CN114239987A - Service early warning management method and early warning management system based on data center - Google Patents
Service early warning management method and early warning management system based on data center Download PDFInfo
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Abstract
The invention belongs to the technical field of task management and data processing, and particularly relates to a service early warning management method and an early warning management system based on a data center. Based on the strong analysis capability of a data center, the method carries out smoothing/fitting processing and multidimensional comparison analysis on the service data by constructing various service data analysis models, converts abnormal data such as service data peak values and the like into service data deviation excavated by the service data analysis models, thereby reducing false alarm caused by various data peak values.
Description
Technical Field
The invention belongs to the technical field of task management and data processing, and particularly relates to a service early warning management method and an early warning management system based on a data center.
Background
Nowadays, in order to discover business problems as early as possible and reduce enterprise losses, business early warning mechanisms are usually introduced in the digital transformation process of business enterprises. The currently used service early warning mechanism usually simply compares the service data analysis result with a preset service early warning threshold value to determine whether to trigger service early warning, however, in this operation mode, the following problems exist:
(1) since the service data has various fluctuations, various data peaks exist in the actual operation process, which easily causes the occurrence of false alarm.
(2) The service early warning threshold is solidified, a fixed service early warning threshold is usually set based on the service experience of operators, and along with the change of the operation scale, the solidified service early warning threshold is often deviated from the actual situation, so that abnormal situations such as false alarm or missed alarm are caused.
Disclosure of Invention
In order to overcome the defects and shortcomings of the existing service early warning mechanism, the invention provides a set of solution. The technical scheme of the invention adopts a multi-level technical means to correct the problems existing in the prior business early warning mechanism. Aiming at the problem (1), based on the strong analysis capability of a data center, various service data analysis models are constructed to carry out smoothing/fitting processing and multi-dimensional comparison analysis on service data, abnormal data such as service data peak values are converted into service data deviation excavated by the service data analysis models, and therefore misinformation caused by various data peak values is reduced; aiming at the problem (2), the invention analyzes various historical data (including data of the enterprise, the benchmarking enterprise and the industry) through the service analyst so as to establish various dynamic time analysis models of the application layer, aiming at service deviation data excavated by the service data analysis model (each enterprise can define an exclusive application layer according to the service characteristics), the dynamic time analysis model carries out secondary intervention on the business deviation data, and the intervention process comprises automatically adjusting the service early warning threshold value, so that the false alarm/false alarm phenomenon caused by the solidification of the service early warning threshold value is reduced, and the assistance is really provided for enterprise management.
Under the design thought, the invention provides a service early warning management method and an early warning management system which comprehensively apply multidimensional service data analysis based on a data middlebox and a service rule judgment technology based on a dynamic time analysis model. The method of the invention is based on the multiple data analysis results of the data center station, and combines the dynamic business rule judgment mechanism of the application layer, thereby greatly improving the accuracy and timeliness of business early warning.
In a first aspect, the invention provides a service early warning management method based on a data center platform, the method configures a service data processing rule, an application layer rule and a service data acquisition rule at an early warning management node, wherein the application layer rule comprises a dynamic time analysis model, and then guides a data acquisition node to complete corresponding service data acquisition and submit acquired service data to the data center platform; the data processing node at the data center station performs multi-dimensional analysis on the received service data according to a service data analysis model selected by a user and submitted by the early warning management node or a service data analysis model automatically matched by the early warning management node according to an enterprise service mode, and submits various service deviation data discovered in the analysis process to the early warning management node; the early warning management node adjusts a service early warning threshold value according to the dynamic time analysis model and judges whether service deviation data from a data center station triggers a service early warning process or not; the task management node controls the whole early warning process and supports various task management mechanisms, wherein the various task management mechanisms comprise a timer task, manual scheduling triggered by management personnel, a real-time task triggered by a data stream provided by the data acquisition node, a secondary analysis task triggered by service deviation data and the like.
Further, the service early warning management method of the invention comprises the following steps:
the method comprises the following steps: the early warning management node stores predefined business data processing rules, application layer rules and business data acquisition rules, wherein the application layer rules comprise various dynamic time analysis models;
step two: a user selects a service data analysis model, configures a data scheme, a dynamic time analysis model and an alarm task handler, constructs an early warning control overall scheme and submits the scheme to an early warning management node;
step three: the early warning management node submits the data acquisition configuration to the data acquisition node, then starts the data acquisition node to acquire service data and submits the acquired service data to the data center station;
step four: the early warning management node submits the data center analysis configuration to the data center, and then starts a data processing node positioned in the data center to analyze the service data;
step five: the data center station performs multi-dimensional analysis on the received service data, triggers service deviation internal processing if abnormality is found, and submits service deviation data and related environment data to an early warning management node;
step six: the early warning management node judges whether the service deviation can form an alarm sign triggering condition or not according to a dynamic time analysis model in the early warning control overall scheme, and if the service deviation meets the triggering condition, an early warning task flow is constructed;
step seven: the early warning management node submits the warning sign information to the task management node, and the task management node confirms the subsequent processing task and the responsible person according to the warning sign processing business flow rule and returns the confirmation result to the early warning management node;
step eight: and the early warning management node adaptively pushes the warning sign information and the related task flow to a data terminal of the user.
Further, the first step of the service early warning management method of the present invention specifically includes the following contents:
(1) the pre-defined service data processing rule stored by the early warning management node comprises a service data analysis model definition and a service deviation definition used by a data center station; the data center platform completes abstract analysis on the service data through a three-layer structure of a service data analysis model, an index system and an index analysis model, wherein service deviation data found by the index analysis model are key points of a data layer of service early warning;
(2) the pre-defined application layer rules stored by the early warning management node comprise various dynamic time analysis models, early warning secondary judgment logic and experience rules, and the application layer rules are determined by a user according to the operation mode and the operation requirement of an enterprise;
(3) the pre-defined business data acquisition rule stored by the early warning management node comprises an application rule of the whole process of extraction-transformation-loading (ETL process) of data, including data source definition, data field definition, extraction rule and acquisition time interval.
Further, the service early warning management method of the invention specifically comprises the following steps:
the user firstly confirms the core service data to be analyzed according to the enterprise operation mode and the operation requirement, and then carries out relevant configuration around the core service data to be analyzed through various data terminals, wherein the configuration comprises the following types:
(1) data acquisition configuration: a user configures related data sources, field definition, extraction, planning and storage models or parameters based on the definition of the ETL process of the data warehouse, and all original data, intermediate data and normalized result data are stored in a data lake positioned in a data center platform in the data acquisition process;
(2) data center analysis configuration: a user configures a service data analysis model and screens an concerned index system based on core service data to be analyzed, configures an index analysis model and a related threshold applied to service deviation, constructs a service data analysis process and service deviation definition, and a data center station analyzes various service data submitted by a data acquisition node according to the configuration;
(3) and (3) application layer rule configuration: a user selects a relevant dynamic time analysis model according to a business mode and configures model parameters according to time dimensions, and when the dynamic time analysis model analyzes business deviation data found by a data center, the dynamic time analysis model comprehensively utilizes the model parameters of various time dimensions to carry out unified judgment;
(4) alarm task handler configuration: and configuring warning sign task handlers to ensure the timeliness of warning sign information processing.
Further, the third step of the service early warning management method of the present invention specifically includes the following contents:
the early warning management node submits data acquisition configuration provided by a user to a data acquisition node, then the data acquisition node is started to acquire service data, and a data acquisition technical scheme matched with the data acquisition node is built in the data acquisition node aiming at different service data sources, wherein the data acquisition technical scheme comprises data synchronization of various databases, reading of text files, extraction of field contents and a data interface based on http specification;
according to data acquisition configuration, a data acquisition node firstly synchronizes or constructs a data dictionary definition, and then extracts and normalizes a service data field from an original data source according to the data dictionary definition;
all the original data, the intermediate data and the normalized result data processed by the data acquisition node are stored in a data lake positioned in a data center for repeated use in subsequent secondary analysis and other business analysis.
Further, the service early warning management method of the present invention specifically includes the following steps:
(1) after the early warning management node submits the data center analysis configuration to the data center, the data center allocates computing resources according to the configuration as a corresponding service data analysis model and an index analysis model, and then a data processing node located in the data center is started to analyze the service data;
(2) the data processing node acquires various service data from the data lake, and in the data analysis process, the data accessed by the data processing node comprises core service data of a client and non-client data selected according to internal logics of a service data analysis model and an index analysis model.
Further, the fifth step of the service early warning management method of the present invention specifically includes the following contents:
(1) the data center takes a three-layer structure of a business data analysis model, an index system and an index analysis model as a basis, relevant business data analysis models and index analysis models are dispatched according to the analysis configuration of the data center provided by a user, the core business data of a client is processed, and if the business indexes are found to deviate in the processing process, the internal processing of the business deviations is immediately triggered;
(2) the service deviation internal processing firstly collects the environment data causing the service deviation, the environment data comprises the service data provided by the client and the external data, the data type included in the environment data is predefined in the service deviation, and the data center station collects the corresponding environment data according to the definition and submits the corresponding environment data to the early warning management node.
Further, the service early warning management method of the invention specifically comprises the following steps in step six:
(1) the dynamic time analysis model analyzes the service deviation data and the environment data and judges whether the service deviation is upgraded to warning sign or not by combining with experience criteria; the dynamic time analysis model is a multivariable judgment model, the variables comprise service index weight, service deviation amplitude, time and other auxiliary variables, and the specific variable number and parameter setting are comprehensively determined by the industry type, the service mode and the channel type of the service;
(2) the early warning management node finally converts the service deviation found by the data console into warning sign information of different levels through an application layer secondary verification mechanism, submits the warning sign information to the task management node, and the task management node performs subsequent processing according to the task management flow of the enterprise.
Further, the seventh step of the service early warning management method of the present invention specifically includes the following contents:
(1) the early warning management node is matched with the data center, after various warning signs are discovered by adopting a secondary verification mechanism, the early warning management node submits warning sign information and relevant service data to the task management node, and the task management node starts a warning sign task processing mechanism;
(2) the task management node is responsible for maintaining an organization system and a transmission system related to the warning sign in the enterprise, and setting different warning sign transmission systems and processing systems aiming at the warning signs with different service indexes and warning signs with different levels; in the initial stage, a user firstly constructs a corresponding organization system and a corresponding transmission system in a task management node according to different service indexes and warning sign levels, so that a warning sign processing service flow rule base is formed; after receiving warning sign information submitted by the warning management node, the task management node constructs warning sign processing service flow according to rules defined in a warning sign processing service flow rule base, wherein the warning sign processing service flow comprises relevant processors for confirming warning signs, relevant acquaintances and subsequent processing tasks;
(3) after the task management node constructs an alarm processing service flow, the alarm processing service flow is returned to the early warning management node;
further, the eighth step of the service early warning management method of the present invention specifically includes the following contents:
after the early warning management node receives the warning sign processing service flow returned by the task management node, the warning sign identification and the establishment of the warning sign processing flow are indicated to be completed; at the moment, the early warning management node internally maintains the service deviation data, the service deviation environment data, the alarm type, the alarm level and the alarm processing flow data of the alarm; and then the early warning management node constructs a service deviation display chart, a warning sign chart and a task flow chart according to the data, and pushes the service deviation display chart, the warning sign chart and the task flow chart to a data terminal of a team member related to the task flow, so that the online processing is carried out.
In a second aspect, the invention provides a service early warning management system based on a data center, which comprises a data acquisition node, an early warning management node, an early warning rule base, a data center, a task management node, an alarm million processing service flow rule base and an automatic control module, wherein the data center comprises a data processing node, a data lake, an analysis engine and a judgment base; wherein:
the data acquisition node has the function of acquiring service data according to a data scheme;
the early warning management node has the functions of managing an early warning rule base and a dynamic time analysis model, analyzing an early warning control overall scheme pushed by a data terminal, controlling a data acquisition node to acquire service data, judging service deviation found by a data center through an application layer secondary verification mechanism, and outputting warning omen information and related task flows in a visual adaptation mode aiming at member authority;
the early warning rule base has the function of storing early warning rule information;
the task management node has the functions of managing warning sign processing service flow rules and constructing warning sign processing service flows according to a warning sign processing service flow rule base;
the warning sign processing service flow rule base has the function of storing warning sign processing service flow rule information;
the data processing node has the functions of carrying out multi-dimensional analysis on the service data based on the enterprise operation mode and submitting the service deviation data and the related environment data appearing in the analysis result to the early warning management node;
the data lake has the functions of storing all the original data, the intermediate data and the normalized result data which are acquired and processed by the data acquisition nodes;
the analysis engine is used for analyzing the service data in cooperation with the data processing node;
the function of the judgment base is to store judgment rule information;
the above components operate according to the service early warning management method of the invention, and the automatic control module automatically organizes and controls the components.
In summary, based on the powerful analysis capability of the data center, the method of the invention performs smoothing/fitting processing and multidimensional comparison analysis on the service data by constructing various service data analysis models, converts abnormal data such as service data peak values and the like into service data deviations excavated by the service data analysis models, thereby reducing false alarm caused by various data peak values.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments are briefly introduced below. It is to be understood that the drawings in the following description are illustrative of some, but not all embodiments of the invention, and that other drawings may be derived therefrom by those skilled in the art without the benefit of the teachings herein.
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a schematic representation of the organization of the method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the embodiments described are merely illustrative of some, but not all, of the present invention and that the invention may be embodied or carried out in various other specific forms, and that various modifications and changes in the details of the specification may be made without departing from the spirit of the invention.
Also, it should be understood that the scope of the invention is not limited to the particular embodiments described below; it is also to be understood that the terminology used in the examples is for the purpose of describing particular embodiments only, and is not intended to limit the scope of the present invention.
Example 1: a business early warning management method based on a data center (see figures 1-2) is characterized in that a business data processing rule, an application layer rule and a business data acquisition rule are configured at an early warning management node, wherein the application layer rule comprises a dynamic time analysis model, and then the data acquisition node is guided to complete corresponding business data acquisition and submit acquired business data to the data center; the data processing node at the data center station performs multi-dimensional analysis on the received service data according to a service data analysis model selected by a user and submitted by the early warning management node or a service data analysis model automatically matched by the early warning management node according to an enterprise service mode, and submits various service deviation data discovered in the analysis process to the early warning management node; the early warning management node adjusts a service early warning threshold value according to the dynamic time analysis model and judges whether service deviation data from a data center station triggers a service early warning process or not; the task management node controls the whole early warning process and supports various task management mechanisms, wherein the various task management mechanisms comprise a timer task, manual scheduling triggered by management personnel, a real-time task triggered by a data stream provided by the data acquisition node, a secondary analysis task triggered by service deviation data and the like.
Specifically, the method comprises the following steps:
the method comprises the following steps: the early warning management node stores predefined business data processing rules, application layer rules and business data acquisition rules, wherein the application layer rules comprise various dynamic time analysis models.
(1) The pre-defined business data processing rules stored by the early warning management node comprise business data analysis model definition and business deviation definition used by the data center. For example, for sales flow data, the data center station provides a commodity value analysis model, a price band analysis model, a brand benefit analysis model, a region/store analysis model, a channel/platform analysis model, an RFM model and the like, and based on the analysis models, a whole set of sales index system is constructed, including sales, order quantity, completion rate, tie rate, growth rate, sales ratio of key commodities, sales ratio of each platform and the like, and then the business deviation existing in the indexes is mined through a regular analysis model/comparison analysis model of the business indexes and the like. And finally, the data center platform completes abstract analysis on the service data through a three-layer structure of a service data analysis model, an index system and an index analysis model, wherein service deviation data found by the index analysis model is a data layer key point of service early warning.
(2) The pre-defined application layer rules stored by the early warning management node comprise various dynamic time analysis models, early warning secondary judgment logic (for example, the early warning can be judged as real business early warning only when three data layer early warnings appear within 10 minutes) and experience rules, and the application layer rules are determined by users according to the business mode and the business requirement of an enterprise. For example, for the sales volume index, the dynamic time analysis model definitions of different industries/different enterprises have great difference, such as different model parameters and fitting curves for each quarter [ slack season/peak season ], month, and week [ working day/resting day ] when analyzing the sales volume index of the clothing sales-type enterprise; therefore, the sales deviation data found by the data center station can be included in the early warning rule as a warning sign to be processed after being analyzed again by the dynamic time analysis model of the application layer.
(3) The pre-defined business data acquisition rule stored by the early warning management node comprises an application rule of the whole process of extraction-transformation-loading (ETL process) of data, including data source definition, data field definition, extraction rule and acquisition time interval.
Step two: and selecting a service data analysis model by a user, configuring a data scheme, a dynamic time analysis model and an alarm task handler, constructing an early warning control overall scheme, and submitting the scheme to an early warning management node.
Firstly, a user confirms core business data to be analyzed, such as sales data, financial data, personnel performance data and the like according to an enterprise operation mode and operation requirements; then, relevant configuration is carried out around core service data to be analyzed through various data terminals (mobile phones, PCs, Web), and the configuration comprises the following types:
(1) data acquisition configuration: a user configures related data sources, field definition, extraction, planning and storage models or parameters based on the definition of the ETL process of the data warehouse, and all original data, intermediate data and normalized result data are stored in a data lake positioned in a data center platform in the data acquisition process;
(2) data center analysis configuration: a user configures a service data analysis model and screens an concerned index system based on core service data to be analyzed, configures an index analysis model and a related threshold applied to service deviation, constructs a service data analysis process and service deviation definition, and a data center station analyzes various service data submitted by a data acquisition node according to the configuration;
(3) and (3) application layer rule configuration: a user selects a relevant dynamic time analysis model according to a business mode and configures model parameters according to time dimensions, such as model parameters used in each quarter, month and week, and when the dynamic time analysis model analyzes business deviation data found by a data center, the model parameters of various time dimensions are comprehensively utilized to carry out unified judgment. For example, for a clothing sales enterprise, a deviation fitting curve is constructed according to different weights based on model parameters of three levels of quarter/month/week. The application layer rules also include common empirical rules, such as for sales day data analysis, the business deviation data is generally updated to alarm under the deviation fitting curve for three consecutive days and reported to the business management personnel.
(4) Alarm task handler configuration: as a complete management system, a warning sign task processor is finally configured to ensure the timeliness of warning sign information processing.
Step three: the early warning management node submits the data acquisition configuration to the data acquisition node, then starts the data acquisition node to acquire the service data and submits the acquired service data to the data center.
The early warning management node submits data acquisition configuration provided by a user to a data acquisition node, then the data acquisition node is started to acquire service data, and a data acquisition technical scheme matched with the data acquisition node is built in the data acquisition node aiming at different service data sources, wherein the data acquisition technical scheme comprises data synchronization of various databases, reading of text files and extraction of field contents, a data interface based on http specification and the like;
according to the data acquisition configuration, the data acquisition nodes first synchronize or construct data dictionary definitions, and then extract and normalize the business data fields from the original data source according to the data dictionary definitions. Such as time field normalization, currency field normalization, etc. For example, for the sales flow data, the constructed data dictionary defines the business fields such as the flow number, the product SKU, the product name, the unit price, the sales volume, the sales date, the sales time, the channel ID, the channel name, the store ID, the store name, and the like, and defines the corresponding relationship, the extraction mode, the conversion mode, and the like between the business fields and various original data content segments.
All the original data, the intermediate data and the normalized result data processed by the data acquisition node are stored in a data lake positioned in a data center for repeated use in subsequent secondary analysis and other business analysis.
Step four: and the early warning management node submits the data center analysis configuration to the data center, and then starts a data processing node positioned in the data center to analyze the service data.
(1) After the early warning management node submits the data center analysis configuration to the data center, the data center allocates computing resources according to the configuration as a corresponding service data analysis model and an index analysis model, and then a data processing node located in the data center is started to analyze the service data;
(2) the data processing node acquires various service data from the data lake, and in the data analysis process, the data accessed by the data processing node comprises core service data of a customer and non-customer data selected according to the service data analysis model and the internal logic of the index analysis model, for example, in the sales flow data analysis process of a clothing retail enterprise, some industry data and public data of channels/platforms, such as competitive price, comment data and the like in an e-commerce platform, can be used.
Step five: and the data center station performs multi-dimensional analysis on the received service data, triggers service deviation internal processing if abnormity is found, and submits service deviation data and related environment data to an early warning management node.
(1) The data center is based on three-layer structure of business data analysis model-index system-index analysis model, relevant business data analysis model and index analysis model are dispatched according to the data center analysis configuration provided by the user, the core business data of the client is processed, and if the business index is found to deviate in the processing process, the internal processing of the business deviation is triggered immediately. For example, in the process of analyzing sales flow data of a clothing retail enterprise, a commodity value analysis model is generally configured to analyze the popularity of various types of clothing (expressed by indexes such as daily sales volume/daily relevance ratio) by analyzing the daily sales flow of a customer; regional/store analysis models are also used for analyzing differences and the like of stores; if the user has defined a business deviation range (e.g., less than 60% of the average daily value of the previous ten days) for the business index of "daily sales" in the data center analysis configuration, the deviation definition of the customer configuration is triggered when the daily sales of the current day is found to trigger the internal processing of the business deviation in the analysis process.
(2) The service deviation internal processing firstly collects environment data causing service deviation, wherein the environment data comprises service data (commodity price data and the like) provided by a client and external data (date/holiday data, store weather data and the like), the data type included in the environment data is predefined in the service deviation, and the data center station collects corresponding environment data according to the definition and submits the environment data to the early warning management node. For example, in the analysis of sales flow data at a retail clothing enterprise, if a deviation definition of the "daily sales" indicator is triggered, environmental data including external data such as store weather, date/holidays, etc. is typically collected according to the configuration.
Step six: and the early warning management node judges whether the service deviation can form an alarm sign triggering condition or not according to a dynamic time analysis model in the early warning control overall scheme, and if the service deviation meets the triggering condition, an early warning task flow is constructed.
(1) The dynamic time analysis model analyzes the service deviation data and the environment data and judges whether the service deviation is upgraded to warning sign or not by combining with experience criteria; the dynamic time analysis model is a multivariable judgment model, the variables comprise service index weight, service deviation amplitude, time and other auxiliary variables, and the specific variable number and parameter setting are comprehensively determined by the industry type, the service mode and the channel type of the service. For example, for the sales flow data analysis of a clothing retail enterprise, for a region/store business model, spatial dimension information such as weather in a region where a store is located, per-capita income in the region where the store is located, and the like is generally taken as a variable to be included in the model; for the e-commerce business model, the occupancy of each e-commerce platform, additional service assessment values (e.g., express service, after sales support) of the e-commerce platform, etc. are typically incorporated into the model as variables.
(2) The early warning management node finally converts the service deviation found by the data console into warning sign information of different levels through an application layer secondary verification mechanism, submits the warning sign information to the task management node, and the task management node performs subsequent processing according to the task management flow of the enterprise.
Dynamic time analysis model sample
For example, for a retail clothing business that takes a regional/store business model, the business bias for the daily sales target is typically incorporated into its dynamic time analysis model with the following variables.
X1 — daily sales weight;
x2 — daily sales volume business deviation magnitude value;
x3- -holiday/week on business date;
x4- -weather of store;
x5-average income of people in the area of the store (last year value);
x6- -estimated number of people in the area where the store is located;
…
these variables construct a multivariate decision function:
w＝F(X1,X2,X3,X4,X5,X6…)；
and performing secondary judgment on the business deviation generated by the daily sales volume index of the clothing retail enterprise by using the multivariable judgment function, wherein the result w is a value in a (0, 1) interval and is used for representing the probability of upgrading the business deviation into warning signs.
By introducing multivariable according to the method, the single service deviation can be evaluated in multiple dimensions. For example, for a traffic deviation with a 60% reduction in sales on the same day, the resulting value of the multivariate decision function may be lower if the day weather of the store area is considered to be extreme weather, or the first working day after eleven long holidays, thereby avoiding upgrading the traffic deviation to alarm.
The application-level rule configuration also includes common empirical criteria that are applied to multiple aspects, such as for the resulting values w of the multivariate decision function, which are typically used to classify w two-fold as follows:
1> w > -0.8 red warning sign;
0.8> w > -0.5 yellow warning;
0.5> w green (normal).
The user can also adopt other classifications for w according to the early warning management system and the business requirements of the enterprise.
The rule configuration of the application layer adopts a mechanism combining an authentication method and a historical experience method. Therefore, in the initial stage of application, a user needs to adjust the rule configuration of the application layer for multiple times by using historical data, evaluate the result of each time, determine the model parameters and experience rules which are most suitable for the enterprise to use from the result, and in the subsequent operation process, the user also needs to track the effect and finely adjust the model parameters.
Step seven: the early warning management node submits the warning sign information to the task management node, and the task management node confirms the subsequent processing task and the responsible person according to the warning sign processing business flow rule and returns the confirmation result to the early warning management node.
(1) The early warning management node is matched with the data center, after various warning signs are discovered by adopting a secondary verification mechanism, the early warning management node submits warning sign information and relevant service data to the task management node, and the task management node starts a warning sign task processing mechanism;
(2) the task management node is responsible for maintaining an organization system and a transmission system related to the warning sign in the enterprise, and setting different warning sign transmission systems and processing systems aiming at the warning signs with different service indexes and the warning signs with different levels (such as red/yellow); in the initial stage, a user firstly constructs a corresponding organization system and a corresponding transmission system in a task management node according to different service indexes and warning sign levels, so that a warning sign processing service flow rule base is formed; after receiving warning sign information submitted by the warning management node, the task management node constructs warning sign processing service flow according to rules defined in a warning sign processing service flow rule base, wherein the warning sign processing service flow comprises relevant processors for confirming warning signs, relevant acquaintances and subsequent processing tasks;
(3) and after constructing the warning million processing service flow, the task management node returns the warning million processing service flow to the warning management node.
Step eight: and the early warning management node adaptively pushes the warning sign information and the related task flow to a data terminal of the user.
After the early warning management node receives the warning sign processing service flow returned by the task management node, the warning sign identification and the establishment of the warning sign processing flow are indicated to be completed; at the moment, the early warning management node internally maintains the service deviation data, the service deviation environment data, the alarm type, the alarm level and the alarm processing flow data of the alarm; and then the early warning management node constructs a service deviation display chart, a warning sign chart and a task flow chart according to the data, and pushes the service deviation display chart, the warning sign chart and the task flow chart to a data terminal of a team member related to the task flow, so that the online processing is carried out.
Example 2: service early warning management system based on data center (see figure 2)
The system consists of a data acquisition node, an early warning management node, an early warning rule base, a data center platform, a task management node, an alarm million processing business flow rule base and an automatic control module, wherein the data center platform comprises a data processing node, a data lake, an analysis engine and a judgment base; wherein:
the data acquisition node has the function of acquiring service data according to a data scheme;
the early warning management node has the functions of managing an early warning rule base and a dynamic time analysis model, analyzing an early warning control overall scheme pushed by a data terminal, controlling a data acquisition node to acquire service data, judging service deviation found by a data center through an application layer secondary verification mechanism, and outputting warning omen information and related task flows in a visual adaptation mode aiming at member authority;
the early warning rule base has the function of storing early warning rule information;
the task management node has the functions of managing warning sign processing service flow rules and constructing warning sign processing service flows according to a warning sign processing service flow rule base;
the warning sign processing service flow rule base has the function of storing warning sign processing service flow rule information;
the data processing node has the functions of carrying out multi-dimensional analysis on the service data based on the enterprise operation mode and submitting the service deviation data and the related environment data appearing in the analysis result to the early warning management node;
the data lake has the functions of storing all the original data, the intermediate data and the normalized result data which are acquired and processed by the data acquisition nodes;
the analysis engine is used for analyzing the service data in cooperation with the data processing node;
the function of the judgment base is to store judgment rule information;
the above components operate according to the service early warning management method in embodiment 1, and the automatic control module performs automatic organization and control on the components.
Example 3: electronic device for business early warning management
The electronic device comprises a processor and a memory, wherein the memory is used for storing programs, and the processor is used for operating the programs so as to realize the service early warning management method based on the data center station in embodiment 1.
The embodiments of the present invention are described in a progressive manner, and the same or similar parts in the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, replacement, or the like that comes within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. A business early warning management method based on a data center is characterized in that a business data processing rule, an application layer rule and a business data acquisition rule are configured at an early warning management node, wherein the application layer rule comprises a dynamic time analysis model, and then the data acquisition node is guided to complete corresponding business data acquisition and submit acquired business data to the data center; the data processing node at the data center station performs multi-dimensional analysis on the received service data according to a service data analysis model selected by a user and submitted by the early warning management node or a service data analysis model automatically matched by the early warning management node according to an enterprise service mode, and submits various service deviation data discovered in the analysis process to the early warning management node; the early warning management node adjusts a service early warning threshold value according to the dynamic time analysis model and judges whether service deviation data from a data center station triggers a service early warning process or not; the task management node controls the whole early warning process and supports various task management mechanisms, wherein the various task management mechanisms comprise a timer task, manual scheduling triggered by management personnel, a real-time task triggered by a data stream provided by the data acquisition node and a secondary analysis task triggered by service deviation data.
2. The business warning management method of claim 1, wherein the method comprises the steps of:
the method comprises the following steps: the early warning management node stores predefined business data processing rules, application layer rules and business data acquisition rules, wherein the application layer rules comprise various dynamic time analysis models;
step two: a user selects a service data analysis model, configures a data scheme, a dynamic time analysis model and an alarm task handler, constructs an early warning control overall scheme and submits the scheme to an early warning management node;
step three: the early warning management node submits the data acquisition configuration to the data acquisition node, then starts the data acquisition node to acquire service data and submits the acquired service data to the data center station;
step four: the early warning management node submits the data center analysis configuration to the data center, and then starts a data processing node positioned in the data center to analyze the service data;
step five: the data center station performs multi-dimensional analysis on the received service data, triggers service deviation internal processing if abnormality is found, and submits service deviation data and related environment data to an early warning management node;
step six: the early warning management node judges whether the service deviation can form an alarm sign triggering condition or not according to a dynamic time analysis model in the early warning control overall scheme, and if the service deviation meets the triggering condition, an early warning task flow is constructed;
step seven: the early warning management node submits the warning sign information to the task management node, and the task management node confirms the subsequent processing task and the responsible person according to the warning sign processing business flow rule and returns the confirmation result to the early warning management node;
step eight: and the early warning management node adaptively pushes the warning sign information and the related task flow to a data terminal of the user.
3. The business warning management method according to claim 2, wherein the first step specifically comprises the following steps:
(1) the pre-defined service data processing rule stored by the early warning management node comprises a service data analysis model definition and a service deviation definition used by a data center station; the data center platform completes abstract analysis on the service data through a three-layer structure of a service data analysis model, an index system and an index analysis model, wherein service deviation data found by the index analysis model are key points of a data layer of service early warning;
(2) the pre-defined application layer rules stored by the early warning management node comprise various dynamic time analysis models, early warning secondary judgment logic and experience rules, and the application layer rules are determined by a user according to the operation mode and the operation requirement of an enterprise;
(3) the pre-defined business data acquisition rule stored by the early warning management node comprises application rules of the whole process of data extraction-conversion-loading, and comprises data source definition, data field definition, extraction rule and acquisition time interval.
4. The business warning management method according to claim 2, wherein the second step specifically comprises the following steps:
the user firstly confirms the core service data to be analyzed according to the enterprise operation mode and the operation requirement, and then carries out relevant configuration around the core service data to be analyzed through various data terminals, wherein the configuration comprises the following types:
(1) data acquisition configuration: a user configures related data sources, field definition, extraction, planning and storage models or parameters based on the definition of the ETL process of the data warehouse, and all original data, intermediate data and normalized result data are stored in a data lake positioned in a data center platform in the data acquisition process;
(2) data center analysis configuration: a user configures a service data analysis model and screens an concerned index system based on core service data to be analyzed, configures an index analysis model and a related threshold applied to service deviation, constructs a service data analysis process and service deviation definition, and a data center station analyzes various service data submitted by a data acquisition node according to the configuration;
(3) and (3) application layer rule configuration: a user selects a relevant dynamic time analysis model according to a business mode and configures model parameters according to time dimensions, and when the dynamic time analysis model analyzes business deviation data found by a data center, the dynamic time analysis model comprehensively utilizes the model parameters of various time dimensions to carry out unified judgment;
(4) alarm task handler configuration: and configuring warning sign task handlers to ensure the timeliness of warning sign information processing.
5. The service early warning management method according to claim 2, wherein the third step specifically includes the following contents:
the early warning management node submits data acquisition configuration provided by a user to a data acquisition node, then the data acquisition node is started to acquire service data, and a data acquisition technical scheme matched with the data acquisition node is built in the data acquisition node aiming at different service data sources, wherein the data acquisition technical scheme comprises data synchronization of various databases, reading of text files, extraction of field contents and a data interface based on http specification;
according to data acquisition configuration, a data acquisition node firstly synchronizes or constructs a data dictionary definition, and then extracts and normalizes a service data field from an original data source according to the data dictionary definition;
all the original data, the intermediate data and the normalized result data processed by the data acquisition node are stored in a data lake positioned in a data center for repeated use in subsequent secondary analysis and other business analysis.
6. The business warning management method according to claim 2, wherein the fourth step specifically includes the following contents:
(1) after the early warning management node submits the data center analysis configuration to the data center, the data center allocates computing resources according to the configuration as a corresponding service data analysis model and an index analysis model, and then a data processing node located in the data center is started to analyze the service data;
(2) the data processing node acquires various service data from the data lake, and in the data analysis process, the data accessed by the data processing node comprises core service data of a client and non-client data selected according to internal logics of a service data analysis model and an index analysis model.
7. The service early warning management method according to claim 2, wherein the fifth step specifically comprises the following steps:
(1) the data center takes a three-layer structure of a business data analysis model, an index system and an index analysis model as a basis, relevant business data analysis models and index analysis models are dispatched according to the analysis configuration of the data center provided by a user, the core business data of a client is processed, and if the business indexes are found to deviate in the processing process, the internal processing of the business deviations is immediately triggered;
(2) the service deviation internal processing firstly collects the environment data causing the service deviation, the environment data comprises the service data provided by the client and the external data, the data type included in the environment data is predefined in the service deviation, and the data center station collects the corresponding environment data according to the definition and submits the corresponding environment data to the early warning management node.
8. The business warning management method according to claim 2, wherein the sixth step specifically comprises the following steps:
(1) the dynamic time analysis model analyzes the service deviation data and the environment data and judges whether the service deviation is upgraded to warning sign or not by combining with experience criteria; the dynamic time analysis model is a multivariable judgment model, the variables comprise service index weight, service deviation amplitude, time and other auxiliary variables, and the specific variable number and parameter setting are comprehensively determined by the industry type, the service mode and the channel type of the service;
(2) the early warning management node finally converts the service deviation found by the data console into warning sign information of different levels through an application layer secondary verification mechanism, submits the warning sign information to the task management node, and the task management node performs subsequent processing according to the task management flow of the enterprise.
9. The business warning management method of claim 2,
the seventh step specifically comprises the following steps:
(1) the early warning management node is matched with the data center, after various warning signs are discovered by adopting a secondary verification mechanism, the early warning management node submits warning sign information and relevant service data to the task management node, and the task management node starts a warning sign task processing mechanism;
(2) the task management node is responsible for maintaining an organization system and a transmission system related to the warning sign in the enterprise, and setting different warning sign transmission systems and processing systems aiming at the warning signs with different service indexes and warning signs with different levels; in the initial stage, a user firstly constructs a corresponding organization system and a corresponding transmission system in a task management node according to different service indexes and warning sign levels, so that a warning sign processing service flow rule base is formed; after receiving warning sign information submitted by the warning management node, the task management node constructs warning sign processing service flow according to rules defined in a warning sign processing service flow rule base, wherein the warning sign processing service flow comprises relevant processors for confirming warning signs, relevant acquaintances and subsequent processing tasks;
(3) after the task management node constructs an alarm processing service flow, the alarm processing service flow is returned to the early warning management node;
the step eight specifically comprises the following contents:
after the early warning management node receives the warning sign processing service flow returned by the task management node, the warning sign identification and the establishment of the warning sign processing flow are indicated to be completed; at the moment, the early warning management node internally maintains the service deviation data, the service deviation environment data, the alarm type, the alarm level and the alarm processing flow data of the alarm; and then the early warning management node constructs a service deviation display chart, a warning sign chart and a task flow chart according to the data, and pushes the service deviation display chart, the warning sign chart and the task flow chart to a data terminal of a team member related to the task flow, so that the online processing is carried out.
10. A service early warning management system based on a data center platform is characterized by comprising a data acquisition node, an early warning management node, an early warning rule base, a data center platform, a task management node, an alarm million processing service flow rule base and an automatic control module, wherein the data center platform comprises a data processing node, a data lake, an analysis engine and a judgment base; wherein:
the data acquisition node has the function of acquiring service data according to a data scheme;
the early warning management node has the functions of managing an early warning rule base and a dynamic time analysis model, analyzing an early warning control overall scheme pushed by a data terminal, controlling a data acquisition node to acquire service data, judging service deviation found by a data center through an application layer secondary verification mechanism, and outputting warning omen information and related task flows in a visual adaptation mode aiming at member authority;
the early warning rule base has the function of storing early warning rule information;
the task management node has the functions of managing warning sign processing service flow rules and constructing warning sign processing service flows according to a warning sign processing service flow rule base;
the warning sign processing service flow rule base has the function of storing warning sign processing service flow rule information;
the data processing node has the functions of carrying out multi-dimensional analysis on the service data based on the enterprise operation mode and submitting the service deviation data and the related environment data appearing in the analysis result to the early warning management node;
the data lake has the functions of storing all the original data, the intermediate data and the normalized result data which are acquired and processed by the data acquisition nodes;
the analysis engine is used for analyzing the service data in cooperation with the data processing node;
the function of the judgment base is to store judgment rule information;
the above components operate according to the service early warning management method of any one of claims 2 to 9, and the automatic control module performs automatic organization and control on the components.
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