CN112308465A

CN112308465A - Service index processing method and device

Info

Publication number: CN112308465A
Application number: CN202011332187.7A
Authority: CN
Inventors: 杨昕
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2020-11-24
Filing date: 2020-11-24
Publication date: 2021-02-02

Abstract

The application provides a method and a device for processing a service index, electronic equipment and a computer readable storage medium; the method comprises the following steps: acquiring a service index of a target service, and monitoring a fluctuation value of the service index; when the fluctuation value of the service index exceeds a fluctuation threshold value, acquiring a plurality of sub-dimensions to which the current dimension corresponding to the fluctuation value belongs; determining a sub-fluctuation value of the business index on each of the sub-dimensions; when the sub-fluctuation value exceeds a sub-fluctuation threshold value, recording the sub-fluctuation value and a sub-dimension corresponding to the sub-fluctuation value; and generating a visual analysis result of the target business based on the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value, and presenting the visual analysis result. By the method and the device, the reason for abnormal fluctuation of the service index can be automatically analyzed, and the data analysis efficiency is improved.

Description

Service index processing method and device

Technical Field

The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for processing a service indicator, an electronic device, and a computer-readable storage medium.

Background

Artificial Intelligence (AI) is a theory, method, technique and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human Intelligence, perceive the environment, acquire knowledge and use the knowledge to obtain the best results. With the continuous development of the internet industry, artificial intelligence technology is widely applied to various industries nowadays.

Taking the internet financial industry as an example, in order to enable each service to run smoothly, in the service operation process, service indexes of each service need to be monitored, and possible abnormal situations are discovered in time. However, the related art has not been an effective solution for how to automatically analyze abnormal fluctuations of the traffic indexes.

Disclosure of Invention

The embodiment of the application provides a method and a device for processing a service index, an electronic device and a computer-readable storage medium, which can automatically analyze the reason for abnormal fluctuation of the service index and improve the data analysis efficiency.

The technical scheme of the embodiment of the application is realized as follows:

the embodiment of the application provides a method for processing a service index, which comprises the following steps:

acquiring a service index of a target service, and monitoring a fluctuation value of the service index;

when the fluctuation value of the service index exceeds a fluctuation threshold value, acquiring a plurality of sub-dimensions to which the current dimension corresponding to the fluctuation value belongs;

determining a sub-fluctuation value of the business index on each of the sub-dimensions;

when the sub-fluctuation value exceeds a sub-fluctuation threshold value, recording the sub-fluctuation value and a sub-dimension corresponding to the sub-fluctuation value;

and generating a visual analysis result of the target business based on the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value, and presenting the visual analysis result.

An embodiment of the present application provides a device for processing a service index, including:

the acquisition module is used for acquiring the service index of the target service;

the monitoring module is used for monitoring the fluctuation value of the service index;

the obtaining module is further configured to obtain a plurality of sub-dimensions to which a current dimension corresponding to the fluctuation value belongs when it is monitored that the fluctuation value of the service index exceeds a fluctuation threshold;

a determining module, configured to determine a sub-fluctuation value of the service indicator in each of the sub-dimensions;

the recording module is used for recording the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value when the sub-fluctuation value exceeds a sub-fluctuation threshold value;

a generating module, configured to generate a visual analysis result of the target service based on the sub-fluctuation value and a sub-dimension corresponding to the sub-fluctuation value;

and the presentation module is used for presenting the visual analysis result.

In the above scheme, the obtaining module is further configured to obtain service data corresponding to the target service; the device also comprises an identification module used for identifying the business scene to which the target business belongs based on a machine learning model; the determining module is further configured to determine, as a service index of the target service, service data associated with the identified service scenario in the service data corresponding to the target service.

In the above scheme, the presenting module is further configured to present a service index setting page; the determining module is further configured to determine, in response to a service index setting operation in the service index setting page, the set service index as the service index of the target service.

In the above scheme, the monitoring module is further configured to compare the service data acquired in the current service period with the service data acquired in the previous service period, so as to determine the fluctuation value of the service index according to the comparison result.

In the above scheme, the obtaining module is further configured to perform, according to a plurality of index dimensions of the service data corresponding to the target service, a dismantling process on a current dimension corresponding to the fluctuation value to obtain a plurality of sub-dimensions to which the current dimension belongs; wherein the plurality of sub-dimensions correspond to the plurality of index dimensions one-to-one.

In the above scheme, the obtaining module is further configured to perform, according to a preset dimension hierarchical relationship table, a dismantling process on a current dimension corresponding to the fluctuation value to obtain a plurality of sub-dimensions to which the current dimension belongs; and the dimension hierarchical relation table is provided with attribution relations corresponding to the dimensions of different hierarchies.

In the above scheme, the generating module is further configured to perform multi-level division on the service data corresponding to the target service according to a preset requirement; and generating the dimension hierarchical relation table according to the result of the multi-level division.

In the foregoing solution, the recording module is further configured to send a storage request carrying the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value to a blockchain network, so that the blockchain network performs the following operations: and calling an intelligent contract to verify the transaction corresponding to the storage request, and storing the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value in a state database of the block chain network after the verification is passed.

In the above scheme, the presenting module is further configured to present a fluctuation analysis description template; the generating module is further configured to fill the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value into a block corresponding to the fluctuation analysis description template, so as to generate a visual analysis result of the target service.

An embodiment of the present application provides an electronic device, including:

a memory for storing executable instructions;

and the processor is used for realizing the service index processing method provided by the embodiment of the application when the executable instruction stored in the memory is executed.

The embodiment of the present application provides a computer-readable storage medium, which stores executable instructions for causing a processor to execute the method for processing a service index provided in the embodiment of the present application.

The embodiment of the application has the following beneficial effects:

by monitoring the fluctuation value of the service index, automatically acquiring a plurality of sub-dimensions to which the current dimension corresponding to the fluctuation value belongs when the fluctuation of the service index is monitored to be abnormal (namely the fluctuation value exceeds a fluctuation threshold), analyzing the sub-motion values respectively corresponding to the service index on each sub-dimension, recording the sub-dimension corresponding to the abnormal sub-fluctuation value (namely the sub-motion value exceeds the sub-motion threshold), then generating a visual analysis result of the target service based on the recorded sub-motion value with abnormal fluctuation and the corresponding sub-dimension, and presenting the visual analysis result, so that the link of manual participation is saved, a large amount of labor cost can be saved, and meanwhile, the final analysis result is prevented from errors caused by negligence, and the comprehensiveness and accuracy of the analysis result are further ensured.

Drawings

FIG. 1 is a schematic diagram of an architecture of a system for processing a business index provided by an embodiment of the present application;

fig. 2 is a schematic structural diagram of a terminal provided in an embodiment of the present application;

fig. 3 is a schematic flowchart of a method for processing a service indicator according to an embodiment of the present application;

fig. 4 is an application scenario diagram of a method for processing a service indicator according to an embodiment of the present application;

fig. 5 is a schematic flowchart of a method for processing a service indicator according to an embodiment of the present application;

fig. 6 is a schematic flowchart of a process for setting a service index by a service person according to an embodiment of the present application;

fig. 7 is a flowchart illustrating a method for processing a service indicator according to an embodiment of the present application.

Detailed Description

In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.

Before further detailed description of the embodiments of the present application, terms and expressions referred to in the embodiments of the present application will be described, and the terms and expressions referred to in the embodiments of the present application will be used for the following explanation.

1) Business indicators, numerical values reflecting the absolute number and scale of business phenomena, such as total production, total sales revenue, staff count, and the like.

2) The data visualization analysis platform is a platform which supports access of various data sources, can automatically analyze service data, and has a visualization operation interface and a drag type exploration analysis.

3) Dimensions, which refer to attributes of the data, for example, the "city" dimension represents the city in which the session was initiated, such as "Beijing" or "Shanghai"; the "web page" dimension represents the address of a web page that the user has browsed.

4) Drill down, add new dimensions from the business size index or look down to the current hierarchical dimension (e.g., year, quarter, month, day). For multidimensional data sources that contain a hierarchy, drill-down is one of the most useful methods of navigating the hierarchy. For example, when a user is viewing the total sales for different years, the user may drill down to view the sales corresponding to each of all months of the year to determine the high-selling season and the low-selling season.

5) A Block chain (Blockchain) is a storage structure for encrypted, chained transactions formed from blocks (blocks).

For example, the header of each block may include hash values of all transactions in the block, and also include hash values of all transactions in the previous block, so as to achieve tamper resistance and forgery resistance of the transactions in the block based on the hash values; newly generated transactions, after being filled into the tiles and passing through the consensus of nodes in the blockchain network, are appended to the end of the blockchain to form a chain growth.

6) A Blockchain Network (Blockchain Network) incorporates new blocks into a set of nodes of a Blockchain in a consensus manner.

7) Ledger (legger) is a general term for blockchains (also called Ledger data) and state databases synchronized with blockchains.

Wherein, the blockchain records the transaction in the form of a file in a file system; the state database records the transactions in the blockchain in the form of different types of Key (Key) Value pairs for supporting fast query of the transactions in the blockchain.

8) Intelligent Contracts (Smart Contracts), also known as chain codes (chaincodes) or application codes, are programs deployed in nodes of a blockchain network, and the nodes execute the intelligent Contracts called in received transactions to perform operations of updating or querying key-value data of the account database.

9) Consensus (Consensus), a process in a blockchain network, is used to agree on transactions in a block among a plurality of nodes involved, the agreed block is to be appended to the end of the blockchain, and the mechanisms for achieving Consensus include Proof of workload (PoW, Proof of Work), Proof of rights and interests (PoS, Proof of equity (DPoS), Proof of granted of shares (DPoS), Proof of Elapsed Time (PoET, Proof of Elapsed Time), and so on.

10) Transactions (transactions), equivalent to the computer term "Transaction," include operations that need to be committed to a blockchain network for execution and do not refer solely to transactions in the context of commerce, which embodiments of the present application follow in view of the convention colloquially used in blockchain technology.

For example, a deployment (deployment) transaction is used to install a specified smart contract to a node in a blockchain network and is ready to be invoked; the Invoke (Invoke) transaction is used to append records of the transaction in the blockchain by invoking the smart contract and to perform operations on the state database of the blockchain, including update operations (including adding, deleting, and modifying key-value pairs in the state database) and query operations (i.e., querying key-value pairs in the state database).

With the development of internet technology, various industries generate a large amount of business data every day in the operation process, such as some report data, and have great research value for detecting whether the data are abnormally fluctuated or not and analyzing the reason of the abnormal fluctuation when the abnormal fluctuation occurs. Especially in the internet financial industry, in daily business, the situation that the payment amount suddenly fluctuates abnormally often occurs, and in order to know the fluctuation reason, the method plays a vital role in subsequent operation decisions of merchants.

In order to meet the requirements, various data visualization analysis platforms are developed, and business personnel can perform visualization analysis on the data visualization analysis platforms, for example, by comparing and drilling down, specific reasons for fluctuation of business indexes are analyzed.

However, although the data visualization analysis platform provides data in an automated manner, does not need to perform data processing, and has a certain visualization analysis capability, when a problem is encountered, for example, when the fluctuation of the service index is abnormal, a great amount of multidimensional and multi-angle drilling analysis still needs to be performed manually by a service worker, which results in a long time for analyzing the abnormal fluctuation of the service index, i.e., the time efficiency still needs to be improved. Meanwhile, manual analysis inevitably causes negligence, and the finally obtained analysis result is not comprehensive enough and has low accuracy.

In view of this, embodiments of the present application provide a method and an apparatus for processing a service index, an electronic device, and a computer-readable storage medium, which can automatically analyze a reason why the service index fluctuates abnormally, and push an analysis result to a data visualization analysis platform for displaying on the data visualization analysis platform, thereby reducing time consumed by service personnel when analyzing the abnormal fluctuation of the service index, ensuring comprehensiveness and accuracy of the analysis result, and avoiding possible mistakes and omissions in manual analysis.

The following describes an exemplary application of the electronic device applying the method for processing a service index provided in the embodiment of the present application, where the electronic device applying the method for processing a service index provided in the embodiment of the present application may be implemented as various types of user terminals such as a notebook computer, a tablet computer, a desktop computer, a set-top box, a mobile device (e.g., a mobile phone, a portable music player, a personal digital assistant, a dedicated messaging device, and a portable game device), may also be implemented as a server, e.g., an independent physical server, a server cluster or a distributed system configured by a plurality of physical servers, may also be a cloud server providing cloud computing services, and may also be implemented in a manner that the terminal and the server cooperate to implement the method for processing a service index provided in the embodiment of the present application.

An exemplary application of the electronic device applying the processing method of the business index when implemented as a server will be described below with reference to fig. 1.

Referring to fig. 1, fig. 1 is a schematic diagram of an architecture of a system 100 for processing a service indicator according to an embodiment of the present application. The system 100 for processing the service index includes: the server 200, the network 300, the terminal 400, and the database 500 are explained below.

In some embodiments, the server 200 may be an independent physical server, may also be a server cluster or a distributed system formed by a plurality of physical servers, and may also be a cloud server providing basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a network service, cloud communication, a middleware service, a domain name service, a security service, a CDN, and a big data and artificial intelligence platform. The terminal 400 may be, but is not limited to, a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, and the like. The terminal 400 and the server 200 may be directly or indirectly connected through wired or wireless communication, and the embodiment of the present application is not limited herein.

The server 200 is configured to obtain service data of a target service from the database 500, monitor a fluctuation value of a service index of the target service (the service index may be automatically determined by the server 200 according to a service scene to which the target service belongs, or specified by a service person), and when it is monitored that the fluctuation value of the service index exceeds a fluctuation threshold, the server 200 obtains a plurality of sub-dimensions to which a current dimension corresponding to the fluctuation value belongs, and determines a sub-fluctuation value of the service index in each sub-dimension. Subsequently, the server 200 analyzes each sub-fluctuation value, and when the sub-fluctuation value exceeds the sub-fluctuation threshold, records the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value. Finally, the server generates a visual analysis result of the target service based on the recorded sub-motion values and the sub-dimensions corresponding to the sub-motion values, and transmits the generated visual analysis result to the terminal 400 through the network 300.

A network 300 for connecting the server 200 and the terminal 400, wherein the network 300 may be a wide area network or a local area network, or a combination thereof.

The terminal 400 is a terminal associated with a service person, and runs thereon a client 410, for example, the client 410 may be a client of various types of data visualization analysis platforms, and the client 410 may also be a browser, which renders the data visualization analysis platform through a page and presents a visualization analysis result of a target service in the data visualization analysis platform. After receiving the visual analysis result of the target service sent by the server 200, the terminal 400 calls the graphical interface of the client 410 to display, so that the service personnel can directly make a subsequent improvement strategy according to the visual analysis result displayed on the client 410.

And a database 500 for storing service data of the target service, wherein the service data may be various types of service data, such as service data of internet finance, government data of digital government affairs, sales data of e-commerce platforms, and the like.

It should be noted that, the method for processing the service index provided in the embodiment of the present application may be implemented independently by the server, or may be implemented independently by the terminal, or implemented cooperatively by the server and the terminal. An exemplary application of the electronic device implementing the method for processing the service index provided by the embodiment of the present application is described below as a terminal.

For example, taking the terminal 400 in fig. 1 as an example, the terminal 400 may obtain, through the network 300, the service data of the target service stored in the database 500, and monitor the fluctuation value of the service index of the target service. Next, when the terminal 400 monitors that the fluctuation value of the service index exceeds the fluctuation threshold, the terminal calls its own operation processing capability to perform the dismantling processing on the current dimension corresponding to the fluctuation value, so as to obtain a plurality of sub-dimensions to which the current dimension belongs. Subsequently, the terminal 400 analyzes the sub-fluctuation value of the service index in each sub-dimension, and when it is determined that the sub-fluctuation value exceeds the sub-fluctuation threshold, records the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value. Finally, the terminal 400 generates a visual analysis result of the target service based on the recorded sub-motion values and the sub-dimensions, and invokes a graphical interface of the client 410 to present.

It should be noted that the analysis program for processing the service index may be integrated in the client of the data visualization analysis platform, and used as an attached function module of the client of the data visualization analysis platform; or independent from the data visualization analysis platform client, that is, after recording the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value, the analysis program generates a corresponding visualization analysis result, and sends the visualization analysis result to the data visualization analysis platform client, so as to present the visualization analysis result of the target service in the user interface of the data visualization analysis platform client.

The method for processing the service index provided by the embodiment of the application can be applied to various types of data analysis scenes. For example, taking a business super scenario as an example, when the server monitors that the total sales volume of the supermarket suddenly increases within a certain time period, the server may drill down the current dimension corresponding to the total sales volume, for example, the server drills down the sales volume corresponding to each commodity, and then, the server determines whether the sales volume corresponding to each commodity abnormally fluctuates. Assuming that the server determines that the sales of the commodity A and the commodity B suddenly increase in a recent period of time, the server records the names of the commodity A and the commodity B and the sales corresponding to the names of the commodity A and the commodity B respectively, and then the server generates a sales analysis report based on the names of the commodity A and the commodity B and the sales corresponding to the names of the commodity A and the commodity B and displays the sales analysis report on a visual analysis platform.

The structure of the terminal 400 in fig. 1 is explained below. Referring to fig. 2, fig. 2 is a schematic structural diagram of a terminal 400 provided in an embodiment of the present application, where the terminal 400 shown in fig. 2 includes: at least one processor 460, memory 450, at least one network interface 420, and a user interface 430. The various components in the terminal 400 are coupled together by a bus system 440. It is understood that the bus system 440 is used to enable communications among the components. The bus system 440 includes a power bus, a control bus, and a status signal bus in addition to a data bus. For clarity of illustration, however, the various buses are labeled as bus system 440 in fig. 2.

The Processor 460 may be an integrated circuit chip having Signal processing capabilities, such as a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like, wherein the general purpose Processor may be a microprocessor or any conventional Processor, or the like.

The user interface 430 includes one or more output devices 431, including one or more speakers and/or one or more visual displays, that enable the presentation of media content. The user interface 430 also includes one or more input devices 432, including user interface components that facilitate user input, such as a keyboard, mouse, microphone, touch screen display, camera, other input buttons and controls.

The memory 450 may be removable, non-removable, or a combination thereof. Exemplary hardware devices include solid state memory, hard disk drives, optical disk drives, and the like. Memory 450 optionally includes one or more storage devices physically located remote from processor 460.

The memory 450 includes either volatile memory or nonvolatile memory, and may include both volatile and nonvolatile memory. The nonvolatile memory may be a Read Only Memory (ROM), and the volatile memory may be a Random Access Memory (RAM). The memory 450 described in embodiments herein is intended to comprise any suitable type of memory.

In some embodiments, memory 450 is capable of storing data, examples of which include programs, modules, and data structures, or a subset or superset thereof, to support various operations, as exemplified below.

An operating system 451, including system programs for handling various basic system services and performing hardware-related tasks, such as a framework layer, a core library layer, a driver layer, etc., for implementing various basic services and handling hardware-based tasks;

a network communication module 452 for communicating to other computing devices via one or more (wired or wireless) network interfaces 420, exemplary network interfaces 420 including: bluetooth, wireless compatibility authentication (WiFi), and Universal Serial Bus (USB), etc.;

a display module 453 for enabling presentation of information (e.g., user interfaces for operating peripherals and displaying content and information) via one or more output devices 431 (e.g., display screens, speakers, etc.) associated with user interface 430;

an input processing module 454 for detecting one or more user inputs or interactions from one of the one or more input devices 432 and translating the detected inputs or interactions.

In some embodiments, the processing device of the service index provided by the embodiment of the present application may be implemented in a software manner, and fig. 2 illustrates the processing device 455 of the service index stored in the memory 450, which may be software in the form of programs and plug-ins, and includes the following software modules: an acquisition module 4551, a monitoring module 4552, a determination module 4553, a recording module 4554, a generation module 4555, a presentation module 4556, and a recognition module 4557, which are logical and thus may be arbitrarily combined or further separated depending on the functions implemented. The functions of the respective modules will be explained below. Different software implementations of the service indicator processing means 455 are illustrated below.

Example one, the processing device of the service index can be a terminal application program and a module

The embodiment of the application can provide a software module designed by using a programming language such as C/C + +, Java, and the like, and embedded into various terminal Apps (for example, game applications and the like) based on systems such as Android or iOS (stored in a storage medium of the terminal as executable instructions and executed by a processor of the terminal), so that tasks such as drilling down and generating a visual analysis result of a target service can be completed directly by using computing resources of the terminal itself, and the generated visual analysis result of the target service can be transmitted to a remote server through various network communication modes periodically or aperiodically or can be stored locally at a mobile terminal.

Example two, the processing device of the business index can be a server application program and a platform

The embodiment of the application can provide application software designed by using programming languages such as C/C + +, Java and the like or a special software module in a large-scale software system, operate in a server end (stored in a storage medium of the server end in an executable instruction mode and operated by a processor of the server end), combine at least one of various kinds of received original data, intermediate data of various levels and final results from other equipment with some data or results existing on the server to train a model and identify a transaction by using the trained model, and then output the model or the result of the transaction identification to other application programs or modules in real time or non-real time for use, and can also write the model or the result of the transaction identification into a database or a file at the server end for storage.

The embodiment of the application can also be provided for carrying a customized and easily interactive network (Web) interface or other User Interfaces (UI) on a distributed and parallel computing platform formed by a plurality of servers to form a UI interface design platform used by individuals, groups or enterprises and the like. The user can upload the existing data packets to the platform in batch to obtain various calculation results, and can also transmit the real-time data stream to the platform to calculate and refresh each stage of results in real time.

Example three, the processing device of the service indicator may be an Application Program Interface (API) and a plug-in

The embodiment of the application can provide an API, a Software Development Kit (SDK) or a plug-in for generating a visual analysis result of the target service for the realization of the server side, so that other server side application program developers can call the API, the SDK or the plug-in, and the API, the SDK or the plug-in is embedded into various application programs.

Example four, the processing device of the service index can be a terminal device client API and a plug-in

The embodiment of the application can also provide an API, an SDK or a plug-in for generating a visual analysis result of the target service for the terminal equipment end, so that other terminal application program developers can call the API, the SDK or the plug-in, and the API, the SDK or the plug-in is embedded into various application programs.

Example five, the processing device of the business index may be a cloud open service

The embodiment of the application can provide a cloud service designed for a UI (user interface) based on artificial intelligence abnormal transaction processing, and the embodiment of the application can also provide an Application Package (API), a Software Development Kit (SDK), a plug-in and the like for designing the cloud service for the UI, and the cloud service can be packaged and packaged into a cloud service which can be used by personnel inside and outside an enterprise in an open mode, or various results can be displayed on various terminal display devices in a proper form for individuals, groups or enterprises.

The following describes a method for processing a service index provided by the embodiment of the present application, with reference to an exemplary application and implementation of a terminal provided by the embodiment of the present application. For example, referring to fig. 3, fig. 3 is a schematic flowchart of a method for processing a service indicator according to an embodiment of the present application, and the steps shown in fig. 3 will be described.

In step S301, a service index of the target service is obtained.

In some embodiments, the target service may be various types of services, which may include, for example, an internet financial service, a digital government service, a map navigation service, and the like. For example, taking internet financial services as an example, the internet financial services can be further subdivided into: payment services, express services, insurance services, and the like.

In some embodiments, the terminal may obtain the service index of the target service by: acquiring service data corresponding to a target service, and identifying a service scene to which the target service belongs based on a machine learning model; and determining the service data associated with the identified service scene in the service data corresponding to the target service as a service index of the target service (namely, a service index which needs to be detected subsequently).

For example, a model, such as a neural network model, for identifying a service scenario to which the target service belongs may be trained by a machine learning method, and the trained neural network model is used to predict the service scenario corresponding to the target service based on the acquired service data.

For example, a portion of known traffic scenarios and corresponding traffic data may be used as a labeled (corresponding traffic scenarios and traffic data) training sample of a scene recognition model such as { traffic scenario; service data }; and training the neural network model by using a machine learning method, so that the trained neural network model has the capability of predicting a service scene based on service data.

Taking training of the neural network model as an example, the neural network model includes three layers, namely an input layer, a hidden layer and an output layer. The input layer is responsible for receiving input training samples and distributing the training samples to the hidden layer, the hidden layer is responsible for required calculation and outputting results to the output layer, and the output layer outputs a service scene to which a target service to be detected belongs. The characteristics (such as the type and source of business data corresponding to a target business) of a training sample input by an input layer of the neural network model are derived variables, and the mapping relation between the variables and business scenes is learned at a hidden layer of the neural network model, so that the neural network model has the performance of predicting the corresponding business scenes on the basis of the business data at an output layer of the neural network. After the neural network training is completed, the business data corresponding to the target business is input into the neural network, and the prediction result of the business scene to which the target business belongs can be obtained. Subsequently, after the prediction result of the service scene is obtained, the service data associated with the service scene may be determined as the service index of the target service from the plurality of service data corresponding to the target service. For example, if the machine learning model identifies that the service scenario to which the target service belongs is an online shopping scenario, the payment amount in the plurality of service data may be used as the service index of the target service; if the machine learning model identifies that the service scene to which the target service belongs is a map navigation scene, the traffic flow in the corresponding service data can be used as the service index of the target service.

In other embodiments, the business index of the target business may also be specified by business personnel. The terminal calls a data visualization analysis platform (for example, a special data visualization analysis platform client or a data visualization analysis platform operated through a browser) operated on the terminal, and presents a service index setting page in a human-computer interaction interface of the data visualization analysis platform; and responding to the service index setting operation in the service index setting page, and determining the set service index as the service index of the target service.

Illustratively, a data visualization analysis platform client is operated on a terminal associated with a service person, and a service index setting page is presented in a user interface of the data visualization analysis platform client. And a plurality of corresponding candidate service indexes are presented in the service index setting page for service personnel to select aiming at different types of target services. For example, taking the type of the target service as the online shopping as an example, a plurality of candidate service indexes such as payment amount, goodness rate, return rate, and the like are presented for the online shopping in the service index setting page. After the business personnel click the button corresponding to the payment amount, the data visualization analysis platform takes the payment amount as a business index of online shopping.

It should be noted that the service index corresponding to the target service may be one or more. For example, when a plurality of service indexes corresponding to the target service are provided, after processing a subsequent service index, the terminal continues to process the next service index until all service indexes are processed.

In step S302, a fluctuation value of the service index is monitored.

In some embodiments, after the service index of the target service is obtained in step S301, the terminal monitors a fluctuation value of the obtained service index to determine whether the fluctuation of the service index is abnormal.

For example, the terminal may monitor the fluctuation value of the service indicator by: and comparing the service data acquired in the current service period with the service data acquired in the last service period to determine the fluctuation value of the service index according to the comparison result. The service period may be determined according to actual needs, for example, one service period may be one month, one week, one day, or the like.

For example, taking the target service as online shopping and the service index of the target service as payment amount as an example, for a certain e-commerce shopping platform, the amount paid by the customer on the e-commerce shopping platform on the same day may be compared with the payment amount on the previous day to determine the fluctuation value of the payment amount. Of course, the payment amount may be compared every other week, that is, the payment amount of the week is compared with the payment amount of the last week, so as to determine the fluctuation value of the payment amount.

In step S303, when it is monitored that the fluctuation value of the service indicator exceeds the fluctuation threshold, a plurality of sub-dimensions to which the current dimension corresponding to the fluctuation value belongs are obtained.

In some embodiments, a normal fluctuation range may be set in advance for the service index of the target service, where the corresponding normal fluctuation ranges may be different for different service indexes of the same target service. For example, taking online shopping as an example, when the corresponding service index is the payment amount, the preset normal fluctuation range may be 10 to 50 ten thousand; when the corresponding service index is a good rating, the preset normal fluctuation range can be 70% -90%. That is, for different service indexes, service personnel can preset corresponding normal fluctuation ranges.

In addition, for the same service index, the normal fluctuation range of the same service index can also be different along with different time periods, for example, the service index is still taken as an example of payment amount, and if the business of a certain off-line supermarket is more explosive at night, the normal fluctuation range of the payment amount can be set to be 30-50 at 8:00-15: 00; and at 16:00-20:00, the normal fluctuation range of the payment amount is set to 80-100. It should be noted that the normal fluctuation range of the service index may be preset according to an actual situation, and the embodiment of the present application is not limited herein.

In other embodiments, after determining the service index to be detected of the target service, obtaining a fluctuation value corresponding to the service index to be detected according to service data corresponding to the target service, then comparing the obtained fluctuation value with a preset normal fluctuation range of the service index, if the obtained fluctuation value is within the normal fluctuation range, indicating that the service index is normal, and continuing to monitor; if the obtained fluctuation value is not in the normal fluctuation range, for example, exceeds or is lower than the normal fluctuation range, it indicates that the service index is abnormal or the service index has abnormal fluctuation.

For example, still taking the service index as the payment amount as an example, for a supermarket under a certain line, assuming that the normal fluctuation range set by a service worker for the payment amount is between 8:00 and 16:00 is between 30 and 50, when the terminal determines that the fluctuation of the payment amount between 8:00 and 16:00 is lower than 30 or exceeds 50 according to the sales data of the supermarket, the fluctuation of the payment amount is abnormal; when the terminal determines that the fluctuation of the payment amount between 8:00 and 16:00 is between 30 and 50 according to the sales data of the supermarket, for example, if the fluctuation range of the payment amount determined by the terminal is 40 to 45, the fluctuation of the payment amount is normal.

In some embodiments, for a case where it is determined that the service indicator abnormally fluctuates (that is, the fluctuation value exceeds the fluctuation threshold), the terminal may perform a dismantling process on the current dimension corresponding to the fluctuation value, so as to further determine which dimension is caused by the abnormal fluctuation.

For example, the terminal may obtain a plurality of sub-dimensions to which the current dimension corresponding to the fluctuation value (i.e. the fluctuation value exceeding the fluctuation threshold) belongs by: according to a plurality of index dimensions of the service data corresponding to the target service, performing disassembly processing on the current dimension corresponding to the fluctuation value to obtain a plurality of sub-dimensions to which the current dimension belongs; and the plurality of sub-dimensions obtained by the disassembly correspond to the plurality of index dimensions one to one. For example, taking the service index as the number of the new population as an example, assuming that when it is monitored that the fluctuation value of the new population of a certain province exceeds the fluctuation threshold, the current dimensionality (namely, province dimensionality) corresponding to the fluctuation value can be decomposed into a plurality of cities to which the province belongs according to the demographic data reported by the cities.

For example, the terminal may also obtain a plurality of sub-dimensions to which the current dimension corresponding to the fluctuation value belongs by: according to a preset dimension hierarchical relation table, performing disassembly processing on the current dimension corresponding to the fluctuation value to obtain a plurality of sub-dimensions to which the current dimension belongs; the dimension hierarchical relation table is preset with attribution relations corresponding to dimensions of different levels.

In some embodiments, the dimension hierarchy table may be obtained by: performing multi-level division on service data corresponding to a target service according to a preset requirement; and generating a dimension hierarchical relation table according to the result of the multi-level division.

For example, taking the service index as the payment amount as an example, for the service index of the payment amount, the type of the current dimension corresponding to the fluctuation value may include occupation, gender, commodity category, and the like. For the career dimension, the career dimension can be decomposed into a plurality of sub-dimensions such as college students, doctors, white collars, blue collars and the like according to a preset dimension hierarchical relation table (for example, a preset career distribution table); for the sex dimension, the sex dimension can be divided into male and female sub-dimensions; for the commodity category dimension, the commodity category dimension can be decomposed into sub-dimensions of food, clothing, books, electronic products and the like according to a preset dimension hierarchical relation table (for example, a preset commodity classification table).

It should be noted that, when the current dimension corresponding to the fluctuation value is a plurality of dimensions of different types, the terminal may perform further dismantling processing on each current dimension corresponding to the fluctuation value according to the actual situation, for example, may perform dismantling processing on the aforementioned occupation dimension, gender dimension, and commodity category dimension at the same time; for example, when a service person only cares about crowd distribution, the terminal may only perform the dismantling process on the professional dimension, which is not limited in the embodiment of the present application.

In step S304, a sub-fluctuation value of the traffic indicator in each sub-dimension is determined.

In some embodiments, after obtaining the multiple sub-dimensions to which the current dimension belongs corresponding to the fluctuation value in step S303, the terminal may further determine the sub-fluctuation value of the service index in each sub-dimension.

In an example, still taking the service index as the payment amount as an example, after monitoring that the fluctuation value of the payment amount exceeds the fluctuation threshold, the terminal performs disassembly processing on the current dimensionality corresponding to the fluctuation value, such as the professional dimensionality, to obtain a plurality of sub-dimensionalities, such as college students, medical staff, white collars, blue collars and the like. And then, the terminal respectively determines the sub-dynamic values of the payment amounts respectively corresponding to the college student, the medical staff, the white collar, the blue collar and the like.

For example, when the terminal is disassembled for the gender dimension, two sub-dimensions of a male and a female can be obtained, and the terminal determines the sub-dynamic value of the payment amount corresponding to the male user and the sub-dynamic value of the payment amount corresponding to the female user respectively.

In step S305, when the sub-fluctuation value exceeds the sub-fluctuation threshold, the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value are recorded.

In some embodiments, after determining the sub-fluctuation value of the service indicator in each sub-dimension through step S304, the terminal analyzes each sub-fluctuation value, determines whether the sub-fluctuation value is abnormal, and records the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value when determining that the sub-fluctuation value is abnormal (i.e., the sub-fluctuation value exceeds the sub-fluctuation threshold); when the sub-fluctuation value is judged not to be abnormal, the sub-fluctuation value is ignored, and the next sub-fluctuation value is continuously judged.

In an example, after receiving the above, the terminal determines the sub-dynamic values of the payment amounts corresponding to the college student, the medical staff, the white collar, the blue collar and the like, and then sequentially judges whether the sub-dynamic values of the payment amounts corresponding to the college student, the medical staff, the white collar, the blue collar and the like are abnormal, and for the sub-dynamic value with abnormal fluctuation, records the corresponding sub-dynamic value and the corresponding sub-dimension. And recording the sub-dimension of the college student and the sub-fluctuation value of the payment amount corresponding to the college student if the terminal determines that the sub-fluctuation value of the payment amount corresponding to the college student exceeds the sub-fluctuation threshold value.

In step S306, a visual analysis result of the target service is generated based on the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value, and the visual analysis result is presented.

In some embodiments, the terminal may generate the visual analysis result of the target service based on the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value by the following methods: calling a user interface of a data visualization platform to present a fluctuation analysis description template; and filling the recorded sub-fluctuation values and the sub-dimensions corresponding to the sub-fluctuation values into the layout blocks corresponding to the presented fluctuation analysis description template so as to generate a visual analysis result of the target business.

For example, taking the service index as the payment amount as an example, the following fluctuation analysis description templates may be presented in the user interface of the data visualization analysis platform client: the fluctuation value of the payment amount of the time to be filled is the specific fluctuation value to be filled and exceeds the normal fluctuation range; the main reasons are: the fluctuation value of the payment amount of the sub-dimension to be filled exceeds the normal fluctuation range, and the corresponding sub-fluctuation value is the sub-fluctuation value to be filled. For example, if the terminal monitors that the fluctuation value of the payment amount is 90 and exceeds the normal fluctuation range on 6/11/2020, then the terminal disassembles the current dimension corresponding to the fluctuation value to obtain that the sub-fluctuation value of the payment amount corresponding to the college student exceeds the sub-fluctuation threshold value, and the corresponding sub-fluctuation value is 50, the plate block corresponding to the fluctuation analysis description template may be filled based on the obtained data, so that the following visual analysis result is presented in the data visual analysis platform client: the fluctuation value of the payment amount of 11/6/2020 is 90, which exceeds the normal fluctuation range; the main reasons are: the fluctuation value of the payment amount of the college student exceeds the normal fluctuation range, and the corresponding sub-fluctuation value is 50. Therefore, business personnel can directly determine that the abnormal fluctuation of the total payment amount is mainly caused by the abnormal fluctuation of the university student group payment amount through the analysis result presented in the data visualization analysis platform.

It should be noted that, in the above example, the reason for the abnormal fluctuation of the payment amount is analyzed only by using the sub-dimension of the university student, in practical applications, the reason for the abnormal fluctuation of the payment amount may be manifold, that is, there may be a plurality of pieces corresponding to the sub-dimension in the fluctuation analysis description template, which are used for filling the sub-dimension and the corresponding sub-dimension value recorded by the plurality of terminals. In addition, the visualization analysis result of the target service presented in the data visualization platform can be presented in a chart manner, for example, in a pie chart or sector chart manner, in addition to the manner described in the above text; or the diagrams are presented in combination with the text, which is not limited in the embodiments of the present application.

The method for processing the service index provided by the embodiment of the application monitors the fluctuation value of the service index, when the fluctuation of the service index is monitored to be abnormal (namely the fluctuation value exceeds a fluctuation threshold value), a plurality of sub-dimensions corresponding to the fluctuation value and subordinate to the current dimension are automatically acquired, and analyzing the sub-fluctuation values respectively corresponding to the service index on each sub-dimension, recording the sub-dimension corresponding to the abnormal sub-fluctuation value (i.e. the sub-fluctuation value exceeds the sub-fluctuation threshold), and then, generating a visual analysis result of the target business based on the recorded sub-fluctuation values of the abnormal fluctuation and the corresponding sub-dimensions, and presents the visual analysis result, thereby saving the link of manual participation, saving a large amount of labor cost, meanwhile, the final analysis result is ensured not to be wrong due to human negligence, and further the comprehensiveness and the accuracy of the analysis result are ensured.

In other embodiments, the method for processing the service indicator provided in the embodiments of the present application may also be implemented by combining a block chain technology.

A blockchain refers to a storage structure of encrypted, chained transactions formed from blocks. The system is a shared database, and data or information stored in the shared database has the characteristics of being unforgeable, traceable and maintained collectively.

For example, referring to fig. 4, fig. 4 is an application schematic diagram of a service indicator processing method provided in an embodiment of the present application, and the application schematic diagram includes a blockchain network 600 (exemplarily showing a consensus node 610-1 to a consensus node 610-3), an authentication center 700, and a service principal 800/900, which are respectively described below.

The type of blockchain network 600 is flexible and may be, for example, any of a public chain, a private chain, or a federation chain. Taking the public chain as an example, the electronic devices (e.g., the server 200 and the terminal 400 in fig. 1) of any service entity can access the blockchain network 600 without authorization to become a client node; taking a federation chain as an example, after being authorized, a business entity can access the electronic device under its jurisdiction to the blockchain network 600 to become a client node.

As an example, when the blockchain network 600 is a federation chain, the business entity 800/900 registers from the certificate authority 700 to obtain respective digital certificates including the public key of the business entity and a digital signature signed by the certificate authority 700 for the public key and identity information of the business entity 800/900, to be appended to the transaction (e.g., a sub-dynamic value for uplink and a sub-dimension corresponding to the sub-dynamic value, or an acquisition request, etc.) together with the digital signature of the business entity for the transaction, and is sent to the blockchain network 600, for the blockchain network 600 to take the digital certificate and the digital signature from the transaction, verify the authenticity of the transaction (i.e., whether it has not been tampered with) and the identity information of the service entity sending the message, and the blockchain network 600 will verify the identity, for example, whether it has the right to initiate the transaction.

In some embodiments, the client node may act as a mere watcher of the blockchain network 600, i.e., provide support for the business entity to initiate transaction functions, and may be implemented by default or selectively (e.g., depending on the specific business requirements of the business entity) for the functions of the consensus node 610 of the blockchain network 600, such as a ranking function, a consensus service, and an ledger function, etc. Therefore, the data and the service processing logic of the service subject can be migrated to the blockchain network 600 to the maximum extent, and the credibility and traceability of the data and service processing process are realized through the blockchain network 600.

Consensus nodes in blockchain network 600 receive transactions submitted by client nodes from different business entities (e.g., business entity 800/900 shown in fig. 4), perform transactions to update the ledger or query the ledger, and various intermediate or final results of performing transactions may be returned for display in the business entity's client nodes.

An exemplary application of a blockchain network is described below, for example, where a server uploads recorded sub-motion values and sub-dimensions corresponding to the sub-motion values to the blockchain network for storage, see fig. 4, where a client node 810 in fig. 4 may correspond to the server 200 in fig. 1.

First, the logic for setting the sub-fluctuation value and the sub-dimension uplink corresponding to the sub-fluctuation value at the client node 810, e.g. when it is determined that the sub-fluctuation value exceeds the sub-fluctuation threshold, the client node 810 sends the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value to the blockchain network 600 and generates a corresponding transaction comprising: the intelligent contract which needs to be called for uploading the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value and the parameters transferred to the intelligent contract; the transaction also includes the client node's 810 digital certificate, signed digital signature, and broadcasts the transaction to the consensus node 610 in the blockchain network 600.

Then, when the transaction is received in the consensus node 610 in the blockchain network 600, the digital certificate and the digital signature carried in the transaction are verified, and after the verification is successful, whether the service entity 800 has the transaction right is determined according to the identity of the service entity 800 carried in the transaction, and any verification error in the digital signature and the right verification will cause the transaction failure. After verification is successful, the consensus node 610 signs its own digital signature (e.g., by encrypting the digest of the transaction using the private key of node 610-1) and continues to broadcast in the blockchain network 600.

Finally, after the consensus node 610 in the blockchain network 600 receives the transaction that is successfully verified, the transaction is filled into a new block and broadcast. When a new block is broadcasted by the consensus node 610 in the block chain network 600, the new block is verified, for example, whether the digital signature of the transaction in the new block is valid is verified, if the verification is successful, the new block is appended to the tail of the block chain stored in the new block, and the state database is updated according to the transaction result to execute the transaction in the new block: for committed transactions that store sub-wave values and sub-dimensions corresponding to the sub-wave values, key-value pairs that include the sub-wave values and the sub-dimensions corresponding to the sub-wave values are added to the state database.

An exemplary application of the blockchain network is described by taking as an example that the terminal sends a request to the blockchain network to obtain the stored sub-motion values and the sub-dimensions corresponding to the sub-motion values. Referring to fig. 4, the client node 910 in fig. 4 may correspond to the terminal 400 in fig. 1.

In some embodiments, the type of data that can be queried by the client node 910 in the blockchain network 600 may be implemented by the consensus node 610 by restricting the authority of a transaction that can be initiated by a client phase of the service body, and when the client node 910 has the authority to initiate query data, a transaction for querying the data may be generated by the client node 910 and submitted to the blockchain network 600, where a key name is carried in the data query request, so that the consensus node 610 executes the transaction to query data corresponding to the key name from the state database. Then, the blockchain network 600 invokes an intelligent contract to obtain a corresponding sub-fluctuation value and a sub-dimension corresponding to the sub-fluctuation value from the state database, and then generates a visual analysis result of the target service based on the obtained sub-fluctuation value and the sub-dimension, and returns the generated visual analysis result to the client node 910, so that the client node 910 invokes a user interface of the data visual analysis platform to present.

Continuing with the exemplary structure of the service indicator processing device 455 provided by the embodiment of the present application implemented as a software module, in some embodiments, as shown in fig. 2, the software module stored in the service indicator processing device 455 of the memory 450 may include: an acquisition module 4551, a monitoring module 4552, a determination module 4553, a recording module 4554, a generation module 4555, a presentation module 4556, and an identification module 4557.

An obtaining module 4551, configured to obtain a service index of a target service; the monitoring module 4552 is configured to monitor a fluctuation value of the service indicator; the obtaining module 4551 is further configured to, when it is monitored that a fluctuation value of the service indicator exceeds a fluctuation threshold, obtain a plurality of sub-dimensions to which a current dimension corresponding to the fluctuation value belongs; a determining module 4553, configured to determine a sub-fluctuation value of the service indicator in each sub-dimension; a recording module 4554, configured to record the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value when the sub-fluctuation value exceeds the sub-fluctuation threshold; a generating module 4555, configured to generate a visual analysis result of the target service based on the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value; a presentation module 4556 configured to present the visual analysis result.

In some embodiments, the obtaining module 4551 is further configured to obtain service data corresponding to the target service; the device 455 for processing the service index further includes an identification module 4557, configured to identify a service scenario to which the target service belongs based on the machine learning model; the determining module 4553 is further configured to determine, as a service index of the target service, service data associated with the identified service scenario in the service data corresponding to the target service.

In some embodiments, the presenting module 4556 is further configured to present a service index setting page; the determining module 4553 is further configured to determine, in response to the service index setting operation in the service index setting page, the set service index as the service index of the target service.

In some embodiments, the monitoring module 4552 is further configured to compare the service data acquired in the current service period with the service data acquired in the previous service period, so as to determine the fluctuation value of the service indicator according to the comparison result.

In some embodiments, the obtaining module 4551 is further configured to, according to a plurality of index dimensions of service data corresponding to the target service, perform a dismantling process on a current dimension corresponding to the fluctuation value to obtain a plurality of sub-dimensions to which the current dimension belongs; the multiple sub-dimensions correspond to the multiple index dimensions one to one.

In some embodiments, the obtaining module 4551 is further configured to perform, according to a preset dimension hierarchical relationship table, a disassembling process on a current dimension corresponding to the fluctuation value to obtain multiple sub-dimensions to which the current dimension belongs; and the dimension hierarchical relation table is provided with attribution relations corresponding to the dimensions of different hierarchies.

In some embodiments, the generating module 4555 is further configured to perform multistage division on service data corresponding to the target service according to a preset requirement; and generating a dimension hierarchical relation table according to the result of the multi-level division.

In some embodiments, the recording module 4554 is further configured to send a storage request carrying the sub-dimension corresponding to the sub-motion value and the sub-motion value to the blockchain network, so that the blockchain network performs the following operations: and calling an intelligent contract to verify the transaction corresponding to the storage request, and storing the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value in a state database of the block chain network after the verification is passed.

In some embodiments, the presenting module 4556 is further configured to present a fluctuation analysis description template; the generating module 4555 is further configured to fill the sub-fluctuation values and the sub-dimensions corresponding to the sub-fluctuation values into the corresponding layout blocks of the fluctuation analysis description template, so as to generate a visual analysis result of the target service.

It should be noted that the description of the apparatus in the embodiment of the present application is similar to the description of the method embodiment, and has similar beneficial effects to the method embodiment, and therefore, the description is not repeated. The technical details that are not used up in the processing device of the service index provided by the embodiment of the present application can be understood from the description of any one of the drawings of fig. 3 and 5.

Next, an exemplary application of the embodiment of the present application in a practical application scenario will be described.

In business businesses, business indexes fluctuate due to various factors, and business personnel (such as product operators) need to spend a great deal of effort to investigate the reasons for the fluctuations.

In view of the above technical problems, the related art provides a data visualization analysis platform, and business personnel can perform visualization analysis on the data visualization analysis platform, for example, by comparing and drilling down, to analyze specific reasons for fluctuation.

However, although the data visualization analysis platform provided by the related art provides data in an automated form, does not need to perform data processing, and has certain visualization analysis capability, the analysis process cannot be reduced when problems occur, a large amount of multidimensional and multi-angle drilling analysis needs to be performed manually by service personnel, and the time efficiency needs to be improved.

In view of this, the embodiment of the present application provides a method for processing a business index, which can automatically analyze the reason for the abnormal fluctuation of the business index, thereby improving data analysis efficiency, and further reducing the labor cost of a business organization in the abnormal fluctuation analysis of the business index.

The method for processing the service index provided by the embodiment of the application can be applied to various types of data analysis scenes, such as an online shopping scene, a digital government scene, a map navigation scene and the like, and can automatically analyze the reason of abnormal fluctuation of the payment amount for the online shopping scene; for a digital government scene, the reasons of abnormal fluctuation of the birth rate of the population can be automatically analyzed; for a map navigation scene, the reason of abnormal fluctuation of the traffic flow can be automatically analyzed.

Illustratively, when the business indexes are abnormally fluctuated due to various factors, the abnormal fluctuation of the business indexes can be automatically found through an analysis program running on a terminal, the business indexes are automatically drilled and analyzed, then, a business fluctuation analysis result is pushed and displayed on a data visualization analysis platform, and therefore time consumption of business personnel in analyzing the reasons of the abnormal fluctuation is reduced through automatically analyzing the abnormal fluctuation of the business indexes.

The following describes a method for processing a service index provided in an embodiment of the present application.

Referring to fig. 5, fig. 5 is a flowchart illustrating a method for processing a service indicator according to an embodiment of the present application, and will be described with reference to the steps shown in fig. 5.

In step S501, the analysis program obtains a service index to be monitored.

In some embodiments, the business indicators to be monitored may be specified by business personnel.

For example, referring to fig. 6, a service person may set a service index to be analyzed in a user interface of an analysis program; or business personnel can also set the business indexes to be analyzed in the user interface of the data visualization analysis platform, so that the data visualization analysis platform sends the business indexes to the analysis program after acquiring the business indexes. For example, for an online shopping scenario, the service person may set the payment amount as a service index to be analyzed, and then the analysis program may monitor for fluctuations in the payment amount set by the service person.

In step S502, the analysis program monitors the fluctuation of the service index.

For example, the analysis program may monitor the fluctuation of the traffic indicator periodically or aperiodically. For example, still taking the service index as the payment amount as an example, the analysis program may monitor the fluctuation of the payment amount every other day or every other 1 hour.

In step S503, the analysis program determines whether the fluctuation of the service index exceeds a threshold, and if the fluctuation exceeds the threshold, step S504 is executed; if the threshold is not exceeded, then step 502 is continued.

For example, in the above, when the analysis program monitors that the fluctuation of the payment amount is abnormal, for example, the fluctuation of the payment amount exceeds a preset normal fluctuation range, the analysis program performs the subsequent drilling analysis operation; and when the analysis program monitors that the fluctuation of the payment amount is within the normal fluctuation range, continuing monitoring.

In step S504, the analysis program drills the corresponding index in each dimension.

For example, taking an offline shopping scenario as an example, when the analysis program monitors that fluctuation of the payment amount is abnormal, the drilling-down processing is performed on each dimension, for example, when the analysis program drills down the professional dimension, the professional dimension may be split into a plurality of sub-dimensions such as college students, medical staff, engineers, and construction workers, and then the analysis program determines fluctuation of the payment amount respectively corresponding to the college students, the medical staff, the engineers, and the construction workers.

In step S505, the analysis program determines whether the dimensional index fluctuation exceeds a threshold, and if so, executes step S506; if the threshold is not exceeded, the process continues to step S504.

For example, after determining the sub-fluctuation value corresponding to each sub-dimension, the analysis program determines whether the sub-fluctuation value on the corresponding sub-dimension fluctuates abnormally, for example, the analysis program determines whether the payment amount corresponding to college students, medical staff, engineers and construction workers fluctuates abnormally, and records the dimension of the payment amount with abnormal fluctuation; and if the dimension of the payment amount which does not have abnormal fluctuation is not recorded.

In step S506, the analysis program records the fluctuation dimension.

For example, assuming that the analysis program determines that the fluctuation of the payment amounts corresponding to college students and medical staff exceeds a threshold, the college student dimensions and the medical staff dimensions are recorded.

In step S507, the analysis program outputs the recorded fluctuation dimension to the visual analysis platform for display.

For example, after recording the dimension of the abnormal fluctuation, the analysis program may push the recorded fluctuation dimension to a data warehouse for storage, for example, in an online shopping scenario, when the analysis program determines that the payment amount corresponding to the university student dimension fluctuates abnormally, the analysis program may push the university student dimension to the data warehouse for storage, and the data visualization analysis platform may subsequently obtain the fluctuation dimension (e.g., the university student dimension) pushed by the analysis program from the data warehouse and display the fluctuation dimension in a corresponding user interface, so that a service person can know at a glance that the fluctuation of the payment amount is mainly caused by the university student.

In other embodiments, referring to fig. 7, in order to further understand a specific reason for the occurrence of the abnormal fluctuation, the analysis program may record the dimension of the abnormal fluctuation and the corresponding fluctuation value at the same time, and push the recorded fluctuation dimension and fluctuation value to the data warehouse for storage, for example, when the analysis program determines that the fluctuation of the payment amount corresponding to the university student dimension and the medical staff dimension exceeds a threshold, the university student dimension and the corresponding fluctuation value, and the medical staff dimension and the corresponding fluctuation value may be sent to the data warehouse for storage. And then, the data visualization analysis platform acquires the data pushed by the analysis program from the data warehouse and displays the dimensionality of the fluctuation of the payment amount, so that business personnel can deduce the specific reason of the abnormal fluctuation of the business index by checking the fluctuation of each dimensionality corresponding to the business index displayed on the data visualization analysis platform. Of course, after recording the dimension of the fluctuation and the corresponding fluctuation value, the analysis program can be directly filled into a fixed analysis template to generate an analysis report, and then, the analysis report is directly pushed to a data visualization analysis platform associated with the service personnel, so that the analysis time of the service personnel is further saved. In addition, it should be noted that "time" in fig. 7 indicates a fluctuation value of the monitoring service index triggered at regular time, for example, the fluctuation value of the monitoring service index is monitored once every day or hour.

According to the embodiment of the application, the time consumed by business personnel during analysis is reduced by automatically analyzing the specific reason of abnormal fluctuation of the business index, meanwhile, the comprehensiveness and the accuracy of an analysis result are guaranteed due to the fact that the business personnel automatically drill down for analysis through an analysis program, and the possible error and leakage of manual analysis are avoided.

Embodiments of the present application provide a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The processor of the computer device reads the computer instruction from the computer-readable storage medium, and executes the computer instruction, so that the computer device executes the method for processing the service index in the embodiment of the present application.

Embodiments of the present application provide a computer-readable storage medium storing executable instructions, which when executed by a processor, will cause the processor to perform a method for processing a service index provided by embodiments of the present application, for example, a method for processing a service index as shown in fig. 3 or 5.

In some embodiments, the computer-readable storage medium may be memory such as FRAM, ROM, PROM, EP ROM, EEPROM, flash memory, magnetic surface memory, optical disk, or CD-ROM; or may be various devices including one or any combination of the above memories.

In some embodiments, executable instructions may be written in any form of programming language (including compiled or interpreted languages), in the form of programs, software modules, scripts or code, and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

By way of example, executable instructions may correspond, but do not necessarily have to correspond, to files in a file system, and may be stored in a portion of a file that holds other programs or data, such as in one or more scripts in a hypertext Markup Language (H TML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code).

By way of example, executable instructions may be deployed to be executed on one computing device or on multiple computing devices at one site or distributed across multiple sites and interconnected by a communication network.

In summary, in the embodiment of the present application, the fluctuation value of the service index is monitored, and when it is monitored that the fluctuation of the service index is abnormal (that is, the fluctuation value exceeds the fluctuation threshold), a plurality of sub-dimensions belonging to the current dimension corresponding to the fluctuation value are automatically obtained, and analyzing the sub-fluctuation values respectively corresponding to the service index on each sub-dimension, recording the sub-dimension corresponding to the abnormal sub-fluctuation value (i.e. the sub-fluctuation value exceeds the sub-fluctuation threshold), and then, generating a visual analysis result of the target business based on the recorded sub-fluctuation values with abnormal fluctuation and the corresponding sub-dimensions, and presenting the visual analysis result, because the whole process does not need manual participation, a large amount of labor cost can be saved, and meanwhile, the final analysis result is ensured not to be mistaken due to human negligence, and further the comprehensiveness and the accuracy of the analysis result are ensured.

The above description is only an example of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, and improvement made within the spirit and scope of the present application are included in the protection scope of the present application.

Claims

1. A method for processing a service index includes:

2. The method of claim 1, wherein the obtaining the service index of the target service comprises:

acquiring service data corresponding to the target service, and identifying a service scene to which the target service belongs based on a machine learning model;

and determining the service data associated with the identified service scene in the service data corresponding to the target service as a service index of the target service.

3. The method of claim 1, wherein the obtaining the service index of the target service comprises:

presenting a service index setting page;

and responding to the service index setting operation in the service index setting page, and determining the set service index as the service index of the target service.

4. The method of claim 1, wherein the monitoring the fluctuation value of the traffic indicator comprises:

and comparing the service data acquired in the current service period with the service data acquired in the last service period to determine the fluctuation value of the service index according to the comparison result.

5. The method according to claim 1, wherein the obtaining a plurality of sub-dimensions to which a current dimension corresponding to the fluctuation value belongs includes:

performing disassembly processing on the current dimensionality corresponding to the fluctuation value according to a plurality of index dimensionalities of the service data corresponding to the target service to obtain a plurality of sub-dimensionalities to which the current dimensionality belongs;

wherein the plurality of sub-dimensions correspond to the plurality of index dimensions one-to-one.

6. The method according to claim 1, wherein the obtaining a plurality of sub-dimensions to which a current dimension corresponding to the fluctuation value belongs includes:

according to a preset dimension hierarchical relation table, performing disassembly processing on the current dimension corresponding to the fluctuation value to obtain a plurality of sub-dimensions to which the current dimension belongs;

and the dimension hierarchical relation table is provided with attribution relations corresponding to the dimensions of different hierarchies.

7. The method according to claim 6, wherein before performing the dismantling process on the current dimension corresponding to the fluctuation value, the method further comprises:

performing multi-level division on the service data corresponding to the target service according to a preset requirement;

and generating the dimension hierarchical relation table according to the result of the multi-level division.

8. The method of claim 1, wherein said recording said sub-fluctuation values and sub-dimensions corresponding to said sub-fluctuation values comprises:

sending a storage request carrying the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value to a blockchain network, so that the blockchain network performs the following operations:

and calling an intelligent contract to verify the transaction corresponding to the storage request, and storing the sub-fluctuation value and the sub-dimension corresponding to the sub-fluctuation value in a state database of the block chain network after the verification is passed.

9. The method of claim 1, wherein generating a visual analysis of the target business based on the sub-fluctuation values and sub-dimensions corresponding to the sub-fluctuation values comprises:

presenting a fluctuation analysis description template;

and filling the sub-fluctuation values and the sub-dimensions corresponding to the sub-fluctuation values into the layout blocks corresponding to the fluctuation analysis description template to generate a visual analysis result of the target business.

10. A device for processing a service indicator, comprising:

and the presentation module is used for presenting the visual analysis result.