WO2021098021A1 - 数据异常统计报警方法、装置及电子设备 - Google Patents

数据异常统计报警方法、装置及电子设备 Download PDF

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WO2021098021A1
WO2021098021A1 PCT/CN2019/130341 CN2019130341W WO2021098021A1 WO 2021098021 A1 WO2021098021 A1 WO 2021098021A1 CN 2019130341 W CN2019130341 W CN 2019130341W WO 2021098021 A1 WO2021098021 A1 WO 2021098021A1
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data
level
time window
current time
alarm
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PCT/CN2019/130341
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English (en)
French (fr)
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林浩生
王博
吕沙沙
谭泽汉
郭强
张康龙
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珠海格力电器股份有限公司
珠海联云科技有限公司
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Priority to US17/771,726 priority Critical patent/US20220391497A1/en
Publication of WO2021098021A1 publication Critical patent/WO2021098021A1/zh

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
    • G05B19/4184—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by fault tolerance, reliability of production system
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/50—Monitoring users, programs or devices to maintain the integrity of platforms, e.g. of processors, firmware or operating systems
    • G06F21/55—Detecting local intrusion or implementing counter-measures
    • G06F21/552—Detecting local intrusion or implementing counter-measures involving long-term monitoring or reporting
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/31—From computer integrated manufacturing till monitoring
    • G05B2219/31088—Network communication between supervisor and cell, machine group
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the present disclosure relates to the technical field of data abnormality detection and alarm, and in particular to a data abnormality statistical alarm method, device and electronic equipment.
  • the quality of the information of different parts or devices in the manufacturing industry or the information exchanged in the information interaction (referred to as “data” in this disclosure) will have different effects on the overall quality of the result of the product or data interaction. Impact, the quality requirements for critical data and accident-prone data must be the most stringent, while for some secondary data, the quality requirements can be appropriately reduced.
  • the traditional method is to calculate the results according to each natural day, week, month and other time periods, and then give an evaluation based on the results.
  • the supplier data quality alarm uses regular statistics and evaluation of supplier data quality, and compares it with a preset threshold. If the statistical result reaches or exceeds the threshold, an alarm is triggered. For example: the system pre-sets data A provided by a certain supplier, and the weekly random inspection pass rate is less than 98% to trigger an alarm; or the number of unqualified daily random inspections is greater than 5 to trigger an alarm, and so on.
  • the present disclosure provides a data abnormal statistical alarm method, device, and electronic equipment, which counts the amount of abnormal data by using a time window, and generates alarm information according to the amount and quality type of abnormal data to give an alarm, effectively alleviating related problems.
  • the problem of missing alarms in the technology can effectively avoid the loss of enterprises due to missing alarms.
  • the present disclosure provides a statistical alarm method for data abnormalities, the method including:
  • Step a Obtain the detection time and the detection result of each data obtained by separately detecting a plurality of data
  • Step b Count the number of target data in the current time window, and generate an alarm signal to prompt when the number of target data obtained by the statistics is greater than a preset number threshold corresponding to the quality level of the data, wherein the target The data is the data whose test result is abnormal;
  • Step c Move back the current time window according to the obtained step duration setting rule corresponding to the quality level of the data, use the moved current time window as the new current time window, and return to step a .
  • the method further includes:
  • the step of moving the current time window backward according to the obtained step duration setting rule corresponding to the quality level of the data includes:
  • the target step duration corresponding to the number of times is obtained from the step duration rule corresponding to the quality level of the data, wherein the step duration setting rule includes multiple alarm signals generated within the preset duration The preset times and the step duration corresponding to each of the preset times;
  • the current time window is moved back according to the target step duration.
  • acquiring the detection time and detection result of each of the data obtained by separately detecting multiple data includes:
  • the quality level of the data includes a first level, a second level, and a third level, and the first level is better than the second level, and the second level is excellent. At the third level.
  • the step duration corresponding to the first level data is less than the step duration corresponding to the second level data
  • the step duration corresponding to the second level data is less than the step duration corresponding to the second level data.
  • the time window length of the current time window is one or two days
  • the step time corresponding to the first level data is ten minutes, twenty minutes, or one day.
  • the step duration corresponding to the second level data is four hours or half a day
  • the step duration corresponding to the third level data is one day or two days.
  • the preset number threshold corresponding to the first-level data is less than the preset number threshold corresponding to the second-level data, which is the same as the second-level data
  • the corresponding preset number threshold is less than the preset number threshold corresponding to the third level of data.
  • Obtaining the detection time and the detection result of each of the data obtained by separately detecting multiple data includes:
  • the detection time and the detection result of each of the data obtained by separately detecting a plurality of data are acquired every interval setting time length, wherein the setting time length is less than or equal to the step time length.
  • the present disclosure provides a data abnormal statistical alarm device, including a processor, wherein the processor is configured to execute the following program modules stored in a memory:
  • An information obtaining module configured to obtain a detection time and a detection result of each of the data obtained by separately detecting a plurality of data
  • the abnormal statistical alarm module is configured to count the number of target data in the current time window, and generate an alarm signal to prompt when the number of target data obtained by the statistics is greater than a preset number threshold corresponding to the quality level of the data, Wherein, the target data is data whose detection result is abnormal;
  • the time window setting module is configured to move back the current time window according to the obtained step time setting rule corresponding to the quality level of the data, and use the moved current time window as the new current time window.
  • the present disclosure provides a storage medium, and a computer program stored in the storage medium can be executed by one or more processors, and can be used to implement the above-mentioned data abnormal statistical alarm method.
  • the present disclosure provides an electronic device including a memory and a processor, and a computer program is stored on the memory.
  • the computer program executes the abnormal data applied to the first terminal.
  • Statistical alarm method When the computer program is executed by the processor, it executes the abnormal data applied to the first terminal.
  • the present disclosure provides a data abnormal statistical alarm method, device and electronic equipment.
  • the method includes: acquiring the detection time and detection result of each of the data obtained by separately detecting multiple data, and counting the target data in the current time window When the number of target data obtained by statistics is greater than the preset number threshold corresponding to the quality level of the data, an alarm signal is generated for prompting, wherein the target data is data whose detection result is abnormal, according to The obtained step time length in the step time length rule corresponding to the quality level of the data moves the current time window back, and uses the moved current time window as the new current time window to recheck the abnormal target data. Perform statistical early warning.
  • the amount of abnormal data is counted in a time window, and alarm information is generated according to the quantity and quality type of abnormal data for alarm, which effectively alleviates the problem of alarm omissions in related technologies, thereby Effectively avoid the loss of enterprises due to missing alarms.
  • FIG. 1 is a schematic flowchart of a method for statistically abnormal data provided by some embodiments of the present disclosure.
  • FIG. 2 is a schematic diagram of time window statistics when performing abnormal statistics on the first type of data provided by some embodiments of the present disclosure.
  • FIG. 3 is a schematic diagram of time window statistics when performing abnormal statistics on the second type of data provided by some embodiments of the present disclosure.
  • FIG. 4 is a schematic diagram of time window statistics when performing abnormal statistics on the third type of data provided by some embodiments of the present disclosure.
  • the present disclosure provides a statistical alarm method for data abnormalities.
  • a sliding time window By adopting a sliding time window, the number of target data whose detection results are abnormal among multiple detected data in different time windows is counted, and the number of target data in the target data
  • an alarm signal is generated to remind the user, thereby effectively alleviating the problem of inaccurate statistics of abnormal data in related technologies.
  • the present disclosure provides a data abnormal statistical alarm method, which can be applied to electronic devices with data processing capabilities such as computers, servers, or tablets.
  • Step S110 to Step S140 can be performed.
  • Step S110 Obtain the detection time and the detection result of each data obtained by separately detecting a plurality of data.
  • Step S120 Count the number of target data in the current time window, and generate an alarm signal to prompt when the number of target data obtained by the statistics is greater than a preset number threshold corresponding to the quality level of the data, wherein the target The data is the data whose test result is abnormal.
  • Step S130 Move the current time window back according to the obtained step duration setting rule corresponding to the quality level of the data, use the moved current time window as the new current time window, and return to step S110 .
  • the method of obtaining the detection time and the detection result may be the detection time and the detection result input by the user, or the detection time and the detection result stored in the device for obtaining the detection data, which is not specifically limited here. Set it according to actual needs.
  • the acquisition method can be the detection time and detection result of the data acquisition in real time, or the detection time and detection result of the data acquisition every set time interval, and It may be to obtain the detection result of the data within the set duration threshold range before the current time.
  • the foregoing step S110 may be to obtain the detection time and the detection result of each of the data obtained by separately detecting multiple data at intervals of a set duration, wherein the set duration is less than or equal to the The length of the step.
  • the above step S110 may also be to obtain the detection time and the detection result of each of the data obtained by separately detecting multiple data within a set duration threshold before the current time, wherein the set duration threshold The length of the time window is greater than the current time window, and the start time of the current time window is within a set time length threshold before the current time.
  • the set duration may be one week, one month, several months or one year, which is not specifically limited here.
  • the time window length of the current time window can be one hour, several hours, one day, two days or one week. There is no specific limitation here, and it will be set according to actual needs.
  • the preset number threshold can be There are several, dozens, or hundreds, which are not specifically limited here, and can be set according to actual needs.
  • the start time of the current time window may be within one day.
  • the time window length of the current time window may be one day or two days.
  • the way of generating an alarm signal for prompting may also be to generate an alarm signal when the ratio of the number of target data to the number of all data detected by the length of the time window of the current time window is greater than a preset value.
  • the quality level of the data may include multiple, and different quality levels have different pros and cons.
  • the quality level of the data may include the best quality level (first level) and the best quality level (second level). Level), the sub-optimal quality level (third level), and the general quality level (fourth level). It can be understood that the quality level of the data may also include more or less levels, which are not specifically limited here.
  • the quality level of the data includes a first level, a second level, and a third level, and the quality of the first level data is better than the quality of the second level data, and the second level The quality of the data is better than the quality of the third-level data.
  • the detection efficiency of the data detection equipment should be the same. In the case of the higher the quality of the data, the number of target data detected in the same time period (within the same time window) It should be less.
  • the preset number threshold corresponding to the first level of data is less than the preset number threshold corresponding to the second level of data, and the preset number threshold corresponding to the second level of data is less than A preset number threshold corresponding to the third level of data.
  • the step time length setting rule may include a step time length, and the step time length may be, but not limited to, five minutes, ten minutes, tens of minutes, several hours, tens of hours, one day or several days.
  • the step duration setting rule can also include the step duration corresponding to different statistical times under the corresponding quality level, or the corresponding different step duration within different time periods under the corresponding quality level, and can also be included in the corresponding quality level.
  • the step duration corresponding to the number of different alarms generated within the preset duration under the level. It can be understood that different data quality levels have different early warning requirements. Therefore, different quality levels correspond to different stepping times. It is understandable that the higher the quality level of the data, the higher the quality level is usually required.
  • the step duration corresponding to the data of the first level is less than the step duration corresponding to the data of the second level, and the step duration corresponding to the data of the second level is less than that of the third level.
  • the step time corresponding to the level data is less than the step duration corresponding to the data of the second level.
  • the step duration corresponding to the first level data is ten minutes, twenty minutes or one hour
  • the step duration corresponding to the second level data is four hours or half a day
  • the step duration corresponding to the third level data is one or two days.
  • the preset statistical rule stores multiple time periods under the corresponding quality level and each of the time periods.
  • the step length corresponding to the step time within the data quality level is obtained, and the step of moving the current time window backward according to the obtained step time length setting rule corresponding to the quality level of the data includes: starting from the step corresponding to the quality level of the data
  • the target time period to which the start time of the current time window belongs and the target step time corresponding to the target time period are determined in the entry time length setting rule, and the current time window is moved back according to the target step time.
  • the statistics of the target data in the current time window are obtained by performing the statistics of the number of target data in the current time window or after performing the above steps.
  • the target step time corresponding to the statistical times is acquired in the time length rule, wherein the step time setting rule includes a plurality of statistical times and a step time corresponding to each of the statistical times, according to the target The step duration will move back the current time window.
  • the preset statistical rule includes predictions corresponding to different preset alarm times within the preset duration.
  • Set the step duration by performing the statistics of the number of target data in the current time window, or after performing the above steps, to obtain the number of alarm signals generated within the preset duration, from the step duration corresponding to the quality level of the data
  • the target step duration corresponding to the number of times is acquired in the rule, wherein the step duration setting rule includes a plurality of preset times of generating an alarm signal within the preset duration and a corresponding corresponding to each of the preset times.
  • the step duration, the current time window is moved backward according to the target step duration.
  • the present disclosure implements the above-mentioned steps S110-S130 to implement different step duration setting rules according to different data quality levels to perform statistical warning of abnormal data in different time windows, so as to effectively alleviate the use of natural weather or natural weather in related technologies.
  • the problem of missing alarms in the statistical early warning of abnormal data during the week can effectively improve the real-time of alarms, so that users can respond to the alarm information in time, thereby effectively avoiding the loss of enterprises due to missing alarms.
  • the quality level includes the first level, the second level, and the third level, and is the same as
  • the step duration corresponding to the quality level includes that the step duration corresponding to the first level is one hour, the step duration corresponding to the second level is half a day, and the step duration corresponding to the third level is one day. Take it as an example.
  • the quality level is the first level
  • the detection time and detection result of each data obtained by detecting multiple data separately in a natural day are obtained, and the starting time in the current time is calculated as before the middle of the natural day.
  • the number of target data within 24 hours after 00:00:00 of a day.
  • step duration (one hour) included in the step duration setting rule corresponding to the level is shifted backward from the start time of the current time window, and the backward shift duration is the step duration (one hour), and the shift backward Start time (00:01:00 of the previous day in a natural day) as the start time of the new current time window, and return to execute again to obtain the detection time and the detection time of each data separately detected on multiple data in the natural day
  • the steps of the detection result are to realize the time-sharing statistics of the number of target data within 24 hours, and the time-sharing time is one hour, so as to effectively avoid the loss of the enterprise due to the lack of alarms.
  • the detection time and the detection result of each data obtained by separately detecting multiple data in a natural day are obtained, and the starting time in the current time is calculated as before the middle of the natural day.
  • step duration (twelve hours) included in the step duration setting rule corresponding to the level is shifted back to the start time of the current time window, and the back shift duration is the step duration (twelve hours), and the next
  • the shifted start time (00:12:00 on the previous day in the natural day) is used as the start time of the new current time window, and the return is executed to obtain the detection of each data separately detected by multiple data in the natural day.
  • the number of target data within 24 hours is counted in a way of achieving misalignment and overlap, and the time-sharing time is twelve hours, so as to effectively avoid the loss of enterprises due to missing alarms.
  • the quality level is the third level
  • the detection time and the detection result of each data obtained by separately detecting multiple data in a natural day are obtained, and the starting time in the current time is calculated as before the middle of the natural day.
  • the number of target data within 24 hours after 00:00:00 of a day.
  • the quality of the data is
  • the step duration (one day) included in the step duration setting rule corresponding to the level is shifted backward from the start time of the current time window, and the backward shift duration is the step duration (one day), and the starting time after shifting backward Start time (00:00:00 on the day after the natural day) as the start time of the new current time window, and return to execute again to obtain the detection time and detection result of each data separately detected on multiple data in the natural day
  • the time-sharing time is one day, so as to effectively avoid the loss of enterprises due to missing alarms.
  • the embodiment of the present disclosure also provides a data abnormal statistical alarm device, including a processor, wherein the processor is configured to execute the following program modules stored in the memory:
  • the information obtaining module is configured to obtain the detection time and the detection result of each of the data obtained by separately detecting a plurality of data.
  • the abnormal statistical alarm module is configured to count the number of target data in the current time window, and generate an alarm signal to prompt when the number of target data obtained by the statistics is greater than a preset number threshold corresponding to the quality level of the data, Wherein, the target data is data whose detection result is abnormal.
  • the time window setting module moves the current time window back according to the obtained step duration corresponding to the quality level of the data, and uses the moved current time window as the new current time window.
  • time window setting module Since the implementation principle of the time window setting module is similar to that of steps S130 and S140 in FIG. 1, no further description will be given here.
  • This embodiment also provides a computer-readable storage medium, such as flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), only Readable memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disks, optical disks, servers, App application malls, etc., on which computer programs are stored, When the computer program is executed by the processor, the method steps in the first embodiment can be realized.
  • a computer-readable storage medium such as flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), only Readable memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disks, optical disks, servers, App application malls, etc., on which computer programs are stored, When the
  • the embodiment of the present disclosure provides a terminal device, including a memory and a processor, wherein, when the computer program stored in the memory is executed by the processor, the data abnormality statistics and early warning method as in the first embodiment is implemented.
  • a terminal device including a memory and a processor, wherein, when the computer program stored in the memory is executed by the processor, the data abnormality statistics and early warning method as in the first embodiment is implemented.
  • the method, device, and electronic equipment for statistical alarming of data abnormalities can collect statistics on the targets in the current time window by acquiring the detection time and detection result of each of the data obtained by separately detecting multiple data.
  • an alarm signal is generated for prompting, wherein the target data is data whose detection result is abnormal, according to
  • the obtained step duration in the step duration setting rule corresponding to the quality level of the data shifts the current time window back, and uses the back shifted current time window as the new current time window, thus realizing the adoption of time
  • the window method counts the quantity of abnormal data, and generates alarm information according to the quantity and quality type of abnormal data for warning, which effectively alleviates the problem of alarm omissions in related technologies, thereby effectively avoiding the loss of enterprises due to alarm omissions.

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Abstract

一种数据异常统计报警方法、装置及电子设备，有效缓解告警缺漏的问题，从而有效避免企业因告警缺漏造成损失的情况，所述方法包括：获取对多个数据分别进行检测得到的每个数据的检测时刻和检测结果，统计当前时间窗口内的目标数据的数量，并在统计得到的目标数据的数量大于与数据的质量等级对应的预设数量阈值时，生成报警信号以进行提示，根据获得的与数据的质量等级对应的步进时长设置规则中包括的步进时长将所述当前时间窗口后移，并将后移后的当前时间窗口作为新的当前时间窗口。

Description

数据异常统计报警方法、装置及电子设备
相关申请
本公开要求2019年11月20日申请的，申请号为201911142964.9，名称为“数据异常统计报警方法、装置及电子设备”的中国专利申请的优先权，在此将其全文引入作为参考。
技术领域
本公开涉及数据异常检测报警技术领域，特别地涉及一种数据异常统计报警方法、装置及电子设备。
背景技术
目前，在制造业中不同零部件或器件的信息或者在信息交互中交互的信息等(在本公开中称之为“数据”)的质量对产品或数据交互的结果的整体质量会产生不同的影响，对于关键数据和容易引起事故的数据的质量要求必须最为严格，而对于一些次要数据，则质量要求可以适当的降低。
且对数据的质量评价，传统的做法是按照每个自然天、每周、每月等时间段统计出结果，再根据结果给出评价。而供应商数据质量告警则通过定时统计和评价供应商数据质量，并与预设的阈值做比较，如果统计结果达到或超出阈值则触发告警。例如：***事先设置某供应商提供的数据A，每周抽检合格率小于98％则触发告警；或者每日抽检不合格个数大于5个则触发告警等等。具体的，当以上一周的周日00:00:00至下周日00:00:00前为一周来统计供应商数据抽检合格率，或者以00:00:00至下一天00:00:00前为一天来统计供应商数据抽检不合格数时，就会根据每周日的00:00:00或者每天的00:00:00为前后周期分界线对数据进行分割，然后再分别进行汇总统计。
经发明人研究发现，以每天不合格总数告警为例，如果我们按自然天的周期来统计并告警都会存在以下问题：在跨前后两个时间段界线附近持续发生了某一供应商的数据A不合格品检出数之和足以达到触发告警条件，但由于在前后两个时间段界线附近，统计时会拆分成两部分分别计入前后两个时间段，这就有可能造成在前后两天的不合格数均达不到告警条件，而没有触发告警，从而被忽略，给企业造成一定的损失。
发明内容
基于此，本公开提供一种数据异常统计报警方法、装置及电子设备，通过采用时间窗口的方式统计异常数据的数量，并根据异常数据的数量及质量类型产生告警信息以进行告警，有效缓解相关技术中存在的告警缺漏的问题，从而有效避免企业因告警缺漏造成损失的情况。
第一方面，本公开提供了一种数据异常统计报警方法，所述方法包括：
步骤a：获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果；
步骤b：统计当前时间窗口内的目标数据的数量，并在统计得到的目标数据的数量大于与所述数据的质量等级对应的预设数量阈值时生成报警信号以进行提示，其中，所述目标数据为检测结果为异常的数据；
步骤c：根据获得的与所述数据的质量等级对应的步进时长设置规则将所述当前时间窗口的后移，将后移后的当前时间窗口作为新的当前时间窗口，并返回执行步骤a。
在一些实施例中，上述数据异常统计报警方法中，所述方法还包括：
获得预设时长内生成报警信号的次数；
根据获得的与所述数据的质量等级对应的步进时长设置规则将所述当前时间窗口的后移的步骤包括：
从与所述数据的质量等级对应的步进时长规则中获取与所述次数对应的目标步进时长，其中，所述步进时长设置规则中包括所述预设时长内生成报警信号的多个预设次数和与每个所述预设次数分别对应的步进时长；
根据所述目标步进时长将所述当前时间窗口后移。
在一些实施例中上述数据异常统计报警方法中，获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，包括：
获取当前时刻之前设定时长阈值内对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，其中，所述设定时长阈值大于所述当前时间窗口的时间窗口长度，且所述当前时间窗口的起始时间位于当前时刻之前设定时长阈值内。
在一些实施例中上述数据异常统计报警方法中，所述数据的质量等级包括第一等级、第二等级以及第三等级，且所述第一等级优于第二等级，所述第二等级优于第三等级。
在一些实施例中与所述第一等级的数据对应的步进时长小于与所述第二等级的数据对应的步进时长，与所述第二等级的数据对应的步进时长小于与所述第三等级的数据对应的步进时长。
在一些实施例中上述数据异常统计报警方法中，所述当前时间窗口的时间窗口长度为一天或两天，与所述第一等级的数据对应的步进时长为十分钟、二十分钟或一小时，与所 述第二等级的数据对应的步进时长为四小时或半天，与所述第三等级的数据对应的步进时长为一天或两天。
在一些实施例中上述数据异常统计报警方法中，与所述第一等级的数据对应预设数量阈值小于与所述第二等级的数据对应的预设数量阈值，与所述第二等级的数据对应的预设数量阈值小于与所述第三等级的数据对应的预设数量阈值。
在一些实施例中上述数据异常统计报警方法中，
获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，包括：
每间隔设定时长获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，其中，所述设定时长小于或等于所述步进时长。
第二方面，本公开提供了一种数据异常统计报警装置，包括处理器，其中，所述处理器被配置为执行存储在存储器中的以下程序模块：
信息获得模块，被配置为获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果；
异常统计报警模块，被配置为统计当前时间窗口内的目标数据的数量，并在统计得到的目标数据的数量大于与所述数据的质量等级对应的预设数量阈值时生成报警信号以进行提示，其中，所述目标数据为检测结果为异常的数据；
时间窗口设置模块，被配置为根据获得的与所述数据的质量等级对应的步进时长设置规则将所述当前时间窗口的后移，将后移后的当前时间窗口作为新的当前时间窗口。
第三方面，本公开提供了一种存储介质，该存储介质存储的计算机程序，可被一个或多个处理器执行，可用来实现如上述的数据异常统计报警方法。
第四方面，本公开提供了一种电子设备，包括存储器和处理器，所述存储器上存储有计算机程序，该计算机程序被所述处理器执行时，执行上述应用于第一终端中的数据异常统计报警方法。
本公开提供的一种数据异常统计报警方法、装置及电子设备，方法包括：获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，统计当前时间窗口内的目标数据的数量，并在统计得到的目标数据的数量大于与所述数据的质量等级对应的预设数量阈值时，生成报警信号以进行提示，其中，所述目标数据为检测结果为异常的数据，根据获得的与所述数据的质量等级对应的步进时长规则中的步进时长将所述当前时间窗口后移，并将后移后的当前时间窗口作为新的当前时间窗口以再次对异常目标数据进行统计预警，通过上述方法，实现了采用时间窗口的方式统计异常数据的数量，并根据异常数据的数量及质量类型产生告警信息以进行告警，有效缓解相关技术中存在的告警缺漏的问 题，从而有效避免企业因告警缺漏造成损失的情况。
附图说明
为了更清楚地说明本公开实施例或相关技术中的技术方案，下面将对实施例或相关技术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公开的实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据公开的附图获得其他的附图。
图1为本公开一些实施例提供的一种数据异常统计方法的流程示意图。
图2为本公开一些实施例提供的对第一类型的数据进行异常统计时的时间窗口统计示意图。
图3为本公开一些实施例提供的对第二类型的数据进行异常统计时的时间窗口统计示意图。
图4为本公开一些实施例提供的对第三类型的数据进行异常统计时的时间窗口统计示意图。
在附图中，相同的部件使用相同的附图标记，附图并未按照实际的比例绘制。
具体实施方式
以下将结合附图及实施例来详细说明本公开的实施方式，借此对本公开如何应用技术手段来解决技术问题，并达到相应技术效果的实现过程能充分理解并据以实施。本公开实施例以及实施例中的各个特征，在不相冲突前提下可以相互结合，所形成的技术方案均在本公开的保护范围之内。
实施例一
本公开提供的一种数据异常统计报警方法，通过采用滑动时间窗口的方式，统计出不同时间窗口内的检测到的多个数据中检测结果为异常的目标数据的数量，并在该目标数据的数量大于该数据的质量等级对应的预设阈值时，生成报警信号，以提示用户，从而有效缓解相关技术中的数据异常统计不准确的问题。
请参阅图1，本公开提供一种数据异常统计报警方法，该方法可以应用于电脑、服务器或平板等具有数据处理能力的电子设备，在所述数据异常统计报警方法应用于所述电子设备时，可以执行步骤S110至步骤S140。
步骤S110：获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果。
步骤S120：统计当前时间窗口内的目标数据的数量，并在统计得到的目标数据的数量大于与所述数据的质量等级对应的预设数量阈值时生成报警信号以进行提示，其中，所述目标数据为检测结果为异常的数据。
步骤S130：根据获得的与所述数据的质量等级对应的步进时长设置规则将所述当前时间窗口的后移，将后移后的当前时间窗口作为新的当前时间窗口，并返回执行步骤S110。
在上述步骤S110中，获取检测时刻和检测结果的方式可以是接收用户输入的检测时刻和检测结果，也可以是获取检测数据的设备中存储的检测时刻和检测结果，在此不做具体限定，根据实际需求进行设置即可。
当采用获取数据设备中存储的检测时刻和检测结果的方式时，其获取方式可以是实时获取数据的检测时刻和检测结果，也可以是每间隔设定时长获取数据的检测时刻和检测结果，还可以是获取当前时刻之前设定时长阈值范围内的数据的检测结果。
在本实施例中，上述步骤S110可以是每间隔设定时长获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，其中，所述设定时长小于或等于所述步进时长。
在本实施例中，上述步骤S110也可以是获取当前时刻之前设定时长阈值内对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，其中，所述设定时长阈值大于所述当前时间窗口的时间窗口长度，且所述当前时间窗口的起始时间位于当前时刻之前设定时长阈值内。
所述设定时长可以是一周、一个月、几个月或者一年，在此不作具体限定。
步骤S120中，所述当前时间窗口的时间窗口长度可以是一小时、几小时、一天、两天或一周在此不做具体限定，根据实际需求将进行设置即可，所述预设数量阈值可以是几个，几十个或几百个，在此不做具体限定，根据实际需求进行设置即可，所述当前时间窗口的起始时间可以是一天内的。
在本实施例中，所述当前时间窗口的时间窗口长度可以是一天或两天。
可以理解的是，生成报警信号以进行提示的方式也可以是在目标数据的数量与当前时间窗口的时间窗口长度检测的所有数据的数量的比值大于预设值时生成报警信号。
所述数据的质量等级可以包括多个，且不同的质量等级的优劣程度不同，例如，所述数据的质量等级可以包括最优的质量等级(第一等级)、优的质量等级(第二等级)、次优的质量等级(第三等级)以及一般的质量等级(第四等级)，可以理解，所述数据的质量等级还可以包括更多或更少的等级，在此不作具体限定。
在一些实施例中，所述数据的质量等级包括第一等级、第二等级以及第三等级，且所述第一等级的数据的质量优于第二等级的数据的质量，所述第二等级的数据的质量优于第 三等级的数据的质量。
可以理解，通常情况下进行数据检测时，数据检测的设备的检测效率应当相同，在数据的质量等级越高的情况下，在相同时间段内(相同时间窗口内)检测到的目标数据的数量应当越少。
在一些实施例中，与所述第一等级的数据对应预设数量阈值小于与所述第二等级的数据对应的预设数量阈值，与所述第二等级的数据对应的预设数量阈值小于与所述第三等级的数据对应的预设数量阈值。
步骤S130中，所述步进时长设置规则中可以包括步进时长，该步进时长可以是但不限于五分钟、十分钟、几十分钟、几小时、几十小时、一天或几天。所述步进时长设置规则中也可以包括在对应质量等级下不同统计次数对应的步进时长，也可以包括在对应质量等级下不同时间段内对应的不同步进时长，还可以包括在对应质量等级下预设时长内生成的不同报警次数对应的步进时长。可以理解，不同的数据的质量等级的预警要求不同，因此，不同的质量等级对应的步进时长不同，可以理解，质量等级越高的数据，其质量等级通常要求越高，因此，在本实施例中，与所述第一等级的数据对应的步进时长小于与所述第二等级的数据对应的步进时长，与所述第二等级的数据对应的步进时长小于与所述第三等级的数据对应的步进时长。
在本实施例中，与所述第一等级的数据对应的步进时长为十分钟、二十分钟或一小时，与所述第二等级的数据对应的步进时长为四小时或半天，与所述第三等级的数据对应的步进时长为一天或两天。
通过将后移后的当前时间窗口作为新的当前时间窗口，并返回步骤S110，以实现进行时间窗口高精度的数据异常统计报警。
为便于在不同时间段内按照不同的步进时进行数据异常统计，在本实施例中，所述预设统计规则中存储有在对应质量等级下的多个时间段和与各所述时间段内分别对应的步进时长，所述根据获得的与所述数据的质量等级对应的步进时长设置规则将所述当前时间窗口的后移的步骤包括：从所述数据的质量等级对应的步进时长设置规则中确定当前时间窗口的起始时刻所属的目标时间段，及该目标时间段对应的目标步进时长，按照该目标步进时长将所述当前时间窗口后移。
为便于在不同的统计次数下执行按照不同的步进时长进行数据异常统计，通过在执行统计当前时间窗口内的目标数据的数量时，或者执行上述步骤之后，获得统计当前时间窗口内目标数据的质量的统计次数，步骤S130中：根据获得的与所述数据的质量等级对应的步进时长设置规则将所述当前时间窗口的后移，包括：从与所述数据的质量等级对应的 步进时长规则中获取与所述统计次数对应的目标步进时长，其中，所述步进时长设置规则中包括多个统计次数和与每个所述统计次数分别对应的步进时长，根据所述目标步进时长将所述当前时间窗口的后移。
为便于根据在预设时长内产生的报警次数确定对应的步进时长进行数据异常统计，在本实施例中，所述预设统计规则中包括预设时长内的不同预设报警次数对应的预设步进时长，通过在执行统计当前时间窗口内的目标数据的数量时，或者执行上述步骤之后，获得预设时长内生成报警信号的次数，从与所述数据的质量等级对应的步进时长规则中获取与所述次数对应的目标步进时长，其中，所述步进时长设置规则中包括预设时长内生成报警信号的多个预设次数和与每个所述预设次数分别对应的步进时长，根据所述目标步进时长将所述当前时间窗口的后移。
可以理解，在预设时长内产生的预设报警次数越多，其对应的预设步进时长应当越短，进而能及时提醒用户数据异常，以便于用户进行处理。
本公开通过执行上述步骤S110-S130，以实现根据不同的数据的质量等级进行采用不同的步进时长设置规则在不同时间窗口内进行异常数据统计预警，以有效缓解相关技术中采用自然天或自然周内数据异常统计预警时存在的告警缺漏的问题，同时能够有效提高报警的实时性，以便于用户能够及时根据报警信息作出应对处理，从而有效避免企业因告警缺漏造成损失的情况。
实施例二
请结合参阅图2、图3和图4，在本实施例中，以所述当前时间窗口的时间窗口长度为一天，所述质量等级包括第一等级、第二等级以及第三等级，且与所述质量等级对应的步进时长包括与所述第一等级对应的步进时长为一小时、与所述第二等级对应的步进时长为半天以及与所述第三等级步进时长为一天为例进行说明。
请参阅图1，当所述质量等级为第一等级时，获取自然天内对多个数据分别检测得到的每个数据的检测时刻和检测结果，统计当前时间内的起始时间为自然天中前一天的00:00:00时刻之后二十四小时内的目标数据的数量，在该目标数据的数量大于该第一质量等级对应的预设数量阈值时，生成报警信号，并根据该数据的质量等级对应的步进时长设置规则中包括的步进时长(一小时)所述当前时间窗口的起始时间后移，且后移时长为所述步进时长(一小时)，并将后移后的起始时间(自然天中前一天的00:01:00)作为新的当前时间窗口的起始时间，并返回执行再次获取自然天内对多个数据分别检测得到的每个数据的检测时刻和检测结果的步骤，以实现分时统计24小时内目标数据的数量，且分时时长为一小时，从而有效避免企业因告警缺漏造成损失的情况。
请参阅图3，当所述质量等级为第一等级时，获取自然天内对多个数据分别检测得到的每个数据的检测时刻和检测结果，统计当前时间内的起始时间为自然天中前一天的00:00:00时刻之后二十四小时内的目标数据的数量，在该目标数据的数量大于该第一质量等级对应的预设数量阈值时，生成报警信号，并根据该数据的质量等级对应的步进时长设置规则中包括的步进时长(十二小时)所述当前时间窗口的起始时间后移，且后移时长为所述步进时长(十二小时)，并将后移后的起始时间(自然天中前一天的00:12:00)作为新的当前时间窗口的起始时间，并返回执行再次获取自然天内对多个数据分别检测得到的每个数据的检测时刻和检测结果的步骤，以实现错位重叠的方式统计24小时内目标数据的数量，且分时时长为十二小时，从而有效避免企业因告警缺漏造成损失的情况。
请参阅图4，当所述质量等级为第三等级时，获取自然天内对多个数据分别检测得到的每个数据的检测时刻和检测结果，统计当前时间内的起始时间为自然天中前一天的00:00:00时刻之后二十四小时内的目标数据的数量，在该目标数据的数量大于该第一质量等级对应的预设数量阈值时，生成报警信号，并根据该数据的质量等级对应的步进时长设置规则中包括的步进时长(一天)所述当前时间窗口的起始时间后移，且后移时长为所述步进时长(一天)，并将后移后的起始时间(自然天中后一天的00:00:00)作为新的当前时间窗口的起始时间，并返回执行再次获取自然天内对多个数据分别检测得到的每个数据的检测时刻和检测结果的步骤，以实现分时间段统计目标数据的数量，且分时时长为一天，从而有效避免企业因告警缺漏造成损失的情况。
实施例三
本公开实施例还提供了一种数据异常统计报警装置，包括处理器，其中，所述处理器被配置为执行存储在存储器中的以下程序模块：
信息获得模块，被配置为获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果。
由于信息获得模块和图1中步骤S110的实现原理类似，因而在此不作更多说明。
异常统计报警模块，被配置为统计当前时间窗口内的目标数据的数量，并在统计得到的目标数据的数量大于与所述数据的质量等级对应的预设数量阈值时生成报警信号以进行提示，其中，所述目标数据为检测结果为异常的数据。
由于异常统计报警模块和图1中步骤S120，步骤S120的实现原理类似，因而在此不作更多说明。
时间窗口设置模块，根据获得的与所述数据的质量等级对应的步进时长将所述当前时间窗口后移，将后移后的当前时间窗口作为新的当前时间窗口。
由于时间窗口设置模块和图1中步骤S130和S140的实现原理类似，因而在此不作更多说明。
实施例四
本实施例还提供一种计算机可读存储介质，如闪存、硬盘、多媒体卡、卡型存储器(例如，SD或DX存储器等)、随机访问存储器(RAM)、静态随机访问存储器(SRAM)、只读存储器(ROM)、电可擦除可编程只读存储器(EEPROM)、可编程只读存储器(PROM)、磁性存储器、磁盘、光盘、服务器、App应用商城等等，其上存储有计算机程序，所述计算机程序被处理器执行时可以实现如上述实施例一中的方法步骤。
上述方法步骤的具体实施例过程可参见实施例一，本实施例在此不再重复赘述。
实施例五
本公开实施例提供了一种终端设备，包括存储器和处理器，其中，所述存储器中存储的计算机程序被所述处理器执行时，实现如实施例一中的数据异常统计预警方法。关于上述方法步骤的具体实施例过程可参见实施例一，本实施例在此不再重复赘述。
综上，本公开提供的一种数据异常统计报警方法、装置及电子设备，通过获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，统计当前时间窗口内的目标数据的数量，并在统计得到的目标数据的数量大于与所述数据的质量等级对应的预设数量阈值时生成报警信号以进行提示，其中，所述目标数据为检测结果为异常的数据，根据获得的与所述数据的质量等级对应的步进时长设置规则中的步进时长将所述当前时间窗口后移，并将后移后的当前时间窗口作为新的当前时间窗口，实现了采用时间窗口的方式统计异常数据的数量，并根据异常数据的数量及质量类型产生告警信息以进行告警，有效缓解相关技术中存在的告警缺漏的问题，从而有效避免企业因告警缺漏造成损失的情况。
在本公开实施例所提供的几个实施例中，应该理解到，所揭露的***和方法，也可以通过其它的方式实现。以上所描述的***和方法实施例仅仅是示意性的。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
虽然本公开所揭露的实施方式如上，但所述的内容只是为了便于理解本公开而采用的实施方式，并非用以限定本公开。任何本公开所属技术领域内的技术人员，在不脱离本公开所揭露的精神和范围的前提下，可以在实施的形式上及细节上作任何的修改与变化，但 本公开的专利保护范围，仍须以所附的权利要求书所界定的范围为准。

Claims (11)

  1. 一种数据异常统计报警方法，其特征在于，所述方法包括：
    步骤a：获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果；
    步骤b：统计当前时间窗口内的目标数据的数量，并在统计得到的目标数据的数量大于与所述数据的质量等级对应的预设数量阈值时生成报警信号以进行提示，其中，所述目标数据为检测结果为异常的数据；
    步骤c：根据获得的与所述数据的质量等级对应的步进时长设置规则，将所述当前时间窗口的后移，将后移后的当前时间窗口作为新的当前时间窗口，并返回执行步骤a。
  2. 根据权利要求1所述的数据异常统计报警方法，其特征在于，所述方法还包括：
    获得预设时长内生成报警信号的次数；
    根据获得的与所述数据的质量等级对应的步进时长设置规则将所述当前时间窗口的后移的步骤包括：
    从与所述数据的质量等级对应的步进时长规则中获取与所述次数对应的目标步进时长，其中，所述步进时长设置规则中包括所述预设时长内生成报警信号的多个预设次数和与每个所述预设次数分别对应的步进时长；
    根据所述目标步进时长将所述当前时间窗口后移。
  3. 根据权利要求1所述的数据异常统计报警方法，其特征在于，获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，包括：
    获取当前时刻之前设定时长阈值内对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，其中，所述设定时长阈值大于所述当前时间窗口的时间窗口长度，且所述当前时间窗口的起始时间位于当前时刻之前设定时长阈值内。
  4. 根据权利要求1所述的数据异常统计报警方法，其特征在于，所述数据的质量等级包括第一等级、第二等级以及第三等级，且所述第一等级优于第二等级，所述第二等级优于第三等级。
  5. 根据权利要求4所述的数据异常统计报警方法，其特征在于，与所述第一等级的数据对应的步进时长小于与所述第二等级的数据对应的步进时长，与所述第二等级的数据对应的步进时长小于与所述第三等级的数据对应的步进时长。
  6. 根据权利要求4所述的数据异常统计报警方法，其特征在于，所述当前时间窗口的时间窗口长度为一天或两天，与所述第一等级的数据对应的步进时长为十分钟、二十分钟或一小时，与所述第二等级的数据对应的步进时长为四小时或半天，与所述第三等级的数 据对应的步进时长为一天或两天。
  7. 根据权利要求4所述的数据异常统计报警方法，其特征在于，与所述第一等级的数据对应预设数量阈值小于与所述第二等级的数据对应的预设数量阈值，与所述第二等级的数据对应的预设数量阈值小于与所述第三等级的数据对应的预设数量阈值。
  8. 根据权利要求1所述的数据异常统计报警方法，其特征在于，获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，包括：
    每间隔设定时长获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果，其中，所述设定时长小于或等于所述步进时长。
  9. 一种数据异常统计报警装置，其特征在于，包括处理器，其中，所述处理器被配置为执行存储在存储器中的以下程序模块：
    信息获得模块，被配置为获取对多个数据分别进行检测得到的每个所述数据的检测时刻和检测结果；
    异常统计报警模块，被配置为统计当前时间窗口内的目标数据的数量，并在统计得到的目标数据的数量大于与所述数据的质量等级对应的预设数量阈值时生成报警信号以进行提示，其中，所述目标数据为检测结果为异常的数据；
    时间窗口设置模块，被配置为根据获得的与所述数据的质量等级对应的步进时长设置规则将所述当前时间窗口后移，将后移后的当前时间窗口作为新的当前时间窗口。
  10. 一种存储介质，其特征在于，其特征在于，该存储介质存储的计算机程序，被一个或多个处理器执行，用来实现如权利要求1-8中任意一项所述的数据异常统计报警方法。
  11. 一种电子设备，其特征在于，包括存储器和控制器，所述存储器上存储有计算机程序，该计算机程序被所述控制器执行时，执行如权利要求1-8任意一项所述的数据异常统计报警方法。
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