CN111861021A - Business risk prediction method, device, equipment and computer readable storage medium - Google Patents

Business risk prediction method, device, equipment and computer readable storage medium Download PDF

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CN111861021A
CN111861021A CN202010735453.4A CN202010735453A CN111861021A CN 111861021 A CN111861021 A CN 111861021A CN 202010735453 A CN202010735453 A CN 202010735453A CN 111861021 A CN111861021 A CN 111861021A
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epc
service data
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business
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CN111861021B (en
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郑圣
朱卫锋
姚勇
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China United Network Communications Group Co Ltd
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China United Network Communications Group Co Ltd
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Abstract

The application provides a business risk prediction method, a device, equipment and a computer readable storage medium, wherein the method comprises the steps of obtaining business data related to EPC business; determining the service data change trend of the EPC service according to the service data; judging whether the EPC service is abnormal or not according to the prestored service data change trend and the service data change trend of the EPC service; if the EPC service is abnormal, the fault in the EPC service is positioned according to the abnormality, and whether the service fails or not can be judged in advance before the fault occurs by combining historical service data, so that the service fault can be prevented before the fault occurs, the EPC service loss is reduced, the waste of manpower and material resources is reduced, and the cost is effectively reduced.

Description

Business risk prediction method, device, equipment and computer readable storage medium
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, a device, and a computer-readable storage medium for predicting business risk.
Background
Nowadays, the competition of the communication industry is increasingly fierce, and with the coming of richer digital services and the world of everything interconnection, a network with high reliability, high stability and high connectivity is used as an expressway for communication information, and has become the foundation and core competitiveness for commercial success of operators. The EPC mobile data service is used as a basic part of increasingly diversified mobile services, and needs to sensitively sense service abnormality and reduce fault influence, and the EPC service bears 2/3/4G standard, has multiple service types, multiple peripheral interfaces, high fault definition difficulty and high requirement on skills of maintenance personnel.
In the related art, the EPC service can be processed passively only after the fault occurs, and the fault is located by means of manual experience.
Therefore, the EPC service processed by the method has large loss, long time for fault recovery, manpower and material resources waste and high cost.
Disclosure of Invention
In order to solve the problems in the prior art, the application provides a business risk prediction method, a business risk prediction device, business risk prediction equipment and a computer readable storage medium.
In order to achieve the above object, the embodiments of the present invention provide the following technical solutions:
in a first aspect, an embodiment of the present application provides a business risk prediction method, which may be executed by a business risk prediction apparatus, and the method includes the following steps:
acquiring service data related to EPC service;
determining the service data change trend of the EPC service according to the service data;
judging whether the EPC service is abnormal or not according to a prestored service data change trend and the service data change trend of the EPC service;
and if the EPC service is abnormal, positioning the fault in the EPC service according to the abnormality.
Here, the embodiment of the application can determine a change trend of the service data according to the service data, and compare the change trend of the service data with a pre-stored change trend of the service data, wherein the pre-stored change trend of the service data can be determined by combining historical service data, so that whether a service fails or not is judged in advance by combining the historical service data before the failure occurs, the service failure is prevented before the failure occurs, the loss of the EPC service is reduced, the waste of manpower and material resources is reduced, and the cost is effectively reduced.
Optionally, the determining a service data change trend of the EPC service according to the service data includes
Inputting the service data into a preset model, wherein the preset model is obtained through service data and service data change trend training;
and acquiring the service data change trend of the EPC service output by the preset model.
The embodiment of the application provides a method for determining a business data change trend, the business data change trend can be obtained by inputting business data into a preset model, and the preset model is obtained by training a large amount of business data and the business data change trend, so that the business data change trend determined by the preset model is accurate, the accuracy of business risk prediction is further improved, the reliability of business is improved, the loss of EPC business is reduced, in addition, the business data are input into the preset model, the automatic fault prediction can be realized, manpower and material resources are reduced, and the cost is reduced.
Optionally, the determining whether the EPC service is abnormal according to a pre-stored service data change trend and a service data change trend of the EPC service includes:
comparing the pre-stored service data change trend with the fluctuation characteristic, the periodicity characteristic, the same-ratio ring ratio fitting characteristic, the statistical characteristic and the distribution characteristic of the service data change trend of the EPC service;
and judging whether the EPC service is abnormal or not according to the comparison result.
Here, because the characteristics of different types of services are different in performance, the embodiment of the present application compares the fluctuation characteristic, the periodicity characteristic, the homographic ring ratio fitting characteristic, the statistical characteristic, and the distribution characteristic of the pre-stored service data change trend and the service data change trend of the EPC service for different services, and by comparing the plurality of characteristics, the abnormal conditions of different types of services can be accurately determined, thereby further improving the accuracy of service risk prediction and improving the reliability of the EPC service.
Optionally, the locating the fault in the EPC service according to the exception includes:
and performing cluster analysis on the abnormity, and positioning faults in the EPC service, wherein the faults comprise number faults, terminal faults, cell faults and network element faults.
Here, the embodiment of the present application may perform label classification on the collected abnormalities and faults by using a clustering method, so as to obtain the fault type of the service abnormality, directly locate the failed network element and the fault location thereof, facilitate adjustment of the service, reduce the fault recovery time, and improve the efficiency of service fault processing.
Optionally, the service data includes a Call History Report (CHR) data, a Call alarm, an operation log, a configuration, and a system log;
the acquiring service data related to the EPC service includes:
and periodically acquiring the speech system, the operation log, the system log and the CHR data, acquiring the alarm in real time, and periodically or in real time configuring.
Here, when acquiring service data, the embodiment of the application acquires session management, operation log, system log and CHR data periodically for different service data acquisition times, and can acquire comprehensive data while saving internal resources of a system, and acquire warning information in real time to ensure timeliness of fault handling, improve service stability and further improve accuracy of service risk prediction.
In a second aspect, an embodiment of the present application provides a business risk prediction apparatus, including:
the system comprises an acquisition module, a service processing module and a service processing module, wherein the acquisition module is used for acquiring service data related to EPC service;
the determining module is used for determining the service data change trend of the EPC service according to the service data;
the judging module is used for judging whether the EPC service is abnormal or not according to the prestored service data change trend and the service data change trend of the EPC service;
and the positioning module is used for positioning the fault in the EPC service according to the abnormity if the EPC service is abnormal.
Optionally, the determining module is specifically configured to:
inputting the service data into a preset model, wherein the preset model is obtained through service data and service data change trend training;
and acquiring the service data change trend of the EPC service output by the preset model.
Optionally, the determining module is specifically configured to:
comparing the pre-stored service data change trend with the fluctuation characteristic, the periodicity characteristic, the same-ratio ring ratio fitting characteristic, the statistical characteristic and the distribution characteristic of the service data change trend of the EPC service;
and judging whether the EPC service is abnormal or not according to the comparison result.
Optionally, the positioning module is specifically configured to:
and performing cluster analysis on the abnormity, and positioning faults in the EPC service, wherein the faults comprise number faults, terminal faults, cell faults and network element faults.
Optionally, the service data includes a call system, an alarm, an operation log, a configuration, a system log, and CHR call history record report data;
the acquisition module is specifically configured to:
and periodically acquiring the speech system, the operation log, the system log and the CHR data, acquiring the alarm in real time, and periodically or in real time configuring.
In a third aspect, an embodiment of the present application provides a business risk prediction device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a business risk prediction method as described in the first aspect or an alternative form of the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored, and when the computer-executable instructions are executed by a processor, the method for predicting business risk according to the first aspect or the alternatives of the first aspect is implemented.
The method determines the change trend of the business data according to the business data, and compares the change trend of the business data with the pre-stored change trend of the business data, wherein the pre-stored change trend of the business data can be determined by combining historical business data, so that whether the business fails or not is judged in advance by combining the historical business data before the failure occurs, the business failure is prevented before the failure occurs, the EPC business loss is reduced, the waste of manpower and material resources is reduced, and the cost is effectively reduced.
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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 business risk prediction system according to an embodiment of the present disclosure;
fig. 2 is a schematic flowchart of a business risk prediction method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of another business risk prediction method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another business risk prediction method according to an embodiment of the present application;
fig. 5 is a schematic flowchart of another business risk prediction method according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a business risk prediction apparatus according to an embodiment of the present application;
fig. 7 is a schematic structural diagram of a business risk prediction device 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.
The EPC, as a fourth-generation mobile communication technology, has incomparable advantages, can rapidly transmit voice, text, video and image information, can meet the requirements of almost all users on wireless services, and is increasingly competitive in the current communication industry, and as increasingly abundant digital services and the world of everything interconnection come, a highly reliable, highly stable and highly connected network is used as an expressway for communication information, which has become the foundation and core competitiveness for commercial success of operators. The EPC mobile data service is used as a basic part of increasingly diversified mobile services, and needs to sensitively sense service abnormality and reduce fault influence, and the EPC service bears 2/3/4G standard, has multiple service types, multiple peripheral interfaces, high fault definition difficulty and high requirement on skills of maintenance personnel. In the related technology, the EPC service can be processed passively only after a fault occurs, the fault is positioned by manual experience, network hidden danger real-time monitoring is lacked, and network risks cannot be identified in advance. Therefore, the EPC service processed by the method has large loss, long time for fault recovery, manpower and material resources waste and high cost.
In order to solve the above problems, embodiments of the present application provide a method, an apparatus, a device, and a computer-readable storage medium for predicting a business risk, where a business data change trend is determined according to business data, and the business data change trend is compared with a pre-stored business data change trend, where the pre-stored business data change trend can be determined in combination with historical business data, so as to determine in advance whether a business will fail in combination with the historical business data before the failure occurs, and prevent the business failure before the failure occurs, thereby reducing EPC business loss, reducing waste of manpower and material resources, and effectively reducing cost.
Optionally, fig. 1 is a schematic view of a business risk prediction and evaluation 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 can be understood that the structure illustrated in the embodiment of the present application does not constitute a specific limitation on the business risk prediction and evaluation architecture. 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. 2 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 determine a change trend of the service data according to the service data, and compare the change trend of the service data with a pre-stored change trend of the service data, where the pre-stored change trend of the service data may be determined in combination with historical service data, so as to determine in advance whether the service fails before the failure occurs, and prevent the service failure before the failure occurs, thereby reducing EPC service loss, reducing waste of manpower and material resources, and effectively reducing cost.
The display device 103 may be used to display the above results and the like.
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 skilled in the art that along with the evolution of the network architecture and the appearance 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:
fig. 2 is a schematic flow chart of a business risk prediction method provided in an embodiment of the present application, an execution subject in the embodiment of the present application may be the processor 102 in fig. 1, and a specific execution subject may be determined according to an actual application scenario. As shown in fig. 2, the method comprises the steps of:
s201: service data related to the EPC service is acquired.
Optionally, the service data related to the EPC service may include data such as a session, an alarm, a configuration, a CHR, an operation log, and a system log related to the EPC service.
Optionally, the service data includes a call system, an alarm, an operation log, a configuration, a system log, and CHR call history record report data; acquiring service data related to EPC service, including: and periodically acquiring a speech system, an operation log, a system log and CHR data, acquiring an alarm in real time, and configuring periodically or in real time.
When the service data is acquired, the session system, the operation log, the system log and the CHR data are acquired periodically according to different service data acquisition times, comprehensive data can be acquired while internal resources of the system are saved, timeliness of fault processing can be guaranteed by acquiring alarm information in real time, accuracy of service risk prediction is further improved while service stability is improved, the service can be configured periodically according to the data, the service can be configured in real time, and flexibility of service processing is achieved.
S202: and determining the service data change trend of the EPC service according to the service data.
S203: and judging whether the EPC service is abnormal or not according to the pre-stored service data change trend and the service data change trend of the EPC service.
Optionally, the determining of whether the EPC service is abnormal may be performed according to a pre-stored service data change trend and a service data change trend of the EPC service, determining a service data change trend safety threshold range according to the pre-stored service data change trend, and if the currently acquired service data change trend is not within the service data change trend safety threshold range, determining that the EPC service is abnormal.
S204: and if the EPC service is abnormal, positioning the fault in the EPC service according to the abnormality.
According to the embodiment of the application, the change trend of the business data can be determined according to the business data, and through comparing the change trend of the business data with the pre-stored change trend of the business data, whether the business fails or not can be judged in advance before the fault occurs by combining historical business data, so that the business fault can be prevented before the fault occurs, the EPC business loss is reduced, the waste of manpower and material resources is reduced, and the cost is effectively reduced.
Optionally, the service data change trend of the EPC service is determined according to the service data, and may be obtained by inputting the service data into a preset model, and accordingly, fig. 3 is a schematic flow diagram of another service risk prediction method provided in this embodiment, as shown in fig. 3, the method includes:
s301: service data related to the EPC service is acquired.
Step S301 is the same as the implementation of step S201, and is not described herein again.
S302: and inputting the service data into a preset model.
The preset model is obtained through business data and business data change trend training.
S303: and acquiring the service data change trend of the EPC service output by the preset model.
Alternatively, the preset model may be a dynamic threshold model established by an Artificial Intelligence (AI) algorithm.
The AI algorithm can realize the automatic identification function aiming at different data characteristics, and the service data input into the dynamic threshold model established by the AI algorithm can be automatically matched with different algorithms according to the characteristics of the service data to obtain the accurate service data change trend.
Optionally, a plurality of different preset models can be trained according to different business data and business data variation trends and different algorithms, so that the accuracy of business risk prediction is improved.
S304: and judging whether the EPC service is abnormal or not according to the pre-stored service data change trend and the service data change trend of the EPC service.
S305: and if the EPC service is abnormal, positioning the fault in the EPC service according to the abnormality.
The steps S304 and S305 are the same as the steps S203 and S204, and are not described herein again.
The embodiment of the application provides a method for determining a business data change trend, the business data change trend can be obtained by inputting business data into a preset model, and the preset model is obtained by training a large amount of business data and the business data change trend, so that the business data change trend determined by the preset model is accurate, the accuracy of business risk prediction is further improved, the reliability of business is improved, the EPC business loss is reduced, in addition, the business data are input into the preset model, the automatic fault prediction can be realized, manpower and material resources are reduced, and the cost is reduced.
Optionally, whether the EPC service is abnormal or not is determined according to a pre-stored service data change trend and a service data change trend of the EPC service, which may be comparing different feature quantities of the pre-stored service data change trend and the service data change trend of the EPC service, where fig. 4 is a flowchart of another service risk prediction method provided in this embodiment, as shown in fig. 4, the method includes:
s401: service data related to the EPC service is acquired.
S402: and determining the service data change trend of the EPC service according to the service data.
The steps S401 and S402 are the same as the steps S201 and S202, and are not described herein again.
S403: comparing the variation trend of the prestored service data with the fluctuation characteristic, the periodicity characteristic, the same-ratio ring ratio fitting characteristic, the statistical characteristic and the distribution characteristic of the variation trend of the service data of the EPC service; and judging whether the EPC service is abnormal or not according to the comparison result.
Optionally, different service data change trends have different characteristics, and comparison needs to be performed in combination with the characteristics of the data when the different service data change trends are judged.
Optionally, for a service data change trend with large volatility, the fluctuation feature of the service data change trend can be compared, for a service data change trend with strong periodicity, the fluctuation feature periodicity feature of the service data change trend can be compared, for a service data change trend with obvious homographic ring ratio fitting feature, statistical feature and distribution feature, the homographic ring ratio fitting feature, statistical feature and distribution feature are compared, the comparability of the data and the accuracy of service anomaly judgment can be further improved, the accuracy of service risk prediction is further improved, and the reliability of service is improved.
S404: and if the EPC service is abnormal, positioning the fault in the EPC service according to the abnormality.
According to the embodiment of the application, the fluctuation characteristics, the periodicity characteristics, the same-proportion ring ratio fitting characteristics, the statistical characteristics and the distribution characteristics of the pre-stored service data change trend and the service data change trend of the EPC service are compared aiming at different services, and the abnormal conditions of different types of services can be accurately determined through comparing the characteristics, so that the accuracy of service risk prediction is further improved, and the reliability of the EPC service is improved.
Optionally, the fault in the EPC service is located according to the abnormality, where the fault may be located by classifying the fault through cluster analysis, fig. 5 is a schematic flow diagram of another service risk prediction method according to the embodiment of the present application, and as shown in fig. 5, the method includes:
s501: service data related to the EPC service is acquired.
S502: and determining the service data change trend of the EPC service according to the service data.
S503: and judging whether the EPC service is abnormal or not according to the pre-stored service data change trend and the service data change trend of the EPC service.
The steps S501 to S503 are the same as the steps S201 to S203, and are not described herein again.
S503: and performing cluster analysis on the abnormity, and positioning faults in the EPC service, wherein the faults comprise number faults, terminal faults, cell faults and network element faults.
Optionally, the cluster analysis may be a method based on hierarchical clustering, partition-based clustering, density-based clustering, grid-based clustering, or model-based clustering, which is not specifically limited in this embodiment of the present application.
Optionally, the faults include, but are not limited to, number faults, terminal faults, cell faults, and network element faults, and the faults are classified into fault classifications of multiple dimensions, so that the faults can be located, and therefore, the faults can be prevented and processed conveniently.
The cluster analysis has the function of dividing a set of physical or abstract objects into a plurality of classes consisting of similar objects, wherein each of the divided classes contains similar characteristics.
According to the method and the device, collected abnormity and faults can be marked and classified by adopting a clustering mode, so that the fault type of abnormal service is obtained, the fault network element and the fault position of the fault network element are directly positioned, the service is conveniently adjusted, the fault recovery time is shortened, and the service fault processing efficiency is improved.
Fig. 6 is a schematic structural diagram of a business risk prediction apparatus provided in an embodiment of the present application, and as shown in fig. 6, the apparatus in the embodiment of the present application includes: an obtaining module 601, a determining module 602, a judging module 603 and a positioning module 604. The business risk prediction device may be the processor 102 itself, or a chip or an integrated circuit that implements the functions of the processor 102. It should be noted that the division of the obtaining module 601, the determining module 602, the determining module 603, and the positioning module 604 is only a division of logical functions, and the two may be integrated or independent physically.
The obtaining module 601 is configured to obtain service data related to an EPC service;
a determining module 602, configured to determine a service data change trend of the EPC service according to the service data;
the determining module 603 is configured to determine whether the EPC service is abnormal according to a pre-stored service data change trend and a service data change trend of the EPC service;
the positioning module 604 is configured to, if the EPC service is abnormal, position a fault in the EPC service according to the abnormality.
Optionally, the determining module 602 is specifically configured to:
inputting the business data into a preset model, wherein the preset model is obtained through business data and business data change trend training;
and acquiring the service data change trend of the EPC service output by the preset model.
Optionally, the determining module 603 is specifically configured to:
comparing the variation trend of the prestored service data with the fluctuation characteristic, the periodicity characteristic, the same-ratio ring ratio fitting characteristic, the statistical characteristic and the distribution characteristic of the variation trend of the service data of the EPC service;
and judging whether the EPC service is abnormal or not according to the comparison result.
Optionally, the positioning module 604 is specifically configured to:
and performing cluster analysis on the abnormity, and positioning faults in the EPC service, wherein the faults comprise number faults, terminal faults, cell faults and network element faults.
Optionally, the service data includes a call system, an alarm, an operation log, a configuration, a system log, and CHR call history record report data;
the obtaining module 601 is specifically configured to:
and periodically acquiring a speech system, an operation log, a system log and CHR data, acquiring an alarm in real time, and configuring periodically or in real time.
Fig. 7 is a schematic structural diagram of a business risk prediction device according to an embodiment of the present application. 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. 7, the business risk prediction apparatus includes: a processor 701 and a memory 702, each connected to each other using a different bus, and may be mounted on a common motherboard or in other manners as needed. Processor 701 may process instructions for execution within a business risk prediction apparatus, 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 an interface). In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. In fig. 7, one processor 701 is taken as an example.
The memory 702, which is 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 method of the business risk prediction apparatus in the embodiment of the present application (for example, the obtaining module 601, the determining module 602, and the determining module 603 shown in fig. 6). The processor 701 executes various functional applications of the server and data processing by running non-transitory software programs, instructions and modules stored in the memory 702, namely, the method of implementing the business risk prediction apparatus in the above method embodiment.
The business risk prediction apparatus may further include: an input device 703 and an output device 704. The processor 701, the memory 702, the input device 703 and the output device 704 may be connected by a bus or other means, and fig. 7 illustrates an example of a connection by a bus.
The input device 703 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the business risk prediction apparatus, such as a touch screen, a keypad, a mouse, or a plurality of mouse buttons, a trackball, a joystick, or the like. The output device 704 may be an output device such as a display device of the business risk prediction 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 service risk prediction 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 principle and the technical effect are similar, which are not described herein again.
The embodiment of the present application further provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when the computer-executable instructions are executed by a processor, the computer-executable instructions are used to implement any one of the business risk prediction 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 can be realized in a form of hardware, and can also be realized in a 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 business risk prediction method is characterized by comprising the following steps:
acquiring service data related to EPC service;
determining the service data change trend of the EPC service according to the service data;
judging whether the EPC service is abnormal or not according to a prestored service data change trend and the service data change trend of the EPC service;
and if the EPC service is abnormal, positioning the fault in the EPC service according to the abnormality.
2. The method according to claim 1, wherein the determining the trend of the EPC service based on the service data comprises
Inputting the service data into a preset model, wherein the preset model is obtained through service data and service data change trend training;
and acquiring the service data change trend of the EPC service output by the preset model.
3. The method for predicting risk of business according to claim 1, wherein the determining whether the EPC service is abnormal according to a pre-stored trend of business data and a trend of business data of the EPC service comprises:
comparing the pre-stored service data change trend with the fluctuation characteristic, the periodicity characteristic, the same-ratio ring ratio fitting characteristic, the statistical characteristic and the distribution characteristic of the service data change trend of the EPC service;
and judging whether the EPC service is abnormal or not according to the comparison result.
4. The method of predicting business risk according to claim 1, wherein the locating the fault in the EPC business according to the anomaly comprises:
and performing cluster analysis on the abnormity, and positioning faults in the EPC service, wherein the faults comprise number faults, terminal faults, cell faults and network element faults.
5. The business risk prediction method of claim 1 wherein the business data comprises session, alarm, operation log, configuration, system log, and CHR session history report data;
the acquiring service data related to the EPC service includes:
and periodically acquiring the speech system, the operation log, the system log and the CHR data, acquiring the alarm in real time, and periodically or in real time configuring.
6. A business risk prediction apparatus, comprising:
the system comprises an acquisition module, a service processing module and a service processing module, wherein the acquisition module is used for acquiring service data related to EPC service;
the determining module is used for determining the service data change trend of the EPC service according to the service data;
the judging module is used for judging whether the EPC service is abnormal or not according to the prestored service data change trend and the service data change trend of the EPC service;
and the positioning module is used for positioning the fault in the EPC service according to the abnormity if the EPC service is abnormal.
7. The apparatus of claim 6, wherein the determining module is specifically configured to:
inputting the service data into a preset model, wherein the preset model is obtained through service data and service data change trend training;
and acquiring the service data change trend of the EPC service output by the preset model.
8. The apparatus of claim 6, wherein the determining module is specifically configured to:
comparing the pre-stored service data change trend with the fluctuation characteristic, the periodicity characteristic, the same-ratio ring ratio fitting characteristic, the statistical characteristic and the distribution characteristic of the service data change trend of the EPC service;
and judging whether the EPC service is abnormal or not according to the comparison result.
9. A business risk prediction apparatus, 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 method of any one of claims 1 to 5.
10. A computer-readable storage medium, wherein computer-executable instructions are stored in the computer-readable storage medium, and when executed by a processor, the computer-executable instructions are configured to implement the business risk prediction method according to any one of claims 1 to 5.
CN202010735453.4A 2020-07-28 Service risk prediction method, device, equipment and computer readable storage medium Active CN111861021B (en)

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