CN116089133A - Abnormal business scene detection method, device, computing equipment and storage medium - Google Patents
Abnormal business scene detection method, device, computing equipment and storage medium Download PDFInfo
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- CN116089133A CN116089133A CN202111316034.8A CN202111316034A CN116089133A CN 116089133 A CN116089133 A CN 116089133A CN 202111316034 A CN202111316034 A CN 202111316034A CN 116089133 A CN116089133 A CN 116089133A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
- G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
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- G06F16/95—Retrieval from the web
- G06F16/955—Retrieval from the web using information identifiers, e.g. uniform resource locators [URL]
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Abstract
The invention discloses an abnormal service scene detection method, an abnormal service scene detection device, computing equipment and a storage medium, wherein the abnormal service scene detection method comprises the following steps: acquiring scene information of historical abnormal service scenes of a service system, constructing an abnormal service scene library, and recording processing strategies corresponding to each historical abnormal service scene in the abnormal service scene library to form a processing strategy library; constructing a corresponding coding rule set according to the URL of the historical abnormal service scene and scene information; when the business system is abnormal, a corresponding target coding rule set is determined according to the URL of the current abnormal business scene, scene information of the current abnormal business scene is matched with the target coding rule set, and if the matching is successful, a processing strategy corresponding to the current abnormal business scene is determined from a processing strategy library. The invention detects and processes the corresponding abnormal service scene in an automatic operation and maintenance mode, thereby assisting the service system to conduct the investigation of the abnormal service scene and improving the working efficiency of the service system.
Description
Technical Field
The present invention relates to the field of communications technologies, and in particular, to a method, an apparatus, a computing device, and a storage medium for detecting an abnormal service scenario.
Background
The development and test of a Web software system are required to be subjected to long development period and repeated for a plurality of times, and in the process of carrying out service test, various problems of service blocking and data abnormality often occur, code investigation is required to be carried out by research personnel, and sometimes, service scene reproduction is required to be carried out by test personnel to assist in investigation of the problems.
Aiming at the problems in the research and development process, the conventional scheme generally comprises the steps of compiling unit tests by research and development personnel after compiling service codes, and executing the unit tests to prove that the behavior of the code segment is consistent with that expected by the research and development personnel. The successful execution of the unit test is to ensure that the service system is not abnormal from the code perspective, the unit test can prevent the situation that the development is out of control due to excessive system code errors (bugs) to a certain extent at the later stage, but from the aspects of functions and services, the unit test cannot ensure that the service flow and the system are not abnormal during normal operation.
In addition, after the system is subjected to strict test, the system is successfully delivered on line to perform production activities, various unpredictable situations, such as network strategy failure, external system abnormality, attack and the like, occur in the process of performing the production activities, once the situation occurs, the business data can be abnormal, so that the business process cannot be normally executed, and at the moment, system operation and maintenance personnel can perform problem investigation and business data restoration, and the process is very time-consuming and labor-consuming.
Disclosure of Invention
The present invention has been made in view of the above problems, and provides an abnormal traffic scenario detection method, apparatus, computing device, and storage medium that overcome or at least partially solve the above problems.
According to one aspect of the present invention, there is provided an abnormal traffic scene detection method, including:
acquiring scene information of historical abnormal business scenes of a business system, constructing an abnormal business scene library according to the scene information, and recording processing strategies corresponding to each historical abnormal business scene in the abnormal business scene library to form a processing strategy library;
constructing a coding rule set corresponding to the URL according to the URL of the historical abnormal business scene in the abnormal business scene library and the scene information;
when the business system is abnormal, a corresponding target coding rule set is determined according to the URL of the current abnormal business scene, scene information of the current abnormal business scene is matched with the target coding rule set, and if the matching is successful, a processing strategy corresponding to the current abnormal business scene is determined from the processing strategy library.
According to another aspect of the present invention, there is provided an abnormal traffic scene detection apparatus including:
the database construction module is used for acquiring scene information of historical abnormal business scenes of the business system, constructing an abnormal business scene library according to the scene information, and recording processing strategies corresponding to each historical abnormal business scene in the abnormal business scene library to form a processing strategy library;
the coding rule set construction module is used for constructing a coding rule set corresponding to the URL according to the URL of the historical abnormal business scene in the abnormal business scene library and the scene information;
and the abnormal service scene detection module is used for determining a corresponding target coding rule set according to the URL of the current abnormal service scene when the service system is abnormal, matching the scene information of the current abnormal service scene with the target coding rule set, and determining a processing strategy corresponding to the current abnormal service scene from the processing strategy library if the matching is successful.
According to yet another aspect of the present invention, there is provided a computing device comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the operation corresponding to the abnormal business scene detection method.
According to still another aspect of the present invention, there is provided a computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the above-described abnormal traffic scene detection method.
According to the abnormal business scene detection method, the device, the computing equipment and the storage medium, the abnormal business scene library is constructed according to scene information by acquiring scene information of historical abnormal business scenes of a business system, and processing strategies corresponding to each historical abnormal business scene in the abnormal business scene library are recorded to form a processing strategy library; constructing a coding rule set corresponding to the URL according to the URL and scene information of the historical abnormal business scene in the abnormal business scene library; when the business system is abnormal, a corresponding target coding rule set is determined according to the URL of the current abnormal business scene, scene information of the current abnormal business scene is matched with the target coding rule set, and if the matching is successful, a processing strategy corresponding to the current abnormal business scene is determined from a processing strategy library. The invention constructs a database by recording the historical abnormal service scene of the service system and the corresponding processing strategy, codes, marks and matches the service scene by abnormality, constructs a coding rule set corresponding to the URL, and detects and processes the corresponding abnormal service scene by an automatic operation and maintenance mode, thereby assisting the service system to carry out the investigation of the abnormal service scene and improving the operation and maintenance work efficiency of the service system.
The foregoing description is only an overview of the present invention, and is intended to be implemented in accordance with the teachings of the present invention in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present invention more readily apparent.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
fig. 1 shows a flowchart of an abnormal service scene detection method provided by an embodiment of the present invention;
FIG. 2 is a diagram showing the entity-relationship of the abnormal business scenario library and the processing policy library provided by the embodiment of the present invention;
FIG. 3 shows a schematic diagram of an abnormal traffic scene coding rule provided by an embodiment of the present invention;
fig. 4 shows a tree structure schematic diagram provided by an embodiment of the present invention;
fig. 5 shows a schematic structural diagram of an abnormal service scene detection device according to an embodiment of the present invention;
FIG. 6 illustrates a schematic diagram of a computing device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present invention are shown in the drawings, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a flowchart of an embodiment of an abnormal traffic scene detection method according to the present invention, as shown in fig. 1, the method includes the following steps:
step S110: acquiring scene information of historical abnormal service scenes of a service system, constructing an abnormal service scene library according to the scene information, and recording processing strategies corresponding to each historical abnormal service scene in the abnormal service scene library to form a processing strategy library.
In an alternative way, the scenario information of the historical abnormal traffic scenario includes one or more of the following information: a uniform resource locator (Uniform Resource Locator, URL) of the historical abnormal business scenario, an operation reproduction step related to the historical abnormal business scenario, historical abnormal business scenario description information, abnormal coding, scenario attribute information, entry time, associated screenshot ID, associated video ID, abnormal feature, and the like.
In the invention, the method for detecting the abnormal business scene is introduced globally by introducing JavaScript (JS) plug-in tools into the head labels in the Web business system entry pages, and the method is started through the related configuration files.
In the invention, abnormal service scene refers to Web page with abnormal service or abnormal function, namely, page with expected display effect inconsistent with expected display effect of service tester or error prompt information; fig. 2 is an entity-contact diagram (Entity Relationship Diagram, E-R structure diagram) of an abnormal service scenario library and a processing policy library of the present application, as shown in fig. 2, in this step, in the whole service system development period, a service tester may collect and record a scenario of a historical abnormal service scenario in which an abnormality occurs, where scenario information of the historical abnormal service scenario includes one or more of the following information: the method comprises the steps of URL of a historical abnormal business scene, operation reproduction steps related to the historical abnormal business scene, historical abnormal business scene description information, abnormal codes, scene attribute information, entry time, associated screenshot ID, associated video ID, abnormal characteristics and the like. And the service operation and maintenance personnel receive a service work order of an abnormal service scene proposed by the service tester according to a background service interface provided by the plug-in tool, conduct investigation of the abnormal service condition, and input an investigation result into an abnormal service scene library through the plug-in tool.
Further, a processing policy library is formed by recording processing policies corresponding to each historical abnormal business scenario in the abnormal business scenario library. In an alternative manner, the processing policy corresponding to the abnormal traffic scenario includes one or more of the following information: the processing strategy codes corresponding to the abnormal business scenes, the processing strategy descriptions corresponding to the abnormal business scenes, the processing strategy creator, the processing time, the abnormal codes of the associated abnormal business scenes and the automatic operation and maintenance application program interfaces (Application Programming Interface, API) corresponding to the processing strategies.
Step S120: and constructing a coding rule set corresponding to the URL according to the URL of the historical abnormal business scene in the abnormal business scene library and scene information.
In an alternative manner, step S120 further includes: aiming at the historical abnormal business scenes with the same URL in the abnormal business scene library, extracting the characteristics of the historical abnormal business scenes to obtain characteristic values, and setting the item values of the characteristic values according to the scene information of the historical abnormal business scenes; and carrying out abnormal classification according to the characteristic values, and constructing a coding rule set corresponding to the URL.
In the step, for the historical abnormal business scene with the same URL in the abnormal business scene library, extracting the characteristics of the historical abnormal business scene to obtain a characteristic value, and setting the item value of the characteristic value according to the scene information of the historical abnormal business scene. It should be specifically noted that, for the situation that an abnormal service scene occurs in the operation and maintenance process of a certain service system, a developer or an operation and maintenance person matches a corresponding abnormal service scene by the method of the invention, and the matched abnormal service scene and a case are compared and analyzed, so as to quickly locate the cause of occurrence of the abnormal service scene and repair the cause, and enrich the characteristic value of the abnormal service scene.
FIG. 3 is a schematic diagram of an abnormal traffic scenario coding rule, as shown in FIG. 3, wherein a traffic operation and maintenance person performs investigation and repair on an abnormal traffic scenario, and codes the abnormal traffic scenario according to the rule shown in FIG. 3; wherein, for the abnormal business scene caused by system defect, after repairing work, archiving operation can be carried out; and (3) carrying out coding record (such as corresponding S000-999 codes in the figure) on the abnormal service scene caused by the non-system defect according to the characteristic value and according to the preset coding rule, and recording the relevant processing strategy and putting the recorded processing strategy into a processing strategy library. Specifically, the entry of the abnormal business scenario is confirmed by the URL wild card of the business system; for example, an abnormal business scenario triggered by an interaction may be marked as an interaction-like anomaly (records may be encoded by encoding Q000-999, etc.); abnormal business scenes caused by business data display or User Interface (UI) problems, marked as display type anomalies (can be recorded by S000-999 codes, etc.), etc.; and continuously and repeatedly extracting the characteristic value of the abnormal service scene in the research and development period and the subsequent production period of the whole service system, so that the term value of the characteristic value is enriched, the abnormal service scene is infinitely subdivided according to the characteristic value until the abnormal service scene can be uniquely marked, and the higher the uniqueness calibration rate of the abnormal service scene of the service system is, the higher the automatic detection rate of the abnormal service scene of the service system is.
It should be noted that, for an abnormal traffic scenario that is uniquely marked, a processing policy corresponding to the abnormal traffic scenario needs to be bound.
Step S130: when the business system is abnormal, a corresponding target coding rule set is determined according to the URL of the current abnormal business scene, scene information of the current abnormal business scene is matched with the target coding rule set, and if the matching is successful, a processing strategy corresponding to the current abnormal business scene is determined from a processing strategy library.
In an alternative manner, step S130 further includes: extracting the URL of the current abnormal service scene, and searching a coding rule set corresponding to the URL of the current abnormal service scene in an abnormal service scene library to serve as a target coding rule set; abstracting the target coding rule set into a tree structure, and calculating the depth of the tree structure; dividing the abnormal code of the current service scene according to the depth of the tree structure, and mapping the division result with the nodes of the target coding rule set to generate an abnormal scene tree structure; traversing the tree structure of the abnormal scene according to the last bit value of the dividing result to obtain a matching result.
In an alternative manner, step S130 further includes: if the last position value of the division result is a leaf node of the abnormal scene tree structure obtained through traversing, the matching is successful, and the historical abnormal service scene corresponding to the leaf node is determined to be a target historical abnormal service scene corresponding to the current abnormal service scene; if the last bit value of the division result obtained through traversing is not a leaf node of the abnormal scene tree structure, the matching is failed.
In an alternative manner, step S130 further includes: and determining the processing strategy corresponding to the target historical abnormal service scene in the processing strategy library as the processing strategy corresponding to the current abnormal service scene.
In an alternative, the method further comprises: if the matching fails, updating an abnormal business scene library and a target coding rule set according to scene information of the current abnormal business scene, and recording a processing strategy corresponding to the current abnormal business scene in a processing strategy library.
Specifically, locating an abnormal scene coding rule set corresponding to the abnormal service scene according to the URL value of the current abnormal service scene to be used as a target coding rule set; abstracting the target coding rule set into a tree structure, wherein fig. 4 is a schematic diagram of the tree structure, and calculating the depth of the tree structure as shown in fig. 4; dividing the current service scene code according to the depth of the tree structure and a preset step length, and mutually mapping the division result and nodes of the abnormal scene code rule set to generate an abnormal scene tree structure; traversing the abnormal scene tree structure according to the last position value of the dividing result, if the traversed last position value is a leaf node of the abnormal scene tree structure, successfully matching, determining a historical abnormal service scene corresponding to the leaf node as a target historical abnormal service scene corresponding to the current abnormal service scene, and determining a processing strategy corresponding to the target historical abnormal service scene in the processing strategy library as a processing strategy corresponding to the current abnormal service scene. For the problem that the system can automatically operate and maintain, the repairing action can be automatically triggered after the unique adaptation of the codes of the abnormal service scene, and the repairing action can be realized by the provided operation and maintenance interface.
If the current abnormal business scene cannot be uniquely matched with the corresponding historical abnormal business scene, extracting the characteristic value of the current abnormal business scene according to the scene information of the current abnormal business scene, and recording the characteristic value into an abnormal business scene library, so that a coding rule set is expanded, and a corresponding processing strategy is recorded and solved in a processing strategy library.
By adopting the method of the embodiment, the scene information of the historical abnormal business scene of the business system is obtained, an abnormal business scene library is constructed according to the scene information, and the processing strategies corresponding to each historical abnormal business scene in the abnormal business scene library are recorded to form a processing strategy library; constructing a coding rule set corresponding to the URL according to the URL and scene information of the historical abnormal business scene in the abnormal business scene library; when the business system is abnormal, a corresponding target coding rule set is determined according to the URL of the current abnormal business scene, scene information of the current abnormal business scene is matched with the target coding rule set, and if the matching is successful, a processing strategy corresponding to the current abnormal business scene is determined from a processing strategy library. The abnormal service scene detection method can be embedded into any Web service system in a static resource introduction mode, a database is constructed by recording the historical abnormal service scene of the service system and the corresponding processing strategy, the service scene is encoded, marked and matched through abnormality, a coding rule set corresponding to the URL is constructed, and the corresponding abnormal service scene is detected and processed in an automatic operation and maintenance mode, so that the investigation of the abnormal service scene by the service system is assisted, and the operation and maintenance work efficiency of the service system is improved.
Fig. 5 shows a schematic structural diagram of an embodiment of an abnormal traffic scene detection device according to the present invention. As shown in fig. 5, the apparatus includes: a database construction module 510, a coding rule set construction module 520, and an abnormal traffic scene detection module 530.
The database construction module 510 is configured to obtain scenario information of historical abnormal service scenarios of the service system, construct an abnormal service scenario library according to the scenario information, and record processing policies corresponding to each historical abnormal service scenario in the abnormal service scenario library to form a processing policy library.
In an alternative way, the scenario information of the historical abnormal traffic scenario includes one or more of the following information: the method comprises the steps of URL of a historical abnormal business scene, operation reproduction steps related to the historical abnormal business scene, historical abnormal business scene description information, abnormal codes, scene attribute information, entry time, associated screenshot ID, associated video ID and abnormal characteristics.
The encoding rule set construction module 520 is configured to construct an encoding rule set corresponding to the URL according to the URL of the historical abnormal service scene in the abnormal service scene library and the scene information.
In an alternative manner, the encoding rule set construction module 520 is further configured to: aiming at the historical abnormal business scenes with the same URL in the abnormal business scene library, extracting the characteristics of the historical abnormal business scenes to obtain characteristic values, and setting the item values of the characteristic values according to the scene information of the historical abnormal business scenes; and carrying out abnormal classification according to the characteristic values, and constructing a coding rule set corresponding to the URL.
The abnormal service scene detection module 530 is configured to determine a corresponding target encoding rule set according to a URL of a current abnormal service scene when a service system is abnormal, match scene information of the current abnormal service scene with the target encoding rule set, and if the matching is successful, determine a processing policy corresponding to the current abnormal service scene from the processing policy library.
In an alternative manner, the abnormal traffic scenario detection module 530 is further configured to: extracting the URL of the current abnormal service scene, and searching a coding rule set corresponding to the URL of the current abnormal service scene in an abnormal service scene library to serve as a target coding rule set; abstracting the target coding rule set into a tree structure, and calculating the depth of the tree structure; dividing the abnormal code of the current service scene according to the depth of the tree structure, and mapping the division result with the nodes of the target coding rule set to generate an abnormal scene tree structure; traversing the tree structure of the abnormal scene according to the last bit value of the dividing result to obtain a matching result.
In an alternative manner, the abnormal traffic scenario detection module 530 is further configured to: if the last position value of the division result is a leaf node of the abnormal scene tree structure obtained through traversing, the matching is successful, and the historical abnormal service scene corresponding to the leaf node is determined to be a target historical abnormal service scene corresponding to the current abnormal service scene; if the last bit value of the division result obtained through traversing is not a leaf node of the abnormal scene tree structure, the matching is failed.
In an alternative manner, the abnormal traffic scenario detection module 530 is further configured to: and determining the processing strategy corresponding to the target historical abnormal service scene in the processing strategy library as the processing strategy corresponding to the current abnormal service scene.
In an alternative manner, the abnormal traffic scenario detection module 530 is further configured to: if the matching fails, updating an abnormal business scene library and a target coding rule set according to scene information of the current abnormal business scene, and recording a processing strategy corresponding to the current abnormal business scene in a processing strategy library.
By adopting the device of the embodiment, scene information of historical abnormal business scenes of a business system is obtained through a database construction module, an abnormal business scene library is constructed according to the scene information, and processing strategies corresponding to each historical abnormal business scene in the abnormal business scene library are recorded to form a processing strategy library; the coding rule set construction module constructs a coding rule set corresponding to the URL according to the URL of the historical abnormal business scene in the abnormal business scene library and scene information; and when the business system is abnormal, the abnormal business scene detection module determines a corresponding target coding rule set according to the URL of the current abnormal business scene, matches the scene information of the current abnormal business scene with the target coding rule set, and determines a processing strategy corresponding to the current abnormal business scene from the processing strategy library if the matching is successful. The device can be embedded into any Web service system in a static resource introduction mode, a database is constructed by recording historical abnormal service scenes and corresponding processing strategies of the service system, the service scenes are encoded, marked and matched through abnormality, a coding rule set corresponding to the URL is constructed, and the corresponding abnormal service scenes are detected and processed in an automatic operation and maintenance mode, so that the service system is assisted in checking the abnormal service scenes, and the operation and maintenance work efficiency of the service system is improved.
The embodiment of the invention provides a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the abnormal business scene detection method in any of the method embodiments.
The executable instructions may be particularly useful for causing a processor to:
acquiring scene information of historical abnormal service scenes of a service system, constructing an abnormal service scene library according to the scene information, and recording processing strategies corresponding to each historical abnormal service scene in the abnormal service scene library to form a processing strategy library;
constructing a coding rule set corresponding to the URL according to the URL and scene information of the historical abnormal business scene in the abnormal business scene library;
when the business system is abnormal, a corresponding target coding rule set is determined according to the URL of the current abnormal business scene, scene information of the current abnormal business scene is matched with the target coding rule set, and if the matching is successful, a processing strategy corresponding to the current abnormal business scene is determined from a processing strategy library.
FIG. 6 illustrates a schematic diagram of an embodiment of a computing device of the present invention, and the embodiments of the present invention are not limited to a particular implementation of the computing device.
As shown in fig. 6, the computing device may include:
a processor (processor), a communication interface (Communications Interface), a memory (memory), and a communication bus.
Wherein: the processor, communication interface, and memory communicate with each other via a communication bus. A communication interface for communicating with network elements of other devices, such as clients or other servers, etc. The processor is configured to execute a program, and may specifically execute relevant steps in the embodiment of the abnormal service scene detection method.
In particular, the program may include program code including computer-operating instructions.
The processor may be a central processing unit, CPU, or specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The one or more processors included by the server may be the same type of processor, such as one or more CPUs; but may also be different types of processors such as one or more CPUs and one or more ASICs.
And the memory is used for storing programs. The memory may comprise high-speed RAM memory or may further comprise non-volatile memory, such as at least one disk memory.
The program may be specifically operative to cause the processor to:
acquiring scene information of historical abnormal service scenes of a service system, constructing an abnormal service scene library according to the scene information, and recording processing strategies corresponding to each historical abnormal service scene in the abnormal service scene library to form a processing strategy library;
constructing a coding rule set corresponding to the URL according to the URL and scene information of the historical abnormal business scene in the abnormal business scene library;
when the business system is abnormal, a corresponding target coding rule set is determined according to the URL of the current abnormal business scene, scene information of the current abnormal business scene is matched with the target coding rule set, and if the matching is successful, a processing strategy corresponding to the current abnormal business scene is determined from a processing strategy library.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general-purpose systems may also be used with the teachings herein. The required structure for a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the above description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. The modules or units or components of the embodiments may be combined into one module or unit or component and, furthermore, they may be divided into a plurality of sub-modules or sub-units or sub-components. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings), may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features but not others included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments can be used in any combination.
Various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components according to embodiments of the present invention may be implemented in practice using a microprocessor or Digital Signal Processor (DSP). The present invention can also be implemented as an apparatus or device program (e.g., a computer program and a computer program product) for performing a portion or all of the methods described herein. Such a program embodying the present invention may be stored on a computer readable medium, or may have the form of one or more signals. Such signals may be downloaded from an internet website, provided on a carrier signal, or provided in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.
Claims (10)
1. The abnormal business scene detection method is characterized by comprising the following steps:
acquiring scene information of historical abnormal business scenes of a business system, constructing an abnormal business scene library according to the scene information, and recording processing strategies corresponding to each historical abnormal business scene in the abnormal business scene library to form a processing strategy library;
constructing a coding rule set corresponding to the URL according to the URL of the historical abnormal business scene in the abnormal business scene library and the scene information;
when the business system is abnormal, a corresponding target coding rule set is determined according to the URL of the current abnormal business scene, scene information of the current abnormal business scene is matched with the target coding rule set, and if the matching is successful, a processing strategy corresponding to the current abnormal business scene is determined from the processing strategy library.
2. The method of claim 1, wherein the context information of the historical abnormal traffic context comprises one or more of the following: the method comprises the steps of URL of a historical abnormal business scene, operation reproduction steps related to the historical abnormal business scene, historical abnormal business scene description information, abnormal codes, scene attribute information, entry time, associated screenshot ID, associated video ID and abnormal characteristics.
3. The method of claim 1, wherein constructing the encoding rule set corresponding to the URL based on the URL of the historical abnormal traffic scene in the abnormal traffic scene library and the scene information further comprises:
performing feature extraction on the historical abnormal business scenes with the same URL in the abnormal business scene library to obtain feature values, and setting item values of the feature values according to scene information of the historical abnormal business scenes;
and dividing the abnormal classification according to the characteristic values, and constructing a coding rule set corresponding to the URL.
4. The method of claim 1, wherein the determining a corresponding set of target encoding rules based on the URL of the current abnormal traffic scene, and matching scene information of the current abnormal traffic scene with the set of target encoding rules further comprises:
extracting the URL of the current abnormal business scene, and searching a coding rule set corresponding to the URL of the current abnormal business scene in the abnormal business scene library to serve as a target coding rule set;
abstracting the target coding rule set into a tree structure, and calculating the depth of the tree structure;
dividing the abnormal codes of the current service scene according to the depth of the tree structure, and mapping the division result with the nodes of the target coding rule set to generate an abnormal scene tree structure;
and traversing the abnormal scene tree structure according to the last bit value of the dividing result to obtain a matching result.
5. The method of claim 4, wherein traversing the outlier tree structure based on the last value of the partitioning result to obtain a matching result further comprises:
if the last position value of the dividing result is a leaf node of the abnormal scene tree structure obtained through traversing, successful matching is achieved, and a historical abnormal service scene corresponding to the leaf node is determined to be a target historical abnormal service scene corresponding to the current abnormal service scene;
if the last bit value of the division result obtained through traversing is not the leaf node of the abnormal scene tree structure, matching fails.
6. The method of claim 5, wherein determining, from the processing policy library, a processing policy corresponding to the current abnormal traffic scenario further comprises:
and determining the processing strategy corresponding to the target historical abnormal service scene in the processing strategy library as the processing strategy corresponding to the current abnormal service scene.
7. The method according to any one of claims 1-6, further comprising:
if the matching is failed, updating the abnormal business scene library and the target coding rule set according to the scene information of the current abnormal business scene, and recording a processing strategy corresponding to the current abnormal business scene in the processing strategy library.
8. An abnormal traffic scene detection device, characterized by comprising:
the database construction module is used for acquiring scene information of historical abnormal business scenes of the business system, constructing an abnormal business scene library according to the scene information, and recording processing strategies corresponding to each historical abnormal business scene in the abnormal business scene library to form a processing strategy library;
the coding rule set construction module is used for constructing a coding rule set corresponding to the URL according to the URL of the historical abnormal business scene in the abnormal business scene library and the scene information;
and the abnormal service scene detection module is used for determining a corresponding target coding rule set according to the URL of the current abnormal service scene when the service system is abnormal, matching the scene information of the current abnormal service scene with the target coding rule set, and determining a processing strategy corresponding to the current abnormal service scene from the processing strategy library if the matching is successful.
9. A computing device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction, where the executable instruction causes the processor to perform an operation corresponding to an abnormal traffic scenario detection method according to any one of claims 1 to 7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to an abnormal traffic scenario detection method according to any one of claims 1-7.
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