CN110851298B - Abnormality analysis and processing method, electronic device and storage medium - Google Patents
Abnormality analysis and processing method, electronic device and storage medium Download PDFInfo
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Abstract
The invention provides an anomaly analysis and processing method, which comprises the steps of sequentially calling data services corresponding to a request according to a preset sequence after receiving the request sent by a user, solving the anomaly problem of the data services when the data services are judged to be anomalous, calculating relevant indexes, processing the request based on the relevant indexes and the called rules after all the data services are called and the relevant indexes are calculated, and feeding back the processing result to a client. The invention also provides an electronic device and a computer storage medium, which can realize the improvement of request processing efficiency while saving data cost and computing resources by carrying out exception analysis and processing on the request.
Description
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to an anomaly analysis and processing method, an electronic device, and a computer readable storage medium.
Background
With the development of the internet, more and more users are used to make user requests on line, but the popularization of online requests provides new challenges for analysis and processing of auditors, such as lengthy audit flow, high data service cost, poor processing efficiency/effect, etc. When the auditing party examines and approves the user request in real time, more detailed and comprehensive user information is required to be obtained, if incomplete user information is utilized to carry out request auditing decision, not only can the waste of data calculation resources be caused, but also decision deviation can be caused, if reprocessing is carried out, the approval program is required to be triggered again, and the system cost is increased.
Taking the example that the user puts out a loan request on line, the user can apply for a loan rapidly through the application of the mobile phone end, and the user does not need to go to a lender for checking, facing sign and the like the traditional loan. The popularization of online loans provides new challenges for the abnormal analysis and processing audit of requests of internet finance companies, and how to control the data cost, save data calculation resources, improve online approval efficiency and realize better processing effects is always a problem explored by the internet finance companies.
Disclosure of Invention
In view of the foregoing, the present invention provides an anomaly analysis and processing method, an electronic device, and a computer readable storage medium, which are mainly aimed at improving request processing efficiency while achieving data cost and computing resources saving by performing anomaly analysis and processing on a request.
In order to achieve the above object, the present invention provides an anomaly analysis and processing method, which includes:
step S1, a request sent by a user through the client is received, a data service list to be called corresponding to the request is determined, the request comprises a user identity, and the data service list to be called comprises data services to be called and a calling sequence;
Step S2, according to the calling sequence, calling the data service in the data service list to be called to the data server in sequence according to the user identity, and receiving user data corresponding to the called data service returned by the data server;
step S3, judging whether the data service is abnormal according to the user data, including: judging that the data service is abnormal when the data service call fails, judging that the data service is abnormal when the data service call is successful and the data label is a first preset label representing data missing, and judging that the data service is not abnormal when the data service call is successful and the data label is a second preset label representing complete data, wherein the user data comprises the data label, the data label comprises the first preset label or the second preset label, and the first preset label and the second preset label are obtained by processing and analyzing the original data of the user by the data server;
step S4, when the data service is abnormal, a preset data service weight configuration table is obtained, and whether the data service is the first type data service is judged according to the data service weight configuration table;
Step S5, when the data service is the first type of data service, determining that the data service is an abnormal data service, recording the abnormal times corresponding to the abnormal data service of the request, and judging whether the abnormal times are greater than or equal to a preset threshold value;
step S6, when the abnormal times are greater than or equal to a preset threshold value, generating early warning information to be fed back to a preset terminal, and receiving an operation instruction fed back by the preset terminal;
step S7, when the operation instruction is a first instruction, calling the abnormal data service, receiving user data returned by the abnormal data service, and returning to the step S3;
step S8, calculating an index value of a preset index corresponding to the data service according to the user data;
step S9, judging whether the data service to be called exists in the data service list, if yes, returning to step S2, and if not, executing step S10;
step S10, calling a rule corresponding to the request from a preset rule engine, processing the request based on the rule and an index value of the preset index, and feeding back a processing result to the client.
In addition, to achieve the above object, the present invention also provides an electronic device including: the system comprises a memory and a processor, wherein the memory stores an abnormality analysis and processing program which can run on the processor, and when the abnormality analysis and processing program is executed by the processor, any step of the abnormality analysis and processing method can be realized.
In addition, in order to achieve the above object, the present invention also provides a computer-readable storage medium including therein an abnormality analysis and processing program which, when executed by a processor, can implement any of the steps in the abnormality analysis and processing method as described above.
According to the anomaly analysis and processing method, the electronic device and the computer readable storage medium, after receiving a request sent by a user through a client, the electronic device sequentially calls data services corresponding to the request according to a preset sequence, when the anomaly occurs in the data service calling process, the anomaly problem of the data service needs to be solved, then other data services are called and related preset indexes are calculated, after all the data services are called, the request is processed based on the calculated preset indexes and preset rules, and the processing result is fed back to the client. 1) By setting an anomaly analysis mechanism, the data service with anomalies is analyzed, and the corresponding processing is carried out on the anomaly data service, so that the request processing risk caused by anomalies of the data service can be avoided; 2) When an abnormality exists in a certain data service, the calling of other data services is suspended, and the subsequent operation is not executed until the current abnormal data service is processed, so that repeated calling of the data service is avoided, the data calling cost is reduced, and the data calculation amount is reduced; 3) For the data service with abnormality, generating early warning information based on the corresponding abnormality information and feeding back the early warning information to the preset terminal, and executing different steps according to different instructions fed back by the preset terminal, thereby improving the efficiency and accuracy of request processing. In conclusion, by carrying out exception analysis on the request, the request processing efficiency is improved while the data cost and the computing resources are saved.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of an anomaly analysis and processing system of the present invention;
FIG. 2 is a schematic diagram of an electronic device according to a preferred embodiment of the invention;
FIG. 3 is a block diagram illustrating an embodiment of the exception analysis and handling process of FIG. 2;
FIG. 4 is a flow chart of an anomaly analysis and processing method according to a preferred embodiment of the present invention;
FIG. 5 is a flow chart of another embodiment of the anomaly analysis and processing method of the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to FIG. 1, a schematic diagram of an anomaly analysis and processing system 1 according to a preferred embodiment of the present invention is shown.
In this embodiment, the anomaly analysis and processing system 1 includes: an electronic device 2, a data server 3 and a client 4. The electronic device 2 communicates with the data server 3 and the client 4 via a network (not identified in the figure).
The electronic device 2 is used for requesting abnormality analysis and processing, for example, approval of a user loan request, and the data server 3 is used for processing and analyzing original data of a user. After the electronic device 2 receives the loan request submitted by the user through the client 4, the electronic device 2 invokes a data service to the data server 3, receives user data corresponding to the data service returned by the data server, judges whether the data service is abnormal based on the returned user data, enters an abnormal processing flow if the data service is abnormal, continues to execute the approval flow to obtain an approval result if the data service is not abnormal, and feeds back the approval result to the user through the client 4.
Referring to FIG. 2, a schematic diagram of an electronic device 2 in the anomaly analysis and processing system 1 according to a preferred embodiment of the present invention is shown.
In this embodiment, the electronic device 2 may be a server, a smart phone, a tablet computer, a portable computer, a desktop computer, or other terminal devices with data processing function, where the server may be a rack server, a blade server, a tower server, or a cabinet server.
The electronic device 2 comprises a memory 11, a processor 12, and a network interface 13.
The memory 11 includes at least one type of readable storage medium including flash memory, a hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 2, such as a hard disk of the electronic device 2.
The memory 11 may in other embodiments also be an external storage device of the electronic apparatus 2, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a flash Card (FlashCard) or the like, which are provided on the electronic apparatus 2. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic apparatus 2.
The memory 11 may be used not only for storing application software installed in the electronic device 2 and various types of data, such as the abnormality analysis and processing program 10, but also for temporarily storing data that has been output or is to be output.
Processor 12 may in some embodiments be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor or other data processing chip for executing program code or processing data stored in memory 11, such as exception analysis and processing program 10.
The network interface 13 may optionally comprise a standard wired interface, a wireless interface (e.g. WI-FI interface), typically used to establish a communication connection between the electronic apparatus 2 and other electronic devices, e.g. a client (not shown), a data server (not shown).
Fig. 2 shows only the electronic device 2 with the components 11-13, it will be understood by those skilled in the art that the structure shown in fig. 2 is not limiting of the electronic device 2 and may include fewer or more components than shown, or may combine certain components, or a different arrangement of components.
Optionally, the electronic device 2 may further comprise a user interface, which may comprise a Display (Display), an input unit such as a Keyboard (Keyboard), and a standard wired interface, a wireless interface.
Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-Emitting Diode (OLED) touch, or the like. The display may also be referred to as a display screen or a display unit for displaying information processed in the electronic device 2 and for displaying a visualized user interface.
In the embodiment of the electronic device 2 shown in fig. 2, as a program code of the abnormality analysis and processing program 10 stored in the memory 11 of a computer storage medium, the processor 12 executes the program code of the abnormality analysis and processing program 10 to realize the following steps:
the method comprises a first receiving step of receiving a request sent by a user through a client, and determining a data service list to be called corresponding to the request, wherein the request comprises a user identity, and the data service list to be called comprises data services to be called and a calling sequence.
The following describes embodiments of the present invention with the electronic device 2 as an execution subject. Taking a request sent by a user through the client 4 as a loan request for example, but not limited to, the loan request submitted by the user through the client 4 comprises a user identity, wherein the user identity comprises information capable of identifying the uniqueness of the user, such as a user name, an identity card number, a passport number and the like.
The client 4 is provided with a client APP by which a user submits a loan request.
In this embodiment, the request further includes request related information, and the determining the to-be-called data service list corresponding to the request includes:
determining the type of the request according to the request related information; a kind of electronic device with high-pressure air-conditioning system
And determining a data service list to be called corresponding to the type of the request according to the type of the request and the predetermined mapping data of the request type and the data service list.
The request-related information includes: application type, application amount, application period, user's academy, work unit address, etc. The application types include, but are not limited to, car credits, house credits, petty credits, and the like.
It will be appreciated that the data services that need to be invoked for different types of loan requests may be the same or different, even though the data services that need to be invoked may be the same, the order of their invocation may be different. In order to improve the accuracy and efficiency of data service calling, mapping data of different types of loan requests and corresponding data service lists are predetermined and stored, wherein the ordering of each data service in the data service list is the called order of each data service. For example, for a class A loan request, the corresponding list of data services is: data service A1, data service A2, data service A3; and B-type loan request, wherein the corresponding data service list is as follows: data service B1, data service B2, data service B3, …; and C-type loan request, wherein the corresponding data service list is as follows: data service C1, data service C2, data service C3, …; and so on.
And a first calling step, namely calling the data services in the service list to be called to the data server in sequence according to the calling sequence and according to the user identity, and receiving user data corresponding to the called data services, which are returned by the data server.
The list of the data services to be called corresponding to the request determined in the step above includes the names of the data services to be called and the called sequence corresponding to each data service to be called. Taking the current loan request as a class-a loan request as an example, assume that the corresponding list of data services to be invoked is: data service A1, data service A2, data service A3, then data service A1 is called first, data service A2 is called second, and data service A3 is called last.
It should be noted that, the electronic device 2 calls the data service to the data server 3, the data server 3 will first determine whether there is cache data corresponding to the called data service in the preset cache, if there is cache data, directly query/obtain the cache data from the cache and return the cache data to the electronic device 2, if there is no cache data, call the called data service to obtain user data, return the obtained user data to the electronic device 2, and store the user data in the preset cache. The preset cache may be a database, and the cache is implemented inside the data service system.
Further, in order to ensure the validity of the data, the user data stored in the preset buffer memory is released once at preset time intervals. For example, the preset time interval is 30minutes.
And a first judging step of judging whether the data service is abnormal or not according to the user data.
It will be appreciated that when it is determined that there is no anomaly in the data service, the steps of calculating and subsequent invoking, requesting, etc. may be performed directly using the received user data.
In this embodiment, the user data further includes a data tag, where the data tag includes a first preset tag and a second preset tag, and the "determining whether the data service has an abnormality" includes:
when the data service call fails, judging that the data service is abnormal;
when the data service is successfully invoked and the data label is a first preset label, judging that the data service is abnormal; a kind of electronic device with high-pressure air-conditioning system
And when the data service is successfully invoked and the data label is a second preset label, judging that the data service is not abnormal.
The first preset label is a data missing label "0", and the second preset label is a data complete label "1".
And after each execution of the data service call, judging the abnormality of the data service based on the returned user data. The data service exception includes: the data service calls for anomalies and user data anomalies. The data service call abnormality is failure of data service call caused by machine downtime, network abnormality and the like corresponding to the data service, and the user data abnormality is abnormality caused by missing key data influencing loan request approval.
It should be noted that, the user data and the data tag thereof are obtained by processing and analyzing the user original data by the data server 3, and the "processing and analyzing the user original data by the data server 3" includes:
acquiring user original data corresponding to the user identity, and processing the user original data to obtain user data corresponding to the user identity;
invoking a pre-configured data integrity judgment rule to carry out integrity judgment on the user data, and determining a data tag of the user data according to a judgment result; a kind of electronic device with high-pressure air-conditioning system
And storing the user data carrying the data tag.
The user original data are social security/public accumulation payment information, consumption information, communication information, credit investigation information and other data of the user. The user identity is in one-to-one correspondence with the original data and the user data. For example, the user identity is an identity card number, and the related data of the user is obtained from a preset database according to the identity card number and is used as the original data of the user.
In this embodiment, the "data processing the user raw data" includes:
and calculating user parameter indexes corresponding to the data services by using the original data.
The user parameter index is related to policies and services, and the calculation mode for calculating the user parameter index is preset and stored.
For example, for social security data, user data obtained after data processing includes: whether the social insurance is matched with the identity card, the number of times of cut-off of the social insurance, the number of medical insurance usage in the last 12 months, and the like.
For another example, for consumption data, the user data obtained after data processing includes: average amount consumed for the last 3 months, average income for the last 6 months, payment amount for the last 6 months, etc.
In this embodiment, the "invoking the preconfigured data integrity determination rule to perform integrity determination on the user data, and determining the data tag of the user data according to the determination result" includes:
invoking an integrity judgment rule corresponding to the request from a preset integrity judgment rule library, and judging whether the user data is complete or not based on the integrity judgment rule;
judging whether the user data is completely missing when judging that the user data is incomplete, if so, judging that the data label is a first preset label, if so, judging that the data label is partially missing, judging that the partially missing data is key data, and if so, judging that the data label is a first preset label;
And when the user data is complete or the user data is incomplete but the partially missing data is not the key data, the data label is regarded as a second preset label.
And a second judging step, when the data service is abnormal, acquiring a preset data service weight configuration table, and judging whether the data service is the first type data service according to the data service weight configuration table.
The storage path of the data service weight configuration table is a configuration center. In the data service weight configuration table, the data service includes two kinds: a first class of data services and a second class of data services. Wherein the first type of data service is a must data service and the second type of data service is a non-must data service. It should be noted that the same data service may be a necessary data service or a non-necessary data service in different types of requests, for example, for a backlog related product: the data services such as the public accumulation data, the social security data and the consumption data are necessary data services, the data services such as the vehicle insurance data and the communication data are general data services, and for the vehicle insurance related products, the data services such as the vehicle insurance data and the consumption data are necessary data services, and the data services such as the public accumulation data and the communication data are general data services.
And a third judging step of determining that the data service is abnormal data service when the data service is the first type of data service, recording the abnormal times corresponding to the abnormal data service of the request, and judging whether the abnormal times are larger than or equal to a preset threshold value.
Also taking the request as a type A user loan request as an example, after receiving the user data returned by the data service A1, executing data service abnormality judging operation, when the data service A1 is abnormal and is necessary data service, recording the abnormal data service A1 corresponding to the current loan request and the abnormal times +1 of the current loan request due to the abnormal data service A1, and executing corresponding processing according to the abnormal times judging result. When the data service A1 is abnormal but is a general data service, it is considered that the user data corresponding to the data service A1 does not substantially affect the processing result of the subsequent request, so that the preset index corresponding to the data service A1 is not calculated, and the fourth judging step is continuously executed.
A second receiving step, when the abnormal times are greater than or equal to a preset threshold value, generating early warning information and feeding back the early warning information to a preset terminal, and receiving an operation instruction fed back by the preset terminal;
In view of the failure of the data call caused by the network abnormality or the downtime of the machine corresponding to the data service, an upper limit (preset threshold) of the retry number is set, and when the abnormality number is smaller than the preset threshold, the electronic device 2 is allowed to execute the first calling step and the subsequent steps again at preset time intervals (for example, 5 min). However, to avoid the waste of computing resources, it may be understood that if the number of anomalies caused by the same data service in a loan request exceeds a preset threshold (for example, 5 times), it is indicated that the data service may have serious faults, and a manual intervention process is required, so that early warning information is sent to the preset terminal based on the anomaly information, so that the related service responsible person processes the anomaly data. The preset terminal is a terminal used by a responsible person corresponding to the current loan request.
And a second calling step, calling the abnormal data service when the operation instruction is the first instruction, receiving the user data returned by the abnormal data service, and returning to the abnormal judging step in the first calling step.
The first instruction is a retry instruction. When a retry instruction is received, clearing the abnormal times corresponding to the current abnormal data service, taking the current abnormal data service as the data service to be called, and continuing to execute the normal processing flow according to the original flow.
And calculating an index value of a preset index corresponding to the data service according to the user data.
Taking the data service A1 in the class a loan request as an example, when the data service A1 is called to obtain corresponding user data and the data label is a label "1" representing the complete data, processing the preset index by using the user data corresponding to the data service A1 returned by the data server 3.
For example, the preset indexes include: the public accumulation fund payment period number, consumption index, social security, call record, pedestrian credit and other related indexes. The purpose of processing the preset indexes is to evaluate the repayment capability of the user and approve the loan request in the subsequent steps. For a certain data service, the above-mentioned processing of the preset index is divided into two types, one is to directly use the user data in the data service, and the other is to generate a composite score by using the user data of a plurality of data services, which will not be described in detail herein.
And a fourth judging step of judging whether the data service which is not called exists in the data service list to be called, if so, returning to the first calling step, and if not, executing the request processing step.
Judging whether the data service to be called exists after the preset index corresponding to one data service is calculated, if so, returning to the step of executing the data service calling and abnormality judgment, for example, if the data service A3 to be called exists after the preset index corresponding to the data service A2 is processed, continuing to call the data service A3 and executing the subsequent steps; otherwise, a request processing step is performed, for example, after processing the preset indexes corresponding to the data service A3, and after all the data services are called, the loan request approval is started based on all the processed preset indexes.
And a request processing step, namely calling a rule corresponding to the request from a preset rule engine, processing the request based on the rule and the index value of the preset index, and feeding back a processing result to the client 4.
The rule engine matches the data with the rules in the rule set one by one and outputs one or more matched results. The rule engine is composed of a rule set, a rule field set and rules. A rule engine may contain one or more rule sets, which may also include one or more rules created using rule fields, one for each rule when executing a matching job.
And approving the loan request based on the rule corresponding to the current loan request in the rule engine and the preset index calculated in the steps, and feeding back the approval result to the client 4.
In the above embodiments, the mapping relation table, the data service weight configuration table, the preset thresholds, and the like of the data services corresponding to the loan requests of each class are configured in the configuration center, instead of being stored in the form of configuration files. The system can be ensured to be modified and validated in real time under the condition of not restarting the system, thereby improving the processing capacity of the system.
In other embodiments, when the processor 12 executes the program code of the exception analysis and handling program 10, the following steps are also implemented:
and a third calling step, when the operation instruction is a second instruction, calling the abnormal data service, receiving user data returned by the abnormal data service, judging whether the user data is abnormal or not, if not, executing the calculating step based on the user data, if not, not calculating a preset index corresponding to the abnormal data service, and executing a fourth judging step.
The second instruction is a forced execution instruction. When a forced execution instruction is received, clearing the abnormal times corresponding to the current abnormal data service, calling the current abnormal data service and receiving corresponding user data, if the current abnormal data service is successfully called and the returned user data is complete, calculating a preset index by using the user data and executing a subsequent calling or approval process, and if the current abnormal data service is failed to be called or is successfully called but the user data is incomplete, not calculating the preset index corresponding to the current abnormal data service, and continuing to execute the subsequent calling or processing process.
Referring to FIG. 3, a block diagram of a preferred embodiment of the anomaly analysis and processing program 10 of FIG. 2 is shown.
The abnormality analysis and processing program 10 may also be divided into one or more modules, one or more modules being stored in the memory 11 and executed by one or more processors (the processor 12 in this embodiment) to implement the present invention, and the modules referred to herein are a series of instruction segments of a computer program capable of performing a specific function, which is more suitable than the program for describing the execution of the abnormality analysis and processing program 10 in the electronic device 2.
In one embodiment, the exception analysis and handling program 10 may include only modules 101-110, with the functions or operational steps performed by the modules 101-110 being similar to those described above and not described in detail herein, for example, wherein:
the first receiving module 101 is configured to receive a request sent by a user through a client, and determine a to-be-called data service list corresponding to the request;
the first calling module 102 is configured to call the data services in the service list to be called to the data server in sequence according to the calling sequence and according to the user identity, and receive user data corresponding to the called data services returned by the data server;
A first judging module 103, configured to judge whether the data service is abnormal according to the user data;
a second judging module 104, configured to obtain a preset data service weight configuration table when the data service is abnormal, and judge whether the data service is a first type data service according to the data service weight configuration table
A third judging module 105, configured to determine that the data service is an abnormal data service when the data service is a first type data service, record an abnormal number of times corresponding to the abnormal data service, and judge whether the abnormal number of times is greater than or equal to a preset threshold;
the second receiving module 106 is configured to generate early warning information and feed back the early warning information to a preset terminal when the abnormal times are greater than or equal to a preset threshold, and receive an operation instruction fed back by the preset terminal;
a second calling module 107, configured to call the abnormal data service and receive user data returned by the abnormal data service when the operation instruction is a first instruction; a kind of electronic device with high-pressure air-conditioning system
A second judging module 108, configured to calculate an index value of a preset index corresponding to the data service according to the user data, and judge whether there is an unrecalled data service in the to-be-called data service list;
A calculation module 109, configured to calculate, according to the user data, an index value of a preset index corresponding to the data service; a kind of electronic device with high-pressure air-conditioning system
The request processing module 110 is configured to invoke a rule corresponding to the request from a preset rule engine, process the request based on the rule and an index value of the preset index, and feed back a processing result to the client 4.
In other embodiments, the exception analysis and handling program 10 may further include a module 111:
and a third calling module 111, configured to call the abnormal data service and receive user data returned by the abnormal data service when the operation instruction is the second instruction, and determine whether the user data has an abnormality.
Referring to FIG. 4, a flow chart of a preferred embodiment of the anomaly analysis and processing method of the present invention is shown. The method is performed by a combination of software and hardware comprised by the electronic device 2 shown in fig. 2.
In this embodiment, the method comprises steps S1-S10.
Step S1, a request sent by a user through a client is received, a data service list to be called corresponding to the request is determined, the request comprises a user identity, and the data service list to be called comprises data services to be called and a calling sequence.
The following describes embodiments of the present invention with the electronic device 2 as an execution subject. Taking a request sent by a user through the client 4 as a loan request for example, but not limited to, the loan request submitted by the user through the client 4 comprises a user identity, wherein the user identity comprises information capable of identifying the uniqueness of the user, such as a user name, an identity card number, a passport number and the like.
The client 4 is provided with a client APP by which a user submits a loan request.
In this embodiment, the request further includes request related information, and the determining the to-be-called data service list corresponding to the request includes:
determining the type of the request according to the request related information; a kind of electronic device with high-pressure air-conditioning system
And determining a data service list to be called corresponding to the type of the request according to the type of the request and the predetermined mapping data of the request type and the data service list.
The request-related information includes: application type, application amount, application period, user's academy, work unit address, etc. The application types include, but are not limited to, car credits, house credits, petty credits, and the like.
It will be appreciated that the data services that need to be invoked for different types of loan requests may be the same or different, even though the data services that need to be invoked may be the same, the order of their invocation may be different. In order to improve the accuracy and efficiency of data service calling, mapping data of different types of loan requests and corresponding data service lists are predetermined and stored, wherein the ordering of each data service in the data service list is the called order of each data service. For example, for a class A loan request, the corresponding list of data services is: data service A1, data service A2, data service A3; and B-type loan request, wherein the corresponding data service list is as follows: data service B1, data service B2, data service B3, …; and C-type loan request, wherein the corresponding data service list is as follows: data service C1, data service C2, data service C3, …; and so on.
And step S2, according to the calling sequence, calling the data services in the service list to be called to the data server in sequence according to the user identity, and receiving user data corresponding to the called data services, which are returned by the data server.
The list of the data services to be called corresponding to the request determined in the step above includes the names of the data services to be called and the called sequence corresponding to each data service to be called. Taking the current loan request as a class-a loan request as an example, assume that the corresponding list of data services to be invoked is: data service A1, data service A2, data service A3, then data service A1 is called first, data service A2 is called second, and data service A3 is called last.
It should be noted that, the electronic device 2 calls the data service to the data server 3, the data server 3 will first determine whether there is cache data corresponding to the called data service in the preset cache, if there is cache data, directly query/obtain the cache data from the cache and return the cache data to the electronic device 2, if there is no cache data, call the called data service to obtain user data, return the obtained user data to the electronic device 2, and store the user data in the preset cache. The preset cache may be a database, and the cache is implemented inside the data service system.
Further, in order to ensure the validity of the data, the user data stored in the preset buffer memory is released once at preset time intervals. For example, the preset time interval is 30minutes.
And step S3, judging whether the data service is abnormal or not according to the user data.
It can be understood that when it is determined that there is no abnormality in the data service, the step S8 may be performed to calculate the preset index corresponding to the data service directly using the received user data, and the subsequent call and request processing steps may be performed.
In this embodiment, the user data further includes a data tag, where the data tag includes a first preset tag and a second preset tag, and the "determining whether the data service has an abnormality" includes:
when the data service call fails, judging that the data service is abnormal;
when the data service is successfully invoked and the data label is a first preset label, judging that the data service is abnormal; a kind of electronic device with high-pressure air-conditioning system
And when the data service is successfully invoked and the data label is a second preset label, judging that the data service is not abnormal.
The first preset label is a data missing label "0", and the second preset label is a data complete label "1".
And after each execution of the data service call, judging the abnormality of the data service based on the returned user data. The data service exception includes: the data service calls for anomalies and user data anomalies. The data service call abnormality is failure of data service call caused by machine downtime, network abnormality and the like corresponding to the data service, and the user data abnormality is abnormality caused by missing key data influencing loan request approval.
It should be noted that, the user data and the data tag thereof are obtained by processing and analyzing the user original data by the data server 3, and the "processing and analyzing the user original data by the data server 3" includes:
acquiring user original data corresponding to the user identity, and processing the user original data to obtain user data corresponding to the user identity;
invoking a pre-configured data integrity judgment rule to carry out integrity judgment on the user data, and determining a data tag of the user data according to a judgment result; a kind of electronic device with high-pressure air-conditioning system
And storing the user data carrying the data tag.
The user original data are social security/public accumulation payment information, consumption information, communication information, credit investigation information and other data of the user. The user identity is in one-to-one correspondence with the original data and the user data. For example, the user identity is an identity card number, and the related data of the user is obtained from a preset database according to the identity card number and is used as the original data of the user.
In this embodiment, the "data processing the user raw data" includes:
and calculating user parameter indexes corresponding to the data services by using the original data.
The user parameter index is related to policies and services, and the calculation mode for calculating the user parameter index is preset and stored.
For example, for social security data, user data obtained after data processing includes: whether the social insurance is matched with the identity card, the number of times of cut-off of the social insurance, the number of medical insurance usage in the last 12 months, and the like.
For another example, for consumption data, the user data obtained after data processing includes: average amount consumed for the last 3 months, average income for the last 6 months, payment amount for the last 6 months, etc.
In this embodiment, the "invoking the preconfigured data integrity determination rule to perform integrity determination on the user data, and determining the data tag of the user data according to the determination result" includes:
invoking an integrity judgment rule corresponding to the request from a preset integrity judgment rule library, and judging whether the user data is complete or not based on the integrity judgment rule;
when the user data is incomplete, judging whether the user data is completely missing, if so, the data label is a first preset label, if so, judging whether the partially missing data is key data, and if so, the data label is a first preset label;
And when the user data is complete or the data which is incomplete but partially missing is not the key data, judging the data label as a second preset label.
And S4, when the data service is abnormal, acquiring a preset data service weight configuration table, and judging whether the data service is the first type of data service according to the data service weight configuration table.
The storage path of the data service weight configuration table is a configuration center. In the data service weight configuration table, the data service includes two kinds: a first class of data services and a second class of data services. Wherein the first type of data service is a must data service and the second type of data service is a non-must data service. It should be noted that the same data service may be a necessary data service or a non-necessary data service in different types of requests, for example, for a backlog related product: the data services such as the public accumulation data, the social security data and the consumption data are necessary data services, the data services such as the vehicle insurance data and the communication data are general data services, and for the vehicle insurance related products, the data services such as the vehicle insurance data and the consumption data are necessary data services, and the data services such as the public accumulation data and the communication data are general data services.
And S5, when the data service is the first type of data service, determining that the data service is abnormal data service, recording the abnormal times corresponding to the abnormal data service of the request, and judging whether the abnormal times are greater than or equal to a preset threshold value.
Also taking the request as a type A user loan request as an example, after receiving the user data returned by the data service A1, executing data service abnormality judging operation, when the data service A1 is abnormal and is necessary data service, recording the abnormal data service A1 corresponding to the current loan request and the abnormal times +1 of the current loan request due to the abnormal data service A1, and executing corresponding processing according to the abnormal times judging result. When the data service A1 is abnormal but is a general data service, it is considered that the user data corresponding to the data service A1 does not substantially affect the processing result of the subsequent request, so that the preset index corresponding to the data service A1 is not calculated, and the fourth judging step is continuously executed.
And S6, when the abnormal times are greater than or equal to a preset threshold value, generating early warning information and feeding back the early warning information to a preset terminal, and receiving an operation instruction fed back by the preset terminal.
In view of the network abnormality or the machine downtime corresponding to the data service causing the data call failure, an upper limit (preset threshold) of the retry number is set, and when the abnormality number is smaller than the preset threshold, the electronic device 2 is allowed to execute the above step S2 and the subsequent steps again at preset time intervals (for example, 5 min). However, to avoid the waste of computing resources, it may be understood that if the number of anomalies caused by the same data service in a loan request exceeds a preset threshold (for example, 5 times), it is indicated that the data service may have serious faults, and a manual intervention process is required, so that early warning information is sent to the preset terminal based on the anomaly information, so that the related service responsible person processes the anomaly data. The preset terminal is a terminal used by a responsible person corresponding to the current loan request.
And S7, when the operation instruction is a first instruction, calling the abnormal data service, receiving user data returned by the abnormal data service, and returning to the step S3.
The first instruction is a retry instruction. When a retry instruction is received, clearing the abnormal times corresponding to the current abnormal data service, taking the current abnormal data service as the data service to be called, and continuously executing normal abnormal judgment and subsequent analysis processing flow according to the original flow.
And S8, calculating an index value of a preset index corresponding to the data service according to the user data.
Taking the data service A1 in the class a loan request as an example, when the data service A1 is called to obtain corresponding user data and the data label is a label "1" representing the complete data, processing the preset index by using the user data corresponding to the data service A1 returned by the data server 3.
For example, the preset indexes include: the public accumulation fund payment period number, consumption index, social security, call record, pedestrian credit and other related indexes. The purpose of processing the preset indexes is to evaluate the repayment capability of the user and approve the loan request in the subsequent steps. For a certain data service, the above-mentioned processing of the preset index is divided into two types, one is to directly use the user data in the data service, and the other is to generate a composite score by using the user data of a plurality of data services, which will not be described in detail herein.
Step S9, judging whether the data service to be called exists in the data service list, if yes, returning to step S2, and if not, executing step S10.
Judging whether the data service to be called exists after the preset index corresponding to one data service is calculated, if so, returning to the step of executing the data service calling and abnormality judgment, for example, if the data service A3 to be called exists after the preset index corresponding to the data service A2 is processed, continuing to call the data service A3 and executing the subsequent steps; otherwise, a request processing step is performed, for example, after processing the preset indexes corresponding to the data service A3, and after all the data services are called, the loan request approval is started based on all the processed preset indexes.
Step S10, invoking a rule corresponding to the request from a preset rule engine, processing the request based on the rule and an index value of the preset index, and feeding back a processing result to the client 4.
The rule engine matches the data with the rules in the rule set one by one and outputs one or more matched results. The rule engine is composed of a rule set, a rule field set and rules. A rule engine may contain one or more rule sets, which may also include one or more rules created using rule fields, one for each rule when executing a matching job.
And approving the loan request based on the rule corresponding to the current loan request in the rule engine and the preset index calculated in the steps, and feeding back the approval result to the client 4.
In the above embodiments, the mapping relation table, the data service weight configuration table, the preset thresholds, and the like of the data services corresponding to the loan requests of each class are configured in the configuration center, instead of being stored in the form of configuration files. The system can be ensured to be modified and validated in real time under the condition of not restarting the system, thereby improving the processing capacity of the system.
Referring to FIG. 5, a flow chart of another embodiment of the anomaly analysis and processing method of the present invention is shown.
In this embodiment, the method includes steps S1-S6 and S8-S11, where steps S1-S6 and S8-S10 are identical to the implementation manners in the foregoing embodiments, and are not repeated herein.
And S11, when the operation instruction is a second instruction, calling the abnormal data service, receiving user data returned by the abnormal data service, judging whether the user data is abnormal or not, if not, executing the step S8 based on the user data, if so, not calculating a preset index corresponding to the abnormal data service, and executing the step S9.
The second instruction is a forced execution instruction. When a forced execution instruction is received, clearing the abnormal times corresponding to the current abnormal data service, calling the current abnormal data service and receiving corresponding user data, if the current abnormal data service is successfully called and the returned user data is complete, calculating a preset index by using the user data and executing a subsequent calling or approval process, and if the current abnormal data service is failed to be called or is successfully called but the user data is incomplete, not calculating the preset index corresponding to the current abnormal data service, and continuing to execute the subsequent calling or processing process.
In order to avoid serious abnormality of the data service to influence the request processing efficiency, the situation of each data service needs to be known in real time and fed back in time so as to improve the request processing efficiency. In other embodiments, the anomaly analysis and processing method further comprises:
acquiring the total abnormal times of the abnormal data service in the preset time, and generating early warning information to feed back to a designated terminal when the total abnormal times are greater than or equal to a preset time threshold;
and receiving an operation instruction fed back by the appointed terminal, and executing the step S7 or the step S11 based on the operation instruction.
The total abnormal times are the total loan application number requiring human intervention caused by abnormal data service in a preset time. The appointed terminal is a terminal used by a responsible person corresponding to the abnormal data service. The method comprises the steps of regularly polling and checking whether each data service has abnormal conditions, if the total abnormal times of a certain data service exceeds M times in a day, considering that the current data service has serious problems, generating early warning information to remind related personnel to process in time; if the total number of anomalies of a certain data service in a day does not exceed M, the batch of loan applications are automatically forced to be executed at a preset time point, and details are not repeated here.
According to the embodiments of the invention, after receiving a request sent by a user through a client 4, the data service corresponding to the request is sequentially called according to a preset sequence, when an abnormality occurs in the data service calling process, the abnormality problem of the data service is required to be solved, then other data services are called and related preset indexes are calculated, after all the data services are called, the request is processed based on the calculated preset indexes and preset rules, and the processing result is fed back to the client 4. 1) By setting an anomaly analysis mechanism, the data service with anomalies is analyzed, and the corresponding processing is carried out on the anomaly data service, so that the request processing risk caused by anomalies of the data service can be avoided; 2) When an abnormality exists in a certain data service, the calling of other data services is suspended, and the subsequent operation is not executed until the current abnormal data service is processed, so that repeated calling of the data service is avoided, the data calling cost is reduced, and the data calculation amount is reduced; 3) For the data service with abnormality, generating early warning information based on the corresponding abnormality information and feeding back the early warning information to the preset terminal, and executing different steps according to different instructions fed back by the preset terminal, thereby improving the efficiency and accuracy of request processing. In conclusion, by carrying out exception analysis and exception processing on the request, the request processing efficiency is improved while the data cost and the computing resources are saved.
In addition, the embodiment of the present invention further provides a computer readable storage medium, where the computer readable storage medium includes an anomaly analysis and processing program 10, and any steps in the anomaly analysis and processing method described above are implemented when the anomaly analysis and processing program 10 is executed by a processor, which is not described herein.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description of the preferred embodiments of the present invention should not be taken as limiting the scope of the invention, but rather should be understood to cover all modifications, equivalents, and alternatives falling within the scope of the invention as defined by the following description and drawings, or by direct or indirect application to other relevant art(s).
Claims (11)
1. An anomaly analysis and processing method is applied to an electronic device, and the electronic device, a data server and a client terminal carry out data transmission, and is characterized in that the method comprises the following steps:
step S1, a request sent by a user through the client is received, a data service list to be called corresponding to the request is determined, the request comprises a user identity, and the data service list to be called comprises data services to be called and a calling sequence;
step S2, according to the calling sequence, calling the data service in the data service list to be called to the data server in sequence according to the user identity, and receiving user data corresponding to the called data service returned by the data server;
step S3, judging whether the data service is abnormal according to the user data, including: judging that the data service is abnormal when the data service call fails, judging that the data service is abnormal when the data service call is successful and the data label is a first preset label representing data missing, and judging that the data service is not abnormal when the data service call is successful and the data label is a second preset label representing complete data, wherein the user data comprises the data label, the data label comprises the first preset label or the second preset label, and the first preset label and the second preset label are obtained by processing and analyzing the original data of the user by the data server;
Step S4, when the data service is abnormal, a preset data service weight configuration table is obtained, and whether the data service is the first type data service is judged according to the data service weight configuration table;
step S5, when the data service is the first type of data service, determining that the data service is an abnormal data service, recording the abnormal times corresponding to the abnormal data service of the request, and judging whether the abnormal times are greater than or equal to a preset threshold value;
step S6, when the abnormal times are greater than or equal to a preset threshold value, generating early warning information to be fed back to a preset terminal, and receiving an operation instruction fed back by the preset terminal;
step S7, when the operation instruction is a first instruction, calling the abnormal data service, receiving user data returned by the abnormal data service, and returning to the step S3;
step S8, calculating an index value of a preset index corresponding to the data service according to the user data;
step S9, judging whether the data service to be called exists in the data service list, if yes, returning to step S2, and if not, executing step S10;
step S10, calling a rule corresponding to the request from a preset rule engine, processing the request based on the rule and an index value of the preset index, and feeding back a processing result to the client.
2. The anomaly analysis and processing method of claim 1, wherein the processing analysis of the user raw data comprises:
acquiring user original data corresponding to the user identity, and processing the user original data to obtain user data corresponding to the user identity;
invoking a preconfigured data integrity judgment rule to carry out integrity judgment on the user data, and determining a first preset label or a second preset label of the user data according to a judgment result; a kind of electronic device with high-pressure air-conditioning system
And storing the user data carrying the first preset label or the second preset label.
3. The anomaly analysis and processing method of claim 2, wherein the "data processing the user raw data" comprises:
and calculating the user data corresponding to each data service by using the user original data.
4. The anomaly analysis and processing method according to claim 3, wherein the step of calling a preconfigured data integrity determination rule to perform integrity determination on the user data, and determining the first preset tag or the second preset tag of the user data according to the determination result comprises:
Invoking an integrity judgment rule corresponding to the request from a preset integrity judgment rule library, and judging whether the user data is complete or not based on the integrity judgment rule;
when the user data is judged to be incomplete, judging whether the user data is completely missing, if so, judging the user data to be a first preset label, if so, judging whether the partially missing data is key data, and if so, judging the user data to be the first preset label;
and when the user data is complete or the data which is incomplete but partially missing is not the key data, judging as a second preset label.
5. The anomaly analysis and processing method of claim 1, wherein the request further includes request related information, and the determining the list of data services to be invoked corresponding to the request includes:
determining the type of the request according to the request related information; a kind of electronic device with high-pressure air-conditioning system
And determining a data service list to be called corresponding to the type of the request according to the type of the request and the predetermined mapping data of the request type and the data service list.
6. The abnormality analysis and processing method according to any one of claims 1 to 5, characterized in that the abnormality analysis and processing method further includes:
When there is no abnormality in the data service, step S8 is performed.
7. The abnormality analysis and processing method according to claim 6, characterized in that the abnormality analysis and processing method further comprises:
and when the data service is not the first type of data service, not calculating a preset index corresponding to the data service, and executing step S9.
8. The abnormality analysis and processing method according to claim 6, characterized in that the abnormality analysis and processing method further comprises:
and when the number of the anomalies corresponding to the anomaly data service is smaller than a first preset threshold value, calling the anomaly data service at intervals of preset time, receiving user data returned by the anomaly data service, and returning to the execution step S3.
9. The abnormality analysis and processing method according to claim 6, characterized in that the abnormality analysis and processing method further comprises:
and S11, when the operation instruction is a second instruction, calling the abnormal data service, receiving user data returned by the abnormal data service, judging whether the user data is abnormal, if not, executing the step S8, if not, not calculating a preset index corresponding to the abnormal data service, and executing the step S9.
10. An electronic device, comprising: a memory, a processor, and an exception analyzing and handling program executable on the processor, wherein the exception analyzing and handling program, when executed by the processor, can implement the steps of the exception analyzing and handling method according to any of claims 1 to 9.
11. A computer-readable storage medium, wherein the computer-readable storage medium includes an abnormality analysis and processing program, and the abnormality analysis and processing program, when executed by a processor, implements the steps of the abnormality analysis and processing method according to any one of claims 1 to 9.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106790723A (en) * | 2017-03-23 | 2017-05-31 | 泰康保险集团股份有限公司 | The monitoring method of application service, load-balancing device and monitoring device |
| CN109146671A (en) * | 2018-08-28 | 2019-01-04 | 卫盈联信息技术（深圳）有限公司 | Air control method, apparatus and computer readable storage medium |
| CN110022373A (en) * | 2019-04-17 | 2019-07-16 | 北京达佳互联信息技术有限公司 | Method for distributing business, device, server and storage medium |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106681849B (en) * | 2015-11-10 | 2020-11-24 | 创新先进技术有限公司 | Data processing method and device |
-
2019
- 2019-11-08 CN CN201911092226.8A patent/CN110851298B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106790723A (en) * | 2017-03-23 | 2017-05-31 | 泰康保险集团股份有限公司 | The monitoring method of application service, load-balancing device and monitoring device |
| CN109146671A (en) * | 2018-08-28 | 2019-01-04 | 卫盈联信息技术（深圳）有限公司 | Air control method, apparatus and computer readable storage medium |
| CN110022373A (en) * | 2019-04-17 | 2019-07-16 | 北京达佳互联信息技术有限公司 | Method for distributing business, device, server and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 基于数据挖掘的个人征信系统异常查询实时监测模型及其应用;姚前等;《大数据》;20160720(第04期);全文 * |
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