WO2019056681A1 - 数据实时监控方法、装置、终端设备及存储介质 - Google Patents

数据实时监控方法、装置、终端设备及存储介质 Download PDF

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WO2019056681A1
WO2019056681A1 PCT/CN2018/073628 CN2018073628W WO2019056681A1 WO 2019056681 A1 WO2019056681 A1 WO 2019056681A1 CN 2018073628 W CN2018073628 W CN 2018073628W WO 2019056681 A1 WO2019056681 A1 WO 2019056681A1
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data
service data
monitoring
historical
obtaining
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PCT/CN2018/073628
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English (en)
French (fr)
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黄度新
金鑫
张川
赵媛媛
温善安
梁永健
王翼
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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
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • 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
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0203—Market surveys; Market polls

Definitions

  • the present application relates to the field of data monitoring, and in particular, to a data real-time monitoring method, device, terminal device, and storage medium.
  • enterprises analyze big data by analyzing historical business data, so as to compare the current business data collected by enterprises in real time, so as to analyze The business development trend of the enterprise.
  • the current business data of the collecting enterprise in any preset time period is compared with the historical business data of the corresponding phase in the previous year to determine whether the current business data is abnormal; or the enterprise is in any preset time period.
  • the current service data is compared with the historical service data of the adjacent time period to determine whether the current service data has an abnormality.
  • historical service data and current service data of a time period need to be collected, so that there is hysteresis in the data monitoring process, and real-time monitoring of current business data of any day cannot be realized.
  • the embodiment of the present application provides a data real-time monitoring method, device, terminal device, and storage medium, so as to solve the problem that real-time monitoring of service data cannot be realized.
  • the embodiment of the present application provides a real-time data monitoring method, including:
  • reference service data is specific historical service data corresponding to the monitoring indicator within the preset time period before the current time
  • the monitoring result is obtained based on the current service data and the historical baseline value.
  • the embodiment of the present application provides a real-time data monitoring device, including:
  • a data monitoring instruction acquiring module configured to acquire a data monitoring instruction, where the data monitoring instruction includes a current time, a preset time period, and a monitoring indicator;
  • the reference service data obtaining module is configured to acquire reference service data according to the data monitoring instruction, where the reference service data is specific historical service data corresponding to the monitoring indicator within the preset time limit before the current time;
  • a historical baseline value obtaining module configured to acquire a historical baseline value based on the reference service data
  • a current service data obtaining module configured to acquire current service data corresponding to the monitoring indicator
  • the monitoring result obtaining module is configured to obtain a monitoring result based on the current service data and the historical baseline value.
  • 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:
  • reference service data is specific historical service data corresponding to the monitoring indicator within the preset time period before the current time
  • the monitoring result is obtained based on the current service data and the historical baseline value.
  • 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:
  • reference service data is specific historical service data corresponding to the monitoring indicator within the preset time period before the current time
  • the monitoring result is obtained based on the current service data and the historical baseline value.
  • the reference service data is obtained by using a data monitoring instruction, where the reference service data includes historical service data carrying time series and time-limited and limited according to a preset time limit. Therefore, the timing of the acquired reference service data is increased, and the real-time performance of the data monitoring is improved.
  • the historical baseline value is obtained based on the reference service data, so that the obtained historical baseline value as a reference basis is more objective.
  • the monitoring result is obtained based on the historical baseline value and the current business data, thereby real-time and objective monitoring of the current business data, ensuring the objectivity and real-time performance of the monitoring result, and improving the accuracy of the data monitoring.
  • Embodiment 1 is a flow chart of a method for real-time monitoring of data in Embodiment 1.
  • FIG. 2 is a specific flow chart of step S20 of FIG. 1.
  • FIG. 3 is a specific flowchart of step S30 in FIG. 1.
  • FIG. 4 is a specific flow chart of step S32 of Figure 3.
  • FIG. 5 is a specific flowchart of step S50 in FIG. 1.
  • FIG. 6 is a schematic block diagram of a real-time data monitoring device in Embodiment 2.
  • Figure 7 is a schematic diagram of a terminal device in Embodiment 4.
  • FIG. 1 is a flow chart showing a method for real-time monitoring of data in the embodiment.
  • the data real-time monitoring method is applied to financial institutions such as banks, securities, insurance, or other terminal equipment, and is used for real-time monitoring of data formed by banks, securities, insurance, and other financial institutions or other institutions during production and operation. To determine if the data is abnormal. As shown in FIG. 1, the data real-time monitoring method includes the following steps:
  • S10 Obtain a data monitoring instruction, where the data monitoring instruction includes a current time, a preset period, and a monitoring indicator.
  • the data monitoring instruction refers to an instruction for controlling the terminal to perform data monitoring.
  • the current time is the system time of the terminal device.
  • the preset period is a predetermined period for dividing the business data with the timing state, and the preset period may be 1 month, 2 months, half a year or other period value.
  • the monitoring indicator refers to the indicator of the business data to be monitored, and the monitoring indicator may correspond to a specific service data, which includes but is not limited to data such as a database connection pool, a transaction amount, a transaction number, and a transaction response time. After obtaining the data monitoring instruction, the terminal device can specifically monitor the specific service data.
  • S20 Acquire reference service data based on the data monitoring instruction, where the reference service data is historical service data corresponding to the monitoring indicator within a preset period before the current time.
  • historical business data is business data formed by financial institutions or other institutions in the production and operation process.
  • the historical service data includes, but is not limited to, a database connection pool, a transaction amount, a transaction number, and a transaction response time, which are stored in databases such as Oracle, Hive, Hadoop, and Hbase.
  • the terminal device may acquire, according to the data monitoring instruction, historical service data corresponding to the monitoring indicator within a preset period before the current time, as the reference service data.
  • the reference service data is specific to the historical service data corresponding to the monitoring indicator within the preset time limit before the current time, and the reference service data is determined by using the time series manner in this embodiment. That is, if the preset period is T and the current time is the first day of the forecast, the historical service data in the past T time before the first day of the prediction is used as the reference service data; if the current time is the second day of the prediction, The historical business data of the first day of the forecast and the past T-1 days is used as the reference business data. If the default period is 1 month, the current time is July 15th, and the day of July 15th is the first day of the forecast, then the reference business data is 1 month from June 15th to July 14th. Historical business data. This "rolling" method determines the reference service data, which makes the reference service data more time-series, makes its reference meaning stronger, and is closer to the current market conditions, which is beneficial to increase the accuracy of the monitoring results.
  • step S20 acquiring reference service data based on the data monitoring instruction specifically includes the following steps:
  • the corresponding monitoring indicators in each data monitoring instruction include one or more, and the monitoring indicators correspond to specific service data such as a database connection pool, a transaction amount, a transaction number, and a transaction response time.
  • Historical business data is the business data of the database connection pool, transaction amount, transaction number and transaction response time formed by financial institutions or other institutions during the production and operation process.
  • the historical business data is stored in Oracle, Hive, Hadoop in order according to the formation time. , Hbase and other databases.
  • the terminal device uses the database query instruction based on the monitoring indicator in the data monitoring instruction, and uses the monitoring indicator as a query field to obtain all historical service data corresponding to the monitoring indicator.
  • all the historical business data corresponding to the monitoring indicator acquired in step S21 is all the transaction amount data corresponding to the transaction amount, and is selected from the database transaction amount.
  • the transaction amount data to be monitored to ensure the relevance of data monitoring and improve the processing efficiency of data monitoring.
  • the current time is the system time of the terminal device
  • the preset time period is a preset time period for dividing the service data with the time-series state, and the part selected from all the historical service data may be determined according to the current time and the preset time limit.
  • Historical business data is used as reference business data. Taking all the transaction amount data obtained above as an example, if the current time is July 15 and the default period is 1 month, the obtained reference service data is within the month from June 15 to July 14. Transaction amount data.
  • processing historical service data refers to grouping historical business data according to unit time, acquiring multiple data groups; calculating and obtaining data average values of each data group, and storing the obtained data average value in Oracle , Hive, Hadoop, Hbase and other databases, as the corresponding historical business data.
  • processing historical service data refers to grouping historical business data according to unit time, acquiring multiple data groups; calculating and obtaining data average values of each data group, and storing the obtained data average value in Oracle , Hive, Hadoop, Hbase and other databases, as the corresponding historical business data.
  • a financial institution or other institution may form a historical business data related to the transaction amount every minute during the production and operation process, and when the historical business data is processed, the historical business data is preset.
  • the unit time (such as 10min) is divided into multiple data groups, and then the average value of each data group is calculated, and the average value of the data is updated to new historical business data, which is beneficial for subsequent use of the historical business data for data monitoring. Save calculations and improve processing efficiency.
  • the transaction time period is 12 hours before and after, and July 15 is the first day of the forecast, and the preset time period is 1 month.
  • the monitoring indicator is the transaction amount
  • the historical transaction amount data is grouped according to the unit time of 5min, forming multiple data groups, then
  • 43200 indicates the historical business data required for the current minute of the transaction amount indicator
  • the data group obtained after the calculation is performed by calculating the average value of the historical business data in each data group, and using the average value as the historical business data corresponding to the data group, thereby facilitating the calculation of subsequent calculation based on the historical business data. the amount.
  • the historical service data may be structured data, semi-structured data, or unstructured data.
  • the structured data can be stored in a two-dimensional table in any database in the relational database and the NoSQL database.
  • the semi-structured and unstructured data can be stored in the server in the form of log files.
  • the log file includes, but is not limited to, a transaction message, a transaction record, a response time, a database connection pool, and the like, and the log file is formed by the user accessing the website through the terminal device (including a mobile phone, a PC, a tablet, etc.).
  • Log file The data stored in the database can be obtained by using any method of Open Database Connectivity (ODBC) or Java Data Base Connectivity (JDBC).
  • ODBC Open Database Connectivity
  • JDBC Java Data Base Connectivity
  • the log file can be obtained by using any log collection tool. And use the log collection tool to convert the semi-structured data or the unstructured data into structured data, and then store it in any database in the relational database and the NoSQL database, so that the database query statement can be directly used for subsequent utilization. Simplify the process of obtaining reference business data.
  • the historical service data corresponding to the preset time period before the current time is selected in the reference service data acquisition process, which simplifies the complexity of processing all historical business data, and ensures the real-time performance of the historical data corresponding to the monitoring index.
  • the historical baseline value is a numerical range formed based on the reference service data, and the numerical range is a reference basis for evaluating whether other data corresponding to the monitoring indicator is abnormal. Since the historical baseline value is based on historical business data before the current time formed by the financial institution or other institutions in the production and operation process and corresponding to the preset period, it has certain objectivity and real-time, and the historical baseline value is used as a reference. It can more objectively understand the development trend of financial institutions or other institutions, and thus decide whether decision adjustment is needed to optimize production and operation activities.
  • Reference business data includes, but is not limited to, historical business data including database connection pools, transaction amounts, transaction counts, and transaction response times.
  • the baseline historical value can be calculated by historical business data within a preset period of one week, half a month, one month, one year or more before the current time. Due to the inconsistent normal trading hours of different industries, the preset deadlines for different industry to determine reference business data are different. For example, the stock trading period of the financial industry is a normal working day; while the clothing sales period of the clothing industry is mainly for non-working days; some products have a large amount of trading in the summer; some products have a large amount of trading in winter, in order to ensure the real-time reference data. Sexuality, generally select historical business data for the last half month or month.
  • step S30 acquiring a historical baseline value based on the reference service data specifically includes the following steps:
  • the average value is specifically the arithmetic mean value, and the arithmetic mean value is the quotient of the sum of all the data and the total number of data, and the average value can collectively present the overall state of the variable.
  • the standard deviation is the average of the distances from which the data deviate from the mean. It is a measure of the degree of dispersion of the average of a set of data. A large standard deviation represents the difference between the majority of the values in the data and the mean of the data. Large; a small standard deviation, which means that most of the values in the data are closer to the average of the data, and the standard deviation can be used as a measure of uncertainty.
  • the reference business data is calculated by the mean value and the standard deviation, and the calculation formula of the average value ⁇ is as follows: Where n is the number of reference service data, and the value of i is 1 to n, and x i is any one of the reference service data.
  • the formula for calculating the standard deviation ⁇ is as follows: N is the number of reference service data, and the value of i is 1-N. x i is any data in the reference service data, and ⁇ is the average value of the reference service data.
  • the historical baseline value is a numerical range formed based on the average value and the standard deviation corresponding to the reference service data, and the numerical range is a reference basis for evaluating whether other data corresponding to the monitoring index is abnormal.
  • the historical baseline value is divided into at least two baseline ranges, and each baseline range corresponds to a data state.
  • the baseline range includes an upper limit value and a lower limit value
  • the upper limit value is the maximum value of the baseline range
  • the lower limit value is the minimum value of the baseline range.
  • the data status is the status corresponding to the data. According to the status of the data, the status can be divided into the normal status, the normal alarm status, the important alarm status, and the critical alarm status.
  • the data is used to evaluate whether the data is normal or stable.
  • step S32 acquiring the historical baseline value based on the average value and the standard deviation specifically includes the following steps:
  • the standard deviation coefficient is a positive number, and may be a positive integer or a positive fraction. By multiplying the standard deviation by the standard deviation coefficient, the corresponding standard deviation product can be obtained. In this embodiment, if the standard deviation coefficient is k, the standard deviation product obtained in step S321 is k* ⁇ .
  • S322 Determine an upper limit value of a baseline range based on the sum of the products of the mean and the standard deviation.
  • the upper limit of the baseline range is the maximum of the baseline range, and the upper limit of the baseline range depends on the mean and standard deviation of the corresponding historical business data, which is calculated as the sum of the product of the mean and the standard deviation. For example, if the average value of the transaction amount data is ⁇ , the standard deviation is ⁇ , and the standard deviation coefficient is k, the upper limit value of the baseline value corresponding to the transaction amount data is ⁇ +k* ⁇ .
  • the upper limit of each baseline range divides the historical baseline value into two baseline ranges, corresponding to different data states.
  • S323 Determine a lower limit of a baseline range based on the difference between the product of the mean and the standard deviation.
  • the lower limit of the baseline range is the minimum of the baseline range, and the lower limit of the baseline range is dependent on the mean and standard deviation of the corresponding historical business data, calculated as the difference between the product of the mean and the standard deviation. For example, if the average value of the transaction amount data is ⁇ , the standard deviation is ⁇ , and the standard deviation coefficient is k, the lower limit value of the baseline value corresponding to the transaction amount data is ⁇ -k* ⁇ .
  • the lower limit of each baseline range divides the historical baseline value into two baseline ranges, corresponding to different data states.
  • the standard deviation coefficient is k
  • the upper limit value of any of the historical baseline values corresponding to the reference service data is ⁇ +k* ⁇ .
  • the lower limit is ⁇ -k* ⁇ . Since the baseline range with a large standard deviation coefficient includes a baseline range with a small standard deviation coefficient, a baseline with a smaller standard deviation coefficient is required to more clearly show the data state corresponding to different baseline ranges. The range is removed from the baseline range where the standard deviation is large to determine that any baseline range is: [[ ⁇ -k* ⁇ , ⁇ -(k-1)* ⁇ ], [ ⁇ +(k-1)* ⁇ , ⁇ +k* ⁇ ]].
  • each baseline range corresponds to a data state
  • the data state includes, but is not limited to, a normal state, a normal alarm state, an important alarm state, and a severe alarm state.
  • the baseline range is: [[ ⁇ - ⁇ , ⁇ ], [ ⁇ , ⁇ + ⁇ ]], ie [ ⁇ - ⁇ , ⁇ + ⁇ ], which is closest to the reference service data.
  • the average value determines that the data state corresponding to the baseline range is a normal state.
  • the baseline range is: [[ ⁇ -2 ⁇ , ⁇ - ⁇ ], [ ⁇ + ⁇ , ⁇ +2 ⁇ ]]]
  • the baseline range is close to the normal state
  • the data state corresponding to the baseline range is determined to be a normal alarm. status.
  • the baseline range is: [[ ⁇ -3 ⁇ , ⁇ -2 ⁇ ], [ ⁇ +2 ⁇ , ⁇ +3 ⁇ ]], the baseline range is close to the normal state, and the data state corresponding to the baseline range is determined to be an important alarm. State, and define the data state outside the baseline range obtained by taking K value as a critical warning state.
  • the monitoring indicator refers to an indicator of service data to be monitored, and the monitoring indicator may correspond to specific service data such as a database connection pool, a transaction amount, a transaction number, and a transaction response time.
  • the current business data is the business data that needs to be monitored, and can be expressed as a specific value relative to the monitoring index. If the monitoring indicator determined in the embodiment is the transaction amount, the current business data corresponding to the monitoring amount of the transaction amount is a specific value of the transaction amount to be monitored.
  • the monitoring result is obtained by monitoring and analyzing the historical service data corresponding to the current service data. Since the current service data corresponds to the monitoring indicator, and the historical baseline value is obtained based on the historical business data corresponding to the monitoring indicator, the current business data is comparable to the historical baseline value.
  • the historical baseline value includes at least two baseline ranges, and each baseline range corresponds to a data state, so the current service data can be compared with at least two baseline ranges, and the current service data is determined according to the comparison result. The data status and the data status is used as the monitoring result data.
  • step S50 acquiring the monitoring result based on the current service data and the historical baseline value specifically includes the following steps:
  • S51 Acquire a target baseline range corresponding to the current service data based on the current service data.
  • the target baseline range is the baseline range that contains current business data. Since the historical baseline values calculated based on the reference service data of the respective monitoring indicators are different, and the corresponding baseline ranges are different, when the corresponding target baseline range is obtained based on the current service data in step S51, the current service data and any baseline range are required. [[ ⁇ -k* ⁇ , ⁇ -(k-1)* ⁇ ], [ ⁇ +(k-1)* ⁇ , ⁇ +k* ⁇ ]] are compared to determine the baseline range in which the current business data is located as the target Baseline range.
  • the data state corresponding to the target baseline range is used as the monitoring result to be obtained, so that the monitoring result can clearly display the current data state of the service data, so that the financial institution or other institution
  • the management can make decision adjustment based on the monitoring results, which is beneficial to improve the operating efficiency of financial institutions or other institutions.
  • step S52 after determining the corresponding target baseline range based on the current service data, determining whether the current service data is abnormal according to the data state of the target baseline range in which the current service data is located, and further determining whether the system in which the data is located is stable.
  • the data state includes, but is not limited to, a normal state, a normal alarm state, an important alarm state, and a severe alarm state
  • the data state other than the normal state may be regarded as abnormal. If the data status of the target baseline range corresponding to the current service data is normal, the current service data is considered to be normal, and then the system in which the current service data is located is determined to be stable.
  • the data status of the target baseline range corresponding to the current service data is a normal alarm state, an important alarm state, and a severe alarm state, it is determined that the current service data is abnormal, and the stability of the current service data system is weak, so that the formed
  • the monitoring results contain relevant content.
  • Spark calculation engine is used for historical baseline value calculation when data monitoring is performed.
  • Spark is a general-purpose engine that can be used to perform SQL query, text processing, machine learning and other operations. It is suitable for data analysis and machine learning and other iterative MapReduce algorithms. Spark enables memory-distributed data sets. In addition to providing interactive queries, Spark can also optimize iterative workloads to achieve second-level, sustainable fault intelligence alerts such as monitoring transactions, database connection pools, response times, and system stability. If there is an abnormality, the current business data to be acquired is compared with the calculated historical baseline value to obtain a corresponding monitoring result.
  • the data monitoring instruction acquired in step S10 may be a timing triggering instruction, such that the data monitoring instruction includes not only the current time, the preset time limit, and the monitoring indicator, but also the triggering time point and the monitoring mailbox.
  • the method further includes: sending the monitoring result to the monitoring mailbox.
  • the triggering time point is a time point for triggering the terminal device to execute the data monitoring instruction to monitor the current service data.
  • the setting of the trigger time point enables the data monitoring to be performed according to the scheduled time, without human monitoring, which is beneficial to improve the monitoring efficiency.
  • the monitoring mailbox is a pre-set mailbox for obtaining monitoring results, so that the person corresponding to the monitoring mailbox can obtain the monitoring result in the monitoring mailbox offline, without manual monitoring, which is beneficial to improve monitoring efficiency.
  • the reference service data is obtained by using the data monitoring instruction, and the reference service data includes historical service data that carries the time series and is limited according to the preset time limit, thereby increasing the reference of the acquisition.
  • the timing of business data improves the real-time performance of data monitoring.
  • the historical baseline value is obtained based on the reference service data, so that the obtained historical baseline value as a reference basis is more objective.
  • the monitoring result is obtained based on the historical baseline value and the current business data, thereby real-time and objective monitoring of the current business data, ensuring the objectivity and real-time performance of the monitoring result, and improving the accuracy of the data monitoring.
  • FIG. 6 is a schematic block diagram showing a real-time data monitoring device corresponding to the real-time data monitoring method in Embodiment 1.
  • the data real-time monitoring device includes a data monitoring instruction acquiring module 10, a reference service data acquiring module 20, a historical baseline value obtaining module 30, a current service data acquiring module 40, and a monitoring result obtaining module 50.
  • the steps of the data monitoring instruction obtaining module 10, the reference service data obtaining module 20, the historical baseline value obtaining module 30, the current service data obtaining module 40, and the monitoring result obtaining module 50, and the data monitoring method in the embodiment are one by one.
  • the present embodiment will not be described in detail.
  • the data monitoring instruction obtaining module 10 is configured to acquire a data monitoring instruction, where the data monitoring instruction includes a current time, a preset period, and a monitoring indicator.
  • the reference service data obtaining module 20 is configured to obtain reference service data according to the data monitoring instruction, where the reference service data is historical service data corresponding to the monitoring indicator within a preset period before the current time.
  • the historical baseline value obtaining module 30 is configured to obtain a historical baseline value based on the reference service data.
  • the current service data obtaining module 40 is configured to acquire current service data corresponding to the monitoring indicator.
  • the monitoring result obtaining module 50 is configured to obtain the monitoring result based on the current service data and the historical baseline value.
  • the reference service data obtaining module 20 includes a historical service data acquiring unit 21 and a reference service data acquiring unit 22.
  • the historical service data acquiring unit 21 is configured to acquire all historical service data corresponding to the monitoring indicator based on the data monitoring instruction.
  • the reference service data obtaining unit 22 is configured to extract, from all historical service data, historical service data that is before the current time and corresponding to the preset time limit to obtain reference service data.
  • the historical baseline value acquisition module 30 includes an average value and standard deviation acquisition unit 31 and a historical baseline value acquisition unit 32.
  • the average and standard deviation obtaining unit 31 is configured to obtain an average value and a standard deviation corresponding to the reference service data based on the reference service data.
  • the historical baseline value acquisition unit 32 is configured to acquire a historical baseline value based on the average value and the standard deviation.
  • the average value and standard deviation acquisition unit 31 includes a standard deviation product acquisition subunit 311, an upper limit value determination subunit 312, and a lower limit value determination subunit 313.
  • a standard deviation product acquisition sub-unit 311, configured to obtain a standard deviation product of the standard deviation and the standard deviation coefficient
  • the upper limit value determining subunit 312 is configured to determine an upper limit value of the baseline range based on a sum of the average value and the standard deviation product;
  • the lower limit value determining sub-unit 313 is configured to determine a lower limit value of the baseline range based on the difference between the average value and the standard deviation product.
  • the monitoring result acquisition module 50 includes a target baseline range obtaining unit 51 and a monitoring result obtaining unit 52.
  • the target baseline range obtaining unit 51 is configured to acquire a target baseline range corresponding to the current service data based on the current service data.
  • the monitoring result obtaining unit 52 is configured to obtain the monitoring result based on the data state corresponding to the target baseline range.
  • the embodiment provides a computer readable storage medium on which computer readable instructions are stored, and when the computer readable instructions are executed by the processor, the data real-time monitoring method in Embodiment 1 is implemented. No longer.
  • the computer readable instructions are executed by the processor, the functions of the modules/units in the data real-time monitoring device in Embodiment 2 are implemented. To avoid repetition, details are not described herein again.
  • FIG. 7 is a schematic diagram of a terminal device provided by this embodiment.
  • the terminal device 70 of this embodiment includes a processor 71, a memory 72, and computer readable instructions 73 stored in the memory 72 and operable on the processor 71.
  • the processor 71 implements various steps of the data real-time monitoring method in Embodiment 1 when the computer readable instructions 73 are executed, such as steps S10, S20, S30, S40, and S50 shown in FIG.
  • the processor 71 executes the computer readable instructions 73
  • the functions of each module/unit of the data real-time monitoring device in Embodiment 2 are implemented, for example, the data monitoring instruction acquiring module 10, the reference service data acquiring module 20, and the historical baseline value shown in FIG.
  • the functions of the acquisition module 30, the current business data acquisition module 40, and the monitoring result acquisition module 50 are obtained.
  • computer readable instructions 73 may be partitioned into one or more modules/units, one or more modules/units being stored in memory 72 and executed by processor 71 to complete the application.
  • the one or more modules/units may be an instruction segment of a series of computer readable instructions capable of performing a particular function, which is used to describe the execution of computer readable instructions 73 in the terminal device 70.
  • the computer readable instructions 73 may be divided into a data monitoring instruction acquisition module 10, a reference service data acquisition module 20, a historical baseline value acquisition module 30, a current service data acquisition module 40, and a monitoring result acquisition module 50.
  • the terminal device 70 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 71, a memory 72. It will be understood by those skilled in the art that FIG. 7 is merely an example of the terminal device 70, and does not constitute a limitation of the terminal device 70, and 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 71 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 72 may be an internal storage unit of the terminal device 70, such as a hard disk or a memory of the terminal device 70.
  • the memory 72 may also be an external storage device of the terminal device 70, such as a plug-in hard disk provided on the terminal device 70, a smart memory card (SMC), a Secure Digital (SD) card, and a flash memory card (Flash). Card) and so on.
  • the memory 72 may also include both an internal storage unit of the terminal device 70 and an external storage device.
  • Memory 72 is used to store computer readable instructions as well as other programs and data required by the terminal device.
  • the memory 72 can also be used to temporarily store data that has been or will 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

一种数据实时监控方法、装置、设备及存储介质。该数据实时监控方法包括：获取数据监控指令，所述数据监控指令包括当前时间、预设期限和监控指标(S10)；基于所述数据监控指令获取参考业务数据，所述参考业务数据具体为当前时间以前所述预设期限内的与所述监控指标相对应的历史业务数据(S20)；基于所述参考业务数据获取历史基线值(S30)；获取与所述监控指标相对应的当前业务数据(S40)；基于所述当前业务数据和所述历史基线值，获取监控结果(S50)。该数据实时监控方法能够对数据进行实时监控，提高系统可靠性和异常数据预警能力。

Description

数据实时监控方法、装置、终端设备及存储介质
本专利申请以2017年9月22日提交的申请号为201710865962.7，名称为“数据实时监控方法、装置、终端设备及存储介质”的中国发明专利申请为基础，并要求其优先权。
技术领域
本申请涉及数据监控领域，尤其涉及一种数据实时监控方法、装置、终端设备及存储介质。
背景技术
随着市场经济的发展，企业之间的竞争也越来越激烈，为了提高企业的竞争力，企业通过对历史业务数据进行大数据分析，以便对企业实时采集的当前业务数据进行比较，从而分析企业的业务发展趋势。当前数据监控过程中，采集企业在任一预设时间段的当前业务数据与往年相应相间段的历史业务数据进行同比比较，以判断当前业务数据是否存在异常；或者采用企业在任一预设时间段的当前业务数据与相邻时间段的历史业务数据进行环比比较，以判断当前业务数据是否存在异常。当前数据监控过程中，需采集一时间段的历史业务数据和当前业务数据，使得数据监控过程中存在滞后性，无法实现对任一天的当前业务数据进行实时监控。
发明内容
本申请实施例提供一种数据实时监控方法、装置、终端设备及存储介质，以解决无法实现对业务数据进行实时监控的问题。
第一方面，本申请实施例提供一种数据实时监控方法，包括：
获取数据监控指令，所述数据监控指令包括当前时间、预设期限和监控指标；
基于所述数据监控指令获取参考业务数据，所述参考业务数据具体为当前时间以前所述预设期限内的与所述监控指标相对应的历史业务数据；
基于所述参考业务数据获取历史基线值；
获取与所述监控指标相对应的当前业务数据；
基于所述当前业务数据和所述历史基线值，获取监控结果。
第二方面，本申请实施例提供一种数据实时监控装置，包括：
数据监控指令获取模块，用于获取数据监控指令，所述数据监控指令包括当前时间、预设期限和监控指标；
参考业务数据获取模块，用于基于所述数据监控指令获取参考业务数据，所述参考业务数据具体为当前时间以前所述预设期限内的与所述监控指标相对应的历史业务数据；
历史基线值获取模块，用于基于所述参考业务数据获取历史基线值；
当前业务数据获取模块，用于获取与所述监控指标相对应的当前业务数据；
监控结果获取模块，用于基于所述当前业务数据和所述历史基线值，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：
获取数据监控指令，所述数据监控指令包括当前时间、预设期限和监控指标；
基于所述数据监控指令获取参考业务数据，所述参考业务数据具体为当前时间以前所述预设期限内的与所述监控指标相对应的历史业务数据；
基于所述参考业务数据获取历史基线值；
获取与所述监控指标相对应的当前业务数据；
基于所述当前业务数据和所述历史基线值，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：
获取数据监控指令，所述数据监控指令包括当前时间、预设期限和监控指标；
基于所述数据监控指令获取参考业务数据，所述参考业务数据具体为当前时间以前所述预设期限内的与所述监控指标相对应的历史业务数据；
基于所述参考业务数据获取历史基线值；
获取与所述监控指标相对应的当前业务数据；
基于所述当前业务数据和所述历史基线值，获取监控结果。
本申请实施例提供的数据实时监控方法、装置、设备及存储介质中，通过数据监控指令获取参考业务数据，该参考业务数据包括时间标注并按照预设期限进行限定的携带时序状态的历史业务数据，从而增加获取的参考业务数据的时序性，提高数据监控的实时性。基于参考业务数据获取历史基线值，以使获取到的作为参考依据的历史基线值更具有客观性。再基于历史基线值 和当前业务数据获取监控结果，从而实现对当前业务数据的实时、客观的监控，保证监控结果的客观性和实时性，提高数据监控的准确率。
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是实施例1中数据实时监控方法的一流程图。
图2是图1中步骤S20的一具体流程图。
图3是图1中步骤S30的一具体流程图。
图4是图3中步骤S32的一具体流程图。
图5是图1中步骤S50的一具体流程图。
图6是实施例2中数据实时监控装置的一原理框图。
图7是实施例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示出本实施例中数据实时监控方法的流程图。该数据实时监控方法 应用在银行、证券、保险等金融机构或者其他机构配置的终端设备上，用于对银行、证券、保险等金融机构或者其他机构在生产经营过程中形成的数据进行实时监控，以判断该数据是否异常。如图1所示，该数据实时监控方法包括如下步骤：
S10：获取数据监控指令，数据监控指令包括当前时间、预设期限和监控指标。
其中，数据监控指令是指用于控制终端执行数据监控的指令。当前时间是终端设备的系统时间。预设期限是预先设定好的用于划分带有时序状态的业务数据的期限，该预设期限可以是1个月、2个月、半年或者其他期限值。监控指标是指所要监控的业务数据的指标，该监控指标可对应一具体业务数据，该具体业务数据包括但不限于数据库连接池、交易金额、交易笔数和交易响应时间等数据。终端设备在获取数据监控指令后，可针对性地对具体业务数据进行监控。
S20：基于数据监控指令获取参考业务数据，参考业务数据具体为当前时间以前预设期限内的与监控指标相对应的历史业务数据。
其中，历史业务数据是金融机构或其他机构在生产经营过程中形成的业务数据。本实施例中，该历史业务数据包括但不限于存储在Oracle、Hive、Hadoop、Hbase等数据库的数据库连接池、交易金额、交易笔数和交易响应时间等业务数据。终端设备在获取数据监控指令后，可基于该数据监控指令获取当前时间以前预设期限内的与监控指标相对应的历史业务数据，作为参考业务数据。
具体地，参考业务数据具体为当前时间以前预设期限内的与监控指标相对应的历史业务数据，说明本实施例中采用时序方式确定参考业务数据。即 若预设期限为T，当前时间为预测的第一天，则将预测的第一天以前过去T时间内的历史业务数据作为参考业务数据；若当前时间为预测的第二天，则将预测的第一天以及过去T-1天内的历史业务数据作为参考业务数据。如预设期限为1个月，当前时间为7月15日，以7月15日当日为预测的第一天，则其参考业务数据为6月15日-7月14日这1个月的历史业务数据。这种“滚动”方式确定参考业务数据，可使得参考业务数据的时序性更强，使其参考意义更强，更接近当前市场行情，有利增加监控结果的准确率。
在一具体实施方式中，步骤S20中，基于数据监控指令获取参考业务数据具体包括如下步骤：
S21：基于数据监控指令，获取与监控指标相对应的所有历史业务数据。
每一数据监控指令中对应的监控指标包括一个或多个，该监控指标对应于数据库连接池、交易金额、交易笔数和交易响应时间等具体业务数据。历史业务数据是金融机构或其他机构在生产经营过程中形成的数据库连接池、交易金额、交易笔数和交易响应时间等业务数据，该历史业务数据按形成时间依序存储在Oracle、Hive、Hadoop、Hbase等数据库。在步骤S21中，终端设备接收到数据监控指令后，会基于数据监控指令中的监控指标，采用数据库查询指令，以监控指标为查询字段，获取与监控指标相对应的所有历史业务数据。若数据监控指令中的监控指标为交易金额，则步骤S21中获取的与监控指标相对应的所有历史业务数据为与交易金额相对应的所有交易金额数据，以从数据库海量的交易金额中选取出所要监控的交易金额数据，以保证数据监控的针对性，并提高数据监控的处理效率。
S22：从所有历史业务数据中提取当前时间以前并与预设期限相对应的历史业务数据，以获得参考业务数据。
其中，当前时间为终端设备的系统时间，预设期限是预先设定好的用于划分带时序状态的业务数据的期限，可根据当前时间和预设期限确定从所有历史业务数据中选取部分的历史业务数据作为参考业务数据。以上述获取的所有交易金额数据为例，若当前时间为7月15日，预设期限为1个月，则获取到的参考业务数据就是6月15日到7月14日这一个月内的交易金额数据。
进一步地，该数据实时监控方法中，步骤S20之前，还包括对历史业务数据进行加工处理，以减少步骤S22中获取的参考业务数据中的数据量，有利于提高后续进行数据监控的监控效率。具体地，对历史业务数据进行加工处理是指将历史业务数据依据单位时间进行分组，获取多个数据组；再计算获取各个数据组的数据平均值，并将获取到的数据平均值存储在Oracle、Hive、Hadoop、Hbase等数据库中，作为相应的历史业务数据。以交易金额为例，金融机构或其他机构在生产经营过程中每分每秒均可能形成一与交易金额相关的历史业务数据，在对历史业务数据进行加工处理时，将历史业务数据按预设的单位时间(如10min)划分成多个数据组，再计算每一数据组的数据平均值，将该数据平均值更新为新的历史业务数据，有利于后续利用该历史业务数据进行数据监控时节省计算量，提高处理效率。
在一具体实施例中，若当前时间是7月15日10点20分，取交易时间段为前后12小时，以7月15日为预测的第一天，采集预设时间段为1个月(6月15日10点20分-7月14日10点20分)的历史业务数据，监控指标为交易金额，按单位时间为5min对历史交易金额数据进行分组，形成多个数据组，则30天内获得的原始交易金额数据为30*24*60＝43200，43200表示交易金额这个指标当前这分钟所需要的历史业务数据，43200/5＝8640为历史交易金额数据按单位时间为5min进行分组后得到的数据组，通过对每组数据组中的历 史业务数据的平均值进行计算，并将该平均值作为该数据组对应的历史业务数据，有利于节省后续基于历史业务数据进行计算的计算量。
本实施例中，历史业务数据可以为结构化数据、半结构化数据或非结构化数据数据。其中，结构化数据可以以二维表形式存储在关系型数据库和NoSQL数据库中任一数据库中，半结构化和非结构化数据可以日志文件形式存储在服务器中。本实施例中，日志文件包括但不限于交易报文，交易记录，响应时间，数据库连接池等日志文件，这些日志文件是用户通过终端设备(包括手机、PC和平板等)访问网站所形成的日志文件。存储在数据库中的数据可以使用开放数据库连接(Open Database Connectivity，简称ODBC)或java数据库连接(Java DataBase Connectivity,简称JDBC)中任一种方法来获取，日志文件可以采用任一个日志采集工具来获取，并采用日志采集工具将半结构化数据或非结构化数据转换为结构化数据，然后存储到关系型数据库和NoSQL数据库中任一数据库中，以便后续利用时可直接采用数据库查询语句进行获取，简化参考业务数据的获取过程。参考业务数据获取过程中选取当前时间以前与预设期限相对应的历史业务数据，简化了对所有历史业务数据处理的复杂性，同时确保了监控指标对应的历史数据的实时性。
S30：基于参考业务数据获取历史基线值。
其中，历史基线值是基于参考业务数据形成的数值范围，该数值范围是评价与监控指标对应的其他数据是否异常的参考依据。由于该历史基线值是基于金融机构或其他机构生产经营过程中形成的当前时间以前并与预设期限相对应的历史业务数据，具有一定的客观性和实时性，以该历史基线值作为参考依据，可更客观地了解金融机构或者其他机构的发展趋势，从而决定是否需要决策调整，优化生产经营活动。
参考业务数据包括但不限于包括数据库连接池、交易金额、交易笔数和交易响应时间等历史业务数据。基线历史值可以选取当前时间以前过去一个星期、半个月、一个月、一年甚至更久的预设期限内的历史业务数据进行计算。由于不同行业的正常交易时段不一致，因此不同行业确定参考业务数据的预设期限不相同。例如金融行业的股票交易时段为正常工作日；而服装行业的服装售卖交易时段则主要在非工作日；有的产品夏季交易量大；有的产品冬季交易量大，为了保证参考业务数据的实时性，一般选用最近半个月或者一个月的历史业务数据。
在一具体实施方式中，步骤S30中，基于参考业务数据获取历史基线值具体包括如下步骤：
S31：基于参考业务数据，获取参考业务数据对应的平均值和标准差。
其中，平均值具体为算数平均值，算数平均值为所有数据之和与数据总个数的商值，平均值可以集中呈现变量的整体状态。标准差具体为各数据偏离平均数的距离的平均数，是一组数据平均值分散程度的一种度量，一个较大的标准差，代表数据中大部分数值和数据的平均值之间差异较大；一个较小的标准差，代表数据中大部分数值较接近数据的平均值，标准差可以当作不确定性的一种测量。
在获得参考业务数据后，对参考业务数据进行平均值和标准差的计算，平均值μ的计算公式如下：
Figure PCTCN2018073628-appb-000001
其中，n为参考业务数据的个数，i的取值为1到n，x i为参考业务数据中的任一项数据。标准差σ的计算公式如下：
Figure PCTCN2018073628-appb-000002
其中，N为参考业务数据的个数，i的取值为1-N，x i为参考业务数据中的任一项数据，μ为参考业务数据的平均值。
S32：基于平均值和标准差，获取历史基线值。
具体地，历史基线值是基于参考业务数据对应的平均值和标准差形成的数值范围，该数值范围是评价与监控指标对应的其他数据是否异常的参考依据。本实施例中，历史基线值划分至少两个基线范围，每一基线范围对应一数据状态。其中，基线范围包括上限值和下限值，上限值为基线范围的最大值，下限值为基线范围的最小值。数据状态为数据对应的状态，根据数据可能出现的状态可划分为正常状态、普通告警状态、重要告警状态和严重告警状态等状态，用于评价数据是否正常或是否稳定。
在一具体实施方式中，步骤S32中，基于平均值和标准差，获取历史基线值具体包括如下步骤：
S321：获取标准差与标准差系数的标准差乘积。
其中，标准差系数为正数，可以为正整数，也可以是正分数。将标准差与标准差系数相乘，即可获取对应的标准差乘积。本实施例中，设标准差系数为k，则步骤S321中获取的标准差乘积为k*σ。
S322：基于平均值和标准差乘积的和值，确定一基线范围的上限值。
基线范围的上限值是基线范围的最大值，基线范围的上限值依赖于相应的历史业务数据的平均值和标准差，具体计算方法为平均值和标准差乘积之和。例如交易金额数据的平均值是μ、标准差是σ、标准差系数为k，则交易金额数据所对应的基线值的上限值为μ+k*σ。每一基线范围的上限值可将历史基线值划分成两个基线范围，对应不同的数据状态。
S323：基于平均值和标准差乘积的差值，确定一基线范围的下限值。
基线范围的下限值是基线范围的最小值，基线范围的下限值依赖于相应的历史业务数据的平均值和标准差，具体计算方法为平均值和标准差乘积之 差。例如交易金额数据的平均值是μ、标准差是σ、标准差系数为k，则交易金额数据所对应的基线值的下限值为μ-k*σ。每一基线范围的下限值可将历史基线值划分成两个基线范围，对应不同的数据状态。
可以理解地，基线范围的上限值和下限值的计算过程中，标准差系数取值越大，基线范围越大。设基于参考业务数据计算出的平均值为μ、标准差为σ、标准差系数为k，则该参考业务数据对应的历史基线值中任一基线范围的上限值为μ+k*σ，下限值为μ-k*σ，由于标准差系数较大的基线范围包含标准差系数较小的基线范围，为更清楚展示不同基线范围对应的数据状态，需将标准差系数较小的基线范围从标准差较大的基线范围中删除，以确定任一基线范围为：[[μ-k*σ,μ-(k-1)*σ]，[μ+(k-1)*σ,μ+k*σ]]。
本实施例中，若每一基线范围对应一数据状态，该数据状态包括但不限于正常状态、普通告警状态、重要告警状态和严重告警状态等状态。当k取1时，基线范围为：[[μ-σ,μ]，[μ,μ+σ]]，即[μ-σ,μ+σ]，该基线范围最接近于参考业务数据对应的平均值，确定该基线范围对应的数据状态为正常状态。当k取2时，基线范围为：[[μ-2σ,μ-σ]，[μ+σ,μ+2σ]]，该基线范围接近正常状态，确定该基线范围对应的数据状态为普通告警状态。当k取3时，基线范围为：[[μ-3σ,μ-2σ]，[μ+2σ,μ+3σ]]，该基线范围接近普通状态，确定该基线范围对应的数据状态为重要告警状态，并定义K取3值获取到的基线范围以外的数据状态为严重警告状态。
S40：获取与监控指标相对应的当前业务数据。
具体地，监控指标是指所要监控的业务数据的指标，该监控指标可对应数据库连接池、交易金额、交易笔数、交易响应时间等具体业务数据。当前业务数据为需要进行监控的业务数据，可以表现为与监控指标相对的具体数 值。如本实施例中确定的监控指标为交易金额，则与交易金额这一监控指标相对应的当前业务数据为该所要监控的交易金额的具体数值。
S50：基于当前业务数据和历史基线值，获取监控结果。
其中，监控结果是通过历史基线值对应当前业务数据进行监控分析后获得的结果。由于当前业务数据与监控指标相对应，而历史基线值是基于与监控指标相对应的历史业务数据进行计算后获取，使得当前业务数据与历史基线值具有可比性。本实施例中，历史基线值包括至少两个基线范围，且每一基线范围对应一数据状态，因此可基于当前业务数据与至少两个基线范围进行比较，根据比较结果确定该当前业务数据对应的数据状态，并将该数据状态作为监控结果数据。
在一具体实施方式中，步骤S50中，基于当前业务数据和历史基线值，获取监控结果具体包括如下步骤：
S51：基于当前业务数据，获取与当前业务数据相对应的目标基线范围。
其中，目标基线范围是包含当前业务数据的基线范围。由于基于各个监控指标的参考业务数据计算出的历史基线值不同，其对应的基线范围不同，在步骤S51中基于当前业务数据获取对应的目标基线范围时，需将当前业务数据与任一基线范围[[μ-k*σ,μ-(k-1)*σ]，[μ+(k-1)*σ,μ+k*σ]]进行比较，确定当前业务数据所在的基线范围作为目标基线范围。
S52：基于目标基线范围对应的数据状态，获取监控结果。
具体地，在获取当前业务数据对应的目标基线范围后，将目标基线范围对应的数据状态作为所要获取的监控结果，以使监控结果可清楚展示当前业务数据对应数据状态，以便金融机构或者其他机构中管理人员可基于监控结果进行决策调整，有利于提高金融机构或者其他机构的经营效益。
步骤S52中，在基于当前业务数据确定对应的目标基线范围后，依据当前业务数据所处的目标基线范围的数据状态判断当前业务数据是否异常，还可进一步判断数据所处系统是否稳定。本实施例中，若数据状态包括但不限于正常状态、普通告警状态、重要告警状态和严重告警状态等状态，可以将除正常状态以外的数据状态认定为异常。若当前业务数据对应的目标基线范围的数据状态为正常状态，则认为当前业务数据正常，进而确定当前业务数据所处系统稳定。若当前业务数据对应的目标基线范围的数据状态为普通告警状态、重要告警状态和严重告警状态等状态，则判定当前业务数据异常，当前业务数据所处系统的稳定性较弱，使其形成的监控结果中包含相关内容。
对于庞大的参考业务数据，进行数据监控时采用Spark这一计算引擎进行历史基线值计算。Spark是一个通用引擎，可用来完成SQL查询、文本处理、机器学习等运算，适用于数据分析与机器学习等需要迭代的MapReduce的算法中。Spark启用了内存分布数据集，除了能够提供交互式查询外，Spark还可以优化迭代工作负载，实现秒级、可持续的故障智能预警功能，比如监控交易、数据库连接池、响应时间和系统稳定性等存在异常，即将获取的当前业务数据与计算出的历史基线值进行比较，得到对应的监控结果。
在一具体实施方式中，步骤S10中获取的数据监控指令可以是定时触发指令，使得该数据监控指令不仅包括当前时间、预设期限和监控指标，还包括触发时间点和监控邮箱。步骤S50中获取监控结果之后还包括：将监控结果发送给监控邮箱。
其中，触发时间点是用于触发终端设备执行该数据监控指令以对当前业务数据进行监控的时间点。触发时间点的设置，可使数据监控可按既定的时间执行，无需人为监控，有利于提高监控效率。监控邮箱是预先设置的用于 获取监控结果的邮箱，以使监控邮箱对应的人员可离线获取监控邮箱中的监控结果，无需进行人工监控，有利于提高监控效率。
本实施例所提供的数据实时监控方法中，通过数据监控指令获取到参考业务数据，该参考业务数据包括时间标注并按照预设期限进行限定的携带时序状态的历史业务数据，从而增加获取的参考业务数据的时序性，提高数据监控的实时性。基于参考业务数据获取历史基线值，以使获取到的作为参考依据的历史基线值更具有客观性。再基于历史基线值和当前业务数据获取监控结果，从而实现对当前业务数据的实时、客观的监控，保证监控结果的客观性和实时性，提高数据监控的准确率。
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6示出与实施例1中数据实时监控方法一一对应的数据实时监控装置的原理框图。如图6所示，该数据实时监控装置包括数据监控指令获取模块10、参考业务数据获取模块20、历史基线值获取模块30、当前业务数据获取模块40和监控结果获取模块50。其中，数据监控指令获取模块10、参考业务数据获取模块20、历史基线值获取模块30、当前业务数据获取模块40和监控结果获取模块50的实现功能与实施例中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。
数据监控指令获取模块10，用于获取数据监控指令，数据监控指令包括当前时间、预设期限和监控指标。
参考业务数据获取模块20，用于基于数据监控指令获取参考业务数据， 参考业务数据具体为当前时间以前预设期限内的与监控指标相对应的历史业务数据。
历史基线值获取模块30，用于基于参考业务数据获取历史基线值。
当前业务数据获取模块40，用于获取与监控指标相对应的当前业务数据。
监控结果获取模块50，用于基于当前业务数据和历史基线值，获取监控结果。
优选地，参考业务数据获取模块20包括历史业务数据获取单元21、参考业务数据获取单元22。
历史业务数据获取单元21，用于基于数据监控指令，获取与监控指标相对应的所有历史业务数据。
参考业务数据获取单元22，用于从所有历史业务数据中提取当前时间以前并与预设期限相对应的历史业务数据，以获得参考业务数据。
优选地，历史基线值获取模块30包括平均值和标准差获取单元31和历史基线值获取单元32。
平均值和标准差获取单元31，用于基于参考业务数据，获取参考业务数据对应的平均值和标准差。
历史基线值获取单元32，用于基于平均值和标准差，获取历史基线值。
优选地，平均值和标准差获取单元31包括标准差乘积获取子单元311、上限值确定子单元312和下限值确定子单元313。
标准差乘积获取子单元311，用于获取所述标准差与标准差系数的标准差乘积；
上限值确定子单元312，用于基于所述平均值和标准差乘积的和值，确定一所述基线范围的上限值；
下限值确定子单元313，用于基于所述平均值和标准差乘积的差值，确定一所述基线范围的下限值。
优选地，监控结果获取模块50包括目标基线范围获取单元51和监控结果获取单元52。
目标基线范围获取单元51，用于基于当前业务数据，获取与当前业务数据相对应的目标基线范围。
监控结果获取单元52，用于基于目标基线范围对应的数据状态，获取监控结果。
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中数据实时监控方法，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中数据实时监控装置中各模块/单元的功能，为避免重复，这里不再赘述。
实施例4
图7是本实施例提供的终端设备的示意图。如图7所示，该实施例的终端设备70包括：处理器71、存储器72以及存储在存储器72中并可在处理器71上运行的计算机可读指令73。处理器71执行计算机可读指令73时实现实施例1中数据实时监控方法的各个步骤，例如图1所示的步骤S10、S20、S30、S40和S50。或者，处理器71执行计算机可读指令73时实现实施例2中数据实时监控装置各模块/单元的功能，例如图6所示数据监控指令获取模块10、参考业务数据获取模块20、历史基线值获取模块30、当前业务数据获取模块40和监控结果获取模块50的功能。
示例性的，计算机可读指令73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72中，并由处理器71执行，以完成本申请。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的指令段，该指令段用于描述计算机可读指令73在终端设备70中的执行过程。例如，计算机可读指令73可以被分割成数据监控指令获取模块10、参考业务数据获取模块20、历史基线值获取模块30、当前业务数据获取模块40和监控结果获取模块50。
该终端设备70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71、存储器72。本领域技术人员可以理解，图7仅仅是终端设备70的示例，并不构成对终端设备70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71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72可以是终端设备70的内部存储单元，例如终端设备70的硬盘或内存。存储器72也可以是终端设备70的外部存储设备，例如终端设备70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72还可以既包括终端设备70的内部存储单元也包括外部存储设备。存储器72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72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. 一种数据实时监控方法，其特征在于，包括：
    获取数据监控指令，所述数据监控指令包括当前时间、预设期限和监控指标；
    基于所述数据监控指令获取参考业务数据，所述参考业务数据具体为当前时间以前所述预设期限内的与所述监控指标相对应的历史业务数据；
    基于所述参考业务数据获取历史基线值；
    获取与所述监控指标相对应的当前业务数据；
    基于所述当前业务数据和所述历史基线值，获取监控结果。
  2. 如权利要求1所述的数据实时监控方法，其特征在于，所述基于所述数据监控指令获取参考业务数据，包括：
    基于所述数据监控指令，获取与所述监控指标相对应的所有历史业务数据；
    从所有历史业务数据中提取所述当前时间以前并与所述预设期限相对应的历史业务数据，以获得所述参考业务数据。
  3. 如权利要求1所述的数据实时监控方法，其特征在于，所述基于所述参考业务数据获取历史基线值，包括：
    基于所述参考业务数据，获取所述参考业务数据对应的平均值和标准差；
    基于所述平均值和所述标准差，获取所述历史基线值。
  4. 如权利要求3所述的数据实时监控方法，其特征在于，所述历史基线值包括至少两个基线范围；
    所述基于所述平均值和所述标准差，获取所述历史基线值，包括：
    获取所述标准差与标准差系数的标准差乘积；
    基于所述平均值和标准差乘积的和值，确定一所述基线范围的上限值；
    基于所述平均值和标准差乘积的差值，确定一所述基线范围的下限值。
  5. 如权利要求4所述的数据实时监控方法，其特征在于，每一所述基线范围对应一数据状态；
    所述基于所述当前业务数据和所述历史基线值，获取监控结果，包括：
    基于所述当前业务数据，获取与所述当前业务数据相对应的目标基线范围；
    基于所述目标基线范围对应的数据状态，获取所述监控结果。
  6. 一种数据实时监控装置，其特征在于，包括：
    数据监控指令获取模块，用于获取数据监控指令，所述数据监控指令包括当前时间、预设期限和监控指标；
    参考业务数据获取模块，用于基于所述数据监控指令获取参考业务数据，所述参考业务数据具体为当前时间以前所述预设期限内的与所述监控指标相对应的历史业务数据；
    历史基线值获取模块，用于基于所述参考业务数据获取历史基线值；
    当前业务数据获取模块，用于获取与所述监控指标相对应的当前业务数据；
    监控结果获取模块，用于基于所述当前业务数据和所述历史基线值，获取监控结果。
  7. 如权利要求6所述的数据实时监控装置，其特征在于，所述参考业务数据获取模块包括：
    历史业务数据获取单元，基于所述数据监控指令，获取与所述监控指标 相对应的所有历史业务数据；
    参考业务数据获取单元，从所有历史业务数据中提取所述当前时间以前并与所述预设期限相对应的历史业务数据，以获得所述参考业务数据。
  8. 如权利要求6所述的数据实时监控装置，其特征在于，所述历史基线值获取模块包括：
    平均值和标准差获取单元，基于所述参考业务数据，获取所述参考业务数据对应的平均值和标准差；
    历史基线值获取单元，用于基于所述平均值和所述标准差，获取所述历史基线值。
  9. 如权利要求8所述的数据实时监控装置，其特征在于，所述历史基线值包括至少两个基线范围；
    所述平均值和标准差获取单元包括：
    标准差乘积获取子单元，用于获取所述标准差与标准差系数的标准差乘积；
    上限值确定子单元，用于基于所述平均值和标准差乘积的和值，确定一所述基线范围的上限值；
    下限值确定子单元，用于基于所述平均值和标准差乘积的差值，确定一所述基线范围的下限值。
  10. 如权利要求9所述的数据实时监控装置，其特征在于，每一所述基线范围对应一数据状态；
    所述监控结果获取模块包括：
    目标基线范围获取单元，用于基于所述当前业务数据，获取与所述当前业务数据相对应的目标基线范围；
    监控结果获取单元，用于基于所述目标基线范围对应的数据状态，获取所述监控结果。
  11. 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：
    获取数据监控指令，所述数据监控指令包括当前时间、预设期限和监控指标；
    基于所述数据监控指令获取参考业务数据，所述参考业务数据具体为当前时间以前所述预设期限内的与所述监控指标相对应的历史业务数据；
    基于所述参考业务数据获取历史基线值；
    获取与所述监控指标相对应的当前业务数据；
    基于所述当前业务数据和所述历史基线值，获取监控结果。
  12. 如权利要求11所述的终端设备，其特征在于，所述基于所述数据监控指令获取参考业务数据，包括：
    基于所述数据监控指令，获取与所述监控指标相对应的所有历史业务数据；
    从所有历史业务数据中提取所述当前时间以前并与所述预设期限相对应的历史业务数据，以获得所述参考业务数据。
  13. 如权利要求11所述的终端设备，其特征在于，所述基于所述参考业务数据获取历史基线值，包括：
    基于所述参考业务数据，获取所述参考业务数据对应的平均值和标准差；
    基于所述平均值和所述标准差，获取所述历史基线值。
  14. 如权利要求13所述的终端设备，其特征在于，所述历史基线值包括 至少两个基线范围；
    所述基于所述平均值和所述标准差，获取所述历史基线值，包括：
    获取所述标准差与标准差系数的标准差乘积；
    基于所述平均值和标准差乘积的和值，确定一所述基线范围的上限值；
    基于所述平均值和标准差乘积的差值，确定一所述基线范围的下限值。
  15. 如权利要求14所述的终端设备，其特征在于，每一所述基线范围对应一数据状态；
    所述基于所述当前业务数据和所述历史基线值，获取监控结果，包括：
    基于所述当前业务数据，获取与所述当前业务数据相对应的目标基线范围；
    基于所述目标基线范围对应的数据状态，获取所述监控结果。
  16. 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：
    获取数据监控指令，所述数据监控指令包括当前时间、预设期限和监控指标；
    基于所述数据监控指令获取参考业务数据，所述参考业务数据具体为当前时间以前所述预设期限内的与所述监控指标相对应的历史业务数据；
    基于所述参考业务数据获取历史基线值；
    获取与所述监控指标相对应的当前业务数据；
    基于所述当前业务数据和所述历史基线值，获取监控结果。
  17. 如权利要求16所述的计算机可读存储介质，其特征在于，所述基于所述数据监控指令获取参考业务数据，包括：
    基于所述数据监控指令，获取与所述监控指标相对应的所有历史业务数 据；
    从所有历史业务数据中提取所述当前时间以前并与所述预设期限相对应的历史业务数据，以获得所述参考业务数据。
  18. 如权利要求17所述的计算机可读存储介质，其特征在于，所述基于所述参考业务数据获取历史基线值，包括：
    基于所述参考业务数据，获取所述参考业务数据对应的平均值和标准差；
    基于所述平均值和所述标准差，获取所述历史基线值。
  19. 如权利要求18所述的计算机可读存储介质，其特征在于，所述历史基线值包括至少两个基线范围；
    所述基于所述平均值和所述标准差，获取所述历史基线值，包括：
    获取所述标准差与标准差系数的标准差乘积；
    基于所述平均值和标准差乘积的和值，确定一所述基线范围的上限值；
    基于所述平均值和标准差乘积的差值，确定一所述基线范围的下限值。
  20. 如权利要求19所述的计算机可读存储介质，其特征在于，每一所述基线范围对应一数据状态；
    所述基于所述当前业务数据和所述历史基线值，获取监控结果，包括：
    基于所述当前业务数据，获取与所述当前业务数据相对应的目标基线范围；
    基于所述目标基线范围对应的数据状态，获取所述监控结果。
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