WO2019051951A1 - 业务数据监控方法、装置、终端设备及存储介质 - Google Patents

业务数据监控方法、装置、终端设备及存储介质 Download PDF

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WO2019051951A1
WO2019051951A1 PCT/CN2017/108534 CN2017108534W WO2019051951A1 WO 2019051951 A1 WO2019051951 A1 WO 2019051951A1 CN 2017108534 W CN2017108534 W CN 2017108534W WO 2019051951 A1 WO2019051951 A1 WO 2019051951A1
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monitoring
service data
data
policy
indicator
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PCT/CN2017/108534
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English (en)
French (fr)
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张川
顾青山
金鑫
梁博
谭志杰
梁永健
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平安科技（深圳）有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/284—Relational databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/245—Query processing
    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2471—Distributed queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/24—Querying
    • G06F16/248—Presentation of query results
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/28—Databases characterised by their database models, e.g. relational or object models
    • G06F16/283—Multi-dimensional databases or data warehouses, e.g. MOLAP or ROLAP
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange

Definitions

  • the present application relates to the field of computer technologies, and in particular, to a service data monitoring method, apparatus, terminal, and storage medium.
  • a large amount of business data is generated in the daily operations of financial institutions such as banks, insurance and securities.
  • Financial institutions are equipped with specialized business personnel to monitor a large amount of business data generated in financial institutions to determine whether there is data anomaly.
  • the monitoring method has high labor costs.
  • the service personnel monitors the service data, the service data is compared with the corresponding monitoring indicators to determine whether the service data meets the monitoring indicators. If the service data does not meet the monitoring indicators, the data is abnormal.
  • the monitoring indicators can be indicators such as the year-on-year and day-to-day ratio.
  • the embodiment of the present invention provides a method, a device, a terminal device, and a storage medium for monitoring service data, so as to solve the problem that the current manual monitoring service data is abnormal.
  • the embodiment of the present application provides a service data monitoring method, including:
  • target service data from the multi-dimensional service data based on the at least one monitoring dimension, and determining whether the target service data all meet at least one of the monitoring indicators;
  • the target service data does not all meet at least one of the monitoring indicators, determining that the target service data is abnormal data, and acquiring the monitoring result.
  • the embodiment of the present application provides a service data monitoring apparatus, including:
  • a service data acquisition module configured to acquire multi-dimensional service data in a big data platform
  • a monitoring policy obtaining module configured to acquire a monitoring policy configured by the user, where the monitoring policy includes at least one monitoring indicator and at least one monitoring dimension;
  • a service data detecting module configured to acquire target service data from the multi-dimensional service data based on the at least one monitoring dimension, and determine whether the target service data all meet at least one of the monitoring indicators;
  • a service data result obtaining module configured to determine that the target service data is abnormal data, and obtain a monitoring result, if the target service data does not all meet at least one of the monitoring indicators.
  • an embodiment of the present application provides a terminal device, including a memory, a processor, and computer readable instructions stored in the memory and executable on the processor, where the processor executes the computer The following steps are implemented when reading the instruction:
  • target service data from the multi-dimensional service data based on the at least one monitoring dimension, and determining whether the target service data all meet at least one of the monitoring indicators;
  • the target service data does not all meet at least one of the monitoring indicators, determining that the target service data is abnormal data, and acquiring the monitoring result.
  • an embodiment of the present application provides a computer readable storage medium, where the computer readable storage medium stores computer readable instructions, and when the computer readable instructions are executed by a processor, the following steps are implemented:
  • target service data from the multi-dimensional service data based on the at least one monitoring dimension, and determining whether the target service data all meet at least one of the monitoring indicators;
  • the monitoring result is obtained.
  • the multi-dimensional service data in the big data platform is obtained, so that the acquired service data is more comprehensive, and the scope of the data monitoring is expanded, so as to obtain more Comprehensive monitoring of business data results.
  • the monitoring policy is configured by the user.
  • the monitoring policy includes at least one monitoring indicator and at least one monitoring dimension.
  • the monitoring policy can be self-configured by the user according to the actual situation.
  • the configuration process is simple and fast, and at least one dimension and at least one monitoring indicator can be implemented. Combine the effects of monitoring to ensure the reliability and comprehensiveness of the final monitoring results.
  • the target service data is obtained from the multi-dimensional service data, and the target service data is used as the service data that needs to be detected, thereby improving the pertinence of the service data monitoring, and ensuring the accuracy of the data monitoring to a certain extent.
  • the target service data is determined to be abnormal data and the monitoring result is obtained, so that the monitoring result can more accurately reflect the abnormal situation of the target service data, so as to implement at least one monitoring.
  • the indicators monitor the target business data, making business data monitoring more accurate and reliable.
  • FIG. 1 is a flowchart of a method for monitoring service data in Embodiment 1 of the present application.
  • FIG. 2 is a specific flow chart of step S10 of FIG. 1.
  • FIG. 3 is a specific flow chart of step S20 of FIG. 1.
  • FIG. 4 is a specific flowchart of step S212 in FIG.
  • FIG. 5 is another specific flowchart of step S20 of FIG. 1.
  • FIG. 6 is a specific flow chart of step S40 of Figure 1.
  • FIG. 7 is a schematic block diagram of a service data monitoring apparatus in Embodiment 2 of the present application.
  • FIG. 8 is a schematic diagram of a terminal device in Embodiment 4 of the present application.
  • FIG. 1 shows a flow chart of a method for monitoring service data in this embodiment.
  • the service data monitoring method can be applied to terminal devices configured by financial institutions such as banks, insurance, and securities, and is used for multi-dimensional monitoring of a large amount of business data generated by financial institutions in daily business activities, thereby achieving high-efficiency monitoring of business data. And make the monitoring results more comprehensive.
  • the terminal device is a device that can perform human-computer interaction with the user, including but not limited to devices such as a computer, a smart phone, and a tablet.
  • the service data monitoring method includes the following steps:
  • the big data platform refers to a data processing platform that integrates data access, data processing, data storage, query and retrieval, analysis and mining, and application interfaces.
  • Business data refers to business-related data generated by financial institutions in the course of production and operation activities.
  • the service data includes, but is not limited to, the quantity corresponding to the specific service data such as the sales amount, the amount of the ticket, the attendance rate of the salesperson, the number of the customer, the amount of the promotion, the number of the active users, and the amount of the newly registered user.
  • a dimension in multi-dimensional business data refers to a parameter object used to represent a business data, including but not limited to a time dimension, an area dimension, a product dimension, and an institutional dimension.
  • the time dimension refers to the time when the business data is formed.
  • the regional dimension refers to the area corresponding to the business data, such as Beijing, Shanghai, and Guangzhou.
  • the product dimension refers to the product corresponding to the business data, such as property insurance and life insurance in insurance institutions.
  • the organization dimension is the organization corresponding to the business data, such as the organization network point corresponding to the business data.
  • a multi-dimensional business data formed in the big data platform is the sales of life insurance products (product dimensions) in the XX institutional outlet (institutional dimension) of Guangzhou (regional dimension) on June 1 (time dimension).
  • the multi-dimensional service data is obtained in the big data platform, so that the subsequent multi-dimensional business data can be combined in any dimension to realize the business data monitoring based on the potential contact between the business data of different dimensions, which can be more comprehensively reflected. Monitoring results of business data.
  • step S10 the multi-dimensional service data in the big data platform is obtained, which specifically includes the following steps:
  • S11 The raw data is collected using the Hadoop big data platform.
  • the original data refers to the business data that has not been processed by the data, and the original data is not classified and counted by the multi-dimensional, that is, the original data is the chaotic unclassified business data obtained by the Hadoop big data collection.
  • the big data platform is specifically a Hadoop big data platform, and the Hadoop big data platform enables the user to develop a distributed program without performing knowledge of the distributed underlying details, and perform high-speed arithmetic and storage of values.
  • Hadoop refers to a distributed system infrastructure, Hadoop implements a distributed file system (Hadoop Distributed File) System, hereinafter referred to as HDFS).
  • HDFS Hadoop Distributed File
  • HDFS is highly fault-tolerant and designed to be deployed on low-cost hardware, and it provides high throughput to access application data for applications with very large data sets.
  • HDFS can access data in the file system as a stream. Understandably, by using the Hadoop big data platform to collect raw data, the efficiency of obtaining raw data is greatly improved, the time required for obtaining a large amount of original data is reduced, and an original data of a certain order of magnitude is efficiently collected.
  • HIVE is a data warehouse tool based on Hadoop.
  • the raw data collected by Hadoop big data platform is stored in HIVE, which provides a safe and effective storage method for a large amount of raw data to map structured data files.
  • HIVE provides a simple SQL query function, which can convert SQL statements into MapReduce tasks without running a dedicated MapReduce application, which has the advantage of low learning cost.
  • the SQL statement refers to the Structured Query Language (Structured Query Language), a database query and programming language for accessing data and querying, counting, updating and managing relational database systems, ie SQL statements. It is a language that operates on a database.
  • MapReduce is a programming model for parallel operations on large data sets (greater than 1TB).
  • the original data can be mapped into a database table, which provides a data source for subsequent data query and operation, and achieves the purpose of safely and effectively storing a large amount of original data. And through HIVE to provide effective support for the subsequent processing of the original data.
  • S13 Perform multi-dimensional statistics on the original data in the HIVE using the SQL statement to obtain multi-dimensional business data.
  • multi-dimensional statistics refers to the classification and statistics of raw data by different dimensions.
  • the original data belonging to the same dimension is first assembled to form a data set of the original data of the dimension to form service data of the same dimension, and then the plurality of service data of the same dimension are statistically calculated.
  • a multi-dimensional business data set composed of different business data subsets of the same dimension to obtain multi-dimensional business data.
  • the original data in the HIVE is subjected to multi-dimensional statistics through the SQL statement to obtain a multi-dimensional business data set composed of a plurality of different service data subsets of the same dimension.
  • step S13 is to convert the complex unordered multi-dimensional raw data into a multi-dimensional business data set containing a plurality of different business data subsets of the same dimension through SQL statement statistical processing, the multi-dimensional business data.
  • the centralized data is multi-dimensional business data.
  • S20 Obtain a monitoring policy configured by the user, where the monitoring policy includes at least one monitoring indicator and at least one monitoring dimension.
  • the monitoring policy is a policy configured by the user according to actual needs, and the policy can implement the purpose of monitoring multi-dimensional business data.
  • the monitoring strategy includes at least one monitoring indicator and at least one monitoring dimension.
  • Monitoring indicator Corresponding to the business data, it is an indicator for evaluating whether the business data is abnormal.
  • the monitoring dimension corresponds to the multi-dimensional business data and is used to define the dimension corresponding to the business data to be monitored.
  • the monitoring strategy is stored in the json data format, and the json data format is relatively common, and supports various hierarchical relationships to facilitate extension and background program calls.
  • the monitoring policy of the user configuration acquired by the terminal device of the financial institution includes at least one monitoring indicator and at least one monitoring dimension.
  • the monitoring indicator monitors the multi-dimensional data corresponding to at least one monitoring dimension, so that the data monitoring is more comprehensive and more efficient. That is, the user can monitor the service data of different dimensions according to actual requirements, and freely combine at least one monitoring indicator according to actual needs to obtain a monitoring strategy configured by the user, and can further contact through potential contacts between business data of different dimensions. Comprehensively reflect the results of business monitoring and improve the reliability and comprehensiveness of monitoring results.
  • a monitoring policy configured by the user is obtained, where the monitoring policy includes at least one monitoring indicator and at least one monitoring dimension, and specifically includes the following steps:
  • S211 Display a configuration interface corresponding to the monitoring policy.
  • the terminal device of the financial institution displays a configuration interface, so that the user implements a self-service configuration monitoring policy through the configuration interface.
  • the configuration interface is an interface that provides the monitoring policy configuration for the user. You can enter the monitoring policy to be configured and confirm the configuration of the monitoring policy.
  • the configuration interface displayed on the terminal device enables the user to automatically configure the monitoring policy, which saves the time cost of the original manual data monitoring, and implements the function of the user self-configuration monitoring policy to improve the efficiency of monitoring the service data.
  • S212 Acquire at least one monitoring indicator and at least one monitoring dimension that are input by the user in the configuration interface, where the monitoring indicator includes the indicator name and the indicator range.
  • the terminal device of the financial institution displays a configuration interface to obtain at least one monitoring indicator and at least one monitoring dimension input by the user in the configuration interface.
  • Each monitoring indicator includes the name of the indicator and the corresponding range of indicators.
  • the indicator name is the specific name of each indicator in the monitoring indicator, and the indicator name corresponds to the business data.
  • the indicator name may be the sales volume or the sales volume week-on-year.
  • the indicator range is the specific numerical range corresponding to the indicator name in the monitoring indicator, and is a numerical range for evaluating whether the business data is abnormal.
  • the terminal device can obtain at least one monitoring indicator and at least one monitoring dimension that are input by the user in the configuration interface, and provide a fast and effective implementation manner for obtaining a monitoring policy configured by the user.
  • S213 Acquire a confirmation instruction input by the user, and acquire a monitoring policy based on the confirmation instruction.
  • the terminal device can obtain the confirmation instruction input by the user, analyze and execute the confirmation instruction input by the user in the background, and acquire the monitoring policy configured by the user according to the confirmation instruction.
  • the confirmation command is an instruction for confirming the final selection that is input after the user configures according to the actual needs of the monitoring policy. For example, if the user can enter the two indicators of the signing amount and the attendant attendance rate in the configuration interface, and the regional dimensions in the monitoring dimension are “Shanghai” and “Shenzhen”, and the time dimension is “July”, the user inputs through the configuration interface. Two monitoring indicators: the amount of the order and the attendance of the salesman, and the regional dimensions in the monitoring dimension are “Shanghai” and “Shenzhen”.
  • the terminal setting After receiving the confirmation command, the terminal setting acquires the corresponding monitoring policy.
  • the user By displaying the configuration interface corresponding to the monitoring policy, the user can implement the self-configuration according to the interface, and provide convenient operation for the self-configuration of the user.
  • the monitoring policy obtained in step S213 is saved in the preset policy database, and the obtained monitoring policy is saved in the preset policy database as a historical monitoring policy.
  • the preset policy library refers to a database for storing the monitoring policies obtained through the autonomous configuration, that is, a database storing the configured monitoring policies. It can be understood that the monitoring policy obtained in steps S211-S213 is saved in the preset policy database, so that the user can directly obtain the corresponding historical monitoring policy according to the same needs in the past, and the work of repeatedly configuring the monitoring policy is omitted. Improve the efficiency of acquiring monitoring strategies.
  • step S212 at least one monitoring indicator and at least one monitoring dimension input by the user in the configuration interface are obtained, and the monitoring indicator includes the indicator name and the indicator range, and specifically includes the following steps:
  • S2121 Acquire at least one indicator name and at least one monitoring dimension entered by the user in the configuration interface.
  • the at least one indicator name and the at least one monitoring dimension that are input by the user in the configuration interface, so that the terminal device can obtain a configuration request including at least one indicator name and at least one monitoring dimension, so that the configuration request is performed based on the configuration request.
  • the indicator name is the specific name of each indicator in the monitoring indicator.
  • the indicator name corresponds to the business data.
  • the indicator name can be the sales volume or the sales volume can be compared with the year-on-year.
  • the monitoring dimension corresponds to the multi-dimensional business data and is used to define the dimension corresponding to the business data to be monitored.
  • the terminal device may obtain the at least one indicator name and the at least one monitoring dimension that are input by the user in the configuration interface, so that the terminal device can obtain the service data to be monitored based on the at least one monitoring dimension, and correspondingly monitor the service based on the at least one monitoring indicator. Data is monitored.
  • the terminal device may obtain at least one indicator name and at least one monitoring dimension that are input by the user in the configuration interface, perform a pairing search according to the indicator name in the HIVE, and obtain multi-dimensional historical data corresponding to the indicator name from the HIVE.
  • the multi-dimensional historical data refers to the multi-dimensional business data previously saved in the HIVE with the same monitoring name as the selected one. If the indicator name entered by the user in the configuration interface is the name of the indicator such as sales volume, user volume, and ticket quantity, the multi-dimensional historical data is all multi-dimensional business data corresponding to sales, user volume, and ticket amount.
  • S2123 Acquire a multivariate linear regression model, and use a multivariate linear regression model to perform regression processing on multi-dimensional historical data to obtain a standard value corresponding to the index name.
  • the standard value is a reference standard line in the indicator range to enable the range of indicators corresponding to the indicator name to be obtained.
  • h ⁇ (x) is a hypothesis function
  • each ⁇ is an angle vector between input values
  • each x is a corresponding feature.
  • x 0 1
  • ⁇ is the row vector
  • the row vector contains the parameters in the linear regression model
  • X is the sample feature matrix.
  • the multivariate linear regression model for obtaining an optimal solution specifically includes the following steps:
  • the feature x is normalized by the feature scaling method to obtain the optimal ⁇ in the multivariate linear regression model.
  • the value of the parameter ⁇ is very large, resulting in a large number of large-value calculations. Therefore, it is necessary to ensure that these features have similar ranges, and the Feature Scaling method will better help the gradient descent algorithm to converge faster.
  • the feature scaling method is to try to scale the range of values of all features to between -1 and 1, with the expression Where x n represents the nth feature, ⁇ n represents the average of all features, and s n represents the standard deviation of all features.
  • Cost Function Cost Function
  • y (i) represents the i-th element in vector y
  • h ⁇ (x (i) ) represents a known hypothesis function
  • m is the training set Quantity.
  • the gradient descent method determines a downward direction by first determining the step size to the next step, and arbitrarily given an initial value ⁇ 0 , ⁇ 1 , ..., ⁇ n , and goes down a predetermined step and updates ⁇ . 0 , ⁇ 1 ,..., ⁇ n , when the descending height is less than a certain defined value, the drop is stopped.
  • the expression of the gradient descent method is Where ⁇ is called the learning rate, which is used to determine the magnitude of the gradient descent.
  • the optimal parameter ⁇ in the multivariate linear regression model is obtained.
  • the multivariate linear regression model of the optimal solution ie, the highest theoretical accuracy
  • the historical data can be input into the multivariate linear regression model for regression processing, and the corresponding standard value can be obtained accurately and accurately.
  • the upper and lower limits range includes an upper limit range and a lower limit range.
  • the standard value corresponding to the indicator name is the quantity corresponding to the specific business data such as the sales amount, the signing amount, the salesman attendance rate, the number of the received customers, the promotional activity amount, the active user number, and the newly registered user amount
  • the upper and lower limits are The range can correspond to the specific quantity; if the standard value corresponding to the indicator name is the ratio corresponding to the specific business data such as the day-to-day ratio, the week-on-year and the month-to-month ratio, the upper and lower limits can be set to plus or minus 5% of the standard value according to the actual situation. Positive and negative 10% and other ranges. It can be understood that the upper and lower limits can be set autonomously according to the actual situation, and the flexibility of the controllable upper and lower limits is
  • S2125 Obtain a range of indicators corresponding to the indicator name based on the standard value and the upper and lower limits.
  • the indicator range corresponding to the sales amount is 40,000-60000;
  • the data is the month-to-year ratio of sales. For example, if the standard value of sales year-on-year is 5%, and the upper and lower limits of the setting are plus or minus 1%, the corresponding range of sales month-on-year is 4%-6. %.
  • the range of indicators corresponding to the name of the indicator is obtained through the standard value and the upper and lower limits, so that the obtained indicator range is closer to the actual range, which is beneficial to improving the rationality and feasibility of setting the monitoring strategy, and improving the based on the indicator.
  • the accuracy of the business data monitoring is monitored by the scope of the monitoring indicators.
  • a monitoring policy configured by the user is obtained, where the monitoring policy includes at least one monitoring indicator and at least one monitoring dimension, and specifically includes the following steps:
  • S221 Display a configuration interface corresponding to the monitoring policy.
  • the terminal device of the financial institution displays a configuration interface, so that the user implements a self-service configuration monitoring policy through the configuration interface.
  • the configuration interface refers to an interface that provides a user with a monitoring policy configuration, and the user can configure the interface. After you enter the monitoring policy of the required configuration and confirm it, you can complete the monitoring policy configuration.
  • the configuration interface displayed on the terminal device enables the user to automatically configure the monitoring policy, which saves the time cost of the original manual data monitoring, and implements the function of the user self-configuration monitoring policy to improve the efficiency of monitoring the service data.
  • the policy query instruction is an instruction for querying a previously saved historical monitoring policy. After obtaining the policy query instruction input by the user, the terminal device may analyze and process the received policy query instruction and output all the saved historical monitoring policies indicated by the policy query instruction to obtain the monitoring policy saved directly through the history. It provides convenience for users to implement self-configuration of monitoring indicators, avoiding the process of re-configuring monitoring policies every time, and improving the efficiency of monitoring policies.
  • S223 Acquire all historical monitoring policies in the preset policy library based on the policy query instruction.
  • the terminal device displays all the historical monitoring policies in the preset policy library through the background output command on the configuration interface according to the policy query instruction input by the user.
  • the preset policy library refers to a database for storing a monitoring policy obtained through autonomous configuration, that is, a database for storing the configured monitoring policies. It can be understood that the terminal device obtains all the historical monitoring policies in the preset policy database based on the obtained policy query instruction, provides the historical monitoring policy for the user, skips the process of each reconfiguration, and provides the user with the monitoring policy. Shortcuts improve the efficiency of acquiring monitoring strategies.
  • S224 Acquire a policy selection instruction input by the user, where the policy selection instruction includes a policy ID.
  • the policy selection instruction refers to an instruction for selecting one of the policies from all historical monitoring strategies.
  • the policy ID is an identifier used to uniquely identify the monitoring policy.
  • the policy ID in the policy selection instruction is an identifier for uniquely identifying the historical monitoring policy selected by the user from the preset policy library, so that the historical monitoring policy selected by the user is obtained based on the policy ID.
  • the policy selection instruction provides the user with a plurality of selection schemes, so that the user can select the historical monitoring policy that has been saved in the preset policy library according to actual needs.
  • the terminal device selects the historical monitoring policy corresponding to the policy ID in the preset policy database according to the policy ID in the policy selection command input by the user, and obtains the historical monitoring policy as the monitoring of the current configuration.
  • Strategy It can be understood that the process of obtaining the corresponding monitoring policy according to the policy ID in the policy selection instruction is simple and convenient, and the accuracy and feasibility of obtaining the corresponding monitoring policy according to the policy ID are improved, and the corresponding monitoring strategy is improved. effectiveness.
  • S30 Obtain target service data from the multi-dimensional service data based on the at least one monitoring dimension, and determine whether the target service data all meet at least one monitoring indicator.
  • step S30 can be summarized as an operation of detecting and determining the target service data based on the monitoring policy.
  • the target service data is service data corresponding to at least one monitoring dimension in the multi-dimensional service data, where the target service data refers to service data that needs to be monitored by the data. Determining whether the target service data all meets at least one monitoring indicator means that the target service data is detected one by one, and whether each target service data satisfies at least one monitoring indicator.
  • the target service data is determined according to at least one monitoring dimension, so as to achieve targeted detection of the target service data, to avoid excessive data volume, and affect service data monitoring efficiency; and, using at least one monitoring indicator to target service data. Monitoring can improve the comprehensiveness of data monitoring and make monitoring results more reliable.
  • the monitoring dimension in the monitoring policy configured by the user includes the area dimension is "Beijing”, the organization dimension is “XX network point”, and the product dimension is “production insurance”, and the target business data that needs to be monitored is determined based on the three monitoring dimensions.
  • the monitoring indicators in the user-configured monitoring strategy are monthly sales of 1 million to 4 million, and the number of receiving customers is 300-800. Then, all the target service data are detected one by one in step S30, and it is determined whether all the two monitoring indicators satisfying the sales amount and the number of received customers are determined.
  • the abnormal data refers to data in which the target business data does not all meet at least one monitoring indicator.
  • the corresponding target service data is normal data, and may not be processed; if the target service data does not all meet at least one monitoring indicator, that is, the target service data does not meet at least one
  • the monitoring indicator determines that the target business data is abnormal data, and the target business data is obtained as the monitoring result of the abnormal data. Specifically, if the target service data is abnormal data, the abnormal data can be extracted, which reduces the burden on the original business personnel to find abnormal data in a large amount of target business data.
  • the target service data and the at least one monitoring indicator need to be judged, instead of being judged based on a single monitoring indicator, thereby improving the comprehensiveness of the data monitoring and the monitoring efficiency, and ensuring the data.
  • the accuracy of monitoring is a simple measure of the accuracy of monitoring.
  • step S40 if the target service data does not all meet at least one monitoring indicator, the target service data is determined to be abnormal data, and then specifically includes the following steps:
  • S41 Query the preset database to obtain a corresponding abnormal reason based on the abnormal data.
  • the preset database refers to a database in which the business personnel summarize and store various abnormal causes in the database according to the experience of the abnormal business data in the past.
  • the terminal device may pair with the abnormal cause in the preset database according to the specific situation of the abnormal data to obtain a corresponding data abnormality cause. If the target service data is the amount of the signed item, if the amount of the signed transaction corresponding to the target service data is too large, and the value exceeds 5% of the corresponding indicator range, the abnormal reason in the query default database is the implementation of the new policy or the holiday. Pass Querying the default database to obtain the corresponding abnormal reason can effectively combine the abnormal reasons derived from previous historical experience, and provide powerful help for relevant business personnel in the analysis of abnormal data.
  • S42 Send abnormal data and abnormal cause to the monitoring mailbox.
  • the monitoring mailbox is a preset mailbox for obtaining monitoring results.
  • the detected abnormal data and the corresponding abnormality obtained by querying the preset database are sent to the monitoring mailbox of the related service personnel, so that the related business personnel can understand the abnormal target business data and may cause the target business.
  • Abnormal causes of data anomalies, in order to more effectively monitor abnormal data, improve the efficiency of data monitoring, and achieve the purpose of quickly processing the monitoring results of the target business data as abnormal data.
  • the method before step S30, further includes: acquiring a timing detection instruction, where the timing detection instruction includes a trigger time point, a monitoring mailbox, and a monitoring policy.
  • the timing detection instruction is an instruction configured by the user to control the terminal device to periodically perform detection based on the monitoring policy to the target service data.
  • the triggering time point is a time point for triggering the terminal device to perform detection of the target service data based on the monitoring policy.
  • the triggering time point in the timing detection command may be set to 1 o'clock every night to control the terminal device to perform the operation of detecting the target service data based on the monitoring policy at 1 o'clock every night.
  • the setting of the triggering time point enables the detection of the target business data based on the monitoring strategy to be performed at a predetermined time.
  • the monitoring policy is the monitoring policy in step S20.
  • the triggering time point and the monitoring mailbox can be configured at the same time to form a timing detection instruction.
  • the monitoring policy is necessary for executing the timing detection instruction, and the operation of detecting the target service data based on the monitoring policy is performed at the triggering time point according to the selected monitoring strategy.
  • the monitoring mailbox is a pre-set mailbox for obtaining monitoring results. After the target service data is detected based on the monitoring policy, the monitoring result can be sent to the monitoring mailbox, so that the relevant personnel can obtain the monitoring result offline.
  • the execution of the timing detection instruction can effectively process the target service data, improve the efficiency of the monitoring, and enable the terminal device to automatically execute the timing detection instruction without manual monitoring.
  • Step S30 specifically includes: performing an operation of detecting target service data based on the monitoring policy at a triggering time point.
  • the terminal device may perform an operation of detecting the target service data based on the monitoring policy at the triggering time according to the timing detection instruction, that is, performing the step of step S30.
  • the monitoring result is obtained based on the monitoring index and the monitoring dimension of the monitoring policy based on the monitoring policy. It can be understood that, at the triggering time point, the operation of detecting the target service data based on the monitoring policy is performed, and the target service data can be effectively timed and the detected target service data can be displayed.
  • step S40 determining that the target service data is abnormal data, and further comprising: sending the abnormal data to the monitoring post box.
  • the abnormal data obtained by the monitoring is sent to the monitoring mailbox for the first time, so that the related service personnel can receive the monitoring result of the target service data as abnormal data in the first time.
  • relevant business personnel can effectively analyze and process abnormal data according to the monitoring results obtained by monitoring the mailbox.
  • the multi-dimensional service data in the big data platform is acquired, and a large amount of original data is quickly and efficiently calculated on the big data platform, so that the multi-dimensional business data is more conveniently obtained.
  • the data is divided into multiple dimensions and combined in any dimension. Through the potential connection between different dimensional data, the business monitoring results can be more fully reflected.
  • the monitoring policy includes at least one monitoring indicator and at least one monitoring dimension.
  • the monitoring policy can be self-configured by the user according to the actual situation.
  • the configuration process is simple and fast, and the monitoring effect of at least one dimension and at least one monitoring indicator can be implemented in any combination to ensure the effect. The reliability and comprehensiveness of the final monitoring results obtained.
  • the target service data is obtained from the multi-dimensional service data, and the target service data is used as the service data that needs to be detected, thereby improving the pertinence of the service data monitoring, and ensuring the accuracy of the data monitoring to a certain extent.
  • the target service data does not all meet the at least one monitoring indicator, the target service data is determined to be abnormal data and the monitoring result is obtained, so that the monitoring result can more accurately reflect the abnormal situation of the service data, so as to implement at least one monitoring indicator pair.
  • Target business data is monitored to make business data monitoring more accurate and reliable.
  • FIG. 7 is a schematic block diagram showing a service data monitoring apparatus corresponding to the service data monitoring method in the first embodiment.
  • the service data monitoring apparatus includes a service data acquiring module 10, a monitoring policy obtaining module 20, a service data detecting module 30, and a service data result obtaining module 40.
  • the implementation functions of the service data acquisition module 10, the monitoring policy acquisition module 20, the service data detection module 30, and the service data result acquisition module 40 correspond to the steps corresponding to the service data monitoring method in the first embodiment, in order to avoid redundancy, The examples are not described in detail.
  • the service data obtaining module 10 is configured to obtain multi-dimensional service data in the big data platform.
  • the monitoring policy obtaining module 20 is configured to obtain a monitoring policy configured by the user, where the monitoring policy includes at least one monitoring indicator and at least one monitoring dimension.
  • the service data detecting module 30 is configured to obtain target service data from the multi-dimensional service data based on the at least one monitoring dimension, and determine whether the target service data all meet at least one monitoring indicator.
  • the service data result obtaining module 40 is configured to determine that the target service data is abnormal data and obtain the monitoring result if the target service data does not all meet the at least one monitoring indicator.
  • the service data obtaining module 10 includes a raw data acquiring unit 11, a raw data storing unit 12, and a raw data counting unit 13.
  • the original data obtaining unit 11 is configured to collect raw data by using a Hadoop big data platform.
  • the original data storage unit 12 is configured to store the original data in the HIVE.
  • the original data statistics unit 13 is configured to perform multi-dimensional statistics on the original data in the HIVE by using the SQL statement to obtain multi-dimensional business data.
  • the monitoring policy obtaining module 20 includes a first configuration interface display unit 21, a monitoring index obtaining unit 22, a confirmation instruction acquiring unit 23, a second configuration interface displaying unit 24, a policy query instruction acquiring unit 25, and a preset monitoring policy acquiring unit 26.
  • the first configuration interface display unit 21 is configured to display a configuration interface corresponding to the monitoring policy.
  • the monitoring indicator acquiring unit 22 is configured to acquire at least one monitoring indicator and at least one monitoring dimension input by the user in the configuration interface, where the monitoring indicator includes the indicator name and the indicator range.
  • the confirmation instruction acquisition unit 23 is configured to acquire a confirmation instruction input by the user, and acquire a monitoring policy based on the confirmation instruction.
  • the second configuration interface display unit 24 is configured to display a configuration interface corresponding to the monitoring policy.
  • the policy query instruction obtaining unit 25 is configured to obtain a policy query instruction input by the user.
  • the preset monitoring policy obtaining unit 26 is configured to obtain all historical monitoring policies in the preset policy library based on the policy query instruction.
  • the policy selection instruction obtaining unit 27 is configured to acquire a policy selection instruction input by the user, and the policy selection instruction includes a policy ID.
  • the monitoring policy obtaining unit 28 is configured to acquire a monitoring policy corresponding to the policy ID.
  • the monitoring index obtaining unit 22 includes an index name obtaining subunit 221, a history data acquiring subunit 222, a standard value obtaining subunit 223, an upper and lower limit obtaining subunit 224, and an index range obtaining subunit 225.
  • the indicator name obtaining sub-unit 221 is configured to obtain at least one indicator name and at least one monitoring dimension input by the user in the configuration interface.
  • the historical data obtaining sub-unit 222 is configured to obtain multi-dimensional historical data in the big data platform based on the indicator name.
  • the standard value acquisition sub-unit 223 is configured to perform regression processing on the historical data by using a linear regression algorithm to obtain a standard value corresponding to the indicator name.
  • the upper and lower limit acquisition sub-unit 224 is configured to obtain an upper and lower limit range input by the user in the configuration interface.
  • the indicator range obtaining sub-unit 225 is configured to obtain a range of indicators corresponding to the indicator name based on the standard value and the upper and lower limits.
  • the service data detecting module 30 includes a timing instruction acquiring unit 31, a service data detecting unit 32, and an abnormal data determining unit 33.
  • the timing instruction acquiring unit 31 is configured to acquire a timing detection instruction, where the timing detection instruction includes a trigger time point, a monitoring mailbox, and a monitoring policy.
  • the service data detecting unit 32 is configured to perform an operation of determining the target service data based on the monitoring policy at the triggering time point.
  • the abnormal data determining unit 33 is configured to determine that the target service data is abnormal data, and further includes: sending the abnormal data to the monitoring mailbox.
  • the service data result obtaining module 40 includes an abnormality reason obtaining unit 41 and a monitoring mailbox sending unit 42.
  • the abnormal cause obtaining unit 41 is configured to query the preset database to obtain a corresponding abnormal cause based on the abnormal data.
  • the monitoring mailbox sending unit 42 is configured to send the abnormal data and the abnormal reason to the monitoring mailbox.
  • the service data acquiring module 10 is configured to acquire multi-dimensional service data in the big data platform, and the module performs fast and effective calculation on a large amount of original data in the big data platform to make In order to conveniently obtain multi-dimensional business data, and divide the data into multiple dimensions and combine any dimension, through the potential contact between different dimensional data, the effect of more comprehensively reflecting the business monitoring results can be realized.
  • the monitoring policy obtaining module 20 is configured to obtain a monitoring policy configured by the user, where the monitoring policy includes at least one monitoring indicator and at least one monitoring dimension, and the at least one monitoring indicator included in the monitoring policy and the at least one monitoring dimension are configured to obtain the user configuration. The purpose of the monitoring strategy.
  • the service data detecting module 30 is configured to obtain target service data from the multi-dimensional service data based on the at least one monitoring dimension, and determine whether the target service data all meets at least one monitoring indicator, and the detecting device can directly and effectively target the service data. The detection is performed, and the more accurate target business data monitoring result can be obtained, and the correct rate of monitoring is improved.
  • the service data result obtaining module 40 is configured to: if the target service data does not all meet at least one monitoring indicator, determine that the target service data is abnormal data, obtain the monitoring result, and the module processes the judgment result of the target service data, and implements The function of extracting abnormal data as a monitoring result provides an abnormal data source for further monitoring of the business personnel, which greatly reduces the burden on the original business personnel to find abnormal data in a large amount of data, and ensures the correctness of the monitoring result.
  • the embodiment provides a computer readable storage medium on which a computer readable storage
  • the instruction that the computer readable instructions are executed by the processor implements the service data monitoring method in Embodiment 1. To avoid repetition, details are not described herein again.
  • the functions of the modules/units in the service data monitoring apparatus in Embodiment 2 are implemented when the computer readable instructions are executed by the processor. To avoid repetition, details are not described herein again.
  • FIG. 8 is a schematic diagram of a terminal device in this embodiment.
  • terminal device 80 includes a processor 81, a memory 82, and computer readable instructions 83 stored in memory 82 and operative on processor 81.
  • the processor 81 implements the various steps of the service data monitoring method of Embodiment 1 when the computer readable instructions 83 are executed, such as steps S10, S20, S30, and S40 shown in FIG.
  • the processor 81 executes the computer readable instructions 83, the functions of the modules/units of the service data monitoring device in Embodiment 2 are implemented.
  • the service data obtaining module 10 the monitoring policy obtaining module 20, and the service data detecting module 30 are shown.
  • the function of the business data result obtaining module 40 are shown in FIG. 7
  • computer readable instructions 83 may be partitioned into one or more modules/units, one or more modules/units being stored in memory 82 and executed by processor 81 to perform the data processing of the present application.
  • the one or more modules/units may be a series of computer readable instruction segments capable of performing a particular function for describing the execution of computer readable instructions 83 in the terminal device 80.
  • the computer readable instructions 80 can be divided into the service data obtaining module 10, the monitoring policy obtaining module 20, the service data detecting module 30, and the service data result obtaining module 40 in Embodiment 2, and the specific functions of each module are as in Embodiment 2. As shown, in order to avoid repetition, we will not repeat them here.
  • the terminal device 80 can be a computing device such as a desktop computer, a notebook, a palmtop computer, and a cloud server.
  • the terminal device may include, but is not limited to, a processor 81, a memory 82. It will be understood by those skilled in the art that FIG. 8 is merely an example of the terminal device 80, does not constitute a limitation of the terminal device 80, may include more or less components than those illustrated, or may combine certain components, or different components.
  • the terminal device may further include an input/output device, a network access device, a bus, and the like.
  • the processor 81 may be a central processing unit (CPU), or may be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, etc.
  • the general purpose processor may be a microprocessor or the processor or any conventional processor or the like.
  • the memory 82 may be an internal storage unit of the terminal device 80, such as a hard disk or a memory of the terminal device 80.
  • the memory 82 may also be an external storage device of the terminal device 80, such as a plug-in hard disk provided on the terminal device 80.
  • the memory 82 may also include both an internal storage unit of the terminal device 80 and an external storage device.
  • Memory 82 is used to store computer readable instructions as well as other programs and data required by the terminal device.
  • the memory 82 can also be used to temporarily store data that has been output or is about to be output.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.
  • the above integrated unit can be implemented in the form of hardware or in the form of a software functional unit.
  • the integrated modules/units if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium.
  • the present application implements all or part of the processes in the foregoing embodiments, and may also be implemented by computer readable instructions, which may be stored in a computer readable storage medium.
  • the computer readable instructions when executed by a processor, may implement the steps of the various method embodiments described above.
  • the computer readable instructions comprise computer readable instruction code, which may be in the form of source code, an object code form, an executable file or some intermediate form or the like.
  • the computer readable medium can include any entity or device capable of carrying the computer readable instruction code, a recording medium, a USB flash drive, a removable hard drive, a magnetic disk, an optical disk, a computer memory, a read only memory (ROM, Read-Only) Memory), random access memory (RAM), electrical carrier signals, telecommunications signals, and software distribution media.
  • a recording medium a USB flash drive
  • a removable hard drive a magnetic disk, an optical disk
  • a computer memory a read only memory (ROM, Read-Only) Memory
  • RAM random access memory

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Abstract

本申请公开了一种业务数据监控方法、装置、终端设备及存储介质。该业务数据监控方法，包括：获取大数据平台中多维度业务数据；获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度；基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标；若所述目标业务数据没有全部符合至少一个所述监控指标，则确定所述目标业务数据为异常数据，获取监控结果。该业务数据监控方法进行业务数据监控时，可以达到监控业务数据效率更高、监控业务数据结果更为全面的效果。

Description

业务数据监控方法、装置、终端设备及存储介质
本专利申请以2017年9月15日提交的申请号为201710834175.6，名称为“业务数据监控方法、装置、终端设备及储存介质”的中国发明专利申请为基础，并要求其优先权。
技术领域
本申请涉及计算机技术领域，尤其涉及一种业务数据监控方法、装置、终端及存储介质。
背景技术
在银行、保险和证券等金融机构每天的经营活动中产生大量的业务数据。金融机构内配置专门的业务人员，对金融机构中产生的大量业务数据进行监控，以确定是否存在数据异常，该监控方式人工成本较高。业务人员对业务数据进行监控时，将任一业务数据与对应的监控指标进行比较，判断该业务数据是否符合监控指标，若业务数据不符合监控指标，则认定数据存在异常。其中，监控指标可以是周同比、日环比等指标。业务人员对业务数据进行监控时，通常是基于单一监控指标进行一维数据监控，而无法实现基于多个监控指标进行多维组合监控，使得数据监控的效率较低。而且，对任一业务数据而言，若采用单一监控指标进行监控时数据可能不存在异常，采用两个或两个以上监控指标进行监控时可能存在异常，会影响数据监控的准确率。因此，当前人工监控业务数据异常时可能存在效率较低、监控不全面、人工成本高且准确率无法保障等问题。
发明内容
本申请实施例提供一种业务数据监控方法、装置、终端设备及存储介质，以解决当前人工监控业务数据异常时存在的问题。
第一方面，本申请实施例提供一种业务数据监控方法，包括：
获取大数据平台中多维度业务数据；
获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度；
基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标；
若所述目标业务数据没有全部符合至少一个所述监控指标，则确定所述目标业务数据为异常数据，获取监控结果。
第二方面，本申请实施例提供一种业务数据监控装置，包括：
业务数据获取模块，用于获取大数据平台中多维度业务数据；
监控策略获取模块，用于获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度；
业务数据检测模块，用于基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标；
业务数据结果获取模块，用于若所述目标业务数据没有全部符合至少一个所述监控指标，则确定所述目标业务数据为异常数据，获取监控结果。
第三方面，本申请实施例提供一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机可读指令，所述处理器执行所述计算机可读指令时实现如下步骤：
获取大数据平台中多维度业务数据；
获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度；
基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标；
若所述目标业务数据没有全部符合至少一个所述监控指标，则确定所述目标业务数据为异常数据，获取监控结果。
第四方面，本申请实施例提供一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可读指令，所述计算机可读指令被处理器执行时实现如下步骤：
获取大数据平台中多维度业务数据；
获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度；
基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标；
若所述目标业务数据没有全部符合至少一个所述监控指标，则确定所述目标业务数 据为异常数据，获取监控结果。
本申请实施例所提供的业务数据监控方法、装置、终端设备及存储介质中，通过获取大数据平台中多维度业务数据，使得获取的业务数据更全面，并且扩大数据监控的范围，以便获取更全面的业务数据的监控结果。之后获取用户配置的监控策略，监控策略包括至少一个监控指标和至少一个监控维度，该监控策略可由用户根据实际情况进行自助配置，配置过程简单快捷，并可实现至少一个维度和至少一个监控指标任意组合进行监控的效果，以保证最终获取的监控结果的可靠性和全面性。接着基于至少一个监控维度，从多维度业务数据中获取目标业务数据，将该目标业务数据作为需要进行检测的业务数据，提高业务数据监控的针对性，在一定程度上保证数据监控的准确率。并且，只有在目标业务数据没有全部符合至少一个监控指标，则确定目标业务数据为异常数据并获取监控结果，使得该监控结果可更准确地反映目标业务数据的异常情况，以实现基于至少一个监控指标对目标业务数据进行监控，使得业务数据监控更为精确和可靠。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对本申请实施例的描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例1中业务数据监控方法的一流程图。
图2是图1中步骤S10的一具体流程图。
图3是图1中步骤S20的一具体流程图。
图4是图3中步骤S212的一具体流程图。
图5是图1中步骤S20的另一具体流程图。
图6是图1中步骤S40的一具体流程图。
图7是本申请实施例2中业务数据监控装置的一原理框图。
图8是本申请实施例4中终端设备的一示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本 申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例，都属于本申请保护的范围。
实施例1
图1示出本实施例中业务数据监控方法的一流程图。该业务数据监控方法可应用在银行、保险和证券等金融机构配置的终端设备中，用于对金融机构每天经营活动中产生的大量业务数据进行多维度监控，可实现对业务数据的高效率监控并使监控结果更为全面的目的。其中，该终端设备是可与用户进行人机交互的设备，包括但不限于电脑、智能手机和平板等设备。如图1所示，该业务数据监控方法包括如下步骤：
S10：获取大数据平台中多维度业务数据。
其中，大数据平台是指一个集数据接入、数据处理、数据存储、查询检索、分析挖掘和应用接口等为一体的数据处理平台。业务数据是指金融机构在生产经营活动过程中产生的与业务相关的数据。业务数据包括但不限于本实施例中的销售额、签单量、业务员出勤率、接待客户数、宣传活动量、活跃用户数量和新注册用户量等具体业务数据对应的数量。多维度业务数据中的维度是指用于表述一业务数据的参数对象，该维度包括但不限于时间维度、区域维度、产品维度和机构维度。其中，时间维度是指业务数据的形成时间。区域维度是指业务数据对应的区域，如北京、上海和广州等。产品维度是指业务数据对应的产品，如保险机构中的产险和寿险等。机构维度是业务数据对应的机构，如形成该业务数据对应的机构网点。如在大数据平台中形成的一多维度业务数据为6月1日(时间维度)广州(区域维度)的XX机构网点(机构维度)中寿险产品(产品维度)的销售额。
本实施例中，通过在大数据平台中获取多维度业务数据，以便于后续对多维度业务数据进行任意维度的组合，实现基于不同维度业务数据间的潜在联系进行业务数据监控，能够更加全面反映业务数据的监控结果。
在一具体实施方式中，如图2所示，步骤S10中，获取大数据平台中多维度业务数据，具体包括如下步骤：
S11：采用Hadoop大数据平台采集原始数据。
其中，原始数据是指未经过数据处理的业务数据，该原始数据并未进行多维度分类和统计，即原始数据是Hadoop大数据采集得到的混乱未分类的业务数据。本实施例中，大数据平台具体为Hadoop大数据平台，Hadoop大数据平台可使用户在不了解分布式底层细节的情况下，开发分布式程序，并进行数值的高速运算和存储。其中，Hadoop是指一种分布式系统基础架构，Hadoop实现了一个分布式文件系统(Hadoop Distributed File  System，以下简称HDFS)。HDFS有高容错性的特点，并且设计用来部署在低廉的硬件上，而且它提供高吞吐量来访问应用程序的数据，适合有着超大数据集的应用程序。HDFS可以以流的形式访问文件系统中的数据。可以理解地，通过采用Hadoop大数据平台采集原始数据，大大提高原始数据的获取效率，降低获取大量原始数据所需时间，并且实现了高效采集一定数量级的原始数据。
S12：将原始数据存储在HIVE中。
其中，HIVE是基于Hadoop的一个数据仓库工具，将Hadoop大数据平台采集的原始数据存储在HIVE中，可为大量的原始数据提供了一种安全有效的存储方式，以将结构化的数据文件映射为一张数据库表。而且，HIVE可提供简单的SQL查询功能，可以将SQL语句转换为MapReduce任务进行运行，而无需开发专门的MapReduce应用，具有学习成本低的优点。其中，SQL语句是指结构化查询语言(Structured Query Language)，结构化查询语言是一种数据库查询和程序设计语言，用于存取数据以及查询、统计、更新和管理关系数据库系统，即SQL语句就是对数据库进行操作的一种语言。MapReduce是一种编程模型，用于大规模数据集(大于1TB)的并行运算。本实施例中，通过将原始数据存储在HIVE中，可将原始数据映射为一张数据库表，为后续的数据查询和操作提供了数据源，实现了将大量的原始数据安全有效存储起来的目的，并通过HIVE为后续对原始数据的进一步处理提供有效的支持。
S13：采用SQL语句对HIVE中的原始数据进行多维度统计，获取多维度业务数据。
其中，多维度统计是指将原始数据按不同维度进行筛选分类并统计。本实施例中，先将属于同一维度的原始数据集合起来，形成该维度的原始数据的数据集，以形成同一维度的业务数据，再将多个同一维度的业务数据进行统计计算，获取由多个不同的同一维度业务数据子集组成的多维度业务数据集，以获取多维度业务数据。具体地，通过SQL语句对HIVE中的原始数据经过多维度统计，得到由多个不同的同一维度业务数据子集组成的多维度业务数据集。可以理解地，步骤S13就是将复杂无序的多维度的原始数据，通过SQL语句统计处理，转换成包含多个不同的同一维度的业务数据子集的多维度业务数据集，该多维度业务数据集中的数据即为多维度业务数据。
S20：获取用户配置的监控策略，监控策略包括至少一个监控指标和至少一个监控维度。
其中，监控策略是用户根据实际需求自行配置的策略，该策略可实现对多维度业务数据进行监控的目的。监控策略包括至少一个监控指标和至少一个监控维度。监控指标 与业务数据相对应，是用于评价业务数据是否存在异常的指标。监控维度与多维度业务数据相对应，用于限定所需监控的业务数据对应的维度。监控策略存储成json数据格式，采用json数据格式比较通用，且支持各种层级关系，方便扩展和后台程序调用。
本实施例中，金融机构的终端设备获取到的用户配置的监控策略包括至少一个监控指标和至少一个监控维度，在基于监控策略对金融机构内部的多维度业务数据进行监控时，可采用至少一个监控指标对至少一个监控维度对应的多维度数据进行监控，使得数据监控更全面且效率更高。即用户可根据实际需求配置对不同维度的业务数据进行监控，并根据实际需求将至少一个监控指标进行自由组合，以获得用户配置的监控策略，通过不同维度的业务数据间潜在的联系，能够更加全面地反映业务监控结果，提高监控结果的可靠性和全面性。
在一种具体实施方式中，如图3所示，步骤S20中，获取用户配置的监控策略，监控策略包括至少一个监控指标和至少一个监控维度，具体包括如下步骤：
S211：显示监控策略对应的配置界面。
本实施例中，金融机构的终端设备显示配置界面，以便用户通过该配置界面实现自助配置监控策略。其中，配置界面是指为用户提供监控策略配置的界面，用户可在配置界面中输入所需配置的监控策略并进行确认后，完成监控策略配置。终端设备显示的配置界面，可使用户自动配置监控策略，节省了原本人工进行数据监控的时间成本，并实现了用户自助配置监控策略的功能，提高用户对业务数据进行监控的效率。
S212：获取用户在配置界面中输入的至少一个监控指标和至少一个监控维度，监控指标包括指标名称和指标范围。
本实施例中，金融机构的终端设备显示配置界面，以获取用户在配置界面中输入的至少一个监控指标和至少一个监控维度。每个监控指标包括指标名称和相对应的指标范围。其中，指标名称为监控指标中各个不同指标的具体名称，指标名称与业务数据相对应，如指标名称可以为销售量，也可以为销售量周同比等。指标范围为监控指标中与指标名称相对应的具体数值范围，是用于评价业务数据是否异常的数值范围。即若检测到的业务数据在该指标范围内，则认定该业务数据是预期无异常的业务数据；若检测到的业务数据不在该指标范围内，则认定该数据是预期异常的业务数据。可以理解地，基于该监控指标中的指标名称和指标范围，可以对业务数据是否异常进行监控，达到有效检测业务数据的效果。本实施例中，终端设备可获取用户在配置界面中输入的至少一个监控指标和至少一个监控维度，为获得用户自主配置的监控策略提供了快捷有效的实现方式。
S213：获取用户输入的确认指令，基于确认指令获取监控策略。
本实施例中，终端设备可获取用户输入的确认指令，在后台对用户输入的确认指令进行分析和执行，并根据确认指令获取用户配置的监控策略。其中，确认指令是指用户根据监控策略实际需要进行配置后输入的用于确认最终选择的指令。例如，如用户可在配置界面中输入签单量和业务员出勤率这两个监控指标，且监控维度中区域维度为“上海”和“深圳”，时间维度为“7月”用户通过配置界面输入签单量和业务员出勤率这两个监控指标，且监控维度中区域维度为“上海”和“深圳”，时间维度为“7月”后，再点击“提交”按钮而输入确认指令，以使终端设置接收到确认指令后，获取相应的监控策略。通过显示监控策略对应的配置界面，使得用户可以根据该界面实现自主配置的目的，为用户的自助配置提供了便捷操作。
S214：将监控策略保存在预设策略库中。
本实施例中，将步骤S213中获得的监控策略保存在预设策略库中，使获取到的监控策略作为历史监控策略保存在预设策略库中。其中，预设策略库是指用于保存通过自主配置获得的监控策略的数据库，即存放以配置好的监控策略的数据库。可以理解地，将步骤S211-S213中获取的监控策略保存在预设策略库中，使得用户可以根据以往相同需求直接获取相应的历史监控策略，省去了每次重复进行监控策略配置的工作，提高了获取监控策略的效率。
在一具体实施方式中，如图4所示，步骤S212中，获取用户在配置界面中输入的至少一个监控指标和至少一个监控维度，监控指标包括指标名称和指标范围，具体包括如下步骤：
S2121：获取用户在配置界面中输入的至少一个指标名称和至少一个监控维度。
本实施例中，通过用户在配置界面中输入的至少一个指标名称和至少一个监控维度，以使终端设备可获取包含至少一个指标名称和至少一个监控维度的配置请求，以使基于该配置请求进行后续处理。指标名称为监控指标中各个不同指标的具体名称，指标名称与业务数据相对应，如指标名称可以为销售量，也可以为销售量周同比等。监控维度与多维度业务数据相对应，用于限定所需监控的业务数据对应的维度。终端设备可通过获取用户在配置界面中输入的至少一个指标名称和至少一个监控维度，使得终端设备可基于至少一个监控维度查询获取需要监控的业务数据，并基于至少一个监控指标对应需要监控的业务数据进行监控。
S2122：基于指标名称，获取大数据平台中多维度的历史数据。
本实施例中，终端设备可获取用户在配置界面中输入的至少一个指标名称和至少一个监控维度，在HIVE中根据指标名称进行配对查找，从HIVE中获取指标名称对应的多维度的历史数据。其中，多维度的历史数据是指先前保存在HIVE中与选取的监控名称相同的多维度业务数据。若用户在配置界面中输入的指标名称为销售额、用户量和签单量等指标名称，则其多维度的历史数据为销售额、用户量和签单量等对应的所有多维度业务数据。
S2123：获取多变量线性回归模型，采用多变量线性回归模型对多维度的历史数据进行回归处理，获取与指标名称相对应的标准值。
其中，标准值是指标范围中的一个参考标准线，以使能够获取与指标名称相对应的指标范围。本实施例中，采用多变量线性回归模型对历史数据进行回归处理，该多变量线性回归模型为hθ(j)＝θ0+θ1x1+θ2x2+…+θnxn，其中，hθ(x)为假设函数，各个θ为输入值间的夹角向量，各个x为对应的特征，在上式中加入x0令x0＝1，则有hθ(x)＝θ0x0+θ1x1+θ2x2+…+θnxn＝θTX。其中，θ是行向量，行向量里包含了线性回归模型中的参数，X是样本特征矩阵。
为了获取准确性高的多变量线性回归模型线性回归，需要计算多变量线性回归模型中参数最优的θ，以获取最优解的多变量线性回归模型。其中，获取最优解的多变量线性回归模型具体包括如下步骤：
首先，采用特征缩放法对特征x进行归一化处理，以获取多变量线性回归模型中的最优θ。在处理多维特征(多变量)的问题时，一般情况下，参数θ的取值差距非常大，导致很多时候要进行大量的大数值的计算。因此需保证这些特征都具有相近的范围，使用特征缩放(Features Scaling)法将更好地帮助梯度下降算法更快的收敛。特征缩放法是尝试将所有特征的取值范围缩放到-1到1之间，其表达式为
Figure PCTCN2017108534-appb-000001
其中，xn代表第n个特征，μn代表所有特征的平均值，sn代表所有特征的标准差。
然后，构建代价函数(Cost Function)，若代价函数越小，说明线性回归地越好。该代价函数表达式如下：
Figure PCTCN2017108534-appb-000002
其中，x(i)表示向量x中的第i个元素，y(i)表示向量y中的第i个元素，hθ(x(i))表示已知的假设函数，m为训练集的数量。
接着，根据梯度下降法获取代价函数的最小值。梯度下降法通过先确定向下一步的步 伐大小，再任意给定一个初始值θ0，θ1，…，θn，确定一个向下的方向，并向下走预先规定的步伐，并更新θ0，θ1，…，θn，当下降的高度小于某个定义的值，则停止下降。该梯度下降法的表达式为
Figure PCTCN2017108534-appb-000003
其中α称为学习率(Learning rate)，用于决定梯度下降步伐大小。根据代价函数的最小值即对应获取多变量线性回归模型中最优的参数θ。
最后，根据代价函数的最小值，获取最优解(即理论上准确性最高)的多变量线性回归模型。可以理解地，将历史数据输入到多变量线性回归模型中进行回归处理，即可有效准确地获取相对应的标准值。
S2124：获取用户在配置界面中输入的上下限范围。
本实施例中，用户在步骤S223获取与指标名称相对应的标准值后，需获取用户在配置界面中输入的上下限范围。其中，上下限范围包括上限范围和下限范围。进一步地，若指标名称对应的标准值为销售额、签单量、业务员出勤率、接待客户数、宣传活动量、活跃用户数量和新注册用户量等具体业务数据对应的数量，则其上下限范围可以对应具体数量；若指标名称对应的标准值为日环比、周同比和月环比等具体业务数据对应的比值，则其上下限范围可根据实际情况自主设置为标准值的正负5％，正负10％等范围。可以理解地，上下限范围可根据需要对实际情形自主设置，提高了可控上下限范围的灵活性。
S2125：基于标准值和上下限范围，获取与指标名称相对应的指标范围。
本实施例中，以业务数据为销售额为例，若获取到销售额的标准值为50000，其设置的上下限范围为正负10000，则销售额对应的指标范围为40000-60000；以业务数据为销售额的月同比为例，若获取到销售额月同比的标准值为5％，其设置的上下限范围为正负1％，则销售额月同比对应的指标范围为4％-6％。可以理解地，通过标准值和上下限范围，获取与指标名称相对应的指标范围，使得获取的指标范围更接近实际范围，有利于提高设置监控策略的合理性和可行性，并提高基于该指标范围形成的监控指标进行业务数据监控的准确率。
在另一具体实施方式中，如图5所示，步骤S20中，获取用户配置的监控策略，监控策略包括至少一个监控指标和至少一个监控维度，具体还可以包括如下步骤：
S221：显示监控策略对应的配置界面。
本实施例中，金融机构的终端设备显示配置界面，以便用户通过该配置界面实现自助配置监控策略。其中，配置界面是指为用户提供监控策略配置的界面，用户可在配置界面 中输入所需配置的监控策略并进行确认后，即可完成监控策略配置。终端设备显示的配置界面，可使用户自动配置监控策略，节省了原本人工进行数据监控的时间成本，并实现了用户自助配置监控策略的功能，提高用户对业务数据进行监控的效率。
S222：获取用户输入的策略查询指令。
其中，策略查询指令是用于查询先前保存的历史监控策略的指令。终端设备获取到用户输入的策略查询指令后，可对接收到的策略查询指令进行分析处理并输出策略查询指令所指的已保存的所有历史监控策略，以获取该直接通过历史保存的监控策略，为用户实现监控指标的自助配置提供了便捷，避免每次都重新进行监控策略配置的过程，提高监控策略的获取效率。
S223：基于策略查询指令，获取预设策略库中所有历史监控策略。
本实施例中，终端设备根据用户输入的策略查询指令，将预设策略库中的所有历史监控策略通过后台输出指令在配置界面显示出来。其中，预设策略库是指用于保存通过自主配置获得的监控策略的数据库，即存放已配置好的监控策略的数据库。可以理解地，终端设备基于获取到的策略查询指令，获取预设策略库中所有历史监控策略，为用户提供了历史监控策略，跳过了每次重新配置的过程，为用户获取监控策略提供了捷径，提高了获取监控策略的效率。
S224：获取用户输入的策略选择指令，策略选择指令包括策略ID。
其中，策略选择指令是指用于从所有历史监控策略中选取其中一个策略的指令。策略ID是用于唯一识别监控策略的标识。策略选择指令中的策略ID是用于唯一识别用户从预设策略库中选择的历史监控策略的标识，以使基于该策略ID获取用户选择的历史监控策略。该策略选择指令为用户提供了多种选择方案，以使用户可根据实际需求选择已保存在预设策略库中的历史监控策略。
S225：获取与策略ID对应的监控策略。
本实施例中，终端设备根据用户输入的策略选择指令中的策略ID，通过分析匹配，找到预设策略库中与策略ID对应的历史监控策略，获取该历史监控策略并作为本次配置的监控策略。可以理解地，根据策略选择指令中的策略ID查询获取对应的监控策略的过程，操作过程简单方便，并可保证根据策略ID获取对应的监控策略的准确性和可行性，提高获取对应监控策略的效率。
S30：基于至少一个监控维度，从多维度业务数据中获取目标业务数据，判断目标业务数据是否全部符合至少一个监控指标。
可以理解地，步骤S30可概括为基于监控策略对目标业务数据进行检测判断的操作。其中，目标业务数据是多维度业务数据中与至少一个监控维度相对应的业务数据，该目标业务数据是指需进行数据监控的业务数据。判断目标业务数据是否全部符合至少一个监控指标，是指逐一检测目标业务数据，判断每一条目标业务数据是否满足至少一个监控指标。本实施例中，根据至少一个监控维度确定目标业务数据，以实现对目标业务数据进行针对性检测，避免数据量过多，而影响业务数据监控效率；并且，采用至少一个监控指标对目标业务数据进行监控，可提高数据监控的全面性，使得监控结果更可靠。
若用户配置的监控策略中的监控维度包括区域维度为“北京”、机构维度为“XX网点”和产品维度为“产险”，并基于这三个监控维度确定需要进行监控的目标业务数据。且用户配置的监控策略中监控指标为每月销售额100万-400万，而接待客户数为300-800个。则步骤S30中对所有目标业务数据进行逐一检测，并判断是否全部符合销售额和接待客户数的两个监控指标。
S40：若目标业务数据没有全部符合至少一个监控指标，则确定目标业务数据为异常数据，获取监控结果。
其中，异常数据是指目标业务数据没有全部符合至少一个监控指标的数据。本实施例中，若目标业务数据全部符合至少一个监控指标，则对应的目标业务数据为正常数据，可以不作处理；若目标业务数据没有全部符合至少一个监控指标，即目标业务数据至少不符合一个监控指标，则确定目标业务数据为异常数据，获取目标业务数据为异常数据的监控结果。具体地，若目标业务数据为异常数据时，可将异常数据提取出来，减轻了原本业务人员在大量目标业务数据中查找异常数据的负担。可以理解地，在判断目标业务数据是否为异常数据时，需将目标业务数据与至少一个监控指标进行判断，而不是基于单一监控指标进行判断，有利提高数据监控的全面性和监控效率，保证数据监控的准确率。
在一具体实施方式中，如图6所示，步骤S40中，若目标业务数据没有全部符合至少一个监控指标，则确定目标业务数据为异常数据，之后具体包括如下步骤：
S41：基于异常数据，查询预设数据库获取对应的异常原因。
其中，预设数据库是指业务人员根据以往异常业务数据情况的经验总结并存储在数据库中的多种异常原因的数据库。本实施例中，终端设备在确定目标业务数据为异常数据时，可根据异常数据的具体情况与预设数据库中的异常原因进行配对，以获取相对应的数据异常原因。如目标业务数据为签单量时，检测到目标业务数据对应的签单量过多，且超出对应指标范围5％时，则查询预设数据库中的异常原因是新政策的实施或节假日等因素。通过 查询预设数据库获取对应的异常原因，可以有效地结合以往历史经验得出的异常原因，为相关业务人员在进行异常数据分析时提供强有力的帮助。
S42：将异常数据和异常原因发送给监控邮箱。
其中，监控邮箱是预先设置的用于获取监控结果的邮箱。本实施例中，把检测出来的异常数据和通过查询预设数据库获取的对应异常原因发送给相关业务人员的监控邮箱，以使相关业务人员可了解存在异常的目标业务数据和可能导致该目标业务数据异常的异常原因，以便对异常数据进行更有效的监控，提高数据监控的效率，并且实现对目标业务数据为异常数据的监控结果进行快速处理的目的。
在一具体实施方式中，该目标业务数据监控方法中，步骤S30之前还包括：获取定时检测指令，定时检测指令包括触发时间点、监控邮箱和监控策略。
其中，定时检测指令是用户配置的用于控制终端设备定时执行基于监控策略对目标业务数据进行检测的指令。触发时间点是用于触发终端设备执行基于监控策略对目标业务数据进行检测的时间点。如可设置定时检测指令中触发时间点为每天晚上1点，以控制终端设备在每天晚上1点执行基于监控策略对目标业务数据进行检测的操作。触发时间点的设置，可使基于监控策略对目标业务数据进行检测按既定的时间执行。监控策略是步骤S20中的监控策略，即在步骤S20配置监控策略时，可同时配置触发时间点和监控邮箱，以形成定时检测指令。监控策略是执行定时检测指令所必备的，根据既定选择好的监控策略在触发时间点中执行基于监控策略对目标业务数据进行检测的操作。监控邮箱是预先设置的用于获取监控结果的邮箱，在基于监控策略对目标业务数据进行检测之后，可将监控结果发送给监控邮箱，以使相关人员可离线获取监控结果。该定时检测指令的执行能够实现对目标业务数据的有效处理，并且提高监控的效率，让终端设备自动执行定时检测指令，无需进行人工监控。
步骤S30具体包括：在触发时间点，执行基于监控策略对目标业务数据进行检测的操作。
本实施例中，终端设备可根据定时检测指令，在触发时间点，执行基于监控策略对目标业务数据进行检测的操作，即执行步骤S30的步骤。在执行基于监控策略对目标业务数据进行检测的操作时，以监控策略为基准，根据该监控策略的监控指标和监控维度，获取监控结果。可以理解地，在触发时间点，执行基于监控策略对目标业务数据进行检测的操作，可以对目标业务数据进行有效定时的检测，将检测到的目标业务数据情况展现出来。
步骤S40中，确定目标业务数据为异常数据，之后还包括：将异常数据发送给监控邮 箱。
本实施例中，在对目标业务数据进行检测判断之后，将监控得出的异常数据第一时间发送到监控邮箱，让相关业务人员能够第一时间接收到目标业务数据为异常数据的监控结果，有效提高处理异常数据的效率，相关业务人员可以根据监控邮箱获取的监控结果，对异常数据进行有效分析和处理。
本实施例所提供的业务数据监控方法中，通过获取大数据平台中多维度业务数据，在大数据平台对大量的原始数据进行快速有效地计算，以使更为便捷地获取多维度业务数据，并将数据分为多种维度并进行任意维度的组合，通过不同维度数据间的潜在联系，能够更加全面反映业务监控结果。监控策略包括至少一个监控指标和至少一个监控维度，该监控策略可由用户根据实际情况进行自助配置，配置过程简单快捷，并可实现至少一个维度和至少一个监控指标任意组合进行监控的效果，以保证最终获取的监控结果的可靠性和全面性。接着基于至少一个监控维度，从多维度业务数据中获取目标业务数据，将该目标业务数据作为需要进行检测的业务数据，提高业务数据监控的针对性，在一定程度上保证数据监控的准确率。并且，在目标业务数据没有全部符合至少一个监控指标，则确定目标业务数据为异常数据并获取监控结果，使得该监控结果可更准确地反映业务数据的异常情况，以实现基于至少一个监控指标对目标业务数据进行监控，使得业务数据监控更为精确和可靠。
应理解，上述实施例中各步骤的序号的大小并不意味着执行顺序的先后，各过程的执行顺序应以其功能和内在逻辑确定，而不应对本申请实施例的实施过程构成任何限定。
实施例2
图7示出与实施例1中业务数据监控方法一一对应的业务数据监控装置的原理框图。如图7所示，该业务数据监控装置包括业务数据获取模块10、监控策略获取模块20、业务数据检测模块30和业务数据结果获取模块40。其中，业务数据获取模块10、监控策略获取模块20、业务数据检测模块30和业务数据结果获取模块40的实现功能与实施例1中业务数据监控方法对应的步骤一一对应，为避免赘述，本实施例不一一详述。
业务数据获取模块10，用于获取大数据平台中多维度业务数据。
监控策略获取模块20，用于获取用户配置的监控策略，监控策略包括至少一个监控指标和至少一个监控维度。
业务数据检测模块30，用于基于至少一个监控维度，从多维度业务数据中获取目标业务数据，判断目标业务数据是否全部符合至少一个监控指标。
业务数据结果获取模块40，用于若目标业务数据没有全部符合至少一个监控指标，则确定目标业务数据为异常数据，获取监控结果。
其中，业务数据获取模块10包括原始数据获取单元11、原始数据存储单元12和原始数据统计单元13。
原始数据获取单元11，用于采用Hadoop大数据平台采集原始数据。
原始数据存储单元12，用于将原始数据存储在HIVE中。
原始数据统计单元13，用于采用SQL语句对HIVE中原始数据进行多维度统计，获取多维度业务数据。
其中，监控策略获取模块20包括第一配置界面显示单元21、监控指标获取单元22、确认指令获取单元23、第二配置界面显示单元24、策略查询指令获取单元25、预设监控策略获取单元26、策略选择指令获取单元27和监控策略获取单元28。
第一配置界面显示单元21，用于显示监控策略对应的配置界面。
监控指标获取单元22，用于获取用户在配置界面中输入的至少一个监控指标和至少一个监控维度，监控指标包括指标名称和指标范围。
确认指令获取单元23，用于获取用户输入的确认指令，基于确认指令获取监控策略。
第二配置界面显示单元24，用于显示监控策略对应的配置界面。
策略查询指令获取单元25，用于获取用户输入的策略查询指令。
预设监控策略获取单元26，用于基于策略查询指令，获取预设策略库中所有历史监控策略。
策略选择指令获取单元27，用于获取用户输入的策略选择指令，策略选择指令包括策略ID。
监控策略获取单元28，用于获取与策略ID对应的监控策略。
其中，监控指标获取单元22包括指标名称获取子单元221、历史数据获取子单元222、标准值获取子单元223、上下限获取子单元224和指标范围获取子单元225。
指标名称获取子单元221，用于获取用户在配置界面中输入的至少一个指标名称和至少一个监控维度。
历史数据获取子单元222，用于基于指标名称，获取大数据平台中多维度的历史数据。
标准值获取子单元223，用于采用线性回归算法对历史数据进行回归处理，获取与指标名称相对应的标准值。
上下限获取子单元224，用于获取用户在配置界面中输入的上下限范围。
指标范围获取子单元225，用于基于标准值和上下限范围，获取与指标名称相对应的指标范围。
其中，业务数据检测模块30包括定时指令获取单元31、业务数据检测单元32和异常数据确定单元33。
定时指令获取单元31，用于获取定时检测指令，定时检测指令包括触发时间点、监控邮箱和监控策略。
业务数据检测单元32，用于在触发时间点，执行基于监控策略对目标业务数据进行判断的操作。
异常数据确定单元33，用于确定目标业务数据为异常数据，之后还包括：将异常数据发送给监控邮箱。
其中，业务数据结果获取模块40包括异常原因获取单元41和监控邮箱发送单元42。
异常原因获取单元41，用于基于异常数据，查询预设数据库获取对应的异常原因。
监控邮箱发送单元42，用于将异常数据和异常原因发送给监控邮箱。
本实施例所提供的业务数据监控装置中，业务数据获取模块10，用于获取大数据平台中多维度业务数据，该模块在大数据平台对大量的原始数据进行快速有效地计算，以使更为便捷地获取多维度业务数据，并将数据分为多种维度并进行任意维度的组合，通过不同维度数据间的潜在联系，实现更为全面反映业务监控结果的效果。监控策略获取模块20，用于获取用户配置的监控策略，监控策略包括至少一个监控指标和至少一个监控维度，通过配置监控策略包括的至少一个监控指标和至少一个监控维度，可实现获取用户配置的监控策略的目的。并且通过不同维度的业务数据之间的联系，提高了监控结果的可靠性和全面性。业务数据检测模块30，用于基于至少一个监控维度，从多维度业务数据中获取目标业务数据，判断目标业务数据是否全部符合至少一个监控指标，通过该检测装置，能够简单有效地对目标业务数据进行检测，并且能够获得更为准确的目标业务数据监控结果，提高了监控的正确率。业务数据结果获取模块40，用于若目标业务数据没有全部符合至少一个监控指标，则确定目标业务数据为异常数据，获取监控结果，该模块对目标业务数据的判断结果进行处理的过程，实现了将异常数据提取出来作为监控结果的功能，为业务人员的进一步监控提供了异常数据源，大大减轻了原本业务人员在大量数据中查找异常数据的负担，并保证监控结果的正确性。
实施例3
本实施例提供一计算机可读存储介质，该计算机可读存储介质上存储有计算机可读 指令，该计算机可读指令被处理器执行时实现实施例1中业务数据监控方法，为避免重复，这里不再赘述。或者，该计算机可读指令被处理器执行时实现实施例2中业务数据监控装置中各模块/单元的功能，为避免重复，这里不再赘述。
实施例4
图8是本实施例中终端设备的示意图。如图8所示，终端设备80包括处理器81、存储器82以及存储在存储器82中并可在处理器81上运行的计算机可读指令83。处理器81执行计算机可读指令83时实现实施例1中业务数据监控方法的各个步骤，例如图1所示的步骤S10、S20、S30和S40。或者，处理器81执行计算机可读指令83时实现实施例2中业务数据监控装置各模块/单元的功能，如图7所示业务数据获取模块10、监控策略获取模块20、业务数据检测模块30和业务数据结果获取模块40的功能。
示例性的，计算机可读指令83可以被分割成一个或多个模块/单元，一个或者多个模块/单元被存储在存储器82中，并由处理器81执行，以完成本申请的数据处理过程。一个或多个模块/单元可以是能够完成特定功能的一系列计算机可读指令段，该指令段用于描述计算机可读指令83在终端设备80中的执行过程。例如，计算机可读指令80可被分割成实施例2中的业务数据获取模块10、监控策略获取模块20、业务数据检测模块30和业务数据结果获取模块40，各模块的具体功能如实施例2所示，为避免重复，此处不一一赘述。
终端设备80可以是桌上型计算机、笔记本、掌上电脑及云端服务器等计算设备。终端设备可包括，但不仅限于，处理器81、存储器82。本领域技术人员可以理解，图8仅仅是终端设备80的示例，并不构成对终端设备80的限定，可以包括比图示更多或更少的部件，或者组合某些部件，或者不同的部件，例如终端设备还可以包括输入输出设备、网络接入设备、总线等。
所称处理器81可以是中央处理单元(Central Processing Unit，CPU)，还可以是其他通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field-Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件等。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
存储器82可以是终端设备80的内部存储单元，例如终端设备80的硬盘或内存。存储器82也可以是终端设备80的外部存储设备，例如终端设备80上配备的插接式硬盘， 智能存储卡(Smart Media Card，SMC)，安全数字(Secure Digital，SD)卡，闪存卡(Flash Card)等。进一步地，存储器82还可以既包括终端设备80的内部存储单元也包括外部存储设备。存储器82用于存储计算机可读指令以及终端设备所需的其他程序和数据。存储器82还可以用于暂时地存储已经输出或者将要输出的数据。
所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，仅以上述各功能单元、模块的划分进行举例说明，实际应用中，可以根据需要而将上述功能分配由不同的功能单元、模块完成，即将所述装置的内部结构划分成不同的功能单元或模块，以完成以上描述的全部或者部分功能。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用软件功能单元的形式实现。
所述集成的模块/单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请实现上述实施例方法中的全部或部分流程，也可以通过计算机可读指令来指令相关的硬件来完成，所述的计算机可读指令可存储于一计算机可读存储介质中，该计算机可读指令在被处理器执行时，可实现上述各个方法实施例的步骤。其中，所述计算机可读指令包括计算机可读指令代码，所述计算机可读指令代码可以为源代码形式、对象代码形式、可执行文件或某些中间形式等。所述计算机可读介质可以包括：能够携带所述计算机可读指令代码的任何实体或装置、记录介质、U盘、移动硬盘、磁碟、光盘、计算机存储器、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、电载波信号、电信信号以及软件分发介质等。需要说明的是，所述计算机可读介质包含的内容可以根据司法管辖区内立法和专利实践的要求进行适当的增减，例如在某些司法管辖区，根据立法和专利实践，计算机可读介质不包括是电载波信号和电信信号。
以上所述实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围，均应包含在本申请的保护范围之内。

Claims (20)

  1. 一种业务数据监控方法，其特征在于，包括：
    获取大数据平台中多维度业务数据；
    获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度；
    基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标；
    若所述目标业务数据没有全部符合至少一个所述监控指标，则确定所述目标业务数据为异常数据，获取监控结果。
  2. 根据权利要求1所述的业务数据监控方法，其特征在于，所述获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度，包括：
    显示所述监控策略对应的配置界面；
    获取用户在所述配置界面中输入的至少一个所述监控指标和至少一个监控维度，所述监控指标包括指标名称和指标范围；
    获取用户输入的确认指令，基于所述确认指令获取所述监控策略；
    所述获取用户在所述配置界面中输入至少一个所述监控指标和至少一个监控维度，所述监控指标包括指标名称和指标范围，包括：
    获取用户在所述配置界面中输入的至少一个指标名称和至少一个监控维度；
    基于所述指标名称，获取大数据平台中多维度的历史数据；
    获取多变量线性回归模型，采用多变量线性回归模型对所述多维度的历史数据进行回归处理，获取与所述指标名称相对应的标准值；其中，所述多变量线性回归模型为hθ(x)＝θ0+θ1x1+θ2x2+···+θnxn，hθ(x)为假设函数，各个θ为输入值间的夹角向量，各个x为对应的特征；
    获取用户在所述配置界面中输入的上下限范围；
    基于所述标准值和所述上下限范围，获取与所述指标名称相对应的所述指标范围。
  3. 根据权利要求2所述的业务数据监控方法，其特征在于，所述获取多变量线性回归模型，包括：
    采用特征缩放法对特征进行归一化处理；所述特征缩放法的表达式为
    Figure PCTCN2017108534-appb-100001
    其 中，xn为第n个特征，μn为平均值，sn为标准差；
    构建代价函数，所述代价函数为
    Figure PCTCN2017108534-appb-100002
    其中，x(i)为向量x中的第i个元素，y(i)为向量y中的第i个元素，hθ(x(i))为已知的假设函数，m为训练集的数量；
    根据梯度下降法获取所述代价函数的最小值，其中，所述梯度下降法的表达式为
    Figure PCTCN2017108534-appb-100003
    α为学习率；
    根据所述代价函数的最小值获取所述多变量线性回归模型。
  4. 根据权利要求1所述的业务数据监控方法，其特征在于，所述获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度，包括：
    显示所述监控策略对应的配置界面；
    获取用户输入的策略查询指令；
    基于所述策略查询指令，获取预设策略库中所有历史监控策略；
    获取用户输入的策略选择指令，所述策略选择指令包括策略ID；
    获取与所述策略ID对应的监控策略。
  5. 根据权利要求1所述的业务数据监控方法，其特征在于，所述基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标，之前还包括：获取定时检测指令，所述定时检测指令包括触发时间点、监控邮箱和所述监控策略；
    所述判断所述目标业务数据是否全部符合至少一个所述监控指标，包括：在所述触发时间点，执行基于所述监控策略对所述目标业务数据进行检测的操作；
    所述确定所述目标业务数据为异常数据，之后还包括：将所述异常数据发送给所述监控邮箱。
  6. 根据权利要求5所述的业务数据监控方法，其特征在于，所述若所述目标业务数据没有全部符合至少一个所述监控指标，则确定所述目标业务数据为异常数据，获取监控结果，包括：
    基于所述异常数据，查询预设数据库获取对应的异常原因；
    将所述异常数据和所述异常原因发送给所述监控邮箱。
  7. 根据权利要求1所述的业务数据监控方法，其特征在于，所述获取大数据平台中多 维度业务数据，包括：
    采用Hadoop大数据平台采集原始数据；
    将所述原始数据存储在HIVE中；
    采用SQL语句对所述HIVE中所述原始数据进行多维度统计，获取所述多维度业务数据。
  8. 一种业务数据监控装置，其特征在于，包括：
    业务数据获取模块，用于获取大数据平台中多维度业务数据；
    监控策略获取模块，用于获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度；
    业务数据检测模块，用于基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，检测所述目标业务数据是否全部符合至少一个所述监控指标；
    业务数据结果获取模块，用于若所述目标业务数据没有全部符合至少一个所述监控指标，则确定所述目标业务数据为异常数据，获取监控结果。
  9. 一种终端设备，包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机可读指令，其特征在于，所述处理器执行所述计算机可读指令时实现如下步骤：
    获取大数据平台中多维度业务数据；
    获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度；
    基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标；
    若所述目标业务数据没有全部符合至少一个所述监控指标，则确定所述目标业务数据为异常数据，获取监控结果。
  10. 根据权利要求9所述的终端设备，其特征在于，所述获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度，包括：
    显示所述监控策略对应的配置界面；
    获取用户在所述配置界面中输入的至少一个所述监控指标和至少一个监控维度，所述监控指标包括指标名称和指标范围；
    获取用户输入的确认指令，基于所述确认指令获取所述监控策略；
    所述获取用户在所述配置界面中输入至少一个所述监控指标和至少一个监控维度，所 述监控指标包括指标名称和指标范围，包括：
    获取用户在所述配置界面中输入的至少一个指标名称和至少一个监控维度；
    基于所述指标名称，获取大数据平台中多维度的历史数据；
    获取多变量线性回归模型，采用多变量线性回归模型对所述多维度的历史数据进行回归处理，获取与所述指标名称相对应的标准值；其中，所述多变量线性回归模型为hθ(x)＝θ0+θ1x1+θ2x2+···+θnxn，hθ(x)为假设函数，各个θ为输入值间的夹角向量，各个x为对应的特征；
    获取用户在所述配置界面中输入的上下限范围；
    基于所述标准值和所述上下限范围，获取与所述指标名称相对应的所述指标范围。
  11. 根据权利要求10所述的终端设备，其特征在于，所述获取多变量线性回归模型，包括：
    采用特征缩放法对特征进行归一化处理；所述特征缩放法的表达式为
    Figure PCTCN2017108534-appb-100004
    其中，xn为第n个特征，μn为平均值，sn为标准差；
    构建代价函数，所述代价函数为
    Figure PCTCN2017108534-appb-100005
    其中，x(i)为向量x中的第i个元素，y(i)为向量y中的第i个元素，hθ(x(i))为已知的假设函数，m为训练集的数量；
    根据梯度下降法获取所述代价函数的最小值，其中，所述梯度下降法的表达式为
    Figure PCTCN2017108534-appb-100006
    α为学习率；
    根据所述代价函数的最小值获取所述多变量线性回归模型。
  12. 根据权利要求9所述的终端设备，其特征在于，所述获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度，包括：
    显示所述监控策略对应的配置界面；
    获取用户输入的策略查询指令；
    基于所述策略查询指令，获取预设策略库中所有历史监控策略；
    获取用户输入的策略选择指令，所述策略选择指令包括策略ID；
    获取与所述策略ID对应的监控策略。
  13. 根据权利要求9所述的终端设备，其特征在于，所述基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标，之前还包括：获取定时检测指令，所述定时检测指令包括触发时间点、监控邮箱和所述监控策略；
    所述判断所述目标业务数据是否全部符合至少一个所述监控指标，包括：在所述触发时间点，执行基于所述监控策略对所述目标业务数据进行检测的操作；
    所述确定所述目标业务数据为异常数据，之后还包括：将所述异常数据发送给所述监控邮箱。
  14. 根据权利要求9所述的终端设备，其特征在于，所述获取大数据平台中多维度业务数据，包括：
    采用Hadoop大数据平台采集原始数据；
    将所述原始数据存储在HIVE中；
    采用SQL语句对所述HIVE中所述原始数据进行多维度统计，获取所述多维度业务数据。
  15. 一种计算机可读存储介质，所述计算机可读存储介质存储有计算机可读指令，其特征在于，所述计算机可读指令被处理器执行时实现如下步骤：
    获取大数据平台中多维度业务数据；
    获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度；
    基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标；
    若所述目标业务数据没有全部符合至少一个所述监控指标，则确定所述目标业务数据为异常数据，获取监控结果。
  16. 根据权利要求15所述的计算机可读存储介质，其特征在于，所述获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度，包括：
    显示所述监控策略对应的配置界面；
    获取用户在所述配置界面中输入的至少一个所述监控指标和至少一个监控维度，所述监控指标包括指标名称和指标范围；
    获取用户输入的确认指令，基于所述确认指令获取所述监控策略；
    所述获取用户在所述配置界面中输入至少一个所述监控指标和至少一个监控维度，所 述监控指标包括指标名称和指标范围，包括：
    获取用户在所述配置界面中输入的至少一个指标名称和至少一个监控维度；
    基于所述指标名称，获取大数据平台中多维度的历史数据；
    获取多变量线性回归模型，采用多变量线性回归模型对所述多维度的历史数据进行回归处理，获取与所述指标名称相对应的标准值；其中，所述多变量线性回归模型为hθ(x)＝θ0+θ1x1+θ2x2+···+θnxn，hθ(x)为假设函数，各个θ为输入值间的夹角向量，各个x为对应的特征；
    获取用户在所述配置界面中输入的上下限范围；
    基于所述标准值和所述上下限范围，获取与所述指标名称相对应的所述指标范围。
  17. 根据权利要求16所述的计算机可读存储介质，其特征在于，所述获取多变量线性回归模型，包括：
    采用特征缩放法对特征进行归一化处理；所述特征缩放法的表达式为
    Figure PCTCN2017108534-appb-100007
    其中，xn为第n个特征，μn为平均值，sn为标准差；
    构建代价函数，所述代价函数为
    Figure PCTCN2017108534-appb-100008
    其中，x(i)为向量x中的第i个元素，y(i)为向量y中的第i个元素，hθ(x(i))为已知的假设函数，m为训练集的数量；
    根据梯度下降法获取所述代价函数的最小值，其中，所述梯度下降法的表达式为
    Figure PCTCN2017108534-appb-100009
    α为学习率；
    根据所述代价函数的最小值获取所述多变量线性回归模型。
  18. 根据权利要求15所述的计算机可读存储介质，其特征在于，所述获取用户配置的监控策略，所述监控策略包括至少一个监控指标和至少一个监控维度，包括：
    显示所述监控策略对应的配置界面；
    获取用户输入的策略查询指令；
    基于所述策略查询指令，获取预设策略库中所有历史监控策略；
    获取用户输入的策略选择指令，所述策略选择指令包括策略ID；
    获取与所述策略ID对应的监控策略。
  19. 根据权利要求15所述的计算机可读存储介质，其特征在于，所述基于至少一个监控维度，从所述多维度业务数据中获取目标业务数据，判断所述目标业务数据是否全部符合至少一个所述监控指标，之前还包括：获取定时检测指令，所述定时检测指令包括触发时间点、监控邮箱和所述监控策略；
    所述判断所述目标业务数据是否全部符合至少一个所述监控指标，包括：在所述触发时间点，执行基于所述监控策略对所述目标业务数据进行检测的操作；
    所述确定所述目标业务数据为异常数据，之后还包括：将所述异常数据发送给所述监控邮箱。
  20. 根据权利要求15所述的计算机可读存储介质，其特征在于，所述获取大数据平台中多维度业务数据，包括：
    采用Hadoop大数据平台采集原始数据；
    将所述原始数据存储在HIVE中；
    采用SQL语句对所述HIVE中所述原始数据进行多维度统计，获取所述多维度业务数据。
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