CN111127213A - Information processing method and device, storage medium and electronic equipment - Google Patents
Information processing method and device, storage medium and electronic equipment Download PDFInfo
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- CN111127213A CN111127213A CN201911135350.8A CN201911135350A CN111127213A CN 111127213 A CN111127213 A CN 111127213A CN 201911135350 A CN201911135350 A CN 201911135350A CN 111127213 A CN111127213 A CN 111127213A
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Abstract
The disclosure belongs to the technical field of information processing computers, and relates to an information processing method and device, a computer readable storage medium and electronic equipment. The method comprises the following steps: acquiring target negative public opinion data, and extracting a first target financial type related to the target negative public opinion data; comparing the position taken data of the first target financial type and the second financial type to determine a second target financial type related to the first target financial type; calculating an evaluation score of a second target financial type based on the first target financial type and the position taken data to rate ranking information of the second target financial type; determining a target prompt message template corresponding to the grade message based on a preset prompt message template; and generating an early warning prompt report according to the target prompt information template and the evaluation score. The method and the system improve the overall risk management and control capacity and management and control efficiency of financial products, improve the accuracy and precision of early warning, provide help for adjusting management and control strategies, and guarantee the safety of financial assets.
Description
Technical Field
The present disclosure relates to the field of information processing computer technologies, and in particular, to an information processing method, an information processing apparatus, a computer-readable storage medium, and an electronic device.
Background
The investment of the annuity combination has the characteristics of long investment period and high risk aversion preference on the whole, so that the safety and steady income is pursued by the annuity combination. In the current market, negative public opinion data is captured and analyzed more and more, and the influence of the negative public opinion data on investment markets, particularly the annual fund combined investment is increasingly prominent. Investment loss cases caused by the fact that the investors do not recognize negative public opinion trends in time are numerous and countless.
At present, a user can access negative public opinion information through a third party or manually collect the negative public opinion information from a plurality of channels such as market public media and the like by self, then carry out integration processing and keyword extraction on the negative public opinion information, and further use related security information of a related main body to check back position data of an annuity combination. The method is not enough for correlation of negative public opinion information and position taking data of the annuity combination and depth and breadth of analysis, and meanwhile, a quantifiable model is lacked for visually and quantitatively analyzing position taking risk levels of the annuity combination, and subsequent risk control operation of standardizing the annuity combination by an intelligent risk control technology is lacked.
In view of the above, there is a need in the art to develop a new information processing method and apparatus.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
An object of the present disclosure is to provide an information processing method, an information processing apparatus, a computer-readable storage medium, and an electronic device, which overcome, at least to some extent, the problem of lack of intelligent prediction of risk level of annuity combination due to limitations of related art.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the disclosure.
According to a first aspect of embodiments of the present invention, there is provided an information processing method including: obtaining target negative public opinion data, and extracting a first target financial type related to the target negative public opinion data; comparing the position taken data of the first target financial type and the second financial type to determine a second target financial type related to the first target financial type; calculating an evaluation score of the second target financial type based on the first target financial type and the position data to predict grade information of the second target financial type; determining a target prompt message template corresponding to the grade message based on a preset prompt message template; and generating an early warning prompt report according to the target prompt information template and the evaluation score, and sending the early warning prompt report.
In an exemplary embodiment of the invention, said calculating an assessment score for said second target financial type based on said first target financial type and said position data comprises: acquiring a first calculation factor for calculating the evaluation score, and acquiring a first preset weight corresponding to the first calculation factor; determining a first calculated score for the second target financial type based on the first target financial type and the position data; and determining the first preset weight corresponding to the first calculated score, and determining the evaluation score of the second target financial type according to the first calculated score and the first preset weight.
In an exemplary embodiment of the invention, the determining a first calculated score for the second target financial type based on the first target financial type and the position data comprises: determining a first indicator of the first calculation factor based on the first target financial type and the position data; and if the first index meets a first preset condition, determining the first calculation score according to a first mapping relation between the first preset condition and the first preset score.
In an exemplary embodiment of the invention, the method further comprises: comparing the position taken data of the first target financial type with the position taken data of the second financial type, and determining that the first target financial type is not included in the second financial type according to a comparison result; acquiring a second calculation factor for calculating the evaluation score, and acquiring a second preset weight of the second calculation factor; determining a second calculated score for the first target financial type from the target negative public opinion data; and determining the second preset weight corresponding to the second calculated score, and determining the evaluation score of the second target financial type according to the second calculated score and the second preset weight.
In an exemplary embodiment of the invention, the determining the second calculated score of the first target financial type according to the target negative public opinion data includes: determining a second index of the second calculation factor according to the target negative public opinion data; and if the second index meets a second preset condition, determining a second calculated score according to a second mapping relation between the second preset condition and the second preset score.
In an exemplary embodiment of the invention, the obtaining target negative public opinion data includes: acquiring initial negative public opinion data, and performing structural processing on the initial negative public opinion data to obtain candidate negative public opinion data; extracting keywords in the candidate negative public opinion data to determine the target negative public opinion data.
In an exemplary embodiment of the invention, the extracting the first target financial type related to the target negative public opinion data includes: performing main body recognition on the target negative public opinion data to obtain a main body related to the target negative public opinion data; extracting securities in the subject that are related to the target negative public opinion data to determine as the first target financial type.
According to a second aspect of the embodiments of the present invention, there is provided an information processing apparatus including: a security determination module configured to obtain target negative public opinion data and extract a first target financial type related to the target negative public opinion data; a data comparison module configured to compare the taken position data of the first target financial type and the second financial type to determine a second target financial type related to the first target financial type; a grade evaluation module configured to calculate an evaluation score of the second target financial type based on the first target financial type and the position data to predict grade information of the second target financial type.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus including: a processor and a memory; wherein the memory has stored thereon computer-readable instructions which, when executed by the processor, implement the information processing method of any of the above-described exemplary embodiments.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the information processing method in any of the above-described exemplary embodiments.
As can be seen from the foregoing technical solutions, the information processing method, the information processing apparatus, the computer storage medium, and the electronic device in the exemplary embodiments of the present invention have at least the following advantages and positive effects:
in the method and the device provided by the exemplary embodiment of the disclosure, through the monitoring and the determined first target financial type of the target negative public opinion data, the grade information evaluation of the second target financial type can be further realized. On one hand, target negative public opinion data are known in time, so that the analysis result can be applied to the risk management and control aspect of the second financial type more deeply and effectively, and the overall risk management and control capacity and management and control efficiency of the second financial type are improved; on the other hand, the level information of the second target financial type is intelligently pre-judged, the accuracy and precision of the early warning of the second financial type are improved, information help is provided for adjusting the management and control strategy of the second financial type in time, and the safety of financial assets is guaranteed.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty.
FIG. 1 schematically illustrates a flow chart of a method of information processing in an exemplary embodiment of the disclosure;
fig. 2 schematically illustrates a flow chart of a method for obtaining target negative public opinion data in an exemplary embodiment of the present disclosure;
FIG. 3 schematically illustrates a flow chart of a method of determining a first target financial type in an exemplary embodiment of the disclosure;
FIG. 4 schematically illustrates a flowchart of a method of calculating an assessment score for a second target financial type in an exemplary embodiment of the disclosure;
FIG. 5 schematically illustrates a flow chart of a method of determining a first calculated score in an exemplary embodiment of the disclosure;
FIG. 6 schematically illustrates a flow chart of another method of determining a valuation score for a second target financial type in an exemplary embodiment of the disclosure;
FIG. 7 schematically illustrates a flowchart of a method of determining a second calculated score in an exemplary embodiment of the disclosure;
fig. 8 schematically shows a block diagram of an information processing method in an application scenario in an exemplary embodiment of the present disclosure;
fig. 9 schematically shows a flowchart of an information processing method in an application scenario in an exemplary embodiment of the present disclosure;
fig. 10 schematically shows a configuration diagram of an information processing apparatus in an exemplary embodiment of the present disclosure;
fig. 11 schematically illustrates an electronic device for implementing an information processing method in an exemplary embodiment of the present disclosure;
fig. 12 schematically illustrates a computer-readable storage medium for implementing an information processing method in an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and the like. In other instances, well-known technical solutions have not been shown or described in detail to avoid obscuring aspects of the present disclosure.
The terms "a," "an," "the," and "said" are used in this specification to denote the presence of one or more elements/components/parts/etc.; the terms "comprising" and "having" are intended to be inclusive and mean that there may be additional elements/components/etc. other than the listed elements/components/etc.; the terms "first" and "second", etc. are used merely as labels, and are not limiting on the number of their objects.
Furthermore, the drawings are merely schematic illustrations of the present disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus their repetitive description will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities.
In view of the problems in the related art, the present disclosure provides an information processing method. Fig. 1 shows a flow chart of an information processing method, which, as shown in fig. 1, comprises at least the following steps:
and S110, acquiring target negative public opinion data, and extracting a first target financial type related to the target negative public opinion data.
And S120, comparing the position taken data of the first target financial type and the second financial type to determine the second target financial type related to the first target financial type.
And S130, calculating an evaluation score of the second target financial type based on the first target financial type and the position data so as to estimate the grade information of the second target financial type.
And S140, determining a target prompt message template corresponding to the grade message based on the preset prompt message template.
And S150, generating an early warning prompt report according to the target prompt information template and the evaluation score.
In an exemplary embodiment of the present disclosure, further rating information evaluation for a second target financial type may be achieved through the monitoring and determination of the target negative public opinion data for the first target financial type. On one hand, target negative public opinion data are known in time, so that the analysis result can be applied to the risk management and control aspect of the second financial type more deeply and effectively, and the overall risk management and control capacity and management and control efficiency of the second financial type are improved; on the other hand, the level information of the second target financial type is intelligently pre-judged, the accuracy and precision of the early warning of the second financial type are improved, information help is provided for adjusting the management and control strategy of the second financial type in time, and the asset safety of the second financial type is guaranteed.
The respective steps of the information processing method are explained in detail below.
In step S110, target negative public opinion data is obtained, and a first target financial type related to the target negative public opinion data is extracted.
In an exemplary embodiment of the disclosure, fig. 2 shows a flowchart of a method for obtaining target negative public opinion data, as shown in fig. 2, the method at least includes the following steps: in step S210, initial negative public opinion data is obtained, and the initial negative public opinion data is subjected to a structuring process to obtain candidate negative public opinion data. Generally, the client can be received by the third-party information server, and the external negative public opinion information data is accessed and uploaded in real time for storage. Therefore, the overall negative public opinion data, namely the initial negative public opinion data, can be acquired in real time. However, the content of the initial negative public opinion data is complex and large, and therefore, the initial negative public opinion data can be structured, useful content of a certain website or application can be captured, and the captured useful content can be determined as candidate negative public opinion data. For example, the capturing frequency may be a preset time or a real time, which is not limited in this exemplary embodiment. For example, when an event of the initial negative public opinion data is identified, the text information of the news information itself, and the text information and the location information of the event subject can be used to input the characteristics into the classification algorithm for event identification, so as to obtain candidate negative public opinion data affecting securities.
In step S220, keywords in the candidate negative public opinion data are extracted to determine the target negative public opinion data. The keywords in the candidate negative public opinion data are extracted, for example, the keywords may include news titles, news content summaries, news Uniform Resource Locators (URLs), early warning types, severity, release time, sources, authors, and associated subjects, which is not limited in this exemplary embodiment. These keywords are the target negative public opinion data related to the securities.
In the exemplary embodiment, the purpose of structuring the acquired initial negative public opinion data and extracting the target negative public opinion data is to acquire the negative public opinion data in real time and determine the public opinion tendency related to the first target financial type in time.
FIG. 3 shows a flow diagram of a method of determining a first target financial type, as shown in FIG. 3, the method comprising at least the steps of: in step S310, subject recognition is performed on the target negative public opinion data to obtain subjects related to the target negative public opinion data. For example, when identifying a subject, news information and text features of the subject can be used to obtain the subject related to the target negative public opinion data. The principal may be an issuing principal, such as an issuing entity, issuing company, or the like, that qualifies for the issuance of securities. For the case where only securities are mentioned in the target negative public opinion data, and the issuing principal is not designated, mapping may be performed to the corresponding issuing principal. For example, the major industry and region can be identified by using a professional dictionary of the financial industry according to the identified major.
In step S320, securities related to the target negative public opinion data in the subject are extracted to be determined as a first target financial type. Securities related to the subject, such as stocks, bonds, pension products, non-standard products, and the like, are extracted and determined as a first target financial type.
In the present exemplary embodiment, a method of determining a first target financial type is given, which improves the accuracy and efficiency of extracting securities to achieve the effect of efficient public opinion monitoring.
In step S120, the taken position data of the first target financial type and the second financial type are compared to determine a second target financial type related to the first target financial type.
In an exemplary embodiment of the present disclosure, the second financial type may be, for example, an investment project, such as an enterprise annuity, a plurality of which is a combination of enterprise annuities. In the annual fund management activity of an enterprise, a trustee can analyze and supervise and manage a large amount of investment conditions of a second financial type of the enterprise, and different second financial types have different management policies and need a large amount of data and index calculation as a basis. These second financial type data and metrics, which may include annuity products, hold and rate of return, etc., are aggregated to arrive at a combined estimate table for the second financial type.
And comparing the position taken data in the second financial type estimation table with the first target financial type, and determining the second financial type holding the first target financial type as the second target financial type.
In step S130, an evaluation score of the second target financial type is calculated based on the first target financial type and the position data to estimate the level information of the second target financial type.
In an exemplary embodiment of the present disclosure, FIG. 4 shows a flowchart of a method of calculating an assessment score for a second target financial type, as shown in FIG. 4, the method comprising at least the steps of: in step S410, a first type factor association table stored in a database is extracted; the first type factor association table comprises a first target financial type, a first calculation factor allocated to the first target financial type and a first preset weight corresponding to the first calculation factor. A factor database storing a first type factor association table is established, and a reference value of a first calculation factor, that is, a first preset weight is set. The first calculated factor and the corresponding first preset weight may be stored in a first type factor association table in a factor database. For example, the first calculation factor includes a combined size/proportion of taken position, a combined rate of return for taken position assets, a combined overall risk indicator value, a combined overall rate of return indicator, and a security early warning level. The combined overall risk index value may include a maximum withdrawal rate, a fluctuation rate, and the like, which is not particularly limited in this exemplary embodiment. And setting corresponding first preset weight aiming at the first calculation factor. Wherein, the setting size of first preset weight can be formulated according to the demand of the applicant. For example, if the applicant pays attention to the profitability, the first preset weight corresponding to the profitability index can be set to be larger than other first calculation factors; if the investment plan of the applicant is conservative, the first preset weight corresponding to the combined overall risk index value can be set to be larger than other first calculation factors. In addition, the preset weight may also be set according to other conditions, and this exemplary embodiment is not particularly limited in this respect.
In step S420, a first calculation factor for calculating the evaluation score is obtained from the first type factor association table, and a first preset weight corresponding to the first calculation factor is obtained. Since the first calculation factor and the corresponding first preset weight are stored in the first type factor association table in the factor database, the first calculation factor and the first preset weight can be obtained from the first type factor association table when the evaluation score is calculated.
In step S430, a first calculated score for a second target financial type is determined based on the first target financial type and the position taken data. Fig. 5 shows a flow diagram of a method of determining a first calculated score, as shown in fig. 5, comprising at least the steps of: in step S510, a first index of a first calculation factor is determined according to the first target financial type and the position taken data. The first index may be an index calculated in real time by the system corresponding to the first calculation factor. For example, for the combined overall profitability indicator in the first calculation factor, the corresponding first indicator may be calculated to be 3% according to the provided combined estimation table data.
In step S520, if the first indicator satisfies a first predetermined condition, a first calculated score is determined according to a first mapping relationship between the first predetermined condition and the first predetermined score. The first preset condition may be a threshold value freely set for the first calculation factor, and different score criteria are given to different threshold values. In view of the plurality of first calculation factors, correspondingly, a plurality of first preset conditions are also set. For example, for the combined overall profitability index in the first calculation factor, when the first index is 3%, the corresponding first preset condition may be satisfied, that is, if the first index is greater than 1%, the score may be 5. Then the first index 3% is greater than 1%, which may be assigned a first calculated score of 5.
In step S440, a first predetermined weight corresponding to the first calculated score is determined, and an evaluation score of the second target financial type is determined according to the first calculated score and the first predetermined weight. According to the determined first calculated score and the corresponding first preset weight, the first calculated score and the corresponding first preset weight can be calculated, and the calculation result is determined as the evaluation score of the second target financial type.
In the present exemplary embodiment, the first calculated score for the second target financial type may be determined by a preset condition set for the first calculation factor. On one hand, the preset conditions can be determined according to a large amount of previous data and human experience, so that the accuracy is higher and the practicability is better; on the other hand, the calculation mode is simple, and the operability is extremely strong.
In an alternative embodiment, FIG. 6 shows a flow diagram of another method for determining a valuation score for a second target financial type, as shown in FIG. 6, the method including at least the steps of: in step S610, the taken position data of the first target financial type and the second financial type are compared, and it is determined that the first target financial type is not included in the second financial type according to the comparison result. Comparing the taken position data in the second financial type estimation table with the first target financial type may cause the taken position data not to hold the first target financial type.
In step S620, extracting a second type factor association table stored in the database; the second type factor association table includes a second target financial type, a second calculation factor assigned to the second target financial type, and a second preset weight corresponding to the second calculation factor. And establishing a factor database for storing a second type factor association table, and setting a reference value of a second calculation factor, namely a second preset weight. The second calculated factor and the corresponding second preset weight may be stored in a second type factor association table in the factor database. It should be noted that the factor database may be the same as the factor database storing the first calculation factor, or may be two databases independent from each other, which is not limited in this exemplary embodiment.
In step S630, a second calculation factor for calculating the evaluation score is obtained from the second factor association table, and a second preset weight of the second calculation factor is obtained. When the first target financial type is not held in the taken position data, a second calculation factor for calculating the evaluation score may be obtained. For example, the second calculation factor includes a security emotional propensity, a security emotional rating, and a subject rating. The securities emotional tendency can comprise three types of positive, middle and negative; the security emotion levels can include five stars, four stars, three stars, two stars and one star; the subject evaluation level may include four types of positive, negative, stable, and developing, and may include other types, which is not particularly limited in the present exemplary embodiment. And setting corresponding second preset weight aiming at the second calculation factor. The setting size of the second preset weight may be determined according to a main aspect of the negative public opinion data, and this exemplary embodiment is not particularly limited thereto.
In step S640, a second calculated score of the first target financial type is determined according to the target negative public opinion data. In an alternative embodiment, fig. 7 shows a flow chart of a method of determining a second calculated score, as shown in fig. 7, the method comprising at least the steps of: in step S710, a second index of a second calculation factor is determined according to the target negative public opinion data. The second index may be an index calculated in real time by the system corresponding to the second calculation factor. For example, for the security emotional tendency in the second calculation factor, the number of the emotion keywords in the target negative public sentiment data may be obtained as the second index of the security emotional tendency.
In step S720, if the second index satisfies a second predetermined condition, a second calculated score is determined according to a second mapping relationship between the second predetermined condition and a second predetermined score. In view of the plurality of second calculation factors, correspondingly, a plurality of second preset conditions are also set. For example, if there are 5 emotion keywords in the target negative public opinion data, which are all positive emotion keywords, and the second preset condition may be that if the number of positive emotion keywords is greater than 3, the second preset score is 6, so that it may be determined that the second calculated score is 6.
In step S650, a second preset weight corresponding to the second calculated score is determined, and an evaluation score of the second target financial type is determined according to the second calculated score and the second preset weight. And calculating the determined second calculated score and the corresponding second preset weight according to the determined second calculated score and the corresponding second preset weight, and determining the calculation result as the evaluation score of the second target financial type.
In the present exemplary embodiment, by judging a case where the first target financial type is not included in the second financial type, a method of calculating the evaluation score in such a case is determined. On one hand, the second calculated value is determined and the second preset weight is set according to previous large amount of data and human experience, so that the accuracy is higher and the practicability is better; on the other hand, the method for calculating the evaluation score is simple and has strong operability.
The calculated evaluation score may be a basis for determining the level information of the second target financial type, and thus, the level information of the second target financial type may be estimated at this time. For example, there may be several levels of information, such as severity, high, medium, low, and none, or there may be other levels of information, which is not limited in this exemplary embodiment.
In step S140, a target prompt information template corresponding to the level information is determined based on a preset prompt information template.
In an exemplary embodiment of the present disclosure, a corresponding preset prompt information template is preset for different level information, so as to issue prompt information to the combined administering person. For example, if the level information of the securities is serious, a first preset prompt message template can be determined as a target prompt message template; if the level information of the securities is low, the fourth preset prompt message template can be determined as the target prompt message template. In addition, other determination manners may be possible, and this exemplary embodiment is not particularly limited to this.
In step S150, an early warning prompt report is generated according to the target prompt information template and the evaluation score.
In an exemplary embodiment of the disclosure, according to the determined target information template, relevant information such as the evaluation score and the like, for example, the score of the first calculation factor or the second calculation factor, and the analysis of the score of the first calculation factor and the second calculation factor are filled in the target information template, and a corresponding early warning prompt report is generated and sent to the administrator, so that the administrator makes a corresponding reflection.
In the exemplary embodiment, a method for sending a corresponding early warning prompt report according to the level information of the second target financial type is provided, so that a manager can conveniently check the risk condition of the second financial type in real time, timely make corresponding operation, and guarantee the asset safety of the second financial type.
The following describes an information processing method in the embodiment of the present disclosure in detail with reference to an application scenario.
Fig. 8 shows a block schematic diagram of an information processing method in an application scenario, as shown in fig. 8, the method at least includes the following steps: in step S810, data is accessed and pre-processed. Generally, the client can be received by the third-party information server, and the external negative public opinion information data is accessed and uploaded in real time for storage. And carrying out structuring processing on the received initial negative public opinion data, capturing useful contents of a certain website or application, and determining the captured useful contents as candidate negative public opinion data.
In step S820, data is extracted and combined taken position associations. The data extraction may be to extract keywords in the candidate negative public opinion data, for example, the keywords may be news titles, news content summaries, news Uniform Resource Locators (URLs), early warning types, severity, release time, sources, authors, and associated subjects. And extracting securities related to the target negative public opinion data according to the extracted keywords. Comparing the position data in the annual fund combination estimation table with the target securities, and determining the annual fund combination holding the target securities as the target annual fund combination.
In step S830, the warning prompt task is generated and processed in batch. And determining the risk level of the target annuity combination according to the determined evaluation score of the target annuity combination, and further determining a target prompt information template. And then, transmitting the target prompt information template generated in batches to the administrator in batches to prompt the administrator.
In step S840, the portfolio risk asset position taking feedback tracking management. And monitoring whether the administrative staff takes measures for managing and controlling the risk assets by tracking and analyzing the position change data of the target annuity combination in real time and comparing the contents of the sent risk prompt letter.
Fig. 9 is a flow chart illustrating an information processing method in an application scenario, and as shown in fig. 9, a target security related to target negative public opinion data can be extracted by a main body of the negative public opinion information data, and there may be a plurality of target securities such as security 1, security 2, … …, and security n. And comparing the target securities with position data of the annuity combination to determine a comparison result. If the annuity combination holds the target securities, the combination asset position taking and the combination overall index parameters can be extracted, namely the first calculation factor can comprise combination position taking scale/ratio, combination position taking asset profitability, combination overall risk index value, combination overall profitability index and security early warning grade; if the annuity combination does not hold the target securities, a second calculation factor can be input according to the target negative public sentiment data, and the second calculation factor can comprise securities emotional tendency, securities emotional level and main body evaluation level. And calculating a corresponding first calculation score and a corresponding second calculation score according to the determined first calculation factor and the second calculation factor, wherein the first calculation score and the second calculation score are used as evaluation scores of the target annuity combination. According to the evaluation score, the risk level of the target annuity combination can be estimated, and whether a corresponding task is triggered or not is judged, namely, an early warning prompt report is sent to a combined administrating person on an intelligent selection line, or whether the early warning prompt report needs to be sent or not is manually confirmed.
For example, through analysis of an information processing method, the early warning level of the A security is identified as 'middle', and it is monitored that in 3 annuity combinations managed by a certain administrator, combination 1 and combination 2 hold the risk asset, and combination 3 does not hold the asset temporarily.
The combination 1 has a high market value and a high space occupation ratio of the securities A, the combination risk assets have a low position taking rate, the combination overall rate of return is lower than a threshold value, and the combination overall risk index is high, so that the combination 1 can calculate and judge that the risk level threshold value of the securities A reaches a serious warning level, automatically generate a corresponding early warning report and send the early warning report to a manager, and the manager is required to carry out position clearing operation on the securities A held by the combination within a limited period.
The combination 2 has a low market value and a low bin space ratio of the securities A, the combination yield is higher than a threshold value, the combination overall risk index is low, the combination 2 is judged to accord with an automatic generation early warning prompt task through model calculation, the risk level threshold value of the securities A reaches a low warning level, a corresponding early warning prompt report is automatically generated and sent to a manager, and the manager is required to perform bin reduction or limit bin adding operation on the securities A held by the combination within a limited period.
The combination 3 does not hold the securities A, the early warning level of the securities A is 'middle', the overall risk index value of the combination 3 is higher, the overall yield index value is lower, the combination 3 is judged to accord with the automatic generation of a corresponding early warning prompt report through model calculation, a signature early warning function is sent to a manager, and the manager is required not to invest the securities within a limited period.
Therefore, aiming at the combination of the assets which do not have the risk temporarily, the advance reminding can be carried out in time, and the occurrence of the risk is practically prevented; for the combination of the existing risk assets, early warning prompt can be timely carried out, so that the risk is reduced to the maximum extent, and the risk in the event is controlled; and after that, the change of the combined position data is tracked in real time, intelligent data analysis is carried out, and whether the administrator takes corresponding risk management measures according to the early warning prompt report is monitored.
Further risk rating assessment of the target annuity combination may be achieved through monitoring of the target negative public sentiment data and the determined target securities. On one hand, target negative public opinion data are known in time, so that the analysis result can be applied to the risk control aspect of the annuity combination more deeply and effectively, and the overall risk control capability and control efficiency of the annuity combination are improved; on the other hand, the risk level of the target annuity combination is intelligently pre-judged, the accuracy and precision of the early warning of the annuity combination are improved, information help is provided for adjusting the management and control strategy of the annuity combination in time, and the asset safety of the annuity combination is guaranteed.
It should be noted that although the above exemplary embodiment implementations describe the various steps of the method in the present disclosure in a particular order, this does not require or imply that these steps must be performed in that particular order, or that all of the steps must be performed, to achieve the desired results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Further, in an exemplary embodiment of the present disclosure, an information processing apparatus is also provided. Fig. 10 shows a schematic configuration diagram of an information processing apparatus, and as shown in fig. 10, the information processing apparatus 1000 may include: a security determination module 1010, a data alignment module 1020, a rank assessment module 1030, a template determination module 1040, and a report generation module 1050. Wherein:
a security determination module 1010 configured to obtain target negative public opinion data and extract a first target financial type related to the target negative public opinion data; a data comparison module 1020 configured to compare the taken position data of the first target financial type and the second financial type to determine a second target financial type associated with the first target financial type; a grade evaluation module 1030 configured to calculate an evaluation score of the second target financial type based on the first target financial type and the position data to estimate grade information of the second target financial type; a template determination module 1040 configured to determine a target prompt information template corresponding to the grade information based on a preset prompt information template; and a report generation module 1050 configured to generate an early warning prompt report according to the target prompt information template and the evaluation score.
The details of the information processing apparatus are already described in detail in the corresponding information processing method, and therefore are not described herein again.
It should be noted that although several modules or units of the information processing apparatus 1000 are mentioned in the above detailed description, such division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
Moreover, although the steps of the methods of the present disclosure are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
An electronic device 1100 according to such an embodiment of the invention is described below with reference to fig. 11. The electronic device 1100 shown in fig. 11 is only an example and should not bring any limitations to the function and the scope of use of the embodiments of the present invention.
As shown in fig. 11, electronic device 1100 is embodied in the form of a general purpose computing device. The components of the electronic device 1100 may include, but are not limited to: the at least one processing unit 1110, the at least one memory unit 1120, a bus 1130 connecting different system components (including the memory unit 1120 and the processing unit 1110), and a display unit 1140.
Wherein the storage unit stores program code that is executable by the processing unit 1110 to cause the processing unit 1110 to perform steps according to various exemplary embodiments of the present invention as described in the above section "exemplary methods" of the present specification.
The storage unit 1120 may include readable media in the form of volatile storage units, such as a random access memory unit (RAM)1121 and/or a cache memory unit 1122, and may further include a read-only memory unit (ROM) 1123.
The storage unit 1120 may also include a program/utility 1124 having a set (at least one) of program modules 1125, such program modules 1125 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 1100 may also communicate with one or more external devices 1300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 1100, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 1100 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 1150. Also, the electronic device 1100 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the internet) via the network adapter 1160. As shown, the network adapter 1140 communicates with the other modules of the electronic device 1100 via the bus 1130. It should be appreciated that although not shown, other hardware and/or software modules may be used in conjunction with the electronic device 1100, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when said program product is run on the terminal device.
Referring to fig. 12, a program product 1200 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a 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.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and 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).
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
Claims (10)
1. An information processing method, characterized in that the method comprises:
obtaining target negative public opinion data, and extracting a first target financial type related to the target negative public opinion data;
comparing the position taken data of the first target financial type and the second financial type to determine a second target financial type related to the first target financial type;
calculating an assessment score for the second target financial type based on the first target financial type and the position data to rate rating information for the second target financial type;
determining a target prompt message template corresponding to the grade message based on a preset prompt message template;
and generating an early warning prompt report according to the target prompt information template and the evaluation score.
2. The information processing method according to claim 1, wherein the calculating an evaluation score of the second target financial type based on the first target financial type and the position data includes:
extracting a first type factor association table stored in a database; the first type factor association table comprises a first target financial type, a first calculation factor allocated to the first target financial type and a first preset weight corresponding to the first calculation factor;
acquiring a first calculation factor for calculating the evaluation score from the first type factor association table, and acquiring a first preset weight corresponding to the first calculation factor;
determining a first calculated score for the second target financial type based on the first target financial type and the position data;
and determining the first preset weight corresponding to the first calculated score, and determining the evaluation score of the second target financial type according to the first calculated score and the first preset weight.
3. The information processing method according to claim 2, wherein the determining a first calculated score for the second target financial type based on the first target financial type and the position data comprises:
determining a first indicator of the first calculation factor based on the first target financial type and the position data;
and if the first index meets a first preset condition, determining the first calculation score according to a first mapping relation between the first preset condition and the first preset score.
4. The information processing method according to claim 3, characterized by further comprising:
comparing the position taken data of the first target financial type with the position taken data of the second financial type, and determining that the first target financial type is not included in the second financial type according to a comparison result;
extracting a second type factor association table stored in a database; the second type factor association table comprises a second target financial type, a second calculation factor allocated to the second target financial type and a second preset weight corresponding to the second calculation factor;
acquiring a second calculation factor for calculating the evaluation score from the second factor association table, and acquiring a second preset weight of the second calculation factor;
determining a second calculated score for the first target financial type from the target negative public opinion data;
and determining the second preset weight corresponding to the second calculated score, and determining the evaluation score of the second target financial type according to the second calculated score and the second preset weight.
5. The information processing method according to claim 4, wherein the determining a second calculated score for the first target financial type from the target negative public opinion data comprises:
determining a second index of the second calculation factor according to the target negative public opinion data;
and if the second index meets a second preset condition, determining a second calculated score according to a second mapping relation between the second preset condition and the second preset score.
6. The information processing method according to claim 1, wherein the obtaining target negative public opinion data comprises:
acquiring initial negative public opinion data, and performing structural processing on the initial negative public opinion data to obtain candidate negative public opinion data;
extracting keywords in the candidate negative public opinion data to determine the target negative public opinion data.
7. The information processing method of claim 1, wherein the extracting the first target financial type related to the target negative public opinion data comprises:
performing main body recognition on the target negative public opinion data to obtain a main body related to the target negative public opinion data;
extracting a first financial type related to the target negative public opinion data in the subject to determine as the first target financial type.
8. An information processing apparatus characterized by comprising:
a security determination module configured to obtain target negative public opinion data and extract a first target financial type related to the target negative public opinion data;
a data comparison module configured to compare the taken position data of the first target financial type and the second financial type to determine a second target financial type related to the first target financial type;
a ranking evaluation module configured to calculate an evaluation score of the second target financial type based on the first target financial type and the position data to predict ranking information of the second target financial type;
the template determination module is configured to determine a target prompt message template corresponding to the grade message based on a preset prompt message template;
and the report generation module is configured to generate an early warning prompt report according to the target prompt information template and the evaluation score.
9. A computer-readable storage medium on which a computer program is stored, the computer program, when being executed by a processor, implementing an information processing method according to any one of claims 1 to 7.
10. An electronic device, comprising:
a processor;
a memory for storing executable instructions of the processor;
wherein the processor is configured to perform the information processing method of any one of claims 1 to 7 via execution of the executable instructions.
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