CN114418775A - Method, device, equipment and medium for checking annual fund investment data - Google Patents
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Abstract
The embodiment of the application provides a method, a device, equipment and a medium for checking annuity investment data, aiming at improving the effectiveness of the annuity investment data which are put in a warehouse, wherein the method comprises the following steps: when target annuity investment data synchronized with a data source are received, data validity verification is respectively carried out on data of the target annuity investment data, wherein the data belong to preset dimensionality; when the data validity check is passed, acquiring information data corresponding to the target annuity investment data and historical position data corresponding to the target annuity investment data; performing simulated valuation verification on the target annuity investment data based on the historical position data and the information data; the simulated valuation check is used for checking whether the target annuity investment data are data with normal valuation or not; and when the simulated valuation verification is passed, storing the target annuity investment data into a preset database.
Description
Technical Field
The application relates to the technical field of annual fund of enterprises, in particular to a method, a device, equipment and a medium for checking annual fund investment data.
Background
The enterprise annuity is a supplementary endowment insurance system, which is a supplementary endowment insurance system independently established by enterprises and workers on the basis of taking part in basic endowment insurance legally. When operating the enterprise annuity, the client initiates and the receiver manages the enterprise annuity generally under the supervision of the bank protection supervision. In the actual operation process, besides the consignor and the consignee, the trustee, the administrating person, the account administrator and the like are involved.
Wherein, the consignee can select, supervise and replace the account manager, the trustee, the administrator and the intermediary service organization, and regularly provide the annual fund management report of the enterprise to the consignor, the beneficiary and the related supervision department. Thus, in order to better perform the operation of an enterprise annuity, trusts and regulatory bodies generally need to service the work on annuity investment.
In the related art, the trustee will connect each distant investment data for investment analysis, for example, the evaluation data connected to the evaluation system and the information data returned by the information provider will be connected, and the data collected by each data source is very different, so that the data synchronized from each data source has difference, and effective statistical analysis of the annuity investment data cannot be performed, and therefore, a scheme for effectively checking the effectiveness of the annuity investment data of each data source is urgently needed.
Disclosure of Invention
In order to solve the above problems, the present application provides a method, an apparatus, a device and a medium for checking annuity investment data, which aim to check validity of annuity investment data of each data source.
In a first aspect of the embodiments of the present application, a method for verifying annuity investment data is provided, where the method includes:
when target annuity investment data synchronized with a data source are received, data validity verification is respectively carried out on data of the target annuity investment data, wherein the data belong to preset dimensionality;
when the data validity check is passed, acquiring information data corresponding to the target annuity investment data and historical position data corresponding to the target annuity investment data;
performing simulated valuation verification on the target annuity investment data based on the historical position data and the information data; the simulated valuation check is used for checking whether the target annuity investment data are data with normal valuation or not;
and when the simulated valuation verification is passed, storing the target annuity investment data into a preset database.
Optionally, the data validity check is performed on the data belonging to the preset dimensionality of the target annuity investment data respectively, and includes:
extracting the account name in the target annuity investment data, and verifying the validity of the account name of the target annuity investment data;
and when the validity of the account name passes the verification, verifying the validity of the data format and/or the numerical value of each preset field in the target annuity investment data.
Optionally, verifying the validity of the account name of the target annuity investment data includes:
determining whether the account name belongs to a plurality of preset account names in a preset rule base, and/or acquiring a text vector of the account name, and inputting the text vector into a named body recognition model to check the validity of the account name;
verifying the validity of the data format and/or the numerical value of each preset field in the target annuity investment data, wherein the verification comprises the following steps:
reading historical investment data corresponding to the account name;
and checking the field value of the preset field in the target annuity investment data based on the field value of the preset field in the historical investment data.
Optionally, the checking the field value of the preset field in the target annuity investment data based on the field value of the preset field in the historical investment data includes:
inputting the historical investment data and the target annuity investment data into a data anomaly detection model so as to carry out data anomaly verification on the target annuity investment data;
the data anomaly detection model is obtained by training a first preset model by taking a plurality of historical anomaly investment data as training samples.
Optionally, the target annuity investment data includes target trading data and target valuation data; based on the historical position data and the information data, performing simulated valuation verification on the target annuity investment data, wherein the simulated valuation verification comprises the following steps:
updating the historical position data based on the information data;
calculating the estimation data based on the updated historical position data and the target transaction data to obtain estimated estimation data;
and checking the validity of the target estimated value data based on the estimated value data.
Optionally, verifying the validity of the target annuity investment data based on the valuation data comprises:
comparing the estimated estimation data with the target estimation data to obtain a difference;
and determining a verification result of the validity of the target evaluation value data based on the difference and a preset difference threshold value.
Optionally, the estimating the estimation data based on the updated historical taken position data and the target transaction data to obtain estimated estimation data includes:
respectively preprocessing the information data and the updated historical position data to obtain standardized information data and standardized historical position data;
inputting the standardized information data and the historical position data into a position data prediction model to obtain estimated estimation data output by the position data prediction model;
the position data prediction model is obtained by training a second preset model by using a plurality of historical position data samples and a plurality of corresponding information data samples.
In a second aspect of the embodiments of the present application, there is provided an apparatus for verifying annuity investment data, the apparatus including:
the system comprises a first checking module, a second checking module and a third checking module, wherein the first checking module is used for respectively checking the data validity of the data belonging to the preset dimensionality of target annuity investment data when the target annuity investment data synchronized with a data source is received;
the data acquisition module is used for acquiring information data corresponding to the target annuity investment data and historical position data corresponding to the target annuity investment data when the data validity check is passed;
the second checking module is used for carrying out simulated valuation checking on the target annuity investment data based on the historical position data and the information data; the simulated valuation check is used for checking whether the target annuity investment data are data with normal valuation or not;
and the storage module is used for storing the target annuity investment data into a preset database when the simulated valuation verification is passed.
In a third aspect of the embodiments of the present application, there is provided an electronic device, including a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor, when executing, implements the method for verifying annuity investment data according to the first aspect.
In a fourth aspect of the embodiments of the present application, there is provided a computer-readable storage medium storing a computer program for causing a processor to execute the method for verifying annuity investment data according to the first aspect.
By adopting the technical scheme of the embodiment of the application, the method at least has the following advantages:
in the embodiment of the application, when target annuity investment data with synchronous data sources are received, data validity check can be respectively carried out on the data of the target annuity investment data, wherein the data belong to preset dimensionality; when the data validity check is passed, acquiring information data corresponding to the target annuity investment data and historical position data; then, based on the historical position data and the information data, carrying out data simulation valuation verification on the target annuity investment data; wherein, the simulated valuation check is used for checking whether the target annuity investment data is data with normal valuation; and when the simulated valuation verification passes, storing the target annuity investment data into a preset database.
After receiving the synchronous annuity investment data, firstly, data validity check is carried out on data of preset dimensions in the target annuity investment data to determine whether the target annuity investment data is valid data or not, wherein the preset dimensions can refer to account names, data formats and the like of the annuity investment data, after the target annuity investment data is determined to be valid data, simulated valuation check can be carried out on the annuity investment data on the basis of historical position data and information data, whether the target annuity investment data to be warehoused currently is data with normal valuation or not is determined through the historical position data and the information data which are checked to pass, and if the target annuity investment data is data with normal valuation, the target annuity investment data are stored, so that effective statistical analysis of an annuity investment plan based on the annuity investment data is guaranteed.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the description of the embodiments or the related art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic view of a communication environment to which a method for verifying annuity investment data according to an embodiment of the present application is applied;
FIG. 2 is a flow chart illustrating steps of a method for verifying annuity investment data according to an embodiment of the present application;
FIG. 3 is a schematic overall flow chart diagram illustrating a method for verifying annuity investment data according to an embodiment of the present application;
FIG. 4 is a flow chart illustrating steps for performing analog valuation verification according to one embodiment of the present application;
fig. 5 is a schematic diagram of a framework of a checking apparatus for annuity investment data according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. 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 application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
In the related art, the annuity investment data from a plurality of data sources are very different, and the annuity investment data is generally analyzed and formatted and then statistically analyzed. However, after parsing and formatting, it is often found that the annuity investment data obtained after processing is erroneous, i.e. cannot be used for statistical analysis, in which case, the data backtracking is performed only by the staff to eliminate the errors. However, this approach is very time consuming, labor intensive, and inefficient.
In view of the above, the applicant proposes a scheme for verifying the validity of the annuity investment data before analyzing and formatting the annuity investment data, so as to perform data validity and simulated estimation verification on the annuity investment data prospectively before the annuity investment data is put into a database, thereby ensuring that the annuity investment data which falls into the database is effectively usable, reducing the error probability and improving the efficiency of investment statistical analysis.
Referring to fig. 1, a schematic diagram of a communication environment applied by the annuity investment data verification method provided by the present application is shown, and as shown in fig. 1, the communication environment includes a plurality of different data sources and an investment supervision and risk management platform interfacing with the plurality of different data sources, wherein the investment supervision and risk management platform is managed and maintained by a trustee in the annual fund of an enterprise, and the plurality of different data sources includes: valuation systems, on-site investment trading systems, alternative investment trading systems, credit evaluation systems, information systems, and other external data sources.
In practice, when synchronizing the annuity investment data of each data source, the investment supervision and risk management platform may obtain the annuity investment data by a timing pull or a timing push of the data source, or may obtain the annuity investment data of the synchronized data source by a web crawler, where the synchronized annuity investment data of the data source may be data of each file type, for example, the synchronized data may include file types such as txt, excel, and Html, and is not limited herein.
Referring to fig. 1, a method for checking annuity investment data according to an embodiment of the present application is described, and referring to fig. 2, a flowchart illustrating steps of a method for checking annuity investment data is shown, and as shown in fig. 2, the method may specifically include the following steps:
step S201: and when target annuity investment data synchronized with a data source are received, data validity verification is respectively carried out on data of the target annuity investment data, wherein the data belong to preset dimensionality.
In this embodiment, the data source may be a plurality of data sources as shown in fig. 1, and the target annuity investment data may refer to investment data of a target annuity plan, which may include valuation data, position taken data, and the like transmitted by the valuation system.
The preset dimension may refer to a dimension related to data validity in the target annuity investment data, for example, a dimension of an account name, a dimension of a numerical format, and the like. In specific implementation, whether data belonging to a preset dimension in the target annuity investment data is valid, for example, whether an account name is valid and a numerical format is correct can be detected.
Step S202: and when the data validity check is passed, acquiring information data corresponding to the target annuity investment data and historical position data corresponding to the target annuity investment data.
In this embodiment, when the data validity check on the data belonging to the preset dimension in the target annuity investment data passes, the data in the target annuity investment data is represented to be valid, for example, the account name is valid, and the numerical format is correct. Thus, information data and historical position data can be further obtained. The information data, the historical position data and the target annuity investment data belong to the same annuity plan of the same account, and the information data and the historical position data are data which are stored in a warehouse after verification.
The information data may be market change information of each asset corresponding to the investment portfolio in the annuity plan, and may reflect rights and interests change details of each asset. The information data includes information such as bargain price, closing price, and rights and interests change, and can reflect market change information of each asset, such as stock from company A and stock from company B. For example, if the asset is a stock, details such as the right-to-stock date, the right-to-remove-to-rest date, etc. may be included.
The historical position data refers to historical variation data of the position taken by the annuity plan, and may be position taken data of the previous trading day, for example. The taken-position amount on the hand of the client constitutes taken-position data by buying and selling the taken-position amount, and therefore the taken-position data can reflect the taken-position details of a plurality of assets held by the client, including, for example, the taken-position number, the taken-position cost, the taken-position market value, and the like of each asset. An asset may be understood, among other things, as the actual security category of a stock, bond, future, etc. Generally speaking, the position taken data is counted in a time period, for example, the position taken amount is counted once a day, so that one position taken data is obtained every day, and the position taken amount change in multiple days can be obtained through counting the position taken amount in multiple days.
Step S203: and performing simulated valuation verification on the target annuity investment data based on the historical position data and the information data.
And the simulated valuation check is used for checking whether the target annuity investment data are data with normal valuation.
In this embodiment, after the information data and the historical taken-position data are obtained, the latest warehousing transaction data can be predicted by using the information data and the historical taken-position data, and the taken-position data in the target annuity investment data, so that the estimation taken-position data which should be warehoused at this time is predicted according to the latest warehousing transaction data, and is compared with the estimation taken-position data which is actually warehoused in the target annuity investment data to perform analog estimation check, if the deviation is large, the target annuity investment data is represented to be abnormal estimation data, alarm information is given, the analog estimation check is failed, and if the deviation is small, the target annuity investment data is represented to be normal estimation data, the analog estimation check is passed.
Step S204: and when the simulated valuation verification is passed, storing the target annuity investment data into a preset database.
In this embodiment, when the simulated valuation check passes, the data representing the target annuity investment data in the preset dimension is valid and normal valuation data, for example, the account name and the numerical format are both valid and are compared with historical investment data, which is also data with a normal valuation, so that the method can be used for subsequent investment statistical analysis, and the target annuity investment data can be stored in a preset database so as to perform later-stage investment statistical analysis.
By adopting the technical scheme of the embodiment, after the synchronous annuity investment data is received, data validity check is firstly carried out on the data with preset dimensionality in the target annuity investment data to determine whether the target annuity investment data is valid data or not, wherein, the preset dimension can refer to the account name, data format, etc. of the annuity investment data, and after the target annuity investment data is determined to be valid data, the data can be based on the historical position data and information data, performing analog valuation verification on the annuity investment data to determine whether the current target annuity investment data to be warehoused is normal valuation data through the historically verified position data and information data, if so, storing the target annuity investment data, therefore, effective statistical analysis of the annuity investment plan based on the annuity investment data is guaranteed.
Referring to fig. 3, fig. 3 is a schematic overall flow chart of a verification method of annuity investment data in an embodiment, as shown in fig. 3, after the target annuity investment data is synchronized, the validity of an account of the target annuity investment data may be verified through a named entity recognition module, after the verification is passed, the validity of a format and a numerical value of data in the target annuity investment data is verified through a data anomaly detection module, after the verification is passed, whether an estimate of the target annuity investment data is normal is verified through an analog estimate module, and after the verification is passed, investment analysis and statistics may be performed on the target annuity investment data.
When any one of the verification links fails to pass the verification, the warning information can be output to prompt a user that the verification fails.
Referring to fig. 3, in an embodiment, the process of performing data validity check on the data belonging to the preset dimension of the target annuity investment data may be:
firstly, extracting the account name in the target annuity investment data, and checking the validity of the account name of the target annuity investment data.
In this embodiment, the preset dimension may refer to a dimension related to data validity in the target annuity investment data, for example, a dimension of an account name, a dimension of a numerical format, and the like. In practice, the account name in the target annuity investment data may be extracted and the validity of the account name may be checked. The account name may be an account of a principal to which the target annuity investment data belongs, and may be an account opened in a bank or an account opened in a consignee.
In practice, the account name may be identified from the target annuity investment data, and then the validity of the account name may be checked, specifically, it may be determined whether the identified account name is in an account library, a plurality of account names opened by the principal may be stored in the account library, and if the identified account name is in the account library, the representation account name is valid, that is, the account of the target annuity investment data has no problem.
And then, when the validity of the account name passes the verification, verifying the validity of the data format and/or the numerical value belonging to each preset field in the target annuity investment data.
The preset field can be set according to actual conditions, for example, the preset field includes a yield, a transaction amount, an expenditure amount and the like; the data format of the preset field may refer to a data format of a field value of the preset field, and the value of the preset field may refer to a size of the field value of the preset field.
The format of the numerical value in the target annuity investment data may be determined, specifically, the format of the numerical value related to the amount may be determined, and if the format of the numerical value related to the amount is numerical type, such as floating point, the numerical value is normally valid data, and if not, the numerical value is abnormally valid data.
In practice, the formats of the numerical values in the target annuity investment data may also be checked in accordance with the formats of the numerical values in the verified historical annuity investment data, for example, if the transaction amount in the warehoused historical annuity investment data is always a floating point, and the transaction amount in the target annuity investment data is a character string, it may be determined that the transaction amount in the target annuity investment data is abnormal, and the check is not passed.
In this embodiment, when the account name of the target annuity investment data is a valid account and the format of the numerical value is correct, the data validity of the target annuity investment data is verified to be passed. In other cases, if the account name is valid but the value format is not, the data validity check on the target annuity investment data may not be passed. When the data validity check is failed, the alarm information can be sent, and the result that the data validity check is failed is displayed, so that the data check can be performed manually.
After the manual data verification is passed, the next simulation evaluation verification can be continued.
In a specific implementation manner, when the validity of the account name of the target annuity investment data is checked, it may be determined whether the account name belongs to a plurality of preset account names in a preset rule base, and/or a text vector of the account name is obtained and input into a named body recognition model to check the validity of the account name.
In this implementation, the following three ways can be adopted for validity check of the account name:
in a first manner, it is determined whether the account name belongs to a plurality of preset account names in a preset rule base. The preset rule base comprises a plurality of legal account names which are opened by the principal, and if the account names of the target annuity investment data are located in the preset rule base, the validity verification of the account names is passed.
In the second mode, a text vector of the account name is obtained and is input into a named body recognition model so as to check the validity of the account name.
In practice, since the preset rule base may not exhaust all the legal account names opened by the principal, for example, when the account name of the target annuity investment data is an account newly opened by the principal, the account name may not exist in the preset rule base, and for example, when the account name of the target annuity investment data is a full name and the preset rule base is a short name, the checking may also be performed by mistake. In practice, the validity check of the account name may be performed using a neural network model.
Specifically, the account name may be extracted from the target annuity investment data, and the account name may be subjected to text vectorization, for example, the account name is converted into a word vector or a sentence vector to obtain a text vector, and then the text vector is input into a named entity recognition model, where the named entity recognition model is used to check the validity of the text vector to determine whether the account name corresponding to the text vector is valid or not.
The process of training to obtain the named body recognition model may be:
a plurality of account name samples are collected, wherein each account name sample carries a label that can characterize whether the account name sample is a legitimate or an illegitimate account name sample. And then, respectively converting the plurality of account name samples into text vector samples, then training a preset neural network model by using the text vector samples, calculating a loss function according to the output of the neural network model and the labels of the account name samples during each training, and then updating parameters in the neural network model according to the calculated loss value to obtain the named body recognition model.
And in the third mode, the validity of the account name is checked by adopting the first mode, and when the first mode fails to check, the validity of the account name is checked by adopting the second mode. That is, whether the account name belongs to a plurality of preset account names in a preset rule base is determined, if not, a text vector of the account name is obtained, and the text vector is input into a named body recognition model to check the validity of the account name.
When the third mode is adopted, the efficiency of account name verification can be improved by combining the preset rule base with the named body recognition model, and manual re-verification is greatly avoided.
Of course, when the third mode is adopted and the data validity check is still displayed to be failed, the warning information can be sent, and the result that the data validity check is failed is displayed, so that the data check can be performed manually. When the result is obtained through manual verification, the result of the manual verification can be used as a label of the account name of the target annuity investment data, so that the account name of the target annuity investment data is used as a training sample, the named entity recognition model is updated, the validity of the account name under the condition can be recognized by the named entity recognition model, and the generalization performance of the named entity recognition model is enhanced.
In a specific implementation manner, when the validity of the data format and/or the numerical value of each preset field in the target annuity investment data is checked, historical investment data corresponding to the account name can be read; and verifying the field value of the preset field name in the target annuity investment data based on the field value of the preset field name in the historical investment data.
In this embodiment, as described above, validity of the data format and/or the numerical value of the preset field in the target annuity investment data may be checked based on the historical investment data that has been checked to pass in the historical process. In specific implementation, it may be determined whether a data format of a preset field in the historical investment data is consistent with a data format of a preset field in the target annuity investment data, and whether a difference between a field value of the preset field in the historical investment data and a field value of the preset field in the target annuity investment data is smaller than a preset difference.
And determining that the validity check of the data format and/or the numerical value of each preset field in the target annuity investment data is passed under the condition that the data format of the preset field in the historical investment data is consistent with the data format of the preset field in the target annuity investment data and the difference between the field values of the preset fields is less than the preset difference.
The condition that the difference value between the field values of the preset fields is smaller than the preset difference value represents that the numerical difference between the historical investment data and the same item in the target annuity investment data is smaller, for example, taking net assets value as an example, if the net assets value is changed from 1000 of the historical investment data to minus 10000 of the target annuity investment data, the net assets value of the target annuity investment data is considered to have serious errors, and the data is invalid and can not be verified.
In an embodiment, the implementation process of checking the field value of the preset field in the target annuity investment data based on the field value of the preset field in the historical investment data may be as follows:
and inputting the historical investment data and the target annuity investment data into a data anomaly detection model so as to carry out data anomaly verification on the target annuity investment data.
The data anomaly detection model is obtained by training a first preset model by taking a plurality of historical anomaly investment data as training samples.
In this embodiment, validity verification may be performed on a preset field in the target annuity investment data by using a data anomaly detection model. The data anomaly detection model can be obtained by training in the following way:
and collecting a plurality of investment data sample pairs, wherein each investment data sample pair comprises investment data to be verified and investment data serving as a reference, and each investment data sample pair carries a label which can represent whether a preset field in the investment data to be verified is legal or illegal. Then, after the plurality of investment data sample pairs are processed into samples which can be processed by the neural network, for example, the samples are converted into a preset data structure, then, the investment data sample pairs are utilized to train a first preset model, the first preset model can be used for extracting preset fields of two investment data samples in the investment data sample pairs, and then the two extracted preset fields are compared, so that whether the preset fields in the investment data to be verified are legal or not is verified.
And then, updating parameters in the first preset model according to the loss value obtained by calculation, thereby obtaining a data anomaly detection model.
In the application, after the data anomaly detection model is obtained, historical investment data and the target annuity investment data can be input into the data anomaly detection model, so that data anomaly verification is performed on the data format and/or the field value size of a preset field in the target annuity investment data.
In practice, a predefined rule and a data anomaly detection model may be combined to check validity of a preset field in the target annuity investment data, specifically, the field value of the preset field in the target annuity investment data may be checked based on the field value of the preset field in the historical investment data, and if the check fails, the historical investment data and the target annuity investment data are input into the data anomaly detection model, so as to perform data anomaly check on the target annuity investment data through the data anomaly detection model.
When the method is adopted, the efficiency of validity verification of the preset field can be improved by utilizing a mode of combining field value verification and a data abnormity detection model, and manual re-verification is greatly avoided. Of course, when the validity check of the preset field is still displayed to be failed, the alarm information can be sent, and the result of the failure check of the validity check of the preset field is displayed, so that the data check can be performed manually. When the result is obtained through manual verification, no matter the result passes through the verification or different results are verified, the target annuity investment data and the historical investment data can be used as a training sample pair, the result of the manual verification is used as a label of the sample pair, and therefore the data anomaly detection model is updated, the effectiveness of the preset field under the condition can be identified through the data anomaly detection model, and the generalization performance of the data anomaly detection model is enhanced.
By adopting the embodiment, the validity of the preset field in the target annuity investment data can be verified based on the historical investment data, so that the verification accuracy of the target annuity investment data is improved, and the abnormity of the data format and the numerical fluctuation can be detected, thereby ensuring the effective availability of the investment data in storage.
Referring now to fig. 4, a flow chart illustrating steps of performing a simulated valuation check is shown, wherein the target annuity investment data includes target trading data and target valuation data; as shown in fig. 4, the method may specifically include the following steps:
step S401: and updating the historical position data based on the information data.
In this embodiment, since the information data is the market change information of each asset corresponding to the investment portfolio in the annuity plan and can reflect the rights and interests change details of each asset, the historical taken-place data that has been put in storage can be updated based on the advisory data.
Illustratively, when the historical position taking data is updated according to the information data, the corresponding position taking needs to be updated according to different types of the assets, wherein:
for a stock: the stock quotes in the information data can be obtained by using the codes in the information data associated with the stock codes, and then the market value in the historical position data is updated, wherein the market value in the historical position data is the price in the quantity stock quotes.
For interest rate debt: the bond interest rate can be obtained by using the basic information of the finance bond in the market code correlation information data of the position bond, and then the interest rate bond in the historical position bond data is updated, and the interest rate bond is counted in quantity and face value and the bond interest rate per year day.
For deposits, claims and trusts: interest rate information can be obtained by using the evaluation security information table in the internal code associated information data of the held position creditor and the trust, and then the interest of the deposit, the creditor and the trust in the historical held position data is updated, and the interest is principal interest rate/365.
For turnable debts: the market can be obtained by using the market table code in the information data related to the securities code, and then the transferable market value in the historical position data is updated, wherein the market value is quantity price.
The cost is as follows: a combined rate calculation is used.
In this way, updated historical position data can be obtained, wherein the updated historical position data can reflect the position data of the latest annuity plan obtained according to the information data under the current condition, and the position data of the latest annuity plan is estimated and can be used as the reference of the target annuity investment data.
Step S402: and calculating the estimation data based on the updated historical position data and the target transaction data to obtain estimated estimation data.
In this embodiment, the target annuity investment data includes target trading data and target valuation data, where the target trading data and the target valuation data are both real investment data to be currently warehoused. The updated historical position data can reflect the latest position data of the annuity plan obtained according to the information data under the current condition, so that the estimation data can be continuously calculated according to the updated historical position data and the target transaction data in the target annuity investment data, and the estimation data can be obtained.
Because the valuation data is the data for valuing each investment asset in the annuity plan, the position taking data reflects the asset change before and after trading the investment assets, and the trading data records the data for trading the investment assets, the target trading data and the position taking data are combined to calculate the valuation data; the estimated estimation data is determined according to the real and effective position data, the real and effective information data and the target transaction data to be put in storage, and can reflect the reasonable range of the estimation data to be put in storage.
Step S403: and checking the validity of the target estimated value data based on the estimated value data.
In this embodiment, the estimated value data may reflect a reasonable range of the estimated value data to be put in storage, so the estimated value data may be used as a reference to compare with the target estimated value data, specifically, a difference between the estimated value data and the target estimated value data may be compared, and when the difference is smaller than a preset difference threshold, validity of the target estimated value data is checked to pass. When the difference is large, the estimation data representing the target is not within the estimated reasonable range, and may be wrong abnormal estimation data.
Accordingly, in one embodiment, when the validity of the target annuity investment data is verified based on the estimation data, the estimation data and the target estimation data can be compared to obtain a difference; and determining a verification result of the validity of the target evaluation value data based on the difference and a preset difference threshold.
Wherein, the difference may refer to a difference between field values of the same field in the estimated value data and the target value data. The preset difference threshold value can be set according to actual conditions, and the simulation valuation verification is the valuation data which is obviously and unreasonably verified, so that the preset difference threshold value can be set to be larger.
By adopting the embodiment, the effectiveness of the target valuation data in the target investment data to be warehoused can be verified according to the warehoused historical position data and the current information data so as to verify whether the target valuation data is reasonable or not, and thus, the accuracy and the comprehensiveness of the verification of the investment data can be improved.
Correspondingly, in one embodiment, when estimation data is calculated based on the updated historical position data and the target transaction data based on the information data to obtain estimated estimation data, the information data and the updated historical position data can be respectively preprocessed to obtain standardized information data and standardized historical position data; and inputting the standardized information data and the historical position data into a position data prediction model to obtain estimated estimation data output by the position data prediction model.
The position data prediction model is obtained by training a second preset model by using a plurality of historical position data samples and a plurality of corresponding information data samples.
In this embodiment, the preprocessing the information data may refer to converting the information data into a preset data structure, for example, converting the information data into an excel data structure, and preprocessing the updated historical position data, or may refer to converting the updated historical position data into a preset data structure, for example, an excel data structure. So that the data input to the position data prediction model is data of a standard data structure.
In the embodiment of the application, the process of training to obtain the position data prediction model may be as follows:
collecting a plurality of groups of training samples, wherein each group of training samples comprises historical position data samples, information data samples and corresponding estimated value data samples, then using the estimated value data samples as labels, inputting the historical position data samples and the information data samples in each group of training samples into a second preset model, wherein the second preset model is used for processing the historical position data samples and the information data samples according to a preset algorithm so as to output estimated value data, then performing loss function calculation according to the output estimated value data and the estimated value data samples, and then performing loss value calculation according to the calculated loss value. And updating the parameters of the second preset model, wherein the second preset model after being updated for many times is the position data prediction model.
The position data prediction model needs to be trained regularly by historical actual position data and actual information data so as to adapt to the influence of position change in the market and products.
When the technical scheme of the embodiment is adopted, estimated estimation data can be predicted by using a neural network model, so that the generalization capability and the calculation efficiency are improved.
By adopting the technical scheme of the embodiment of the application, the method has the following advantages:
1. by utilizing the technology of combining the neural network and the predefined rule, the multi-level verification of the target annuity investment data is carried out, so that the quality problem of the investment transaction data can be discovered and solved as early as possible. In addition, the data quality can be rapidly detected by adopting a predefined rule (a preset rule base and the like); the neural network can be used as a supplement when the predefined rule fails, and the performance of data quality detection is effectively improved by continuously correcting and perfecting the warehoused historical data.
2. The accuracy of the investment data statistical analysis can be effectively improved, and the workload of backtracking when the data statistical report is in problem can be greatly reduced, so that the accuracy and the timeliness of investment decision and risk management are improved.
Based on the same inventive concept, in an embodiment, there is further provided a verification apparatus for annuity investment data, and referring to fig. 5, a block diagram of the verification apparatus for annuity investment data is shown, the apparatus includes:
the first checking module 501 is configured to, when target annuity investment data synchronized with a data source is received, perform data validity checking on data belonging to a preset dimension of the target annuity investment data respectively;
a data obtaining module 502, configured to obtain information data corresponding to the target annuity investment data and historical position data corresponding to the target annuity investment data when the data validity check passes;
a second checking module 503, configured to perform simulated valuation checking on the target annuity investment data based on the historical position data and the information data; the simulated valuation check is used for checking whether the target annuity investment data are data with normal valuation or not;
and the storage module 504 is configured to store the target annuity investment data into a preset database when the simulated valuation verification is passed.
Optionally, the first verification module 501 includes:
the first checking unit is used for extracting the account name in the target annuity investment data and checking the validity of the account name of the target annuity investment data;
and the second checking unit is used for checking the validity of the data format and/or the numerical value of each preset field in the target annuity investment data when the validity check of the account name passes.
Optionally, the first verifying unit is specifically configured to determine whether the account name belongs to a plurality of preset account names in a preset rule base, and/or obtain a text vector of the account name, and input the text vector into a named body recognition model to verify the validity of the account name;
the second verification unit is specifically used for reading historical investment data corresponding to the account name; and checking the field value of the preset field in the target annuity investment data based on the field value of the preset field in the historical investment data.
Optionally, the second verifying unit is specifically configured to input the historical investment data and the target annuity investment data into a data anomaly detection model, so as to perform data anomaly verification on the target annuity investment data;
the data anomaly detection model is obtained by training a first preset model by taking a plurality of historical anomaly investment data as training samples.
Optionally, the target annuity investment data includes target trading data and target valuation data; the second check module 503 includes:
the updating unit is used for updating the historical position data based on the information data;
the estimation unit is used for calculating estimation data based on the updated historical position data and the target transaction data to obtain estimated estimation data;
and the checking unit is used for checking the validity of the target estimated value data based on the estimated value data.
Optionally, the checking unit is specifically configured to compare the estimated value data with the target estimated value data to obtain a difference; and determining a verification result of the validity of the target evaluation value data based on the difference and a preset difference threshold.
Optionally, the update unit includes:
the preprocessing subunit is used for respectively preprocessing the information data and the updated historical position data to obtain standardized information data and standardized historical position data;
the input subunit is used for inputting the standardized information data and the historical position data into a position data prediction model to obtain estimated estimation data output by the position data prediction model;
the position data prediction model is obtained by training a second preset model by using a plurality of historical position data samples and a plurality of corresponding information data samples.
By adopting the verification device for the annuity investment data of the embodiment of the application, after receiving the synchronous annuity investment data, firstly, data validity check is carried out on data of preset dimensions in the target annuity investment data to determine whether the target annuity investment data is valid data or not, wherein, the preset dimension can refer to the account name, data format, etc. of the annuity investment data, and after the target annuity investment data is determined to be valid data, the data can be based on the historical position data and information data, performing analog valuation verification on the annuity investment data to determine whether the current target annuity investment data to be warehoused is normal valuation data through the historically verified position data and information data, if so, storing the target annuity investment data, therefore, effective statistical analysis of the annuity investment plan based on the annuity investment data is guaranteed.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The method, the device, the equipment and the medium for checking the annuity investment data provided by the invention are introduced in detail, a specific example is applied in the text to explain the principle and the implementation mode of the invention, and the description of the embodiment is only used for helping to understand the method and the core idea of the invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A method of verifying annuity investment data, the method comprising:
when target annuity investment data synchronized with a data source are received, data validity verification is respectively carried out on data of the target annuity investment data, wherein the data belong to preset dimensionality;
when the data validity check is passed, acquiring information data corresponding to the target annuity investment data and historical position data corresponding to the target annuity investment data;
performing simulated valuation verification on the target annuity investment data based on the historical position data and the information data; the simulated valuation check is used for checking whether the target annuity investment data are data with normal valuation or not;
and when the simulated valuation verification is passed, storing the target annuity investment data into a preset database.
2. The method according to claim 1, wherein the data validity check of the data belonging to the preset dimension of the target annuity investment data respectively comprises:
extracting the account name in the target annuity investment data, and verifying the validity of the account name of the target annuity investment data;
and when the validity of the account name passes the verification, verifying the validity of the data format and/or the numerical value of each preset field in the target annuity investment data.
3. The method of claim 2, wherein verifying the validity of the account name of the target annuity investment data comprises:
determining whether the account name belongs to a plurality of preset account names in a preset rule base, and/or acquiring a text vector of the account name, and inputting the text vector into a named body recognition model to check the validity of the account name;
verifying the validity of the data format and/or the numerical value of each preset field in the target annuity investment data, wherein the verification comprises the following steps:
reading historical investment data corresponding to the account name;
and checking the field value of the preset field in the target annuity investment data based on the field value of the preset field in the historical investment data.
4. The method of claim 3, wherein checking the field value of the preset field in the target annuity investment data based on the field value of the preset field in the historical investment data comprises:
inputting the historical investment data and the target annuity investment data into a data anomaly detection model so as to carry out data anomaly verification on the target annuity investment data;
the data anomaly detection model is obtained by training a first preset model by taking a plurality of historical anomaly investment data as training samples.
5. The method of claim 1, wherein the target annuity investment data includes target trading data and target valuation data; based on the historical position data and the information data, performing simulated valuation verification on the target annuity investment data, wherein the simulated valuation verification comprises the following steps:
updating the historical position data based on the information data;
calculating the estimation data based on the updated historical position data and the target transaction data to obtain estimated estimation data;
and checking the validity of the target estimated value data based on the estimated value data.
6. The method of claim 4, wherein verifying the validity of the target annuity investment data based on the valuation data comprises:
comparing the estimated estimation data with the target estimation data to obtain a difference;
and determining a verification result of the validity of the target evaluation value data based on the difference and a preset difference threshold value.
7. The method of claim 4, wherein calculating estimate data based on the updated historical position data and the target transaction data to obtain estimated estimate data comprises:
respectively preprocessing the information data and the updated historical position data to obtain standardized information data and standardized historical position data;
inputting the standardized information data and the historical position data into a position data prediction model to obtain estimated estimation data output by the position data prediction model;
the position data prediction model is obtained by training a second preset model by using a plurality of historical position data samples and a plurality of corresponding information data samples.
8. A method and apparatus for verifying annuity investment data, the apparatus comprising:
the system comprises a first checking module, a second checking module and a third checking module, wherein the first checking module is used for respectively checking the data validity of the data belonging to the preset dimensionality of target annuity investment data when the target annuity investment data synchronized with a data source is received;
the data acquisition module is used for acquiring information data corresponding to the target annuity investment data and historical position data corresponding to the target annuity investment data when the data validity check is passed;
the second checking module is used for carrying out simulated valuation checking on the target annuity investment data based on the historical position data and the information data; the simulated valuation check is used for checking whether the target annuity investment data are data with normal valuation or not;
and the storage module is used for storing the target annuity investment data into a preset database when the simulated valuation verification is passed.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor when executing implementing a method of verifying annuity investment data according to any one of claims 1-7.
10. A computer-readable storage medium storing a computer program for causing a processor to execute the method of verifying annuity investment data according to any one of claims 1-7.
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| CN115576958B (en) * | 2022-12-08 | 2023-03-07 | 杭银消费金融股份有限公司 | Data verification method, equipment and medium for production equipment supervision report |
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