CN117592063A - Data risk early warning method, device, equipment and storage medium - Google Patents

Data risk early warning method, device, equipment and storage medium Download PDF

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CN117592063A
CN117592063A CN202311345385.0A CN202311345385A CN117592063A CN 117592063 A CN117592063 A CN 117592063A CN 202311345385 A CN202311345385 A CN 202311345385A CN 117592063 A CN117592063 A CN 117592063A
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data
risk
early warning
risk data
platform
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程夏莹
易力
万仕龙
朱勇
王恺显
胡燕
顾永兴
邹悦
梁东梅
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Ouye Yunshang Co ltd
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Ouye Yunshang Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/23—Updating
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/903—Querying
    • G06F16/9035—Filtering based on additional data, e.g. user or group profiles

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Abstract

The application provides a risk early warning method, device, equipment and storage medium for data, and relates to the technical field of data processing. A risk early warning method of data is applied to a big data platform, and comprises the following steps: synchronizing the early warning rule table of the big data platform with the early warning rule table applied by the rear end; screening initial data of the big data platform according to the early warning rule table to obtain first risk data; outputting the first risk data according to a preset period of time to match with the second risk data of the back-end application; and generating risk feedback information according to the first risk data and the received matching result of the first risk data and the second risk data. According to the embodiment of the application, the risk early warning condition of the data can be flexibly set, and the synchronization of the large data platform and the application data is realized.

Description

Data risk early warning method, device, equipment and storage medium
Technical Field
The present application relates to the field of data processing technologies, and in particular, to a method, an apparatus, a device, and a storage medium for risk early warning of data.
Background
With the different expansion of the business scope of enterprises and the application of big data technology, the data acquisition capacity of the enterprises reaches the unprecedented scale, but the informatization system of the enterprises is gradually dispersed, so that the data is isolated.
The current data risk early warning technical scheme is characterized in that a fixed risk early warning threshold value is usually set, and early warning conditions are set more singly and inflexibly. In addition, when the risk early warning threshold is updated or the risk early warning condition is increased according to the service requirement, the current data risk early warning technical scheme can apply a new early warning rule after a fixed release period, so that the updating frequency of the data risk early warning condition is low and the updating is not timely. Moreover, the current data risk early warning technical scheme cannot carry out full-flow tracking, so that no explicit data record exists in the generation, feedback and elimination of risk early warning.
Therefore, current data risk early warning solutions have been difficult to cope with increasing demands for data and services.
Disclosure of Invention
According to an aspect of the present application, there is provided a risk early warning method for data, applied to a big data platform, including: synchronizing the early warning rule table of the big data platform with the early warning rule table applied by the rear end; screening initial data of the big data platform according to the early warning rule table to obtain first risk data; outputting the first risk data according to a preset period of time to match with the second risk data of the back-end application; and generating risk feedback information according to the first risk data and the received matching result of the first risk data and the second risk data.
According to some embodiments, the early warning rule table includes a plurality of early warning categories, wherein each early warning category corresponds to a plurality of early warning rule groups from high to low according to early warning levels; each early warning rule group comprises a plurality of early warning conditions; and the plurality of early warning conditions are in a relation with each other.
According to some embodiments, according to the early warning rule table, the screening the initial data of the big data platform to obtain the first risk data includes: analyzing the initial data; according to the early warning rule table, acquiring the first risk data based on the analyzed initial data, wherein the first risk data comprises a risk code; and storing the first risk data into a risk result table.
According to some embodiments, according to the pre-warning rule table, obtaining the first risk data based on the parsed initial data includes: generating a screening expression according to the early warning conditions in the early warning rule table; merging the screening expressions according to the early warning level; screening the analyzed initial data through the combined screening expression; and acquiring the first risk data according to the screening result.
According to some embodiments, based on the first risk data, generating the corresponding risk code by a preset algorithm; determining whether the risk code is repeated; if yes, the first risk data corresponding to the risk codes are invalidated; and if not, marking the first risk data through the risk coding.
According to an aspect of the present application, there is provided a risk early warning method of data, applied to a backend application, including: synchronizing the early warning rule table to a big data platform; acquiring first risk data generated by the big data platform according to a preset period; matching the first risk data and second risk data, the second risk data being marked with a risk code; updating the second risk data according to the matching result; and sending the matching result to the big data platform.
According to some embodiments, matching the first risk data and the second risk data comprises: preprocessing the first risk data; screening the second risk data; the preprocessed first risk data and the screened second risk data are matched according to the risk codes.
According to some embodiments, updating the second risk data according to the matching result comprises: if first risk data of a risk coding mark exist and second risk data of the risk coding mark do not exist, taking the first risk data of the risk coding mark as newly-built second risk data; if the first risk data of the risk coding mark does not exist and the second risk data of the risk coding mark exists, updating the early warning state of the second risk data of the risk coding mark; and if the first risk data and the second risk data of the risk coding mark exist, updating the second risk data according to the difference between the first risk data and the second risk data.
According to an aspect of the present application, there is provided a risk early warning device for data, applied to a big data platform, including: the data receiving module is used for acquiring initial data; acquiring an early warning rule table of back-end application synchronization; obtaining a matching result of the first risk data and the second risk data; the data processing module screens the initial data according to the early warning rule table to acquire the first risk data; generating risk feedback information according to the first risk data and the matching result; and the data output module outputs the first risk data to the back-end application.
According to an aspect of the present application, there is provided a risk early warning device for data, applied to a backend application, including: the data receiving module is used for receiving first risk data generated by the big data platform; the data processing module is used for matching the first risk data with the second risk data; updating the second risk data according to the matching result; the data output module is used for sending an early warning rule table to the big data platform; and sending the matching result to the big data platform.
According to an aspect of the present application, there is provided an electronic apparatus including: one or more processors; a storage means for storing one or more programs; the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the methods as described above.
According to an aspect of the present application, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method as described above.
According to the embodiment of the application, the screening conditions can be set according to the early warning rule table synchronous with the back-end application, so that the initial data of the large data platform can be screened, the risk early warning of the data is more flexible, the updating frequency of the data is improved, the large data platform and the database of the back-end application can simultaneously provide data service, and the stability of the application service is improved.
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 application.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application.
Fig. 1 shows a flowchart of a risk early warning method for data according to an example embodiment of the present application.
FIG. 2 illustrates a flow chart of a big data platform screening initial data according to an example embodiment of the present application.
FIG. 3 illustrates a flow chart of data matching of a backend application with a large data platform according to an example embodiment of the present application.
Fig. 4 shows a block diagram of a risk early warning device applied to data of a big data platform according to an example embodiment of the present application.
Fig. 5 shows a block diagram of a risk early warning device applied to data of a back-end application according to an example embodiment of the present application.
Fig. 6 shows a block diagram of an electronic device according to an example embodiment of the present application.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. However, the exemplary embodiments can be embodied in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted.
The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the present application. One skilled in the relevant art will recognize, however, that the aspects of the application can be practiced without one or more of the specific details, or with other methods, components, materials, devices, operations, etc. In these instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail.
The flow diagrams depicted in the figures are exemplary only, and do not necessarily include all of the elements and operations/steps, nor must they be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the order of actual execution may be changed according to actual situations.
The terms first, second and the like in the description and in the claims of the present application and in the above-described figures, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
The application provides a data risk early warning method, device, equipment and storage medium, which can set conditions of multiple data risk early warning, so that the data risk early warning is more flexible, and the updating frequency of data can be improved through synchronization of a large data platform and a back-end application.
A method, apparatus, device and storage medium for risk early warning of data according to embodiments of the present application will be described in detail below with reference to the accompanying drawings.
Fig. 1 shows a flowchart of a risk early warning method for data according to an example embodiment of the present application.
As shown in fig. 1, in step S110, the big data platform synchronizes the early warning rule table with the early warning rule table applied by the back end.
For example, in step S110, the large data platform acquires the early warning rule table applied by the back end, and synchronizes the data in the early warning rule table applied by the back end to the large data platform, and covers the data in the early warning rule table of the large data platform.
According to some embodiments, the alert rule table of the big data platform includes a plurality of alert categories corresponding to cts_rule_collection in the table, including, for example, inventory alert, expiration of a credit, expiration of a delivery, expiration of no warehouse entry, arrearage of a sales contract, and the like.
Each early warning category in the early warning rule table of the big data platform can be divided into a plurality of early warning rule groups from high to low according to early warning levels, and the early warning rule groups correspond to cts_rule_groups in the table, wherein the same early warning category and the same early warning level can correspond to the plurality of early warning rule groups.
In the early warning rule table of the big data platform, each early warning rule group comprises a plurality of early warning conditions, such as the business type of the transaction, the overdue days and the like, and corresponds to the cts_rule_detail in the table. And when the plurality of early warning conditions in the same early warning rule group are met at the same time, the result of calculation of the early warning rule group is true.
According to some embodiments, the early warning levels in the early warning rule table of the big data platform may be determined according to the early warning conditions to set the levels, and may be sequentially set to red, yellow, and blue from high to low.
In step S120, the big data platform screens the initial data according to the early warning rule table to obtain the first risk data.
For example, in step S120, the big data platform first obtains the initial data stored in the big data platform, stores the initial data in the risk source table for data query, and parses the initial data according to the early warning category. The parsed initial data may be used for screening of the first risk data.
According to some embodiments, the risk source table includes at least one piece of default data, so that the data of the risk source table is not empty when the data query is performed, thereby avoiding error reporting in the data query process. Default data is automatically excluded when a data query is made.
The big data platform firstly acquires the analyzed initial data in the risk source table. And the big data platform generates a screening expression according to the early warning rule table, and performs screening calculation on each piece of analyzed initial data through the screening expression. If a screening expression with a true return result exists, the big data platform determines that the piece of parsed initial data is first risk data.
And the big data platform screens all screening expressions with the returned result of the first risk data being true, and selects the screening expression with the highest early warning level from the screening expressions, so that the early warning level of the early warning rule group corresponding to the screening expression with the highest early warning level is used as the early warning level of the first risk data.
The big data platform traverses the first risk data, and generates a risk code corresponding to the first risk data by calling a preset algorithm according to information contained in the first risk data, wherein the risk code is a field of the first risk data. For marking the first risk data corresponding thereto.
According to some embodiments, the first risk data may include information of an early warning category, a company name, a correction value, and the like, wherein different fields in the first risk data may be adopted as the correction value according to the early warning category.
To ensure the uniqueness of the risk codes, the big data platform searches the first risk data which has completed the screening calculation and generates the risk codes to determine whether repeated risk codes exist. If so, the big data platform invalidates the first risk data corresponding to the repeated risk codes. If not, the big data platform uses the risk code to mark the corresponding first risk data.
And the big data platform performs screening calculation on the parsed initial data piece by piece according to the step 120, and stores the obtained first risk data into a risk result table.
In step S130, the big data platform outputs the first risk data according to the preset period to match with the second risk data of the back-end application.
For example, in step S130, after the big data platform generates the first risk data, the backend application acquires the risk result table, which is output by the big data platform and stores the first risk data, according to the preset period. And the back-end application matches the first risk data with second risk data local to the back-end application, and updates the second risk data according to a matching result.
And outputting a risk result table to the back-end application by the big data platform according to a preset period of time according to the call instruction of the back-end application.
According to some embodiments, the preset time period may be adjusted according to actual requirements of the working time period. For example, 8:15-22:15 a day, with a frequency of once an hour, i.e., the backend application obtains a risk results table from the big data platform once an hour during a period of 8:15-22:15 a day.
After the risk result table is received, the back-end application preprocesses the first risk data in the risk result table so as to merge the data of specific early warning categories (such as entry exceeding and inventory early warning) in the first risk data, and determine responsible persons corresponding to all early warning category data in the first risk data.
Further, the back-end application reads local second risk data, and screens the second risk data according to the early warning state of the data so as to remove the data with the cancelled early warning state. Each piece of the second risk data includes a corresponding risk code and is marked using the risk code.
According to some embodiments, the early warning status of the data in the second risk data includes newly created, to be fed back, fed back and cancelled.
The back-end application matches the preprocessed first risk data and the screened second risk data piece by piece according to respective risk codes so as to update the local second risk data of the back-end application.
And the back-end application forms risk log data according to the matching result of the first risk data and the second risk data, and sends the matching result of the first risk data and the second risk data and the risk log data to the big data platform.
In step S140, the big data platform generates risk feedback information according to the first risk data and the received matching result of the first risk data and the second risk data.
For example, in step S140, after receiving the matching result of the first risk data and the second risk data and the risk log data pushed by the back-end application, the big data platform generates risk feedback information by combining the first risk data and the matching result of the first risk data and the second risk data, and stores the risk feedback information in the risk feedback information table. The big data platform provides the risk feedback information table for corresponding responsible persons, so that the responsible persons can query data with risk early warning to determine whether the data need to be processed or not, and track the result of data processing.
According to the embodiment of the application, the big data platform can flexibly set screening conditions through the early warning rule table synchronous with the back-end application, so that initial data of the big data platform can be screened, the initial data can be compared with risk data recorded by the back-end application, and the efficiency of risk early warning of the data and the stability of service are improved.
FIG. 2 illustrates a flow chart of a big data platform screening initial data according to an example embodiment of the present application.
As shown in fig. 2, in step S210, according to the analyzed early warning category of the initial data, the big data platform obtains the corresponding early warning condition in the early warning rule table.
For example, in step S210, according to the pre-warning category of any piece of data in the parsed initial data, the big data platform queries all pre-warning rule sets in the pre-warning category corresponding to the piece of data in the pre-warning rule table synchronized with the back-end application, and obtains all pre-warning conditions in the pre-warning rule sets.
In step S220, the big data platform generates a screening expression according to the early warning condition.
For example, in step S220, according to each early warning condition corresponding to any one piece of data in the parsed initial data, the big data platform generates a corresponding screening expression respectively, and combines the screening expressions corresponding to each early warning condition to form a screening expression of the early warning rule set corresponding to the piece of data.
For example, a screening expression of a certain early warning rule set is sob=1234 and biz_type=10 and measure >10, where the sob item represents the early warning condition as an organization (or company) name, the biz_type item represents the early warning condition as a transaction type (such as an online transaction or an offline transaction), and the measure item represents the early warning condition as an out-of-date day. Because of the relationship between the plurality of early warning conditions in the same early warning rule group, the screening expression of the early warning rule group can be expressed as follows: data satisfying three conditions of company name 1234, transaction type 10 (online transaction), and more than 10 days out of date are screened simultaneously.
And the big data platform combines the screening expressions of all the early warning rule groups under the same early warning level so that the same early warning level corresponds to one screening expression, wherein the early warning rule groups under the same early warning level are in OR relation.
For example, the early warning class is overdue, the early warning class is red, and the early warning class comprises a first early warning rule set and a second early warning rule set. The screening expression of the first early warning rule set is sob=1234 and biz_type=10 and measure >10, and represents data with company name 1234, transaction type 10 (online transaction) and more than 10 days of expiration days. The screening expression of the second early warning rule group is measure_5>0 and represents data with excess amount larger than 0. Thus, the final screening expression is (sob=1234 and biz_type=10 and measure > 10) or (measure_ 5>0).
In step S230, the big data platform performs a screening calculation on the parsed initial data through a screening expression to obtain first risk data.
For example, in step S230, the big data platform reads the parsed initial data, traverses each piece of data therein, brings the data value into the screening expression, and obtains the result of true or false through the screening expression. And the big data platform determines the data with the return result of the screening expression being true as first risk data.
And the big data platform screens all screening expressions with the return result of true, and selects the screening expression with the highest early warning level from the screening expressions, so that the early warning level of the early warning rule group corresponding to the screening expression with the highest early warning level is used as the early warning level of the first risk data.
For example, the screening expression is sob=1234 and biz_type=10 and measure >10, the data value of a certain piece of data in the parsed initial data is sob=1234, biz_type=10, measure=20, and the screening expression is 1234=1234 and 10=10 and20>10 after the data value is brought in, so the result returned by the screening expression is true, and the piece of data is the first risk data. If the pre-warning level of the pre-warning rule set corresponding to the screening expression is red as compared with other screening expressions with true returned results, the pre-warning level of the first risk data is set to be red.
In step S240, the big data platform generates a risk code corresponding to the first risk data.
For example, in step S240, the big data platform acquires the first risk data that has been screened, and generates risk codes according to the first risk data through a preset algorithm, where the risk codes are in one-to-one correspondence with the first risk data.
The big data platform traverses the first risk data, acquires information such as early warning category, early warning grade, company name, correction value and the like in each piece of first risk data, invokes a preset algorithm, and generates a risk code based on the information in the first risk data.
According to some embodiments, the big data platform may employ the values of different fields in the first risk data as correction values in the first risk data according to the pre-warning category, e.g., for the inventory pre-warning category, correction values may employ the current date (year, month, day) field; for the trusted overdue category, the correction value can adopt a trusted tracking date and time limit (days) field; for the class which is not warehouse-in for an out-of-date period, a warehouse-in single number field can be adopted as the correction value; for the bill of lading unreturned class, the correction value can use the bill of lading number field; for the shipping costs outstanding categories, correction values may use an out-of-date field.
According to some embodiments, the preset algorithm may employ a hash algorithm (SHA-256).
In order to ensure the uniqueness of the risk codes and avoid errors when the first risk data are matched with the second risk data, the big data platform searches the first risk data which are subjected to screening calculation and are subjected to risk code generation so as to remove the first risk data corresponding to repeated risk codes.
According to the embodiment of the application, the big data platform can flexibly set the screening conditions of the initial data, any field in the initial data can be used for risk early warning of the data, and the diversity of risk early warning condition setting is increased.
FIG. 3 illustrates a flow chart of data matching of a backend application with a large data platform according to an example embodiment of the present application.
As shown in fig. 3, in step S310, the back-end application pre-processes the received first risk data.
For example, in step S310, the backend application acquires the first risk data in the risk result table from the big data platform according to the preset period, and performs preprocessing on the first risk data so as to match with the second risk data.
The preprocessing of the first risk data by the back-end application includes merging of specific pre-warning category data. For example, for two types of data with early warning categories of entry expiration and inventory early warning, the back-end application performs merging processing according to risk codes, and merges the data with consistent risk codes into one piece of data. And in the combined first risk data, the maximum value of the out-of-date days is stored in an out-of-date day field, the minimum value of the out-of-date days is stored in other index fields, and the out-of-date quantity field and the sum field are subjected to summation treatment.
The preprocessing of the first risk data by the back-end application further comprises determination of a responsible person, so that the responsible person for related business can inquire corresponding risk early warning data to be processed. The back-end application can determine the responsible person through automatically calculating the weight score according to the early warning category corresponding to the first risk data.
For example, if the product in the first risk data is product energy pre-sale or stock and the corresponding pre-warning category is credit overdue or item overdue, the back-end application determines that the responsible person corresponding to the data is a fixed person or a person with highest weight score in the personnel configuration list.
If the early warning category corresponding to the first risk data is inventory early warning and price drop early warning, the back-end application determines that responsibility corresponding to the data is a marketer. The back-end application determines responsibility corresponding to the first risk data corresponding to other early warning categories as a salesman.
If the first risk data does not meet the two conditions, or the field of the marketer in the personnel configuration list is empty, the back-end application matches the person with the highest weight score in the personnel configuration list with the responsible person corresponding to the data.
According to some embodiments, the information of the current person in the person configuration list includes 4 dimensions of pre-warning major categories (transactions, logistics), pre-warning categories, business types and areas. If the values corresponding to the 4 dimensions are all null, the weight score of the current person is unchanged. If the values corresponding to the 4 dimensions are not null and the values corresponding to the 4 dimensions all exist, the weight score of the current person is increased by 1. If the values corresponding to the 4 dimensions are not null and the existing values corresponding to the dimensions are different from the values corresponding to the dimensions in the first risk data, deleting the current person from the person configuration list. If the value corresponding to the 4 dimensions is not null and the value corresponding to the existing dimension is the same as the value of the dimension corresponding to the first risk data, the weight score of the current person is increased by 1.
In step S320, the backend application screens the second risk data.
For example, in step S320, the backend application reads the second risk data stored in the local database and marked with the risk code, and removes the data with the early warning status cancelled from the second risk data by filtering.
In step S330, the backend application matches the preprocessed first risk data and the screened second risk data by risk coding.
For example, in step S330, the backend application matches the preprocessed first risk data and the screened second risk data piece by piece according to the risk codes, and updates the second risk data locally applied to the backend according to the matching result.
If the first risk data of a certain risk coding mark exists and the second risk data of the risk coding mark does not exist, the back-end application takes the first risk data of the risk coding mark as newly-built second risk data.
If the first risk data of a certain risk coding mark does not exist and the second risk data of the risk coding mark exists, the early warning state of the second risk data of the risk coding mark is cancelled by the back-end application.
If the first risk data and the second risk data of a certain risk code mark exist, the back end application updates the second risk data corresponding to the risk code according to the difference between the first risk data and the second risk data corresponding to the risk code.
And the back-end application sends the matching result of the first risk data and the second risk data to a big data platform for forming risk feedback information.
According to the embodiment of the application, the back-end application can provide data service with the big data platform at the same time, update and feedback of risk early-warning data are carried out on time, and the efficiency of data risk early-warning and the stability of data application service are improved.
Fig. 4 shows a block diagram of a risk early warning device applied to data of a big data platform according to an example embodiment of the present application.
As shown in fig. 4, the risk early warning device 400 applied to the data of the big data platform includes a data receiving module 410, a data processing module 420, and a data output module 430.
The data receiving module 410 obtains the initial data of the big data platform, stores the initial data in the risk source table, and analyzes the initial data according to the early warning category.
The data receiving module 410 receives the early warning rule table pushed by the back-end application, and covers the data in the early warning rule table of the big data platform with the data in the early warning rule table applied by the back-end application, so as to complete synchronization of the early warning rule table.
According to the synchronized pre-warning rule table, the data processing module 420 generates a screening expression, and performs screening calculation on each piece of parsed initial data through the screening expression. The data processing module 420 determines first risk data in the parsed initial data according to the returned result of the screening expression, wherein the first risk data is stored in a risk result table.
The data processing module 420 screens out all screening expressions with the returned result of the first risk data being true, and selects the screening expression with the highest early warning level from the screening expressions, so that the early warning level of the early warning rule group corresponding to the screening expression with the highest early warning level is used as the early warning level of the first risk data.
The data processing module 420 traverses the first risk data, and generates a risk code corresponding to the first risk data by calling a preset algorithm according to information contained in the first risk data.
The data output module 430 outputs a risk result table containing the first risk data to the back-end application according to the preset period, so as to match the second risk data of the back-end application.
The data receiving module 410 is further configured to receive a matching result of the first risk data and the second risk data pushed by the back-end application and the risk log data.
The data processing module 420 generates risk feedback information by combining the first risk data and the matching result of the first risk data and the second risk data.
Fig. 5 shows a block diagram of a risk early warning device applied to data of a back-end application according to an example embodiment of the present application.
As shown in fig. 5, the risk early warning device 500 applied to data of the back-end application includes a data receiving module 510, a data processing module 520, and a data output module 530.
Before the big data platform performs the screening of the initial data, the data output module 530 sends the early warning rule table applied by the back end to the big data platform to synchronize with the early warning rule table of the big data platform.
The data receiving module 510 is configured to receive a risk result table including first risk data sent by the big data platform according to a preset period.
The data processing module 520 pre-processes the first risk data and screens the second risk data stored in the back-end application local database.
The data processing module 520 matches the preprocessed first risk data and the screened second risk data piece by piece according to respective risk codes to update the second risk data locally applied to the back end. The data processing module 520 obtains a matching result of the first risk data and the second risk data and risk log data after the matching is completed.
The data output module 530 transmits the matching result of the first risk data and the second risk data and the risk log data to the big data platform.
Fig. 6 shows a block diagram of an electronic device according to an example embodiment of the present application.
As shown in fig. 6, the electronic device 600 is only an example, and should not impose any limitation on the functionality and scope of use of the embodiments of the present application.
As shown in fig. 6, the electronic device 600 is in the form of a general purpose computing device. Components of electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different system components (including the memory unit 620 and the processing unit 610), a display unit 640, etc. In which a storage unit stores program code that can be executed by the processing unit 610, such that the processing unit 610 performs the methods described herein according to various exemplary embodiments of the present application. For example, the processing unit 610 may perform the method as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile storage units, such as Random Access Memory (RAM) 6201 and/or cache memory unit 6202, and may further include Read Only Memory (ROM) 6203.
The storage unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
Bus 630 may be a local bus representing one or more of several types of bus structures including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or using any of a variety of bus architectures.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), one or more devices that enable a user to interact with the electronic device 600, and/or any device (e.g., router, modem, etc.) that enables the electronic device 600 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 650. Also, electronic device 600 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the Internet, through network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown, other hardware and/or software modules may be used in connection with electronic device 600, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, data backup storage systems, and the like.
From the description of the embodiments above, those skilled in the art will readily appreciate that the example embodiments described herein may be implemented in software, or may be implemented in software in combination with the necessary hardware. The technical solution according to the embodiments of the present application may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (may be a CD-ROM, a U-disc, a mobile hard disk, etc.) or on a network, and includes several instructions to cause a computing device (may be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the method according to the embodiments of the present application.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable storage medium may also be any readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of remote computing devices, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., connected via the Internet using an Internet service provider).
The computer-readable medium carries one or more programs which, when executed by one of the devices, cause the computer-readable medium to perform the aforementioned functions.
Those skilled in the art will appreciate that the modules may be distributed throughout several devices as described in the embodiments, and that corresponding variations may be implemented in one or more devices that are unique to the embodiments. The modules of the above embodiments may be combined into one module, or may be further split into a plurality of sub-modules.
According to some embodiments of the application, the technical scheme timely synchronizes the risk early warning rule of the data through the data interaction between the big data platform and the back-end application, and adopts a calculation mode of converting the early warning condition into the screening expression to screen the initial data of the big data platform, so that the risk early warning of the data is more flexible, and the efficiency of the risk early warning of the data is also improved.
The foregoing embodiments have been described in some detail to provide an understanding of the methods and concepts underlying the present application. Meanwhile, based on the ideas of the present application, those skilled in the art can make changes or modifications on the specific embodiments and application scope of the present application, which belong to the scope of the protection of the present application. In view of the foregoing, this description should not be construed as limiting the application.

Claims (12)

1. The risk early warning method of the data is applied to a big data platform and is characterized by comprising the following steps:
synchronizing the early warning rule table of the big data platform with the early warning rule table applied by the rear end;
screening initial data of the big data platform according to the early warning rule table to obtain first risk data;
outputting the first risk data according to a preset period of time to match with the second risk data of the back-end application;
and generating risk feedback information according to the first risk data and the received matching result of the first risk data and the second risk data.
2. The method of claim 1, wherein the pre-warning rule table comprises a plurality of pre-warning categories, wherein,
each early warning category corresponds to a plurality of early warning rule groups from high to low according to early warning grades;
each early warning rule group comprises a plurality of early warning conditions; and, in addition, the processing unit,
and the plurality of early warning conditions are in a relation with each other.
3. The method of claim 1, wherein screening initial data of the big data platform according to the pre-warning rule table to obtain first risk data comprises:
analyzing the initial data;
according to the early warning rule table, acquiring the first risk data based on the analyzed initial data, wherein the first risk data comprises a risk code;
and storing the first risk data into a risk result table.
4. A method according to claim 2 or 3, wherein obtaining the first risk data based on the parsed initial data according to the pre-warning rule table comprises:
generating a screening expression according to the early warning conditions in the early warning rule table;
merging the screening expressions according to the early warning level;
screening the analyzed initial data through the combined screening expression;
and acquiring the first risk data according to the screening result.
5. A method according to claim 3, further comprising:
generating the corresponding risk codes through a preset algorithm based on the first risk data;
determining whether the risk code is repeated;
if yes, the first risk data corresponding to the risk codes are invalidated;
and if not, marking the first risk data through the risk coding.
6. The risk early warning method of the data is applied to the back-end application and is characterized by comprising the following steps:
synchronizing the early warning rule table to a big data platform;
acquiring first risk data generated by the big data platform according to a preset period;
matching the first risk data and second risk data, the second risk data being marked with a risk code;
updating the second risk data according to the matching result; and
and sending the matching result to the big data platform.
7. The method of claim 6, wherein matching the first risk data and the second risk data comprises:
preprocessing the first risk data;
screening the second risk data;
the preprocessed first risk data and the screened second risk data are matched according to the risk codes.
8. The method of claim 6, wherein updating the second risk data based on the matching result comprises:
if first risk data of a risk coding mark exist and second risk data of the risk coding mark do not exist, taking the first risk data of the risk coding mark as newly-built second risk data;
if the first risk data of the risk coding mark does not exist and the second risk data of the risk coding mark exists, updating the early warning state of the second risk data of the risk coding mark;
and if the first risk data and the second risk data of the risk coding mark exist, updating the second risk data according to the difference between the first risk data and the second risk data.
9. The utility model provides a risk early warning device of data, is applied to big data platform, its characterized in that includes:
the data receiving module is used for acquiring initial data; acquiring an early warning rule table of back-end application synchronization; obtaining a matching result of the first risk data and the second risk data;
the data processing module screens the initial data according to the early warning rule table to acquire the first risk data; generating risk feedback information according to the first risk data and the matching result;
and the data output module outputs the first risk data to the back-end application.
10. A risk early warning device of data is applied to backend application, characterized in that includes:
the data receiving module is used for receiving first risk data generated by the big data platform;
the data processing module is used for matching the first risk data with the second risk data; updating the second risk data according to the matching result;
the data output module is used for sending an early warning rule table to the big data platform; and sending the matching result to the big data platform.
11. An electronic device, comprising:
one or more processors;
a storage means for storing one or more programs;
when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-8.
12. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-8.
CN202311345385.0A 2023-10-17 2023-10-17 Data risk early warning method, device, equipment and storage medium Pending CN117592063A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311345385.0A CN117592063A (en) 2023-10-17 2023-10-17 Data risk early warning method, device, equipment and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311345385.0A CN117592063A (en) 2023-10-17 2023-10-17 Data risk early warning method, device, equipment and storage medium

Publications (1)

Publication Number Publication Date
CN117592063A true CN117592063A (en) 2024-02-23

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