence time as a fourth time when the calculation cannot be obtained, and entering S15;
s14: judging whether the first time is consistent with the second time or the third time, if so, determining that the first time is the real time of the second data, otherwise, determining that the second time or the third time is the real time of the second data, and marking the real time of the data;
s15: marking the second data information as pending timeline data.
5. The artificial intelligence based timeline security asset management system of claim 4, wherein: the working process of the integration unit comprises the following steps:
s21: extracting real time of different data of the same object, and sequencing according to the time sequence to form a development time line of the main object;
s22: different data correspond to the time point corresponding to the real time and form the storage data of the object;
s23: when new data is added, extracting the real time of the data, inserting the real time into the development time line of the main object, and correspondingly storing the data content corresponding to the data and the real time.
6. The artificial intelligence based timeline security asset management system of claim 5, wherein: the working process of the modification unit comprises the following steps:
s31: checking whether a development time line in the integration unit has a coincidence time point, judging whether data on the coincidence time point conforms to a set rule, if so, not modifying the data, and if not, extracting the time point and the data corresponding to the time point;
s32: judging the extracted data by using a learning model, judging that the data is correct, returning the extracted data, sending the data to a worker without modification and judgment errors, manually carrying out secondary judgment, returning the error data after modification, and manually selecting and deleting the data which cannot be judged;
s33: receiving undetermined time line data, judging the authenticity of the data by a learning model, deleting the data with a judgment result of data false or error, judging the data with a judgment result of true or correct data, reserving the data, searching through the whole network, giving the true time of the undetermined time line data, and returning the data.
7. The artificial intelligence based timeline security asset management system of claim 1, wherein: the working process of the computing unit comprises the following steps:
s41: setting a corresponding total proportion factor for the main object, wherein the sum of all the total proportion factors is 1;
s42: setting a time-interval ratio factor according to corresponding data information in different main objects;
s43: calculating the risk bearing capacity and credit worthiness of the client according to the retrieved related storage information to obtain the investment capacity value of the client;
s44: for security products already held by a customer that do not meet the customer investment ability value, the calculation unit gives relevant suggestions.
8. The artificial intelligence based timeline security asset management system of claim 7, wherein: the time-interval-based ratio factor is defined as: and segmenting the time line, wherein each segment of time line is provided with a corresponding proportion factor.
9. The artificial intelligence based timeline security asset management system of claim 8, wherein: the S42 specifically includes the following steps:
s421: extracting time lines of different subject objects, and taking nodes on different time lines to divide time periods;
s422: setting time period priority levels according to the distance from the current time, wherein the time period closest to the current time has the highest priority level;
s423: and sequentially setting the ratio according to the priority level, wherein the time period with the highest priority level has the highest ratio.
10. The artificial intelligence based timeline security asset management system of claim 9, wherein: the node is a turning point of data or an event, or the node is a segmentation point of an equal time period.
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