CN116708251A - Method, device, equipment and storage medium for analyzing service data - Google Patents
Method, device, equipment and storage medium for analyzing service data Download PDFInfo
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- H—ELECTRICITY
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- H04L43/00—Arrangements for monitoring or testing data switching networks
- H04L43/12—Network monitoring probes
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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- H—ELECTRICITY
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Abstract
The application relates to the field of financial science and technology, and provides a method, a device, equipment and a storage medium for analyzing business data, wherein the method comprises the following steps: when detecting that a micro service accesses a gateway layer of a service system, determining an item type corresponding to the micro service; determining a data acquisition node aiming at the micro service according to the item type; acquiring buried point data of the micro service based on the data acquisition node; determining a data type of the buried point data based on a preset buried point data classification model, wherein the data type comprises: normal data, abnormal data; and if the number of the abnormal data is larger than a first preset number threshold value in the first preset time, outputting abnormal alarm information based on the abnormal data. The cost and complexity of acquiring the micro-service buried point data are reduced, and the operation and maintenance costs are reduced in a financial system connected with a plurality of micro-services.
Description
Technical Field
The present application relates to the field of financial science and technology, and in particular, to a method, an apparatus, a device, and a storage medium for analyzing service data.
Background
In order to monitor a financial service system in real time, in the prior art, a service end or a client of the financial service system is usually selected to perform embedding points, for example, embedding point data of a refueling service, a car washing service, a parking service and the like of a car service platform are respectively acquired through embedding points of the client. Therefore, before a new service is on-line, a developer needs to spend a lot of time to preset the buried points for the service scene of the new service, and a mature service system usually needs to bear tens of thousands of buried points, so that a lot of time cost and labor cost are consumed for burying the new service system. How to quickly and efficiently realize the embedded point of a new service becomes a problem to be solved.
Disclosure of Invention
The application mainly aims to provide a method, a device, equipment and a storage medium for analyzing service data, which aim to solve the problem that a great deal of time cost and labor cost are consumed when a new micro service is buried.
In a first aspect, the present application provides a method for analyzing service data, where the method for analyzing service data includes the following steps:
when detecting that a micro service accesses a gateway layer of a service system, determining an item type corresponding to the micro service;
determining a data acquisition node aiming at the micro service according to the item type;
acquiring buried point data of the micro service based on the data acquisition node;
determining a data type of the buried point data based on a preset buried point data classification model, wherein the data type comprises: normal data, abnormal data;
and if the number of the abnormal data is larger than a first preset number threshold value in the first preset time, outputting abnormal alarm information based on the abnormal data.
In a second aspect, the present application also provides an analysis device for service data, where the analysis device for service data includes:
the system comprises an item type determining module, a service system and a service system, wherein the item type determining module is used for determining the item type corresponding to the micro service when detecting that the micro service is accessed to a gateway layer of the service system;
the acquisition node determining module is used for determining a data acquisition node aiming at the micro service according to the item type;
the embedded point data acquisition module is used for acquiring embedded point data of the micro service based on the data acquisition node;
the embedded point data classification module is used for determining the data type of the embedded point data based on a preset embedded point data classification model, wherein the data type comprises: normal data, abnormal data;
and the alarm information output module is used for outputting abnormal alarm information based on the abnormal data if the quantity of the abnormal data is larger than a first preset quantity threshold value in a first preset time.
In a third aspect, the present application also provides a computer device, the computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program when executed by the processor implements a method for analyzing service data as described above.
In a fourth aspect, the present application also provides a computer readable storage medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements a method for analyzing service data as described above.
The application provides a method, a device, equipment and a computer storage medium for analyzing service data, wherein when detecting that a micro service is accessed to a gateway layer of a service system, the method determines the item type corresponding to the micro service; determining a data acquisition node aiming at the micro service according to the item type; acquiring buried point data of the micro service based on the data acquisition node; determining a data type of the buried point data based on a preset buried point data classification model, wherein the data type comprises: normal data, abnormal data; and if the number of the abnormal data is larger than a first preset number threshold value in the first preset time, outputting abnormal alarm information based on the abnormal data. The embedded point data is acquired by automatically determining the data acquisition nodes of the micro service, so that the cost and the complexity for acquiring the embedded point data of the micro service are reduced, and the operation and maintenance cost is reduced in a financial system connected with a plurality of micro services.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for analyzing service data according to an embodiment of the present application;
FIG. 2 is a view of a usage scenario of a method for analyzing service data according to an embodiment of the present application;
FIG. 3 is a schematic block diagram of an apparatus for analyzing service data according to an embodiment of the present application;
fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The flow diagrams depicted in the figures are merely illustrative and not necessarily all of the elements and operations/steps are included or performed in the order described. For example, some operations/steps may be further divided, combined, or partially combined, so that the order of actual execution may be changed according to actual situations.
The embodiment of the application provides a method and a device for analyzing service data, computer equipment and a computer readable storage medium.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings. The following embodiments and features of the embodiments may be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flow chart of a method for analyzing service data according to an embodiment of the application. The analysis method of the service data can be used in a terminal or a server to acquire and analyze the service data. The terminal can be electronic equipment such as a mobile phone, a tablet personal computer, a notebook computer, a desktop computer, a personal digital assistant, wearable equipment and the like; the server may be an independent server, a server cluster, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), basic cloud computing services such as big data and artificial intelligence platforms, and the like.
Referring to fig. 2, fig. 2 is a usage scenario diagram according to an embodiment of the present application. As shown in fig. 2, the gateway layer is used for transmitting information between a plurality of micro services and clients, and the micro services connected with the gateway layer include micro service 1, micro service 2, … … and micro service n; similarly, the number of clients connected to the gateway layer may be plural, which is not limited herein. The business data analysis system provided by the embodiment of the application detects whether a new micro-service is accessed in a gateway layer, and determines a data acquisition node and acquires buried data according to the item type of the accessed micro-service.
As shown in fig. 1, the analysis method of the service data includes steps S101 to S105.
Step S101, when detecting that the micro service accesses the gateway layer of the service system, determining the item type corresponding to the micro service.
After the new micro service finishes developing the access gateway layer, the method for analyzing the service data provided by the application determines the item type of the micro service so as to determine the data acquisition node according to the item type of the micro service.
Illustratively, a plurality of item types are preset for the characteristics of the financial system. Specifically, the item types may include, for example: the user authentication microservices, mall browsing microservices, transaction microservices, etc., are not limited herein.
The item type of the micro service may be determined by detecting the name of the micro service, or may be determined by scanning a code analysis of the micro service, which is not limited herein.
In some embodiments, when detecting that a micro service accesses a gateway layer of a service system, determining an item type corresponding to the micro service includes: determining target features of the micro-service based on a preset feature recognition algorithm; and according to the target characteristics, matching is carried out in a preset item type database, and the item type corresponding to the micro service is determined.
Illustratively, based on a preset feature recognition algorithm, determining a target feature of the name of the micro service according to the name of the micro service, and determining an item type matched with the target feature of the name of the micro service in a preset item type library; the project type library comprises target features of names of various preset project types.
Illustratively, determining target features of the code of the micro service according to the code of the micro service based on a preset feature recognition algorithm, and determining item types matched with the target features of the code of the micro service in a preset item type library; the item type library comprises target features of codes of a plurality of preset item types.
Step S102, determining a data acquisition node aiming at the micro service according to the item type.
For example, the micro services of the same item type have certain common characteristics, for example, the mall browsing micro services have the functions of detail browsing, commodity collection and the like, so that corresponding data acquisition nodes can be determined for the micro services of the same item type.
In some embodiments, the determining a data collection node for the micro-service according to the item type includes: inputting preset operation data to the micro-service based on a target data template corresponding to the item type; acquiring target data output by the micro-service based on the preset operation data, wherein the target data is used for reflecting the validity of the preset operation data; and determining the data acquisition node according to the target data.
For example, since micro services belonging to the same item type have similar functions, target data templates may be preset for different item types. The target data template at least comprises preset operation data.
For example, taking a mall browsing micro-service as an example, if it is determined that the item type to which the micro-service belongs is the mall browsing micro-service, the preset operation data may include an operation instruction for instructing the micro-service to execute functions such as detail browsing, commodity collection, and the like. Specifically, preset operation data is input to the micro service based on the target data template, the micro service is tested, it is determined which preset operation data the micro service can return effective target data, and then a data acquisition node is determined according to the preset operation data capable of returning effective data.
For example, if the preset operation data is an operation instruction for instructing to execute the commodity collection operation, the operation instruction for instructing to execute the commodity collection operation is input to the micro-service, and if the micro-service can return valid data for the operation instruction, it is indicated that the micro-service cannot execute the operation instruction, and an operation corresponding to the operation instruction can be used as the data collection node. Otherwise, if the micro-service cannot return valid data for the operation instruction, the micro-service cannot execute the operation instruction, and the operation corresponding to the operation instruction is not required to be used as the data acquisition node.
Step S103, acquiring buried point data of the micro service based on the data acquisition node.
Illustratively, according to the data collection node determined in step S102, buried point data when the micro service interacts with the client is obtained at the gateway layer.
For example, if the commodity collection operation is taken as a buried point for acquiring the related business data, that is, when the gateway layer detects an operation instruction for executing the commodity collection operation, the interaction data of the micro service and the client is acquired as the buried point data, which is not limited to this.
Step S104, determining the data type of the buried point data based on a preset buried point data classification model, wherein the data type comprises the following steps: normal data, abnormal data.
Illustratively, according to the data characteristics of the obtained buried point data, the data type of the buried point data is determined based on the buried point data classification model. The data type of the buried point data may also include unknown data, that is, buried point data that cannot be classified into normal data or abnormal data, which is not limited to this.
In some embodiments, the determining the data type of the buried point data based on the preset buried point data classification model includes: acquiring first data characteristics of the target data; comparing the first data characteristic with the second data characteristic of the buried point data, and determining that the buried point data belongs to normal data or abnormal data.
Illustratively, the data types of the buried point data acquired later are classified according to target data obtained by inputting preset operation data to the micro service. Specifically, extracting a first data feature of the target data and a second data feature of the buried point data, and if the similarity between the first data feature and the second data feature is greater than a preset threshold value, indicating that the buried point data is normal data; and if the similarity between the first data feature and the second data feature is smaller than a preset threshold value, indicating that the buried point data is abnormal data.
Step 105, if the number of the abnormal data is greater than a first preset number threshold in a first preset time, outputting abnormal alarm information based on the abnormal data.
For example, if the number of the detected abnormal information is greater than the first preset number threshold value in a certain period of time, the abnormal alarm information is sent to the user, so that the user can timely learn about the abnormal condition of the micro-service and process the abnormality.
The abnormal data may be abnormal data of a certain micro service, for example, the obtained embedded point data is parsed to obtain a service identifier of the micro service that sends or receives the embedded point data, and when an abnormal message corresponding to the certain micro service is greater than a first preset number threshold, abnormal alarm information is output for the micro service.
In some embodiments, if the number of the abnormal data is greater than a first preset number threshold in a first preset time, outputting abnormal alarm information based on the abnormal data includes: if the number of the abnormal data is larger than a first preset number threshold value in a first preset time, acquiring a preset target alarm identifier corresponding to the micro service; and outputting the abnormal alarm information based on the abnormal data according to the preset target alarm identifier.
The target alarm identifier may be, for example, a mailbox address of a technician corresponding to the micro service, but not limited to, a phone number of the technician, and the method for indicating the user to receive the abnormal alarm information is not limited to.
In some embodiments, if the number of the abnormal data is greater than the first preset number threshold in the first preset time, before the abnormal alarm information is output based on the abnormal data, the method further includes: determining a peak time period and a valley time period in a second preset time based on the number of buried data in the second preset time, wherein the time length of the first preset time is smaller than that of the second preset time; and respectively determining a first preset quantity threshold corresponding to the peak time period and the valley time period according to the quantity of the buried data in the peak time period and the quantity of the buried data in the valley time period.
For example, the first preset number threshold may be determined according to an actual number of buried data, and different first preset number thresholds may be determined at different time periods.
The second preset time may be, for example, 24 hours, and the peak time period and the underestimated time period within the second preset time are determined by taking the 24 hours as a period, which is not limited to this, and the second preset time period may be one week or one month, which is not limited herein.
Illustratively, the peak period and the valley period are determined according to a change in the magnitude of the number of buried data within the second preset time. Specifically, the period in which the number of buried data is greater than the third preset number threshold is determined as the peak period, and the period in which the number of buried data is less than or equal to the third preset number threshold is determined as the valley period, although the present application is not limited thereto.
The first preset number threshold is determined according to the data amount of the buried point data in different periods, for example, 60% of the average value of the buried point data in the peak period is determined as the first preset number threshold in the peak period, and 60% of the average value of the buried point data in the valley period is determined as the first preset number threshold in the valley period, which is not limited thereto.
In some embodiments, the method further comprises: acquiring request information received by the server at the gateway layer; analyzing the request information and determining an original interface for transmitting the request information; and outputting a flow alarm based on the original interface if the number of the request information sent by the original interface is larger than a second preset number threshold.
For example, besides the occurrence of a large amount of abnormal data, the sudden increase of the amount of the request information sent by a certain original interface of the client may be an abnormal embodiment, so that the request information sent by the client to the server is acquired at the gateway layer, and if the amount of the request information of the original interface is too large, a flow alarm based on the original interface is output to prompt the user to analyze and check the condition of the original interface.
According to the analysis method of the service data, when the gateway layer of the micro service access service system is detected, the item type corresponding to the micro service is determined; determining a data acquisition node aiming at the micro service according to the item type; acquiring buried point data of the micro service based on the data acquisition node; determining a data type of the buried point data based on a preset buried point data classification model, wherein the data type comprises: normal data, abnormal data; and if the number of the abnormal data is larger than a first preset number threshold value in the first preset time, outputting abnormal alarm information based on the abnormal data. The embedded point data is acquired by automatically determining the data acquisition nodes of the micro service, so that the cost and the complexity for acquiring the embedded point data of the micro service are reduced, and the operation and maintenance cost is reduced in a financial system connected with a plurality of micro services.
Referring to fig. 3, fig. 3 is a schematic diagram of an apparatus for analyzing service data according to an embodiment of the present application, where the apparatus for analyzing service data may be configured in a server or a terminal, and is configured to execute the foregoing method for analyzing service data.
As shown in fig. 4, the analysis device for service data includes: the system comprises an item type determining module 110, an acquisition node determining module 120, a buried data obtaining module 130, a buried data classifying module 140 and an alarm information outputting module 150.
An item type determining module 110, configured to determine an item type corresponding to a micro service when detecting that the micro service accesses a gateway layer of a service system;
a collection node determining module 120, configured to determine a data collection node for the micro service according to the item type;
a buried point data acquisition module 130, configured to acquire buried point data of the micro service based on the data acquisition node;
the buried data classification module 140 is configured to determine a data type of the buried data based on a preset buried data classification model, where the data type includes: normal data, abnormal data;
and the alarm information output module 150 is configured to output abnormal alarm information based on the abnormal data if the number of the abnormal data is greater than a first preset number threshold in a first preset time.
The item type determination module 110 also illustratively includes a target feature determination sub-module, an item type determination sub-module.
The target feature determining sub-module is used for determining target features of the micro-service based on a preset feature recognition algorithm;
and the item type determining submodule is used for matching in a preset item type database according to the target characteristics and determining the item type corresponding to the micro service.
The acquisition node determination module 120 includes, for example, an operation data input sub-module, a target data acquisition sub-module, and an acquisition node determination sub-module.
The operation data input sub-module is used for inputting preset operation data to the micro-service based on the target data template corresponding to the item type;
the target data acquisition sub-module is used for acquiring target data output by the micro-service based on the preset operation data, wherein the target data is used for reflecting the validity of the preset operation data;
and the acquisition node determining submodule is used for determining the data acquisition node according to the target data.
Illustratively, the buried data classification module 140 further includes a feature extraction sub-module, a buried data classification sub-module.
The feature extraction sub-module is used for acquiring first data features of the target data;
and the buried point data classification sub-module is used for comparing the first data characteristic with the second data characteristic of the buried point data and determining whether the buried point data belongs to normal data or abnormal data.
The alarm information output module 150 further includes an alarm identifier acquisition sub-module and an alarm information output sub-module.
The alarm identification acquisition sub-module is used for acquiring a preset target alarm identification corresponding to the micro-service if the number of the abnormal data is larger than a first preset number threshold value in a first preset time;
and the alarm information output sub-module is used for outputting the abnormal alarm information based on the abnormal data according to the preset target alarm identifier.
The analysis device of the service data further comprises a time period determination module and a quantity threshold determination module.
A time period determining submodule, configured to determine a peak time period and a valley time period in a second preset time based on a buried data amount in the second preset time, where a time length of the first preset time is smaller than a time length of the second preset time;
and the quantity threshold value determining submodule is used for respectively determining a first preset quantity threshold value corresponding to the peak time period and the valley time period according to the quantity of the buried data in the peak time period and the quantity of the buried data in the valley time period.
The analysis device of the service data further comprises a request message acquisition module, an original interface analysis module and a flow alarm output module.
A request message acquisition module, configured to acquire, at the gateway layer, request information received by the server side;
the original interface analysis module is used for analyzing the request information and determining an original interface for transmitting the request information;
and the flow alarm output module is used for outputting a flow alarm based on the original interface if the number of the request information sent by the original interface is larger than a second preset number threshold value.
It should be noted that, for convenience and brevity of description, specific working processes of the above-described apparatus and each module, unit may refer to corresponding processes in the foregoing method embodiments, which are not repeated herein.
The methods and apparatus of the present application are operational with numerous general purpose or special purpose computing system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The above-described methods, apparatus may be implemented, for example, in the form of a computer program that is executable on a computer device as shown in fig. 4.
Referring to fig. 4, fig. 4 is a schematic block diagram of a computer device according to an embodiment of the present application. The computer device may be a server or a terminal.
As shown in fig. 4, the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a storage medium and an internal memory.
The storage medium may store an operating system and a computer program. The computer program comprises program instructions which, when executed, cause the processor to perform any of a number of methods of analyzing business data.
The processor is used to provide computing and control capabilities to support the operation of the entire computer device.
The internal memory provides an environment for the execution of a computer program in a storage medium that, when executed by a processor, causes the processor to perform any of a number of methods for analyzing business data.
The network interface is used for network communication such as transmitting assigned tasks and the like. It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
It should be appreciated that the processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. Wherein the general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein in one embodiment the processor is configured to run a computer program stored in the memory to implement the steps of:
when detecting that a micro service accesses a gateway layer of a service system, determining an item type corresponding to the micro service;
determining a data acquisition node aiming at the micro service according to the item type;
acquiring buried point data of the micro service based on the data acquisition node;
determining a data type of the buried point data based on a preset buried point data classification model, wherein the data type comprises: normal data, abnormal data;
and if the number of the abnormal data is larger than a first preset number threshold value in the first preset time, outputting abnormal alarm information based on the abnormal data.
In one embodiment, when the processor determines that the item type corresponding to the micro service is detected when implementing the gateway layer of the micro service access service system, the processor is configured to implement:
determining target features of the micro-service based on a preset feature recognition algorithm;
and according to the target characteristics, matching is carried out in a preset item type database, and the item type corresponding to the micro service is determined.
In one embodiment, the processor, when implementing the determining the data collection node for the micro service according to the item type, is configured to implement:
inputting preset operation data to the micro-service based on a target data template corresponding to the item type;
acquiring target data output by the micro-service based on the preset operation data, wherein the target data is used for reflecting the validity of the preset operation data;
and determining the data acquisition node according to the target data.
In one embodiment, when implementing the classification model of buried point data based on the preset, the processor is configured to implement:
acquiring first data characteristics of the target data;
comparing the first data characteristic with the second data characteristic of the buried point data, and determining that the buried point data belongs to normal data or abnormal data.
In one embodiment, when the processor implements that the number of the abnormal data is greater than the first preset number threshold in the first preset time, the processor is configured to implement:
if the number of the abnormal data is larger than a first preset number threshold value in a first preset time, acquiring a preset target alarm identifier corresponding to the micro service;
and outputting the abnormal alarm information based on the abnormal data according to the preset target alarm identifier.
In one embodiment, before implementing the step of outputting the abnormality alert information based on the abnormality data if the number of the abnormality data is greater than a first preset number threshold within a first preset time, the processor is configured to implement:
determining a peak time period and a valley time period in a second preset time based on the number of buried data in the second preset time, wherein the time length of the first preset time is smaller than that of the second preset time;
and respectively determining a first preset quantity threshold corresponding to the peak time period and the valley time period according to the quantity of the buried data in the peak time period and the quantity of the buried data in the valley time period.
In one embodiment, the processor, when implementing the method for analyzing service data, is configured to implement:
acquiring request information received by the server at the gateway layer;
analyzing the request information and determining an original interface for transmitting the request information;
and outputting a flow alarm based on the original interface if the number of the request information sent by the original interface is larger than a second preset number threshold.
It should be noted that, for convenience and brevity of description, a specific working process of the foregoing description of the analysis of the service data may refer to a corresponding process in the foregoing embodiment of the analysis control method of the service data, which is not described herein again.
Embodiments of the present application also provide a computer readable storage medium having a computer program stored thereon, where the computer program includes program instructions, where the method implemented when the program instructions are executed may refer to various embodiments of the method for analyzing service data according to the present application.
The computer readable storage medium may be an internal storage unit of the computer device according to the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, which are provided on the computer device.
It is to be understood that the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments. While the application has been described with reference to certain preferred embodiments, it will be understood by those skilled in the art that various changes and substitutions of equivalents may be made and equivalents will be apparent to those skilled in the art without departing from the scope of the application. Therefore, the protection scope of the application is subject to the protection scope of the claims.
Claims (10)
1. A method for analyzing service data, the method comprising:
when detecting that a micro service accesses a gateway layer of a service system, determining an item type corresponding to the micro service;
determining a data acquisition node aiming at the micro service according to the item type;
acquiring buried point data of the micro service based on the data acquisition node;
determining a data type of the buried point data based on a preset buried point data classification model, wherein the data type comprises: normal data, abnormal data;
and if the number of the abnormal data is larger than a first preset number threshold value in the first preset time, outputting abnormal alarm information based on the abnormal data.
2. The method for analyzing service data according to claim 1, wherein when detecting that a micro service accesses a gateway layer of a service system, determining an item type corresponding to the micro service includes:
determining target features of the micro-service based on a preset feature recognition algorithm;
and according to the target characteristics, matching is carried out in a preset item type database, and the item type corresponding to the micro service is determined.
3. The method for analyzing service data according to claim 1, wherein the determining a data collection node for the micro service according to the item type includes:
inputting preset operation data to the micro-service based on a target data template corresponding to the item type;
acquiring target data output by the micro-service based on the preset operation data, wherein the target data is used for reflecting the validity of the preset operation data;
and determining the data acquisition node according to the target data.
4. The method for analyzing service data according to claim 3, wherein determining the data type of the buried point data based on a predetermined buried point data classification model comprises:
acquiring first data characteristics of the target data;
comparing the first data characteristic with the second data characteristic of the buried point data, and determining that the buried point data belongs to normal data or abnormal data.
5. The method for analyzing service data according to claim 1, wherein if the number of the abnormal data is greater than a first preset number threshold in a first preset time, outputting abnormal alarm information based on the abnormal data, includes:
if the number of the abnormal data is larger than a first preset number threshold value in a first preset time, acquiring a preset target alarm identifier corresponding to the micro service;
and outputting the abnormal alarm information based on the abnormal data according to the preset target alarm identifier.
6. The method for analyzing service data according to any one of claims 1 to 5, wherein if the number of abnormal data is greater than a first preset number threshold in a first preset time, before outputting abnormal alarm information based on the abnormal data, the method further comprises:
determining a peak time period and a valley time period in a second preset time based on the number of buried data in the second preset time, wherein the time length of the first preset time is smaller than that of the second preset time;
and respectively determining a first preset quantity threshold corresponding to the peak time period and the valley time period according to the quantity of the buried data in the peak time period and the quantity of the buried data in the valley time period.
7. The method for analyzing service data according to any one of claims 1 to 5, further comprising:
acquiring request information received by the server at the gateway layer;
analyzing the request information and determining an original interface for transmitting the request information;
and outputting a flow alarm based on the original interface if the number of the request information sent by the original interface is larger than a second preset number threshold.
8. An analysis device for service data, characterized in that the analysis device for service data comprises:
the system comprises an item type determining module, a service system and a service system, wherein the item type determining module is used for determining the item type corresponding to the micro service when detecting that the micro service is accessed to a gateway layer of the service system;
the acquisition node determining module is used for determining a data acquisition node aiming at the micro service according to the item type;
the embedded point data acquisition module is used for acquiring embedded point data of the micro service based on the data acquisition node;
the embedded point data classification module is used for determining the data type of the embedded point data based on a preset embedded point data classification model, wherein the data type comprises: normal data, abnormal data;
and the alarm information output module is used for outputting abnormal alarm information based on the abnormal data if the quantity of the abnormal data is larger than a first preset quantity threshold value in a first preset time.
9. A computer device, characterized in that it comprises a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when being executed by the processor, realizes the steps of the method of analyzing traffic data according to any of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the method of analyzing service data according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310763079.2A CN116708251A (en) | 2023-06-26 | 2023-06-26 | Method, device, equipment and storage medium for analyzing service data |
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| CN202310763079.2A CN116708251A (en) | 2023-06-26 | 2023-06-26 | Method, device, equipment and storage medium for analyzing service data |
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| CN116708251A true CN116708251A (en) | 2023-09-05 |
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| CN202310763079.2A Pending CN116708251A (en) | 2023-06-26 | 2023-06-26 | Method, device, equipment and storage medium for analyzing service data |
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