CN115878633A - Risk processing method, device, equipment and storage medium - Google Patents
Risk processing method, device, equipment and storage medium Download PDFInfo
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Abstract
The application provides a risk processing method, a risk processing device, risk processing equipment and a storage medium, wherein the method is used for acquiring change data of a system to be processed; inputting the change data into a preset training model, and determining abnormal information in the system to be processed and an auditing rule corresponding to the abnormal information according to the output result of the preset training model; according to the abnormal information and the auditing rule corresponding to the abnormal information, data restoration is carried out on the abnormal information, and the problems that risks cannot be timely and accurately found and processed in a risk processing mode in the prior art, the timeliness and the accuracy of risk processing are poor, and user service handling interruption possibly occurs in the risk processing process are solved.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a risk processing method, apparatus, device, and storage medium.
Background
With the rapid increase of the number of users carried by the system, the more diversified service scenarios and the arrival of the multi-dimensional charging era of the fifth Generation Mobile Communication Technology (5G), the charging system is more and more complicated, so the risk faced by the charging system is higher and higher, any data abnormality may cause inaccurate charging, and the occurrence of inaccurate charging may affect the perception of the user, thereby causing complaints of the user and affecting the image of the enterprise, and on this basis, risk management becomes more important.
The existing risk processing method mainly comprises two parts of data auditing and abnormity monitoring, wherein the data auditing is to regularly audit newly added data according to a certain rule to generate an auditing result and then to manually repair, and the abnormity monitoring is to perform daily monitoring aiming at high cost, discover abnormity and start call ticket rollback.
However, the risk processing method in the prior art cannot timely and accurately find and process the risk, the timeliness and accuracy of risk processing are poor, and the user service acceptance may be interrupted in the risk processing process.
Disclosure of Invention
The application provides a risk processing method, a risk processing device, risk processing equipment and a storage medium, so that the technical problems of long time consumption and low efficiency in risk processing in the prior art are solved.
In a first aspect, the present application provides a risk processing method, including:
acquiring change data of a system to be processed;
inputting the change data into a preset training model, and determining abnormal information in the system to be processed and an auditing rule corresponding to the abnormal information according to an output result of the preset training model;
and performing data restoration on the abnormal information according to the abnormal information and the auditing rule corresponding to the abnormal information.
The application provides a risk processing method capable of performing data restoration in real time, which can acquire change data of a system to be processed in real time in a charging process, input the change data into a preset training model, and directly determine abnormal information and an audit rule corresponding to the abnormal information according to an output result of the model, so that whether the charging risk exists or not in the system to be processed can be identified and judged timely and quickly.
Optionally, before the inputting the change data into a preset training model, the method further includes:
acquiring historical change data of a system to be processed;
obtaining an auditing rule corresponding to the historical change data;
and inputting the historical change data and the audit rule corresponding to the historical change data into a training model for training to obtain a preset training model.
The application provides a training method of a preset training model, which includes acquiring historical change data of a system to be processed and audit rules corresponding to the historical change data, inputting the historical change data and the audit rules corresponding to the historical change data into the training model for model training, so that the accurate and efficient preset training model can be obtained, the change data can be output according to the model, abnormal information with charging risks can be accurately identified through the model, the audit rules corresponding to the abnormal information can be obtained, and the accuracy and the efficiency of risk processing are further improved.
Optionally, after obtaining the historical change data of the system to be processed, the method further includes:
the historical change data is stored in the form of database tables or files.
Here, after the history change data is acquired, the history change data can be stored according to a database table or a file form, so that the history change data can be classified according to different storage modes and data sources, different audit rules are determined, the history change data can be called in time, and data support is provided for real-time processing of the charging risk.
Optionally, after the storing the historical change data in the form of a database table or a file, the method further includes:
and acquiring newly added abnormal data, and storing the newly added abnormal data in a database table or file form.
The method and the device can further acquire newly-added abnormal data, wherein the newly-added abnormal data can be abnormal data which are not appeared in acquired historical change data when real-time data of a system to be processed is acquired, or abnormal data which are newly added by workers, and a data set of a preset training model can be updated according to the abnormal data, so that the preset training model can identify and process more abnormal conditions, deal with different charging risks, process different risks in time, further improve timeliness and comprehensiveness of risk processing, and improve user experience.
Optionally, the obtaining the audit rule corresponding to the historical change data includes:
and acquiring the auditing rule corresponding to the historical change data according to a preset auditing rule.
The application can acquire the auditing rules corresponding to the historical change data through the preset auditing rules, wherein the preset auditing rules can be pre-stored in the database, and the auditing rules corresponding to each historical change data are determined according to the auditing rules stored in the database.
Optionally, the obtaining the audit rule corresponding to the historical change data includes:
and acquiring an audit rule corresponding to the historical change data input by the user.
The auditing rule corresponding to the historical change data input by the user can be acquired, so that specific processing can be performed according to the setting of the user aiming at the specific historical change data or the historical change data without the specific auditing rule, and the comprehensiveness and the efficiency of risk processing are further improved.
Optionally, the acquiring change data of the system to be processed includes:
and acquiring data static parameter data, tariff static parameter data and newly added base station cell static parameter data of the system to be processed.
Here, the application obtains all the change data of the system to be processed in real time, including data static parameter data, tariff static parameter data and newly added base station cell static parameter data, so as to comprehensively and completely carry out investigation and repair of the charging risk according to the change data, and further improve the security and user experience of the charging system.
In a second aspect, the present application provides a risk processing apparatus, comprising:
the acquisition module is used for acquiring the change data of the system to be processed;
the input module is used for inputting the change data into a preset training model and determining abnormal information in the system to be processed and an auditing rule corresponding to the abnormal information according to an output result of the preset training model;
and the repairing module is used for repairing the data of the abnormal information according to the abnormal information and the auditing rule corresponding to the abnormal information.
Optionally, before the input module inputs the change data to a preset training model, the apparatus further includes:
a training module to: acquiring historical change data of a system to be processed;
obtaining an auditing rule corresponding to the historical change data;
and inputting the historical change data and the audit rule corresponding to the historical change data into a training model for training to obtain a preset training model.
Optionally, after the training module obtains the historical change data of the system to be processed, and after the training module obtains the historical change data of the system to be processed, the training module is further configured to:
the historical change data is stored in a database table or file.
Optionally, after the training module stores the historical change data in the form of a database table or a file, the apparatus further includes:
and the updating module is used for acquiring newly added abnormal data and storing the newly added abnormal data in a database table or file form.
Optionally, the training module is specifically configured to: and acquiring an audit rule corresponding to the historical change data according to a preset audit rule.
Optionally, the training module is further specifically configured to: and acquiring an auditing rule corresponding to the historical change data input by the user.
Optionally, the obtaining module is specifically configured to:
and acquiring data static parameter data, tariff static parameter data and newly added base station cell static parameter data of the system to be processed.
In a third aspect, the present application provides a risk processing device, comprising: at least one processor and a memory;
the memory stores computer-executable instructions;
the at least one processor executes the computer-executable instructions stored by the memory to cause the at least one processor to perform the risk processing method as set forth in the first aspect above and in various possible designs of the first aspect.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer-executable instructions that, when executed by a processor, implement the risk processing method as set forth in the first aspect above and in various possible designs of the first aspect.
In a fifth aspect, the present invention provides a computer program product comprising a computer program which, when executed by a processor, implements a risk processing method as described above in the first aspect and various possible designs of the first aspect.
The method can repair data in real time, specifically, change data of a system to be processed can be obtained in real time in a charging process, the change data is input into a preset training model, abnormal information and an auditing rule corresponding to the abnormal information can be directly determined according to an output result of the model, so that whether the abnormal information exists in the system to be processed or not, namely whether a charging risk exists or not can be identified and judged timely and quickly.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic diagram of a load balancing system according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a risk processing method according to an embodiment of the present application;
fig. 3 is a schematic flow chart of another risk processing method provided in the embodiment of the present application;
fig. 4 is a schematic structural diagram of a charging wind control system according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a risk processing apparatus according to an embodiment of the present disclosure;
fig. 6 is a schematic structural diagram of a risk processing apparatus according to an embodiment of the present application.
With the foregoing drawings in mind, certain embodiments of the disclosure have been shown and described in more detail below. These drawings and written description are not intended to limit the scope of the disclosed concepts in any way, but rather to illustrate the concepts of the disclosure to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
The terms "first," "second," "third," and "fourth," if any, in the description and claims of this application and the above-described figures are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
With the popularization of intelligent mobile terminals and the development of mobile networks, the mobile payment mode of operator charging gradually comes into the view of users, mobile payment actually covers a series of products and mechanisms, and the whole value chain also involves the benefits of multiple parties including mobile operators, service providers, equipment manufacturers, various large merchants, terminal users and the like. However, the charging system may have charging risks such as data eye-to-eye and data abnormality, which affects user experience, and therefore risk management of the charging system is very important. The traditional risk control mostly adopts modes of asynchronous auditing of data, background repairing of data, call ticket rollback, monitoring of abnormally high cost and the like for prevention and control.
With the rapid increase of the load of users carried by the current support system and the introduction of the leading-edge technology of the charging system architecture, the data processed by the charging system per second reaches tens of millions, and the traditional measures such as asynchronous audit monitoring and the like cannot meet the current risk challenge, so that a novel risk prevention and control strategy is urgently needed. The risk processing in the prior art is as follows: in the data auditing stage, generating data auditing logic according to a certain service rule; generating incremental change data, and performing batch verification on abnormal data; generating an audit result regularly, repairing data manually, scanning system bill data regularly in an abnormal monitoring stage, and giving an alarm (experts set a cost limit) aiming at the data exceeding a certain cost; acquiring system processing error data at regular time, and alarming if the system processing error data exceeds a set threshold; after the daily on-duty personnel receive the alarm information, the manual intervention stops the related application process. However, when the pre-processed data reaches tens of millions of users per second, the traditional asynchronous auditing and timing monitoring cannot find abnormal blocking risks in the first time, and tens of thousands of users may be affected when the abnormal blocking risks are audited or monitored; asynchronous data repair may cause interruption of service acceptance, affecting user perception. Therefore, the prior art has the technical problems that risks cannot be timely and accurately found and processed, the timeliness and the accuracy of risk processing are poor, and user service handling interruption can be caused in the risk processing process.
In order to solve the above problems, embodiments of the present application provide a risk processing method, device, equipment, and storage medium, where the method may perform real-time identification and processing on an abnormal charging result by auditing all change data in real time and combining with a preset training model of an artificial intelligence algorithm.
Optionally, fig. 1 is a schematic diagram of a risk processing system architecture provided in an embodiment of the present application. In fig. 1, the above-described architecture includes at least one of a receiving device 101, a processor 102, and a display device 103.
It is understood that the illustrated architecture of the embodiments of the present application does not constitute a specific limitation on the architecture of the risk processing system. In other possible embodiments of the present application, the foregoing architecture may include more or less components than those shown in the drawings, or combine some components, or split some components, or arrange different components, which may be determined according to practical application scenarios, and is not limited herein. The components shown in fig. 1 may be implemented in hardware, software, or a combination of software and hardware.
In a specific implementation process, the receiving device 101 may be an input/output interface or a communication interface.
The processor 102 may perform real-time audit on all the changed data, and combine with a preset training model of an artificial intelligence algorithm, may perform real-time recognition on the abnormal billing result, process the abnormal billing result, and the display device 103 may be used to display the above result, and may also perform interaction with a user through the display device.
The display device may also be a touch display screen for receiving user instructions while displaying the above-mentioned content to enable interaction with a user.
It should be understood that the processor may be implemented by reading instructions in the memory and executing the instructions, or may be implemented by a chip circuit.
In addition, the network architecture and the service scenario described in the embodiment of the present application are for more clearly illustrating the technical solution of the embodiment of the present application, and do not constitute a limitation to the technical solution provided in the embodiment of the present application, and it can be known by a person of ordinary skill in the art that, along with the evolution of the network architecture and the occurrence of a new service scenario, the technical solution provided in the embodiment of the present application is also applicable to similar technical problems.
The technical scheme of the application is described in detail by combining specific embodiments as follows:
optionally, fig. 2 is a schematic flow chart of a risk processing method provided in the embodiment of the present application. The execution subject of the embodiment of the present application may be the processor 102 in fig. 1, and the specific execution subject may be determined according to an actual application scenario. As shown in fig. 2, the method comprises the steps of:
s201: and acquiring the change data of the system to be processed.
Optionally, the obtaining change data of the system to be processed includes: and acquiring data static parameter data, tariff static parameter data and newly added base station cell static parameter data of the system to be processed.
The change data is acquired in real time when the system to be processed operates normally, and the data is acquired synchronously without asynchronous execution when the system operates.
Alternatively, the change data may be obtained from a network management system of the system to be processed.
Here, the embodiment of the application obtains all the change data of the system to be processed in real time, including the static parameter data of the data, the static parameter data of the tariff and the static parameter data of the newly added base station cell, so as to comprehensively and completely perform investigation and repair of the charging risk according to the change data, and further improve the security of the charging system and the user experience.
S202: and inputting the change data into a preset training model, and determining abnormal information in the system to be processed and an auditing rule corresponding to the abnormal information according to an output result of the preset training model.
The preset training model can identify according to the input change data to determine whether the change data has abnormal information with charging risk, and if so, the abnormal information and the corresponding audit rule are output.
Optionally, the audit rules include data logic audit rules, data difference comparison analysis rules and difference data repair rules.
S203: and performing data restoration on the abnormal information according to the abnormal information and the auditing rule corresponding to the abnormal information.
The embodiment of the application can automatically and intelligently repair abnormal data according to the audit rule without manual participation. Optionally, the data repair instruction may be automatically generated based on the audit rule, and the data may be corrected in real time.
Optionally, different coping strategies may be adopted according to the formulated risk level for the abnormal information, for example, if the abnormal value of the abnormal information is greater than a preset threshold, the process is triggered to be blown, so as to protect the security of the system.
It is understood that the preset threshold may be determined according to actual situations, and the embodiment of the present application is not particularly limited thereto.
The embodiment of the application provides a risk processing method capable of performing data restoration in real time, change data of a system to be processed can be obtained in real time in a charging process, the change data are input into a preset training model, abnormal information and an auditing rule corresponding to the abnormal information can be determined directly according to an output result of the model, and therefore whether the charging risk exists or not in the system to be processed can be identified and judged timely and quickly.
Optionally, in the embodiment of the present application, the model may be trained in advance, so as to identify the system risk according to the model, and accordingly, fig. 3 is a schematic flow diagram of another risk processing method provided in the embodiment of the present application, and as shown in fig. 3, the method includes:
s301: and acquiring the change data of the system to be processed.
S302: and acquiring historical change data of the system to be processed.
Optionally, the historical change data may be historical change data in a preset time period, where the preset time period may be determined according to an actual situation, and the embodiment of the present application does not specifically limit this.
Optionally, the historical change data may include historical data static parameter data, historical tariff static parameter data, and historical added base station cell static parameter data.
Alternatively, the historical change data can be obtained from historical webmaster data.
Optionally, after obtaining the historical change data of the system to be processed, the method further includes:
the historical change data is stored in the form of database tables or files. In order to conveniently manage and control the storage space of the host and the database, storage and cleaning strategies can be formulated.
Here, after the history change data is acquired, the history change data may be stored according to a database table or a file form, so as to classify the history change data according to different storage modes and data sources, determine different audit rules, and also facilitate timely retrieving the history change data, thereby providing data support for real-time processing of charging risks.
Optionally, after storing the historical change data in a database table or a file, the method further includes:
and acquiring newly added abnormal data, and storing the newly added abnormal data in a database table or file form.
Here, newly-added abnormal data may also be obtained in the embodiment of the present application, where the newly-added abnormal data may be abnormal data that does not appear in the obtained history change data when the real-time data of the system to be processed is obtained, or newly-added abnormal data that is newly added by a worker, and a data set of a preset training model may be updated according to the abnormal data, so that the preset training model may identify and process more abnormal situations, deal with different charging risks, and process different risks in time, thereby further improving timeliness and comprehensiveness of risk processing, and improving user experience.
S303: and obtaining an auditing rule corresponding to the historical change data.
Optionally, the obtaining of the audit rule corresponding to the historical change data includes:
and acquiring the auditing rule corresponding to the historical change data according to the preset auditing rule.
Here, in the embodiment of the application, the audit rule corresponding to the historical change data may be obtained through a preset audit rule, where the preset audit rule may be stored in a database in advance, and the audit rule corresponding to each historical change data is determined according to the audit rule stored in the database.
Optionally, the obtaining of the audit rule corresponding to the historical change data includes:
and acquiring an audit rule corresponding to historical change data input by a user.
Here, the embodiment of the application may further obtain the audit rule corresponding to the history change data input by the user, so that specific processing may be performed according to the setting of the user for specific history change data or history change data without a specific audit rule, and further comprehensiveness and efficiency of risk processing are improved.
S304: and inputting the historical change data and the audit rule corresponding to the historical change data into a training model for training to obtain a preset training model.
Here, the embodiment of the present application may perform model training based on the data, and when building a model, obtain a model parameter matrix: and performing data cleaning, feature selection, model construction, learning algorithm formulation and user model data acquisition on the basis of the user historical bills and the tariff data.
Optionally, in the preset training model, S represents all the change data, S 0 The auditing process matches different auditing rules to audit data logic according to different data sources, records a main Key Key value data source for the auditing abnormal data and writes the data source into a memory bank in real time.
J denotes all audit rule configuration actions, J 0 The data source and the data key factors are matched with the audit rule factor1 and the audit rule factor2 … … and the audit rule factor n, if the matching is successful, the changed data are in accordance with the rules, and the changed data are written into the memory base in an anti-regular mode.
R represents all data repair actions, R 0 And e.g. R, according to the configuration rule, triggering data repair to generate an instruction to trigger data correction.
S305: and inputting the change data into a preset training model, and determining abnormal information in the system to be processed and an auditing rule corresponding to the abnormal information according to an output result of the preset training model.
S306: and performing data restoration on the abnormal information according to the abnormal information and the auditing rule corresponding to the abnormal information.
Here, the implementation of steps S305 to S306 is similar to that of steps S202 to S203, and is not described herein again.
The embodiment of the application provides a training method of a preset training model, historical change data of a system to be processed and audit rules corresponding to the historical change data are obtained, the historical change data and the audit rules corresponding to the historical change data are input into the training model for model training, so that the accurate and efficient preset training model can be obtained, the change data can be output according to the model, abnormal information with charging risks can be accurately identified through the model, the audit rules corresponding to the abnormal information are obtained, and the accuracy and the efficiency of risk processing are further improved.
Optionally, fig. 4 is a schematic structural diagram of a charging wind control system provided in an embodiment of the present application, and as shown in fig. 4, the system may perform real-time auditing on changed data (real-time information), feed back charging core applications on auditing results in real time, and block abnormal charging results in real time by using an artificial intelligence algorithm and model data of a multi-level distributed cache technology in combination with user historical data, so as to implement throwing and monitoring blocking of abnormal information and prevent charging inaccuracy from affecting user perception.
Fig. 5 is a schematic structural diagram of a risk processing apparatus according to an embodiment of the present application, and as shown in fig. 5, the apparatus according to the embodiment of the present application includes: an acquisition module 501, an input module 502, and a repair module 503. The risk processing means here may be the processor itself described above, or a chip or an integrated circuit that implements the functionality of the processor. It should be noted here that the division of the obtaining module 501, the inputting module 502, and the repairing module 503 is only a division of logical functions, and the two may be integrated physically or may be independent.
The system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring the change data of a system to be processed;
the input module is used for inputting the change data into a preset training model and determining abnormal information in the system to be processed and an auditing rule corresponding to the abnormal information according to the output result of the preset training model;
and the repairing module is used for repairing the data of the abnormal information according to the abnormal information and the auditing rule corresponding to the abnormal information.
Optionally, before the input module inputs the change data to the preset training model, the apparatus further includes:
a training module to: acquiring historical change data of a system to be processed;
obtaining an auditing rule corresponding to historical change data;
and inputting the historical change data and the audit rule corresponding to the historical change data into a training model for training to obtain a preset training model.
Optionally, after the training module acquires the historical change data of the system to be processed and acquires the historical change data of the system to be processed, the training module is further configured to:
the historical change data is stored in the form of database tables or files.
Optionally, after the training module stores the historical change data in the form of a database table or a file, the apparatus further includes:
and the updating module is used for acquiring the newly added abnormal data and storing the newly added abnormal data in a database table or file form.
Optionally, the training module is specifically configured to: and acquiring the auditing rule corresponding to the historical change data according to the preset auditing rule.
Optionally, the training module is further specifically configured to: and acquiring an audit rule corresponding to historical change data input by a user.
Optionally, the obtaining module is specifically configured to:
and acquiring data static parameter data, tariff static parameter data and newly added base station cell static parameter data of the system to be processed.
Fig. 6 is a schematic structural diagram of a risk processing device according to an embodiment of the present application, where the risk processing device may be a server. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not limiting to the implementations of the present application described and/or claimed herein.
As shown in fig. 6, the risk processing apparatus includes: a processor 601 and a memory 602, the various components being interconnected using different buses, and may be mounted on a common motherboard or in other manners as desired. The processor 601 may process instructions executed within the risk processing device, including instructions for graphical information stored in or on a memory for display on an external input/output device (such as a display device coupled to the interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, if desired. In fig. 6, one processor 601 is taken as an example.
The memory 602, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the methods of the risk processing device in the embodiments of the present application (e.g., the obtaining module 501, the inputting module 502, and the repairing module 503 shown in fig. 5). The processor 601 executes various functional applications of the authentication platform and risk processing, i.e. the method of implementing the risk processing device in the above method embodiments, by running non-transitory software programs, instructions and modules stored in the memory 602.
The risk processing device may further include: an input device 603 and an output device 604. The processor 601, the memory 602, the input device 603 and the output device 604 may be connected by a bus or other means, and fig. 6 illustrates the connection by a bus as an example.
The input device 603 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the risk processing device, such as a touch screen, keypad, mouse, or multiple mouse buttons, track ball, joystick, or other input device. The output device 604 may be an output device such as a display device of a risk processing device. The display device may include, but is not limited to, a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display, and a plasma display. In some implementations, the display device can be a touch screen.
The risk processing device in the embodiment of the present application may be configured to execute the technical solutions in the method embodiments of the present application, and the implementation principles and technical effects are similar, which are not described herein again.
The embodiment of the application also provides a computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the computer-readable storage medium is used for implementing any one of the risk processing methods.
An embodiment of the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the computer program is configured to implement any one of the risk processing methods described above.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a division of a unit is merely a logical division, and an actual implementation may have another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (10)
1. A method of risk processing, comprising:
acquiring change data of a system to be processed;
inputting the change data into a preset training model, and determining abnormal information in the system to be processed and an auditing rule corresponding to the abnormal information according to an output result of the preset training model;
and performing data restoration on the abnormal information according to the abnormal information and the auditing rule corresponding to the abnormal information.
2. The method of claim 1, prior to said inputting said change data into a preset training model, further comprising:
acquiring historical change data of a system to be processed;
obtaining an auditing rule corresponding to the historical change data;
and inputting the historical change data and the audit rule corresponding to the historical change data into a training model for training to obtain a preset training model.
3. The method of claim 2, after obtaining historical change data of the pending system, further comprising:
the historical change data is stored in a database table or file.
4. The method of claim 3, wherein after storing the historical change data in a database table or file, further comprising:
and acquiring newly added abnormal data, and storing the newly added abnormal data in a database table or file form.
5. The method according to any one of claims 2 to 4, wherein the obtaining of the audit rule corresponding to the historical change data comprises:
and acquiring an audit rule corresponding to the historical change data according to a preset audit rule.
6. The method according to any one of claims 2 to 4, wherein the obtaining of the audit rule corresponding to the historical change data comprises:
and acquiring an auditing rule corresponding to the historical change data input by the user.
7. The method according to any one of claims 1 to 4, wherein the acquiring change data of the system to be processed comprises:
and acquiring data static parameter data, tariff static parameter data and newly added base station cell static parameter data of the system to be processed.
8. A risk processing apparatus, comprising:
the acquisition module is used for acquiring the change data of the system to be processed;
the input module is used for inputting the change data into a preset training model and determining abnormal information in the system to be processed and an auditing rule corresponding to the abnormal information according to an output result of the preset training model;
and the repairing module is used for repairing the data of the abnormal information according to the abnormal information and the auditing rule corresponding to the abnormal information.
9. A risk processing device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the risk processing method of any one of claims 1 to 7.
10. A computer-readable storage medium having computer-executable instructions stored therein, which when executed by a processor, are configured to implement the risk processing method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111131265.1A CN115878633A (en) | 2021-09-26 | 2021-09-26 | Risk processing method, device, equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111131265.1A CN115878633A (en) | 2021-09-26 | 2021-09-26 | Risk processing method, device, equipment and storage medium |
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| Publication Number | Publication Date |
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| CN115878633A true CN115878633A (en) | 2023-03-31 |
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| CN202111131265.1A Pending CN115878633A (en) | 2021-09-26 | 2021-09-26 | Risk processing method, device, equipment and storage medium |
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| CN (1) | CN115878633A (en) |
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