CN115907986A - Asset checking method and device, medium and computer equipment - Google Patents
Asset checking method and device, medium and computer equipment Download PDFInfo
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Abstract
The embodiment of the disclosure provides an asset checking method, an asset checking device, a medium and computer equipment, wherein the method comprises the following steps: determining a target asset model to be checked, wherein the target asset model is used for recording a plurality of target asset tables and asset change information among the target asset tables; obtaining subscription information of a target checking model, wherein the target checking model is obtained by training based on a plurality of first sample asset tables and asset change information among the plurality of first sample asset tables; and creating a first asset checking task for calling the target checking model to check the target asset model based on the subscription information.
Description
Technical Field
The present disclosure relates to the field of risk control technologies, and in particular, to an asset checking method and apparatus, a medium, and a computer device.
Background
In order to ensure the accuracy of the asset change information and avoid the problem of loss as much as possible during the transaction of the user, it is necessary to perform asset verification. In the related art, a collation model for performing asset collation is generally generated based on expert experience. However, the number of transactions is often very large, information related to each transaction is very complicated, and the collation rules of the collation model are also various. Therefore, the method of generating the collation model based on the expert experience has a problem of high labor cost.
Disclosure of Invention
In a first aspect, an embodiment of the present disclosure provides an asset checking method, where the method includes: determining a target asset model to be checked, wherein the target asset model is used for recording a plurality of target asset tables and asset change information among the target asset tables; obtaining subscription information of a target checking model, wherein the target checking model is obtained by training based on a plurality of first sample asset tables and asset change information among the plurality of first sample asset tables; and creating a first asset checking task for calling the target checking model to check the target asset model based on the subscription information.
In some embodiments, the determining the target asset model to be checked includes: acquiring search information input by a user and a pre-established topological model; the topological model comprises a plurality of nodes and edges connecting the nodes, each node corresponds to a search object corresponding to one item of search information, and each edge is used for representing the incidence relation between two search objects or representing the incidence relation between one search object and the target asset model; and determining a target asset model to be checked based on the search information and the topological model.
In some embodiments, the search information comprises at least one of: identification information of asset transfer links corresponding to a plurality of target asset tables in the target asset model; an order number in any target asset table in the target asset model; identification information of any target asset table in the target asset model; identifying information for use in connection with an application for operating a plurality of target asset tables in the target asset model; identification information of the created target asset model.
In some embodiments, the method further comprises: pushing at least one candidate collation model in the model database to the user; determining the target collation model from the at least one candidate collation model based on selection instructions sent by the user.
In some embodiments, said pushing at least one candidate reconciliation model in the model database to the user comprises: verifying the candidate verification model based on a plurality of second sample asset tables and asset change information among the plurality of second sample asset tables; and pushing the candidate verification models which are verified successfully to the user.
In some embodiments, the method further comprises: generating a first report, the first report including at least one of: the number of newly added candidate collation models in the model database; the number of newly added target asset models; the total number of the newly added target fields to be checked; the number of updated target asset models; the number of candidate verification models which are verified successfully; the verification success rate of each candidate verification model; the ratio of the number of successfully verified candidate verification models to the total number of candidate verification models; and verifying the candidate verification model through the second sample asset table every time.
In some embodiments, the method further comprises: and updating the target checking model under the condition that a preset updating triggering condition is met.
In some embodiments, the update trigger condition comprises at least one of: reaching a preset updating time interval; the checking accuracy of the target asset model is lower than a preset accuracy threshold; and receiving an updating instruction input by a user.
In some embodiments, after creating a first asset reconciliation task for reconciling a target asset model with the target reconciliation model based on the subscription information, the method further comprises: and outputting alarm information if the checking result of the first asset checking task is abnormal.
In some embodiments, the method further comprises: if the checking result of the first asset checking task is abnormal, acquiring first marking information of the target asset model and second marking information of the target checking model; the first marking information is used for representing whether the target asset model is correct or not, and the second marking information is used for representing whether the checking result is correct or not; and if the target asset model and the check result are both correct, generating a correlation event corresponding to the check result, wherein the correlation event is used for carrying out exception handling on the check result.
In some embodiments, the method further comprises: in an asset table generated in a target asset transfer scene, if the number of first asset tables is greater than a preset first number threshold, determining the first asset tables as first sample asset tables of the target asset transfer scene; and/or determining the first asset change information as the asset change information among the plurality of first sample asset tables if the number of the first sample asset tables meeting the first asset change information is greater than a preset second number threshold.
In some embodiments, the method further comprises: under the condition that the target asset model is not obtained, an empty second asset checking task is created; after the target asset model is obtained, adding the target asset model to the second asset reconciliation task.
In some embodiments, the method further comprises: under the condition that the target check model is not obtained, an empty third asset check task is created; and after the target verification model is obtained, adding the target asset model into the third asset verification task.
In some embodiments, the method further comprises: generating a second report; the second report includes at least one of the following information: the number of target asset models that are checked; the number of target collation models used for asset collation; the alarm times when the checking result of the first asset checking task is abnormal; the discovery rate of asset change information anomalies.
In some embodiments, the method further comprises: acquiring a configuration instruction input by a user; and configuring parameters of the target verification model and/or target fields needing verification in the target asset tables based on the configuration instructions.
In a second aspect, an embodiment of the present disclosure provides an asset reconciliation apparatus, the apparatus comprising: the system comprises a determining module, a checking module and a checking module, wherein the determining module is used for determining a target asset model to be checked, and the target asset model is used for recording a plurality of target asset tables and asset change information among the target asset tables; the system comprises an acquisition module, a comparison module and a comparison module, wherein the acquisition module is used for acquiring subscription information of a target verification model, and the target verification model is obtained based on a plurality of first sample asset tables and asset change information training among the first sample asset tables; and the creating module is used for creating a first asset checking task for calling the target checking model to check the target asset model based on the subscription information.
In a third aspect, the embodiments of the present disclosure provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the method according to any of the embodiments.
In a fourth aspect, the present disclosure provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method according to any embodiment.
According to the method and the device, after subscription information of the target verification model is acquired, a first asset verification task for calling the target verification model to verify the target asset model can be automatically established on the basis of the subscription information. And automatically checking the target asset model through the target checking model by running the first asset checking task. In addition, the target check model can be obtained based on the plurality of first sample asset tables and the asset change information training among the plurality of first sample asset tables, so that the target check model does not need to be generated based on expert experience, the dependence on manpower in the process of generating the target check model is reduced, and the labor cost is reduced.
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.
FIG. 1 is a flow chart of an asset reconciliation method of an embodiment of the present disclosure.
FIG. 2 is a schematic diagram of a topology model of an embodiment of the present disclosure.
Fig. 3 is an overall flow diagram of an embodiment of the present disclosure.
FIG. 4 is a schematic diagram of a training and freshness keeping process of an object verification model according to an embodiment of the disclosure.
FIG. 5 is a block diagram of an asset reconciliation apparatus of an embodiment of the present disclosure.
Fig. 6 is a schematic structural diagram of a computer device according to an embodiment of the present disclosure.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below do not represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terminology used in the disclosure is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. As used in this disclosure and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present disclosure. The word "if," as used herein, may be interpreted as "at \8230; \8230when" or "when 8230; \823030when" or "in response to a determination," depending on the context.
In order to make the technical solutions in the embodiments of the present disclosure better understood and make the above objects, features and advantages of the embodiments of the present disclosure more obvious and understandable, the technical solutions in the embodiments of the present disclosure are further described in detail below with reference to the accompanying drawings.
In order to ensure the accuracy of the asset change information and avoid the problem of loss as much as possible during the transaction of the user, etc., it is necessary to perform asset verification. Asset reconciliation is a type of refined monitoring process for asset security scenarios for risk checking of data related to assets. The object of asset verification is called an asset model, the asset model is an abstraction of a data model in asset loss control, and the asset model comprises a plurality of asset tables, a trigger table, an association relation among the asset tables and the like. The asset table refers to a table related to asset information, for example, an account balance table, a transaction list, and the like. The triggering table refers to an asset table in which asset information is changed, for example, if the asset information in an account balance table is an account balance, if the account balance in one account balance table is changed, the account balance table can be used as the triggering table. The association relationship between the multiple asset tables is used to represent asset change information between the multiple asset tables, and taking two asset tables as an example, assuming that account a pays 10 yuan to account B, in the account balance table of account a, the balance of account a is decreased by 10 yuan (i.e. the balance change value of account a is-10), in the account balance table of account B, the balance of account B is increased by 10 yuan (i.e. the balance change value of account B is 10), and the association relationship between the account balance table of account a and the account balance table of account B is that the asset of account a is transferred to account B.
When performing asset checking, a plurality of asset tables in the asset model are usually checked based on a pre-generated checking model. The checking model is a specific logic for carrying out risk check on the asset model, and comprises target fields to be checked in a plurality of asset tables and relations between the target fields in the asset tables. For example, in the above example where account a pays account B for 10 dollars, the target fields to be checked are the balance change value of account a and the balance change value of account B. The relationship between the target fields in each asset table is that the balance of account a is decreased by an amount equal to the balance of account B is increased by an amount (i.e., the sum of the balance change value for account a and the balance change value for account B is equal to 0). Those skilled in the art will appreciate that the above is merely illustrative, and in other examples, the target fields to be checked in different asset tables may be different, and the relationship between the target fields may be other relationships.
In the related art, a collation model for performing asset collation is generally generated based on expert experience. However, the number of transactions is often very large, information related to various transactions is very complicated, and collation rules of collation models are also diversified. Therefore, the method of generating the collation model based on the expert experience has a problem of high labor cost.
Based on this, the present disclosure provides an asset reconciliation method, see fig. 1, the method comprising:
step 102: determining a target asset model to be checked, wherein the target asset model is used for recording a plurality of target asset tables and asset change information among the target asset tables;
step 104: obtaining subscription information of a target checking model, wherein the target checking model is obtained by training based on a plurality of first sample asset tables and asset change information among the plurality of first sample asset tables;
step 106: and creating a first asset checking task for calling the target checking model to check the target asset model based on the subscription information.
In step 102, the target asset model may be various types of asset models, and the type of asset model may be determined according to the asset transfer scenario. For example, in a transfer scenario, the target asset model is an asset model that includes account balance tables for a plurality of users and balance change information between the account balance tables for the plurality of users. For another example, in an interest settlement scenario, the target asset model is an asset model that includes multiple interest settlement tables for the same user's account at different times and interest change information between the multiple interest settlement tables. And the balance change information and the interest change information are asset change information under corresponding scenes.
In some embodiments, the target asset model may be searched for by search information entered by the user. The search information may be identification information of the target asset model itself that has been created (for example, a model ID of the target asset model), or identification information of other objects related to the target asset model (referred to as search objects). The search object may include, but is not limited to, at least one of an asset transfer link corresponding to a plurality of target asset tables in the target asset model, a target asset table, an application for operating a plurality of target asset tables in the target asset model, and the like, and accordingly, the search information may include identification information (denoted as trace) of the asset transfer link corresponding to a plurality of target asset tables in the target asset model, an order number of any target asset table in the target asset model, identification information (e.g., a form ID of any target asset table) of any target asset table in the target asset model, or identification information related to an application for operating a plurality of target asset tables in the target asset model. The following describes specific ways to determine the target asset model.
In the case of using the identification information of the target asset model itself as the search information, one or more target asset models may be created manually or automatically, each target asset model that is successfully created may correspond to a model ID, and the target asset model may be searched by inputting the model ID.
In the case of using the identification information of the asset transfer link as the search information, an asset output path from one asset transferring-out party to one asset transferring-in party is referred to as one asset transfer link. For example, when transferring to account B through account a, the transfer path from account a to account B is an asset transfer link; when transferring to account C through account a, the transfer path from account a to account C is another asset transfer link. Each asset transfer link may correspond to an identification information (referred to as a link ID) that identifies the asset transfer link. The corresponding asset transfer link can be searched by using the link ID as search information, so that the target asset model related to the asset transfer link is obtained.
Under the condition that the order number of any target asset table in the target asset model is used as search information, one transaction can correspond to one order number and is used for uniquely identifying the transaction. Since at least two asset tables related to the same transaction share the same order number, the respective asset tables sharing the order number can be searched by using the order number as search information, and the target asset models related to the respective asset tables can be searched.
In the case of using the identification information of any one of the target asset tables in the target asset model as the search information, each target asset table may include an identification information (referred to as a form ID) for uniquely identifying the target asset table. By using the form ID as search information, a corresponding target asset table can be searched out, and thus a target asset model including the target asset table can be searched out.
In the case of using identification information related to an application as search information, a target asset table may be operated by one or more applications, an application may operate one or more target asset tables, and the operation result is used to update a field value of a target field related to an asset in the target asset table. For example, the transfer application may operate the user balance table to update the balance field in the user balance table. The identification information related to the application may be identification information of the application itself (i.e., application ID). Since each application may include one or more databases, each database included in an application may store a set of target asset tables that the application is capable of operating on, and different databases may store different target asset tables that the application is capable of operating on. Thus, the identification information related to the application may also be identification information of a database comprised by the application. By using the identification information related to the application as the search information, each target asset table that can be operated by the application can be searched out, and further, the target asset model related to each target asset table can be searched out.
Those skilled in the art will appreciate that the above examples are illustrative only. In addition to the above listed search information and search objects, other information may also be used as the search information, and accordingly other objects may be used as the search objects, which are not listed here.
In some embodiments, a topology model may be established in advance, where the topology model includes a plurality of nodes and edges connecting the nodes, each node corresponds to a search object corresponding to one item of search information, and each edge is used to represent an association relationship between two search objects or between one search object and the target asset model. Referring to FIG. 2, the search objects may include a human model, an automated model, an application, a physical database listing, a physical database, a logical database listing, a logical database, and a target asset table. The artificial model is a target asset model created in an artificial mode, the automatic model is a target asset model obtained through training based on a plurality of first sample asset tables and asset change information among the first sample asset tables, and the application can comprise one or more physical databases and one or more logical databases. The relationships between the target asset table and the artificial model and the relationships between the target asset table and the automatic model may be stored in the model nodes.
After the topological model is built, the target asset model can be determined based on search information input by a user and the topological model together. Taking the search object as an application as an example, a physical database list corresponding to the application can be searched based on the identification information of the physical database included in the application, and then the corresponding physical database is searched from the physical database list, and then the target asset list in the physical database is searched. Then, based on the relationship between the target asset table stored in the model node and the automated model, the automated model may be searched. The above lists examples of searching for the target asset model by applying the related search information, and the manner of searching for the target asset model by using other search information is similar to the above-mentioned manner, and is not described herein again. It should be noted that the figures list the case where the application includes a physical database and a logical database, and in an actual application, the application may include only the physical database and not the logical database.
According to the embodiment, the target asset model can be searched based on the search information of various search objects by establishing the topological model, and the user can acquire the target asset model only by inputting the search information, so that the efficiency and flexibility of acquiring the target asset model are improved, and the operation complexity of acquiring the target asset model is reduced.
In step 104, subscription information for the target collation model may be acquired, and the subscription information may include identification information of the target collation model, and may further include information indicating a subscription mode, such as subscription start time and subscription end time.
The target check model can be various types of check models, the number of the target check models can be greater than or equal to 1, and different target check models can adopt different check rules to check the target asset model or check different target fields in a plurality of target asset tables. For example, in the aforementioned example of transferring to account B through account a, the target field of one target reconciliation model reconciliation may be the balance change values in the multiple target asset tables, and the reconciliation rule is that the sum of the balance change values in the multiple target asset tables is equal to 0. In other examples, other object verification models may use other verification rules to verify object fields other than the balance change value.
The target verification model may be trained based on a plurality of first sample asset tables and asset change information between the plurality of first sample asset tables. The target checking model can automatically identify target fields needing checking in the first sample asset tables and automatically learn the association relation between the target fields.
In some embodiments, the plurality of first sample asset tables may be determined based on: in an asset table generated in a target asset transfer scenario, if the number of first asset tables is greater than a preset first number threshold, determining the first asset tables as first sample asset tables of the target asset transfer scenario. Wherein the asset table generated under the target asset transfer scenario may be an asset table generated over a historical period of time (e.g., the last week or the last month). For example, in the asset table generated in the transfer scenario within the last week, the number of the account balance tables is greater than 90% of the total number of the first sample asset tables, and thus, the account balance table may be determined as the first sample asset table of the transfer scenario. Of course, the above is only an exemplary illustration, the first quantity threshold used in this embodiment may be set to other values according to actual needs, and the target asset transfer scenario in this embodiment may also be other scenarios besides the transfer scenario.
In some embodiments, asset change information between the first plurality of sample asset tables may be obtained based on: and determining the first asset change information as the asset change information among the plurality of first sample asset tables if the number of the first sample asset tables satisfying the first asset change information is greater than a preset second number threshold. For example, in a transfer scenario, if the number of the first sample asset tables which satisfy that the sum of the balance change value of the account and the balance change value of the account of another first sample asset table is equal to 0 is greater than 95% of the total number of the first sample asset tables, determining the first asset change information as that the sum of the balance change values in the two first sample asset tables is equal to 0. The second quantity threshold may be equal to or different from the first quantity threshold, and the specific value of the second quantity threshold may be set according to actual needs.
One or more candidate verification models may be trained and stored in the model database in the manner described above. Wherein different candidate verification models may be trained using different first sample asset tables. At least one candidate verification model in the database may be pushed to the user when required by the user, and the target verification model may be determined from the at least one candidate verification model based on a selection instruction sent by the user.
The trained candidate collation model may be somewhat noisy, i.e., the collation accuracy of some of the candidate collation models may be low. In some embodiments, the candidate verification model may be verified based on a plurality of second sample asset tables and asset change information between the plurality of second sample asset tables, and the candidate verification model that is verified successfully may be pushed to the user. And if the verification fails, retraining the candidate checking model. The above process is referred to as gradation management of the candidate collation model. Through gray scale management, only the verified candidate verification model is pushed to the user, so that the user can be prevented from being disturbed. In some embodiments, the verification is determined to be successful if the number of second sample asset tables satisfying the reconciliation rule corresponding to the candidate reconciliation model is greater than a predetermined third number threshold. For example, if the reconciliation rule of a candidate reconciliation model is that the sum of the balance change values in two asset tables is equal to 0, and the number of second sample asset tables satisfying that the sum of the balance change value of an account and the balance change value of an account in another second sample asset table is equal to 0 is greater than 90% of the total number of the second sample asset tables, it is determined that the candidate reconciliation model is verified successfully; otherwise, determining that the candidate verification model fails to be verified. The third number threshold may be the same as or different from any one of the first number threshold and the second number threshold in the foregoing embodiments.
In some embodiments, during the gamma management process, a first report may also be generated to determine the progress of the gamma management process. Optionally, the first report includes at least one of the following information:
the number of candidate collation models newly added to the model database. Assuming that the number of candidate collation models in the model database at the last time of statistics is N1 and the number of candidate collation models in the model database at the present time of statistics is N2, the number of candidate collation models newly added in the model database is equal to N2-N1. In some embodiments, the number of candidate verification models newly added to the model database may be counted at preset time intervals, or triggered by preset statistical instructions, or based on other conditions.
The number of newly added target asset models. The statistical manner of the number of the newly added target asset models is similar to the statistical manner of the number of the newly added candidate collation models in the model database. Assuming that the number of the target asset models in the last statistics is M1, and the number of the target asset models in the current statistics is M2, the number of the newly added target asset models is equal to M2-M1.
The total number of newly added target fields. Assuming that the number of the target fields in the target asset table of each target asset model in the last statistical time is K1, and the number of the target fields in the target asset table of each target asset model in the current statistical time is K2, the total number of the newly added target fields is equal to K2-K1.
The number of updated target asset models. The target asset model may be updated because the target asset model itself may be erroneous, for example, due to an erroneous selection of target fields of multiple target asset tables in the target asset model. Each time a target asset model is updated, the number of updated target asset models may be increased by 1.
The number of candidate verification models that are verified successfully. Optionally, each candidate verification model that is successfully verified may be pushed to the user, such that the number of candidate verification models that are successfully verified is equal to the number of candidate verification models that are pushed to the user. By counting the number of the candidate collation models successfully verified, the noise amount of the candidate collation model can be determined. The greater the amount of noise, the fewer the number of candidate collation models that are successfully collated.
The verification success rate of each candidate verification model. The verification success rate of a candidate collation model may be determined based on a ratio of the number of second sample asset tables satisfying the collation rule corresponding to the candidate collation model to the total number of second sample asset tables.
The ratio of the number of successfully verified candidate verification models to the total number of candidate verification models. Assuming that the number of successfully verified candidate verification models is M and the total number of candidate verification models is N, the ratio is equal to M/N.
And verifying the candidate verification model through the second sample asset table every time. And if a second sample asset table meets the checking rule corresponding to the candidate checking model, judging that the checking result is successful, otherwise, judging that the checking result is failed.
In addition to the various information listed above, other information may be included in the first report and is not listed here.
In step 106, a first asset reconciliation task may be created. By operating the first asset checking task, the target asset model can be checked through the target checking model to obtain a checking result. The stage of running the first asset reconciliation task may also be referred to as an online stage of the target reconciliation model. In the online phase, a second report may be generated; the second report includes at least one of the following information: the number of the checked target asset models, the number of the target checking models used for asset checking, the number of times of warning when the checking result of the first asset checking task is abnormal, and the rate of finding out an abnormal condition of the asset change information. By generating the second report, the user can conveniently master the asset checking condition of the online stage of the target checking model.
Further, if the target asset model is not acquired in step 102, an empty second asset reconciliation task may be created in this step and added to the second asset reconciliation task after the target asset model is acquired. In practical applications, due to the large number of target asset tables, asset change information between some target asset tables and other target asset tables may be difficult to mine in time, resulting in portions of the target asset tables not being included in any target asset model. With the continuous creation of the target asset model, the excavated asset change information is gradually increased, so that more target asset models can be built. The above process is also referred to as a training or derivation process of the target asset model. That is, aspects of embodiments of the present disclosure may train a target asset model and a target verification model simultaneously. Therefore, in the case where the target asset model is not obtained, an empty second asset checking task may be created first, and the second asset checking task does not include the target asset model. After the target asset model is established, the target asset model may be automatically added to the second asset reconciliation task.
Similarly, if the target reconciliation model was not obtained in step 104, an empty third asset reconciliation task may be created in this step. The third asset verification task does not include the target verification model, but may include the target asset model or may not include the target asset model. After the target reconciliation model is obtained, the target asset model may be added to a third asset reconciliation task.
In this manner, a user may create an asset reconciliation task regardless of the presence of a target asset model and/or a target reconciliation model. In this way, the user can generate the asset checking task for the risk object (such as the target asset table) concerned by the user at any time, and immediately start the asset checking task after the target asset model and/or the target checking model is created, so that the asset checking efficiency is improved. In addition, the user does not need to pay attention to whether the target asset model and/or the target verification model is generated or not, and the operation convenience of the user is improved.
Further, in this step, the life cycle of the target verification model may be managed. The life cycle may include a start time, an end time, and/or an update time period (also referred to as a freshness time) for the target collation model to perform asset collation. The starting time is the time when the target verification model starts to perform asset verification, that is, the time when the first asset verification task starts to run. The end time is a time when the target collation model terminates the asset collation, that is, a time when the first asset collation task ends the operation. The start time and the end time of the asset reconciliation by the target reconciliation model can be determined based on the subscription start time and the subscription end time obtained in step 104, respectively. In addition, since the asset transition scenario may change, which causes the asset change information to change, and thus causes the verification result of the originally accurate target verification model to become inaccurate, an update time period may also be set in advance, and the target verification model is updated based on the update time period in this step. After the updating is completed, the updated target verification model can be called to verify the target asset model in the first asset verification task, so that the accuracy of asset verification is improved.
After the first asset checking task is created, if the checking result of the first asset checking task is abnormal, alarm information may be output. Still taking the scenario that the user a transfers money to the user B as an example, assuming that the check rule of the target check model is that the sum of the balance change values in the two account balance tables is equal to 0, but when the account balance table of the user a and the account balance table of the user B are checked through the target check model, it is found that the sum of the balance change values in the two account balance tables is not equal to 0, for example, the balance change value in the account balance table of the user a is-10, and the balance change value in the account balance table of the user B is 9, at this time, it may be determined that the check result is abnormal, and alarm information is output.
Further, when the check result is abnormal, a related event corresponding to the check result may be generated, where the related event is used to perform exception handling on the check result. For example, the associated event may be stored in a log and actively sent to the relevant processing party, or the log may be actively obtained by the processing party, so that the processing party can obtain the exception information and perform exception processing on the check result, for example, contacting the property owner for reimbursement, querying the exception cause, and the like. The related events may include, but are not limited to, part or all of the time when the anomaly of the check result is detected, the target asset table related to the anomaly check result, the category of the anomaly check result, the asset limit related to the anomaly check result, the anomaly level, and the like.
However, the reasons that may cause the verification result to be abnormal are as follows: (1) errors occur in the asset change process; (2) the target verification model is in error; and (3) the target asset model generates errors. That is, the anomaly in the verification result may be a misjudgment and does not cause any damage to the property owner. In order to reduce the above misjudgment, when the verification result of the first asset verification task is abnormal, the first marking information of the target asset model and the second marking information of the target verification model may be acquired. The first marking information is used for representing whether the target asset model is correct or not, and the second marking information is used for representing whether the checking result is correct or not. And if the target asset model and the check result are both correct, generating a correlation event corresponding to the check result. In this embodiment, if the target asset model and the verification result are both correct, the cause of the abnormal verification result is generally an error in the asset change process, and therefore, only when the target asset model and the verification result are both correct, the associated event is generated. Therefore, the misjudgment situation can be effectively reduced.
In some embodiments, the target verification model may be updated, as described above. The update process may be performed in a case where a preset update trigger condition is satisfied. Wherein the update trigger condition comprises at least one of: reaching a preset updating time interval; the checking accuracy of the target asset model is lower than a preset accuracy threshold; and receiving an updating instruction input by a user. The process of updating the target verification model may also be referred to as freshness keeping process of the target verification model. Through preservation processing, the target verification model can adapt to the change of an asset transfer scene, and therefore the verification accuracy of the target verification model is guaranteed.
In the process of the fresh-keeping treatment, a configuration instruction input by a user can be obtained, and the parameters of the target checking model and/or the target fields needing checking in the target asset tables are configured based on the configuration instruction. The parameters may include, but are not limited to, a life cycle (e.g., a start time, an end time, and/or an update time period) of the object collation model, the first quantity threshold described above, the second quantity threshold, and/or the third quantity threshold.
The asset reconciliation flow of the disclosed embodiment is described below in conjunction with fig. 3 and 4. FIG. 3 is a general flow chart of an implementation of the present disclosure, the asset reconciliation flow of an embodiment of the present disclosure comprises the following steps:
(1) And (5) a risk search example, namely acquiring a target asset model to be checked. Risk objects (e.g., asset tables) of interest to the user may be searched out by trace, order number, identification information of the application, form ID of the asset table, model ID, etc.
(2) And (5) deploying subscription. I.e. selecting a target asset model and a target verification model. First, it is determined whether a risk object has a corresponding model (including a target asset model and a target verification model). And if the target asset model and the target verification model both exist, selecting the target asset model and the target verification model. One or more candidate verification models in the model database may be recommended to the user so that the user may determine and subscribe to a target verification model from the candidate verification models.
(3) An asset reconciliation task is generated. If the target asset model or the target verification model does not exist in the step (2), generating an empty asset verification task; and (3) if the target asset model and the target checking model exist in the step (2), generating a non-empty asset checking task. Then, the asset reconciliation task is lifecycle managed. If the target asset model does not exist, a risk object of interest (e.g., an asset table) may also be entered by the user to facilitate generation of the target asset model.
(4) Exception/event management. And if the asset checking result is abnormal, outputting an abnormal alarm, marking (namely marking the first marking information and the second marking information) by a user, and generating a correlation event based on the first marking information and the second marking information.
The training and refreshing process of the target verification model can refer to fig. 4. Initialization is first performed. After initialization, automatic models (i.e., target collation models generated by training) are not included in the asset collation task. Thus, at least one candidate verification model may be generated by training. And for empty asset checking tasks (for example, the asset checking tasks without any target asset models), entering a training state, continuously and automatically deducing target asset models and target checking models according to risk objects concerned by the asset checking tasks, and if new asset models and new checking models are output, informing a user to subscribe the asset models and the new checking models.
The asset reconciliation task subscribing to the asset model and the reconciliation model enters a gray level in-process state so as to verify the validity of the subscribed reconciliation model, realize the noise reduction of the reconciliation model and output a gray level running report (i.e. a first report).
After the gray scale is finished or the user triggers, the check model enters an online state, and the user starts to normally receive abnormal alarm information and an operation report (namely a second report) generated by the asset check task.
The asset checking task preservation can be appointed by a user or triggered at regular time, and automatic derivation and updating of the asset model and the checking model are carried out, so that the timeliness of the asset model and the checking model is guaranteed. The updated asset model and the updated checking model can be subjected to gray scale management again, and are on-line again after the verification in the gray scale management process is successful.
The embodiment of the disclosure carries out training of the check model and the asset model in advance, and precipitates a set of the check model and the asset model to provide for a user to select and subscribe, so that the cost of the user to get familiar with related businesses of the asset from beginning is reduced, and the burden of manually creating the check model and the asset model is reduced; meanwhile, the asset checking task can automatically keep the checking model and the asset model fresh, capture changes caused by business and code changes in real time and ensure timeliness of asset checking.
It will be understood by those of skill in the art that in the above method of the present embodiment, the order of writing the steps does not imply a strict order of execution and does not impose any limitations on the implementation, as the order of execution of the steps should be determined by their function and possibly inherent logic.
Referring to fig. 5, an embodiment of the present disclosure further provides an asset reconciliation apparatus, including:
a determining module 502, configured to determine a target asset model to be checked, where the target asset model is used to record multiple target asset tables and asset change information among the multiple target asset tables;
an obtaining module 504, configured to obtain subscription information of a target verification model, where the target verification model is obtained through training based on a plurality of first sample asset tables and asset change information between the plurality of first sample asset tables;
a creating module 506, configured to create, based on the subscription information, a first asset matching task for calling the target matching model to match the target asset model.
In some embodiments, functions of or modules included in the apparatus provided in the embodiments of the present disclosure may be used to execute the method described in the above method embodiments, and for specific implementation, reference may be made to the description of the above method embodiments, and for brevity, details are not described here again.
Embodiments of the present specification also provide a computer device, which at least includes a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any of the foregoing embodiments when executing the program.
Fig. 6 is a schematic diagram illustrating a more specific hardware structure of a computing device according to an embodiment of the present disclosure, where the computing device may include: a processor 602, a memory 604, an input/output interface 606, a communication interface 608, and a bus 610. Wherein the processor 602, memory 604, input/output interface 606, and communication interface 608 enable communication connections within the device with each other via a bus 610.
The processor 602 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits, and is configured to execute related programs to implement the technical solutions provided in the embodiments of the present specification. The processor 602 may further include a display card, which may be an Nvidia titan X display card or a 1080Ti display card, etc.
The Memory 604 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, or the like. The memory 604 can store an operating system and other application programs, and when the technical solutions provided by the embodiments of the present specification are implemented by software or firmware, the relevant program codes are stored in the memory 604 and called to be executed by the processor 602.
The input/output interface 606 is used for connecting an input/output module to realize information input and output. The i/o module may be configured as a component within the device (not shown) or may be external to the device to provide corresponding functionality. The input devices may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output devices may include a display, a speaker, a vibrator, an indicator light, etc.
The communication interface 608 is used for connecting a communication module (not shown in the figure) to implement communication interaction between the present device and other devices. The communication module can realize communication in a wired mode (for example, USB, network cable, etc.), and can also realize communication in a wireless mode (for example, mobile network, WIFI, bluetooth, etc.).
The bus 610 includes a path that transfers information between various components of the device, such as the processor 602, the memory 604, the input/output interface 606, and the communication interface 608.
It should be noted that although the above-described device only shows the processor 602, the memory 604, the input/output interface 606, the communication interface 608 and the bus 610, in a specific implementation, the device may also include other components necessary for normal operation. In addition, those skilled in the art will appreciate that the above-described apparatus may also include only those components necessary to implement the embodiments of the present description, and not necessarily all of the components shown in the figures.
The embodiments of the present disclosure also provide a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the method of any of the foregoing embodiments.
Computer-readable media, including both permanent and non-permanent, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
From the above description of the embodiments, it is clear to those skilled in the art that the embodiments of the present disclosure can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the embodiments of the present specification or portions thereof contributing to the prior art may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, or the like, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods described in the embodiments or some portions of the embodiments of the present specification.
The systems, apparatuses, modules or units described in the above embodiments may be specifically implemented by a computer chip or an entity, or implemented by a product with certain functions. A typical implementation device is a computer, which may be in the form of a personal computer, laptop, cellular telephone, image capture device telephone, smart phone, personal digital assistant, media player, navigation device, email messaging device, game console, tablet computer, wearable device, or a combination of any of these devices.
All the embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the apparatus embodiment, since it is substantially similar to the method embodiment, it is relatively simple to describe, and reference may be made to some descriptions of the method embodiment for relevant points. The above-described apparatus embodiments are merely illustrative, and the modules described as separate components may or may not be physically separate, and the functions of the modules may be implemented in one or more software and/or hardware when implementing the embodiments of the present disclosure. And part or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The foregoing is merely a detailed description of the embodiments of the present disclosure, and it should be noted that modifications and embellishments could be made by those skilled in the art without departing from the principle of the embodiments of the present disclosure, and should be considered as the scope of the embodiments of the present disclosure.
Claims (18)
1. An asset reconciliation method, the method comprising:
determining a target asset model to be checked, wherein the target asset model is used for recording a plurality of target asset tables and asset change information among the target asset tables;
obtaining subscription information of a target checking model, wherein the target checking model is obtained by training based on a plurality of first sample asset tables and asset change information among the plurality of first sample asset tables;
and creating a first asset checking task for calling the target checking model to check the target asset model based on the subscription information.
2. The method of claim 1, the determining a target asset model to be verified, comprising:
acquiring search information input by a user and a pre-established topological model; the topological model comprises a plurality of nodes and edges connecting the nodes, each node corresponds to a search object corresponding to one item of search information, and each edge is used for representing the incidence relation between two search objects or representing the incidence relation between one search object and the target asset model;
and determining a target asset model to be checked based on the search information and the topological model.
3. The method of claim 2, the search information comprising at least one of:
identification information of asset transfer links corresponding to a plurality of target asset tables in the target asset model;
an order number in any target asset table in the target asset model;
identification information of any target asset table in the target asset model;
identifying information for use in connection with an application for operating a plurality of target asset tables in the target asset model;
identification information of the created target asset model.
4. The method of claim 1, further comprising:
pushing at least one candidate collation model in the model database to the user;
determining the target collation model from the at least one candidate collation model based on selection instructions sent by the user.
5. The method of claim 4, wherein pushing at least one candidate reconciliation model in the model database to the user comprises:
verifying the candidate verification model based on a plurality of second sample asset tables and asset change information among the plurality of second sample asset tables;
and pushing the candidate verification models which are verified successfully to the user.
6. The method of claim 5, further comprising:
generating a first report, the first report comprising at least one of:
the number of newly added candidate collation models in the model database;
the number of newly added target asset models;
the total number of newly added target fields to be checked;
the number of updated target asset models;
the number of candidate collation models which are successfully verified;
the verification success rate of each candidate verification model;
the ratio of the number of successfully verified candidate verification models to the total number of candidate verification models;
and verifying the candidate verification model through the second sample asset table every time.
7. The method of claim 1, further comprising:
and updating the target checking model under the condition that a preset updating triggering condition is met.
8. The method of claim 7, the update trigger condition comprising at least one of:
reaching a preset updating time interval;
the checking accuracy of the target asset model is lower than a preset accuracy threshold;
and receiving an updating instruction input by a user.
9. The method of claim 1, after creating a first asset reconciliation task invoking the target reconciliation model to reconcile a target asset model based on the subscription information, the method further comprising:
and if the checking result of the first asset checking task is abnormal, outputting alarm information.
10. The method of claim 9, further comprising:
if the checking result of the first asset checking task is abnormal, acquiring first marking information of the target asset model and second marking information of the target checking model; the first marking information is used for representing whether the target asset model is correct or not, and the second marking information is used for representing whether the checking result is correct or not;
and if the target asset model and the check result are both correct, generating a correlation event corresponding to the check result, wherein the correlation event is used for carrying out exception handling on the check result.
11. The method of claim 1, further comprising:
in an asset table generated in a target asset transfer scene, if the number of first asset tables is greater than a preset first number threshold, determining the first asset tables as first sample asset tables of the target asset transfer scene; and/or
And determining the first asset change information as the asset change information among the plurality of first sample asset tables if the number of the first sample asset tables satisfying the first asset change information is greater than a preset second number threshold.
12. The method of claim 1, further comprising:
under the condition that the target asset model is not obtained, an empty second asset checking task is created;
after the target asset model is obtained, adding the target asset model to the second asset reconciliation task.
13. The method of claim 1, further comprising:
under the condition that the target checking model is not obtained, an empty third asset checking task is created;
after the target reconciliation model is obtained, adding the target asset model to the third asset reconciliation task.
14. The method of claim 1, further comprising:
generating a second report; the second report includes at least one of the following information:
the number of target asset models that are checked;
the number of target collation models used for asset collation;
the alarm times when the checking result of the first asset checking task is abnormal;
the discovery rate of asset change information anomalies.
15. The method of claim 1, further comprising:
acquiring a configuration instruction input by a user;
and configuring parameters of the target verification model and/or target fields needing verification in the target asset tables based on the configuration instructions.
16. An asset reconciliation apparatus, the apparatus comprising:
the system comprises a determining module, a checking module and a checking module, wherein the determining module is used for determining a target asset model to be checked, and the target asset model is used for recording a plurality of target asset tables and asset change information among the target asset tables;
the system comprises an acquisition module, a comparison module and a comparison module, wherein the acquisition module is used for acquiring subscription information of a target checking model, and the target checking model is obtained based on a plurality of first sample asset tables and asset change information training among the plurality of first sample asset tables;
and the creating module is used for creating a first asset checking task for calling the target checking model to check the target asset model based on the subscription information.
17. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method of any one of claims 1 to 15.
18. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of any one of claims 1 to 15 when the program is executed by the processor.
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