Method, device and equipment for analyzing business data and storage medium

Abstract

The application relates to the field of financial technology, and provides a business data analysis method and device, equipment and a storage medium. The method comprises the following steps: when detecting that a microservice accesses a gateway layer of a business system, determining a project type corresponding to the microservice; according to the project type, determining a data collection node for the microservice; based on the data collection node, acquiring buried point data of the microservice; based on a preset buried point data classification model, determining a data type of the buried point data, wherein the data type comprises normal data and abnormal data; and if the number of abnormal data is greater than a first preset number threshold within a first preset time, outputting abnormal alarm information based on the abnormal data. The cost and complexity of acquiring the microservice buried point data are reduced, and the operation and maintenance cost of a financial system connected with numerous microservices is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of financial technology, and in particular to a business data analysis method and device, equipment and a storage medium. BACKGROUND

[0002] In order to monitor the financial business system in real time, the existing technology usually selects to perform point embedding on the service end or the client end of the financial business system, for example, through client-side point embedding to obtain point embedding data of the refueling business, car washing business and parking business of the car service platform. Therefore, before the new business goes online, the developer needs to spend a lot of time to pre-set the point embedding for the business scenarios of the new business. A mature business system usually needs to carry tens of thousands of points, and it takes a lot of time and cost to perform point embedding on the new business system. How to quickly and efficiently implement point embedding for new businesses has become a problem to be solved. SUMMARY

[0003] The main purpose of the present application is to provide a business data analysis method, device, equipment and storage medium, which aims to solve the problem of consuming a large amount of time and cost to perform point embedding on new microservices.

[0004] In a first aspect, the present application provides a business data analysis method, which comprises the following steps:

[0005] When detecting that a microservice accesses the gateway layer of a business system, determine the project type corresponding to the microservice;

[0006] According to the project type, determine the data collection node for the microservice;

[0007] Based on the data collection node, obtain the point embedding data of the microservice;

[0008] Based on a preset point embedding data classification model, determine the data type of the point embedding data, wherein the data type includes normal data and abnormal data;

[0009] If the number of abnormal data is greater than a first preset number threshold within a first preset time, output an abnormal alarm information based on the abnormal data.

[0010] In a second aspect, the present application further provides a business data analysis device, which comprises:

[0011] A project type determination module is configured to determine the project type corresponding to the microservice when detecting that the microservice accesses the gateway layer of a business system;

[0012] The collection node determination module is configured to determine a data collection node for the microservice according to the project type;

[0013] The buried point data acquisition module is configured to acquire buried point data of the microservice based on the data collection node;

[0014] The buried point data classification module is configured to determine a data type of the buried point data based on a preset buried point data classification model, wherein the data type includes normal data and abnormal data.

[0015] The alarm information output module is configured to output abnormal alarm information based on the abnormal data if a quantity of the abnormal data is greater than a first preset quantity threshold within a first preset time.

[0016] In a third aspect, the present application also provides a computer device, which comprises a processor, a memory, and a computer program stored in the memory and executable by the processor, wherein the computer program is executed by the processor to implement the business data analysis method as described above.

[0017] In a fourth aspect, the present application also provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the business data analysis method as described above.

[0018] The present application provides a business data analysis method, device, equipment and computer storage medium. The present application determines the project type corresponding to the microservice when detecting the gateway layer of the microservice accessing the business system, determines the data collection node for the microservice according to the project type, acquires the buried point data of the microservice based on the data collection node, determines the data type of the buried point data based on a preset buried point data classification model, wherein the data type includes normal data and abnormal data, and outputs abnormal alarm information based on the abnormal data if the quantity of the abnormal data is greater than a first preset quantity threshold within a first preset time. By automatically determining the data collection node of the microservice and then acquiring the buried point data, the cost and complexity of acquiring the buried point data of the microservice are reduced, and the operation and maintenance cost of the financial system connected with numerous microservices is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0020] Figure 1 A flowchart of a business data analysis method provided by an embodiment of the present application is shown in FIG. 1.

[0021] Figure 2 A use scenario diagram of a business data analysis method provided by an embodiment of the present application is shown in FIG. 2.

[0022] Figure 3 A schematic block diagram of a business data analysis apparatus provided by an embodiment of the present application is shown in FIG. 3.

[0023] Figure 4 A structural schematic block diagram of a computer device related to an embodiment of the present application is shown in FIG. 4. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the scope of the present application.

[0025] The flowchart shown in the drawings is only an example and does not necessarily include all the contents and operations / steps, nor does it necessarily be executed in the described order. For example, some operations / steps can be further decomposed, combined or partially merged, so that the actual execution order may be changed according to the actual situation.

[0026] The embodiments of the present application provide a business data analysis method and apparatus, a computer device and a computer readable storage medium.

[0027] Some embodiments of the present application will be described in detail below with reference to the drawings. In the case of no conflict, the embodiments described below and the features in the embodiments can be combined with each other.

[0028] Please refer to Figure 1 , Figure 1 A flowchart of a business data analysis method provided by an embodiment of the present application is shown in FIG. 1. The business data analysis method can be used in a terminal or a server to achieve acquisition and analysis of business data. The terminal can be an electronic device such as a mobile phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant and a wearable device. The server can be a standalone server, a server cluster, 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 distribution networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms.

[0029] Referring to Figure 2 , Figure 2 is a use scenario provided by an embodiment of the present application. As shown in Figure 2 , the gateway layer is used to transmit information between multiple microservices and clients, and the microservices connected with the gateway layer include microservice 1, microservice 2, …, and microservice n. Similarly, the number of clients connected with the gateway layer can also be multiple, which is not limited herein. The business data analysis system provided by the embodiment of the present application detects whether there is a new microservice accessing the gateway layer, and determines a data collection node and acquires a point data according to a project type to which the microservice belongs.

[0030] As shown in Figure 1 , the business data analysis method includes steps S101 to S105.

[0031] Step S101, when detecting that a microservice accesses the gateway layer of a business system, determining a project type corresponding to the microservice.

[0032] For example, after a new microservice completes development and accesses the gateway layer, the project type of the microservice is determined by the business data analysis method provided by the present application, so as to subsequently determine a data collection node according to the project type of the microservice.

[0033] For example, a plurality of project types are pre-set according to the characteristics of a financial system. Specifically, the project types can include, for example, a user authentication microservice, a mall browsing microservice, a transaction microservice, and the like, which are not limited herein.

[0034] For example, the project type of the microservice can be determined by detecting the name of the microservice, or can be determined by analyzing and scanning the code of the microservice, which is not limited herein.

[0035] In some embodiments, when detecting that a microservice accesses the gateway layer of a business system, determining a project type corresponding to the microservice includes: determining a target feature of the microservice based on a pre-set feature recognition algorithm; and determining the project type corresponding to the microservice in a pre-set project type database according to the target feature.

[0036] For example, based on a pre-set feature recognition algorithm, the target feature of the name of the microservice is determined according to the name of the microservice, and a project type matching the target feature of the name of the microservice is determined in a pre-set project type library; wherein the project type library includes target features of names of a plurality of pre-set project types.

[0037] According to the preset feature recognition algorithm, a target feature of the code of the microservice is determined according to the code of the microservice, and a project type matching the target feature of the code of the microservice is determined in a preset project type library; the project type library includes target features of codes of multiple preset project types.

[0038] In step S102, a data collection node for the microservice is determined according to the project type.

[0039] For example, microservices of the same project type have certain common features, such as shopping mall browsing microservices having functions such as detail browsing and product collection, so for microservices belonging to the same project type, corresponding data collection nodes can be determined.

[0040] In some embodiments, the data collection node for the microservice is determined according to the project type, including: inputting preset operation data to the microservice based on the target data template corresponding to the project type; obtaining target data output by the microservice based on the preset operation data, the target data reflecting the effectiveness of the preset operation data; and determining the data collection node according to the target data.

[0041] For example, microservices belonging to the same project type have similar functions, so target data templates can be set in advance for different project types. The target data template at least includes preset operation data.

[0042] For example, taking a shopping mall browsing microservice as an example, if it is determined that the project type to which the microservice belongs is a shopping mall browsing microservice, the preset operation data can include operation instructions for instructing the microservice to perform functions such as detail browsing and product collection. Specifically, based on the target data template, preset operation data is input to the microservice to test the microservice, and determine which preset operation data the microservice can return valid target data, and then determine the data collection node according to the preset operation data that can return valid data.

[0043] For example, if the preset operation data is an operation instruction for instructing to perform a product collection operation, the operation instruction for instructing to perform the product collection operation is input to the microservice, and if the microservice can return valid data for the operation instruction, it means that the microservice cannot execute the operation instruction, and the operation corresponding to the operation instruction can be used as the data collection node. Conversely, if the microservice cannot return valid data for the operation instruction, it means that the microservice cannot execute the operation instruction, and the operation corresponding to the operation instruction does not need to be used as the data collection node.

[0044] Step S103: Based on the data acquisition node, obtain the data collection point data of the microservice.

[0045] For example, based on the data acquisition node determined in step S102, the embedded data during the interaction between the microservice and the client is obtained at the gateway layer.

[0046] For example, if the product collection operation is used as a tracking point for obtaining relevant business data, that is, when the gateway layer detects the operation instruction to execute the product collection operation, the interaction data between the microservice and the client is obtained as the tracking data. Of course, it is not limited to this and is not restricted here.

[0047] Step S104: Based on a preset data classification model, determine the data type of the data, wherein the data type includes: normal data and abnormal data.

[0048] For example, based on the data characteristics of the acquired tracking data, the data type of the tracking data is determined according to the tracking data classification model. The data type of the tracking data may also include unknown data, i.e., tracking data that cannot be classified as normal or abnormal data, but it is not limited to this and is not restricted here.

[0049] In some implementations, determining the data type of the tracking data based on a preset tracking data classification model includes: acquiring a first data feature of the target data; comparing the first data feature with a second data feature of the tracking data to determine whether the tracking data belongs to normal data or abnormal data.

[0050] For example, based on the target data obtained by inputting preset operation data into the microservice, the data types of the subsequently acquired event tracking data are classified. Specifically, a first data feature of the target data and a second data feature of the event tracking data are extracted. If the similarity between the first data feature and the second data feature is greater than a preset threshold, the event tracking data is considered normal data; if the similarity between the first data feature and the second data feature is less than the preset threshold, the event tracking data is considered abnormal data.

[0051] Step S105: If the number of abnormal data exceeds a first preset threshold within a first preset time period, output an abnormal alarm message based on the abnormal data.

[0052] For example, if the number of abnormal information detected cumulatively within a certain period of time exceeds a first preset threshold, an abnormal alarm message is sent to the user so that the user can be informed of the abnormal situation of the microservice in a timely manner and handle the abnormality.

[0053] For example, the abnormal data can be abnormal data of a microservice, for example, the obtained buried point data is parsed to obtain a service identifier of a microservice sending or receiving the buried point data, and in a case where the abnormal message corresponding to a microservice is greater than a first preset quantity threshold, abnormal alarm information is output for the microservice.

[0054] In some embodiments, if the quantity of abnormal data is greater than the first preset quantity threshold within the first preset time, the abnormal alarm information is output based on the abnormal data, including: if the quantity of abnormal data is greater than the first preset quantity threshold within the first preset time, a preset target alarm identifier corresponding to the microservice is obtained; and the abnormal alarm information is output based on the abnormal data according to the preset target alarm identifier.

[0055] For example, the target alarm identifier can represent a way in which a user receives the abnormal alarm information, for example, can be an email address of a technical personnel corresponding to the microservice, and of course is not limited thereto, and the target alarm identifier can also be a telephone number of the technical personnel, which is not limited herein.

[0056] In some embodiments, before the abnormal alarm information is output based on the abnormal data if the quantity of abnormal data is greater than the first preset quantity threshold within the first preset time, the method further includes: determining a peak time period and a valley time period within a second preset time based on a quantity of buried point data within the second preset time, wherein a time length of the first preset time is less than a time length of the second preset time; and determining a first preset quantity threshold corresponding to the peak time period and the valley time period according to the quantity of buried point data within the peak time period and the quantity of buried point data within the valley time period.

[0057] For example, the first preset quantity threshold can be determined according to an actual quantity of buried point data, and different first preset quantity thresholds can be determined in different time periods.

[0058] For example, the second preset time can be 24 hours, and the peak time period and the valley time period within the second preset time are determined in a 24-hour cycle, and of course are not limited thereto, and the second preset time period can also be one week or one month, which is not limited herein.

[0059] For example, the peak time period and the valley time period are determined according to a size change of the quantity of buried point data within the second preset time. Specifically, a time period in which the quantity of buried point data is greater than a third preset quantity threshold is determined as the peak time period, and a time period in which the quantity of buried point data is less than or equal to the third preset quantity threshold is determined as the valley time period, and of course is not limited thereto.

[0060] For example, the first preset quantity threshold is determined according to the data quantity of the buried point data in different time periods. For example, 60% of the average value of the quantity of the buried point data in a peak time period is determined as the first preset quantity threshold of the peak time period, and 60% of the average value of the quantity of the buried point data in a valley time period is determined as the first preset quantity threshold of the valley time period. Of course, the first preset quantity threshold is not limited to this, and is not limited herein.

[0061] In some embodiments, the method further comprises: obtaining, at the gateway layer, request information received by the server; parsing the request information to determine an original interface that sends the request information; and if the quantity of request information sent by the original interface is greater than a second preset quantity threshold, outputting a traffic alarm based on the original interface.

[0062] For example, in addition to a large amount of abnormal data, the quantity of request information sent by a certain original interface of the client may suddenly increase, which may also be an abnormality. Therefore, in the gateway layer, request information sent by the client to the server is obtained, and in the case that the quantity of request information of the original interface is too large, a traffic alarm based on the original interface is outputted to prompt the user to analyze and troubleshoot the situation of the original interface.

[0063] The business data analysis method provided in the above embodiments determines the project type corresponding to the microservice when detecting the gateway layer of the microservice access business system; determines the data collection node for the microservice according to the project type; obtains the buried point data of the microservice based on the data collection node; determines the data type of the buried point data based on a preset buried point data classification model, wherein the data type includes normal data and abnormal data; and if the quantity of abnormal data is greater than a first preset quantity threshold within a first preset time, abnormal alarm information is outputted based on the abnormal data. By automatically determining the data collection node of the microservice to obtain the buried point data, the cost and complexity of obtaining the buried point data of the microservice are reduced, and the operation and maintenance cost of the financial system connected with numerous microservices is reduced.

[0064] Please refer to Figure 3 , Figure 3 FIG. 1 is a schematic diagram of a business data analysis device according to an embodiment of the present application. The business data analysis device can be configured in a server or a terminal, and is used to execute the business data analysis method described above.

[0065] As shown in Figure 4 , the business data analysis device comprises a project type determination module 110, a collection node determination module 120, a buried point data obtaining module 130, a buried point data classification module 140, and an alarm information output module 150.

[0066] The project type determination module 110 is configured to determine a project type corresponding to the microservice when detecting that the microservice accesses a gateway layer of a business system.

[0067] The collection node determination module 120 is configured to determine a data collection node for the microservice according to the project type.

[0068] The buried point data acquisition module 130 is configured to acquire buried point data of the microservice based on the data collection node.

[0069] The buried point data classification module 140 is configured to determine a data type of the buried point data based on a preset buried point data classification model, wherein the data type includes normal data and abnormal data.

[0070] The alarm information output module 150 is configured to output abnormal alarm information based on the abnormal data if a quantity of the abnormal data is greater than a first preset quantity threshold within a first preset time.

[0071] The project type determination module 110 further includes a target feature determination sub-module and a project type determination sub-module.

[0072] The target feature determination sub-module is configured to determine a target feature of the microservice based on a preset feature recognition algorithm.

[0073] The project type determination sub-module is configured to determine a project type corresponding to the microservice by matching in a preset project type database according to the target feature.

[0074] The collection node determination module 120 includes an operation data input sub-module, a target data acquisition sub-module, and a collection node determination sub-module.

[0075] The operation data input sub-module is configured to input preset operation data to the microservice based on a target data template corresponding to the project type.

[0076] The target data acquisition sub-module is configured to acquire target data output by the microservice based on the preset operation data, and the target data is used to reflect validity of the preset operation data.

[0077] The collection node determination sub-module is configured to determine the data collection node according to the target data.

[0078] The buried point data classification module 140 further includes a feature extraction sub-module and a buried point data classification sub-module.

[0079] The feature extraction sub-module is configured to acquire a first data feature of the target data.

[0080] The buried point data classification submodule is configured to compare the first data feature with a second data feature of the buried point data, and determine whether the buried point data belongs to normal data or abnormal data.

[0081] The alarm information output module 150 further includes an alarm identifier acquisition submodule and an alarm information output submodule.

[0082] The alarm identifier acquisition submodule is configured to acquire a preset target alarm identifier corresponding to the microservice if the number of abnormal data is greater than a first preset number threshold within a first preset time.

[0083] The alarm information output submodule is configured to output the abnormal alarm information based on the abnormal data according to the preset target alarm identifier.

[0084] The business data analysis device further includes a time period determination submodule and a number threshold determination submodule.

[0085] The time period determination submodule is configured to determine a peak time period and a valley time period within a second preset time based on the number of buried point data within the second preset time, wherein the time length of the first preset time is less than the time length of the second preset time.

[0086] The number threshold determination submodule is configured to determine a first preset number threshold corresponding to the peak time period and the valley time period, respectively, according to the number of buried point data within the peak time period and the number of buried point data within the valley time period.

[0087] The business data analysis device further includes a request message acquisition submodule, an original interface analysis submodule, and a traffic alarm output submodule.

[0088] The request message acquisition submodule is configured to acquire request information received by the server side at the gateway layer.

[0089] The original interface analysis submodule is configured to analyze the request information and determine an original interface that sends the request information.

[0090] The traffic alarm output submodule is configured to output a traffic alarm based on the original interface if the number of request information sent by the original interface is greater than a second preset number threshold.

[0091] It should be noted that, for the convenience and brevity of description, the specific working processes of the device, the modules, and the units described above can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0092] The methods and apparatus of this application can be used in a wide variety of general-purpose or special-purpose computing system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0093] For example, the above-described method and apparatus can be implemented as a computer program, which can be used in, for example... Figure 4 It runs on the computer device shown.

[0094] Please see Figure 4 , Figure 4 This is a schematic block diagram illustrating the structure of a computer device provided in an embodiment of this application. The computer device may be a server or a terminal.

[0095] like Figure 4 As shown, the computer device includes a processor, a memory, and a network interface connected via a system bus, wherein the memory may include a storage medium and internal memory.

[0096] The storage medium can store the operating system and computer programs. These computer programs include program instructions that, when executed, cause the processor to perform any method of analyzing business data.

[0097] The processor provides computing and control capabilities, supporting the operation of the entire computer device.

[0098] Internal memory provides an environment for the execution of computer programs stored in the storage medium. When these computer programs are executed by the processor, the processor can perform any method of analyzing business data.

[0099] This network interface is used for network communication, such as sending assigned tasks. Those skilled in the art will understand that... Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0100] It should be understood that the processor can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among these, a general-purpose processor can be a microprocessor or any conventional processor.

[0101] In one embodiment, the processor is configured to run a computer program stored in memory to perform the following steps:

[0102] When the gateway layer of the microservice access business system is detected, the project type corresponding to the microservice is determined;

[0103] Based on the project type, determine the data collection nodes for the microservice;

[0104] Based on the data acquisition node, obtain the event tracking data of the microservice;

[0105] Based on a preset data classification model, the data type of the data is determined, wherein the data type includes: normal data and abnormal data;

[0106] If the number of abnormal data exceeds a first preset threshold within a first preset time period, an abnormal alarm message is output based on the abnormal data.

[0107] In one embodiment, when the processor determines the project type corresponding to the microservice upon detecting the gateway layer accessing the business system via a microservice, it is configured to:

[0108] Based on a preset feature recognition algorithm, the target features of the microservice are determined;

[0109] Based on the target characteristics, a matching process is performed in a preset project type database to determine the project type corresponding to the microservice.

[0110] In one embodiment, when the processor implements the step of determining the data collection node for the microservice based on the project type, it is configured to:

[0111] input preset operation data to the microservice based on the target data template corresponding to the project type;

[0112] obtain target data output by the microservice based on the preset operation data, the target data being used to reflect validity of the preset operation data;

[0113] determine the data collection node according to the target data.

[0114] In one embodiment, when implementing the preset buried point data classification model to determine the data type of the buried point data, the processor is configured to:

[0115] obtain a first data feature of the target data;

[0116] compare the first data feature with a second data feature of the buried point data to determine whether the buried point data belongs to normal data or abnormal data.

[0117] In one embodiment, when implementing the preset buried point data classification model to determine the data type of the buried point data, the processor is configured to:

[0118] if the number of abnormal data is greater than a first preset number threshold within a first preset time, obtain a preset target alarm identifier corresponding to the microservice;

[0119] output the abnormal alarm information based on the abnormal data according to the preset target alarm identifier.

[0120] In one embodiment, before implementing the preset buried point data classification model to determine the data type of the buried point data, the processor is configured to:

[0121] determine a peak time period and a trough time period within a second preset time based on the number of buried point data within the second preset time, wherein a time length of the first preset time is less than a time length of the second preset time;

[0122] determine a first preset number threshold corresponding to the peak time period and the trough time period according to the number of buried point data within the peak time period and the number of buried point data within the trough time period.

[0123] In one embodiment, when implementing the business data analysis method, the processor is configured to:

[0124] obtain request information received by the service end at the gateway layer;

[0125] analyzing the request information, determining an original interface from which the request information is sent;

[0126] If the number of request information sent by the original interface is greater than a second preset number threshold, outputting a traffic alarm based on the original interface.

[0127] It should be noted that, for the convenience and brevity of description, the specific working process of the above-described analysis of the business data can refer to the corresponding process in the foregoing embodiment of the analysis control method of the business data, which will not be described here.

[0128] The embodiment of the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, the computer program includes program instructions, and the method implemented when the program instructions are executed can refer to each embodiment of the analysis method of the business data of the present application.

[0129] The computer readable storage medium can be an internal storage unit of the computer device, for example, a hard disk or a memory of the computer device. The computer readable storage medium can also be an external storage device of the computer device, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc.

[0130] It should be understood that the terms used in the present application specification are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the present application specification and the appended claims, unless otherwise clearly indicated by the context, the singular forms "a", "an" and "the" are intended to include the plural forms as well.

[0131] It should also be understood that the term "and / or" used in the present application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. It should be noted that in this document, the term "comprising" or "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or other elements inherent to such a process, method, article or system. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of other identical elements in the process, method, article or system including the element.

[0132] The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments. The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method of analyzing business data, characterized by, The method comprises: When detecting that a microservice accesses a gateway layer of a business system, determining a project type corresponding to the microservice; According to the project type, determining a data collection node for the microservice; Based on the data collection node, obtaining the microservice's buried point data; Based on a preset buried point data classification model, determining the data type of the buried point data, wherein the data type includes normal data and abnormal data; If the number of abnormal data is greater than a first preset number threshold within a first preset time, outputting abnormal alarm information based on the abnormal data; Wherein, according to the project type, the data collection node for the microservice is determined, comprising: Based on the target data template corresponding to the project type, inputting preset operation data to the microservice; Obtain the target data output by the microservice based on the preset operation data, which is used to reflect the validity of the preset operation data; According to the preset operation data that can return valid target data, determine the data collection node; The data type of the buried point data is determined based on the preset buried point data classification model, comprising: Obtain the first data feature of the target data; Compare the first data feature with the second data feature of the buried point data to determine whether the buried point data belongs to normal data or abnormal data.

2. The analysis method of business data according to claim 1, characterized in that, When detecting that a microservice accesses a gateway layer of a business system, determining a project type corresponding to the microservice, comprising: Based on a preset feature recognition algorithm, determine the target feature of the microservice; According to the target feature, match in the preset project type database to determine the project type corresponding to the microservice.

3. The analysis method of business data according to claim 2, characterized in that, The target feature of the microservice is determined based on the preset feature recognition algorithm, comprising: Based on the preset feature recognition algorithm, determine the target feature of the microservice according to the name and / or code of the microservice, and the project type database includes the target feature of the name and / or code of multiple preset project types.

4. The analysis method of service data according to claim 1, characterized by, If the number of abnormal data is greater than a first preset number threshold within a first preset time, output abnormal alarm information based on the abnormal data, comprising: If the number of abnormal data is greater than a first preset number threshold within a first preset time, obtain the preset target alarm identifier corresponding to the microservice; According to the preset target alarm identifier, output the abnormal alarm information based on the abnormal data.

5. The method of claim 1-4, wherein, If the number of abnormal data is greater than a first preset number threshold within a first preset time, output abnormal alarm information based on the abnormal data, further comprising: Based on the number of buried point data within a second preset time, determine the peak time period and the trough time period within the second preset time, wherein the time length of the first preset time is less than the time length of the second preset time; According to the number of buried point data in the peak time period and the number of buried point data in the trough time period, respectively determine the first preset number threshold corresponding to the peak time period and the trough time period.

6. The method of claim 1-4, wherein, The method further comprises: Obtain the request information received by the server at the gateway layer; The request information is parsed to determine an original interface from which the request information is sent; If the number of request information sent by the original interface is greater than a second preset number threshold, a traffic alarm is output based on the original interface.

7. The analysis method of service data according to claim 1, characterized by, The data collection node is used to collect the trace data of the microservice, including: When a preset operation instruction is detected at the gateway layer, the data collection node is used to collect the interaction data between the microservice and the client as the trace data.

8. An analysis device of service data, characterized by, The business data analysis device includes: A project type determination module is configured to determine a project type corresponding to the microservice when the gateway layer through which the microservice accesses the business system is detected. A collection node determination module is configured to determine a data collection node for the microservice according to the project type. A trace data collection module is configured to collect the trace data of the microservice based on the data collection node. A trace data classification module is configured to determine a data type of the trace data based on a preset trace data classification model, wherein the data type includes normal data and abnormal data. An alarm information output module is configured to output abnormal alarm information based on the abnormal data if the number of abnormal data is greater than a first preset number threshold within a first preset time. The data collection node for the microservice is determined according to the project type, including: The preset operation data is input to the microservice based on a target data template corresponding to the project type. Target data output by the microservice based on the preset operation data is obtained, and the target data is used to reflect the validity of the preset operation data. The data collection node is determined according to the preset operation data that can return valid target data. The data type of the trace data is determined based on the preset trace data classification model, including: A first data feature of the target data is obtained. The first data feature is compared with a second data feature of the trace data to determine whether the trace data belongs to normal data or abnormal data.

9. A computer device, comprising: The computer device includes a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program is executed by the processor to implement the steps of the business data analysis method according to any one of claims 1 to 7.

10. A computer readable storage medium characterized by The computer readable storage medium stores a computer program, wherein the computer program is executed by the processor to implement the steps of the business data analysis method according to any one of claims 1 to 7.

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