Data processing method and device, equipment and storage medium

Abstract

The embodiment of the invention provides a data processing method and device, equipment and a storage medium. The method comprises the following steps: determining a data processing index and an index subitem under the data processing index; according to the data of each index subitem in the associated time window of the target time period, determining whether the data of the current time window satisfies a preset condition, the current time window being a time window corresponding to the current time point in the associated time window; and if the data of the current time window does not meet the preset condition, determining an abnormal classification factor item corresponding to the current time window from each index subitem. According to the embodiment of the invention, the effectiveness of attribution processing when the data does not meet the condition judgment can be improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of data processing technology, and in particular, to a data processing method, apparatus, device and storage medium. Background Art

[0002] The existing solutions for data anomaly attribution are relatively limited, and there is still a lack of effective solutions. Summary of the invention

[0003] The embodiments of the present application provide a data processing method, apparatus, device and storage medium to improve the effectiveness of attribution processing.

[0004] In a first aspect, an embodiment of the present application provides a data processing method, the method comprising:

[0005] Determine data processing indicators and indicator sub-items under the data processing indicators;

[0006] Determine whether the data in the current time window meets a preset condition according to the data in the associated time window of each indicator sub-item in the target period, wherein the current time window is a time window in the associated time window corresponding to the current time point;

[0007] If the data in the current time window does not meet the preset conditions, the abnormal attribution factor item corresponding to the current time window is determined from each of the indicator sub-items.

[0008] In a second aspect, an embodiment of the present application provides a data processing device, the device comprising:

[0009] A processing index determination module, used to determine a data processing index and index sub-items under the data processing index;

[0010] A window data determination module, used to determine whether the data in the current time window meets the preset conditions according to the data of each indicator sub-item in the associated time window of the target period, wherein the current time window is a time window corresponding to the current time point in the associated time window;

[0011] The data attribution processing module is used to determine the abnormal attribution factor item corresponding to the current time window from each of the indicator sub-items if the data in the current time window does not meet the preset conditions.

[0012] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising:

[0013] A processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the data processing method provided in the first aspect of the present application.

[0014] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium for storing a computer program, wherein the computer program enables a computer to execute the data processing method provided in the first aspect of the present application.

[0015] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program / instructions, characterized in that when the computer program / instructions are executed by a processor, the data processing method provided in the first aspect of the present application is implemented.

[0016] The data processing method, device, equipment and storage medium provided by the embodiment of the present application first determine the data processing index and each index sub-item under the data processing index. Then, according to the data in each associated time window of each index sub-item in the target period, determine whether the data in the current time window meets the preset conditions. If the data in the current time window does not meet the preset conditions, the corresponding abnormal attribution factor item in the current time window is determined from each index sub-item, thereby improving the effectiveness of attribution processing when the data does not meet the condition determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 A flowchart of a data processing method shown in an embodiment of the present application;

[0019] Figure 2 A schematic diagram of each indicator sub-item of the data processing indicator shown in the embodiment of the present application;

[0020] Figure 3 A flowchart of another data processing method shown in an embodiment of the present application;

[0021] Figure 4a This is a schematic diagram of the principle of performing attribution processing on the situation where the data in the current time window does not meet the preset conditions under the overall attribution processing of indicators shown in an embodiment of the present application;

[0022] Figure 4b This is a schematic diagram showing the principle of performing attribution processing on the situation where the data in the current time window does not meet the preset conditions under the indicator sub-item attribution processing shown in an embodiment of the present application;

[0023] Figure 4cThis is a schematic diagram of the principle of performing attribution processing on the situation where the data in the current time window does not meet the preset conditions under the indicator sub-item ratio attribution processing shown in an embodiment of the present application;

[0024] Figure 5 A principle block diagram of a data processing device shown in an embodiment of the present application;

[0025] Figure 6 A schematic block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or scheme described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or schemes. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0029] Taking the live broadcast scenario as an example, the business party can access a variety of business functions in the live broadcast scenario, such as live broadcast interaction, etc. The above business functions correspond to one or more business operations.

[0030] If the business operations at a certain time / time period meet certain conditions, such as the same business operation volume is greater than a threshold, it will cause business data fluctuations and cause certain data anomalies.

[0031] Then, in order to avoid business application failures due to data anomalies, it is usually necessary to detect whether data anomalies occur during the operation of business applications, and attribute the data anomalies so as to effectively determine the cause of the data anomalies and quickly resolve the data anomalies. Based on this, the present application designs a solution that can effectively attribute data anomalies, thereby improving the effectiveness of data anomaly attribution.

[0032] Next, the technical solution of this application is described in detail:

[0033] Figure 1 A flowchart of a data processing method shown in an embodiment of the present application. The method can be executed by a data processing device provided in an embodiment of the present application, wherein the data processing device can be implemented by any software and / or hardware. Exemplarily, the data processing device can be applied to any electronic device, which may include but is not limited to tablet computers, mobile phones (such as folding screen mobile phones, large screen mobile phones, etc.), wearable devices, vehicle-mounted devices, augmented reality (AR) / virtual reality (VR) devices, laptop computers, ultra-mobile personal computers (UMPC), netbooks, personal digital assistants (PDA), smart TVs, smart screens, high-definition TVs, 4K TVs, smart speakers, smart projectors and other devices. This application does not impose any restrictions on the specific types of electronic devices.

[0034] Specifically, Figure 1 As shown, the method may include the following steps:

[0035] S110, determining a data processing indicator and indicator sub-items under the data processing indicator.

[0036] The data processing indicators may be related to the execution objects of various business operations within the business application and various factors that may cause data anomalies.

[0037] For any business application, by analyzing various factors that may cause data anomalies in the business application, the data flow fluctuation after the business application performs corresponding business operations on the factor is determined. Therefore, the application can select a factor that may cause data anomalies as the corresponding data processing indicator.

[0038] After the data processing indicator is determined, each indicator sub-item under the data processing indicator can be determined by analyzing each actual indicator information configured under the data processing indicator in the business application.

[0039] In order to achieve multi-level analysis and processing of data anomaly attribution in business applications, such as Figure 2 As shown, the present application can set each indicator sub-item under the data processing indicator as a dimensional item of the data processing indicator in a single dimension, or, it can also be a dimensional item of the data processing indicator in multiple dimensions (for example, a double dimension consisting of a first dimension and a second dimension).

[0040] In this application, when a data anomaly occurs in a business application at a certain moment, it is possible to analyze which indicator sub-item is the cause of this data anomaly from all indicator sub-items under the data processing indicator. Then, when the indicator sub-item under the data processing indicator is a dimension item under a dual dimension, the data anomaly can be simultaneously attributed at multiple levels to ensure the effectiveness of the data anomaly attribution processing.

[0041] S120, determining whether the data in the current time window meets a preset condition based on the data of each indicator sub-item in the associated time window of the target period.

[0042] Since whether there is data anomaly in the business application at the current time point needs to be judged in combination with the change trend of the business data statistics (that is, data traffic) in the past period of time, this application can set a target period, which can be a historical period divided forward from the current time point according to the preset time length.

[0043] Then, by analyzing the change trend of the entire data traffic in the target period to see whether there is a sudden increase or decrease at the current time point, it is possible to determine whether there is data anomaly at the current time point. As for the sudden increase or decrease of data traffic at the current time point, it is not judged by the specific value of data traffic at the current time point, but needs to be analyzed in combination with the specific changes in data traffic within a short period of time at the current time point.

[0044] Moreover, a sudden increase or decrease in data traffic means that the data traffic changes significantly in a short period of time. Therefore, by analyzing the rate of change of data traffic at the current time point and the historical time points, it is also possible to determine whether there is a sudden increase or decrease in data traffic at the current time point. However, since only the rate of change between the data traffic at the current time point and the data traffic at a single historical time point is compared, misjudgment may occur due to the jitter of data traffic in a short period of time. Therefore, on the basis of analyzing whether there is a sudden increase or decrease in data traffic at the current time point in combination with the specific changes in data traffic within a short period of time where the current time point is located, it is also necessary to further combine the rate of change between the business traffic at the current time point and multiple historical time points to analyze whether there is a sudden increase or decrease in data traffic at the current time point.

[0045] From the above content, it can be seen that in order to ensure effective and timely determination of data anomalies at the current time point, the present application can set a sliding time window. Then, by controlling the sliding time window to slide dynamically within the target time period, the target time period can be divided into multiple associated time windows.

[0046] Each associated time window may correspond to the last time point represented by the associated time window in the target time period. Since the current time point is the last time point in the target time period, the current time window may be a time window corresponding to the current time point in each associated time window, that is, the last time window in the associated time window.

[0047] For example, the window length of the sliding time window in the present application may be 5 minutes, and the sliding step length may be 1 minute. Then, a correlation time window is divided every 1 minute in the target time period, and the length of each correlation time window is 5 minutes.

[0048] In this application, before attributing the data anomaly, it is first necessary to determine whether there is a data anomaly at the current time point. Then, for each indicator sub-item under the data processing indicator, this application can first find out the large amount of data generated by each indicator sub-item during the target period due to the execution of various business operations. Then, for each indicator sub-item, by analyzing the specific generation timestamp of each data of the indicator sub-item during the target period, each data in the target period can be divided into corresponding associated time windows, thereby obtaining the data of each indicator sub-item in each associated time window during the target period.

[0049] Then, for each associated time window, statistics may be performed on each data within the associated time window to obtain data statistics corresponding to the associated time window.

[0050] Since each associated time window corresponds to the end time point represented by the associated time window in the target time period, and the end time point of the last associated time window among the associated time windows is the current time point, the last associated time window can be used as the current time window, and the end time points of other associated time windows can be various historical time points before the current time point.

[0051] Therefore, for the current time window in each associated time window, the data statistics in the current time window can be used as the data flow at the current time point. And for each other associated time window in each associated time window, the data statistics of the other associated time window can be used as the data flow at the corresponding historical time point.

[0052] Moreover, in order to effectively determine whether there is data anomaly, the present application can pre-set a determination condition for the change trend of data flow as a preset condition in the present application. Then, the present application can determine whether the data in any time window satisfies the preset condition to determine whether the data in the time window is abnormal.

[0053] The preset condition may be a data flow variation range within which data in any time window can maintain stable changes when the business application is operating normally.

[0054] Then, according to the change between the data flow at each historical time point and the current time point in the target period, the data flow change rate at the current time point can be determined. Then, by determining whether the data flow change rate at the current time point meets the requirement of the maximum change rate threshold that can be achieved when the data flow changes in the above preset conditions, it is determined whether the data in the current time window meets the preset conditions.

[0055] Therefore, if the data in the current time window meets the above preset conditions, it means that the data flow change rate of the current time window is within the maximum change rate threshold range supported by the preset conditions, and it can be determined that there is no abnormality in the data in the current time window.

[0056] If the data in the current time window does not meet the above preset conditions, it means that the data flow change rate of the current time window has exceeded the range of the maximum change rate threshold supported by the preset conditions, and it can be determined that the data in the current time window is abnormal.

[0057] In some possible implementations, for the data flow rate change rate at the current time point, the present application can first calculate the average data flow at each historical time point, and then determine the data flow rate change rate at the current time point by analyzing the change between the data flow at the current time point and the average data flow, so as to avoid misjudgment of data anomalies due to jitter of data flow in a short period of time, and ensure effective judgment of whether the data in the current time window meets the preset conditions.

[0058] As an optional implementation scheme in the present application, in order to more quickly and effectively obtain the data of each associated time window of each indicator sub-item in the target time period, after obtaining the source data in the target time period, the present application can partition and slice the source data in the target time period according to each indicator sub-item under the data processing indicator to obtain the data of each indicator sub-item in the target time period.

[0059] That is to say, after obtaining the entire source data within the target period, the present application can first analyze the correspondence between each source data and each indicator sub-item of the data processing indicator. Then, according to each indicator sub-item under the data processing indicator, the source data corresponding to the indicator sub-item is divided into a partition, thereby obtaining the data partition corresponding to each indicator sub-item.

[0060] Moreover, since the source data in the subsequent target time period also needs to be divided into various associated time windows, for the data partition corresponding to each indicator sub-item, the data in the data partition corresponding to the indicator sub-item can be sliced ​​and processed at the time granularity according to a certain time interval, so as to obtain the data of each indicator sub-item in the target time period.

[0061] Then, by analyzing the data time in each shard after the data partition corresponding to each indicator sub-item is split, each shard in the data partition corresponding to each indicator sub-item can be quickly and effectively divided into each associated time window in the target time period, thereby obtaining the data of each indicator sub-item in each associated time window in the target time period, thereby improving the efficiency of determining whether the data meets the preset conditions.

[0062] S130: If the data in the current time window does not meet the preset conditions, determine the abnormal attribution factor item corresponding to the current time window from each indicator sub-item.

[0063] If the data in the current time window does not meet the preset conditions, it means that there is a sudden increase or decrease in data flow at the current time point, which may cause data anomalies in the current time window. Then, by performing corresponding statistical analysis on the data of each indicator sub-item in the current time window, the degree of change in data flow of each indicator sub-item in the current time window can be determined. Then, according to the degree of change in data flow of each indicator sub-item in the current time window, the degree of influence of each indicator sub-item on the conditional judgment of the data in the current time window can be determined.

[0064] It is understandable that the influence degree of each indicator sub-item on the conditional determination of the data in the current time window in the present application can be the contribution degree of each indicator sub-item to the occurrence of data anomaly. When it is determined that the data in the current time window does not meet the preset conditions, it is determined that anomaly occurs.

[0065] Then, based on the degree of influence of each indicator sub-item on the conditional judgment of the data in the current time window, each indicator sub-item can be sorted in order from high to low in terms of influence, and the indicator sub-item sequence in the current time window can be obtained. It can be seen from this that the first few indicator sub-items in the indicator sub-item sequence in the current time window have a greater degree of influence on the judgment that the data in the current time window does not meet the preset conditions, so it can be determined which indicator sub-item is the reason why the data in the current time window does not meet the preset conditions, and the abnormal attribution factor item corresponding to the current time window can be obtained from each indicator sub-item, thereby realizing accurate attribution processing of the data in each time window from each indicator sub-item of the data processing indicator when the preset conditions are not met.

[0066] However, if the data in the current time window meets the preset conditions, it means that there is no data anomaly in the current time window, that is, there is no sudden increase or decrease in business traffic at the current time point. In this case, there is no need to perform data anomaly attribution analysis at the current time point, and the current time window can be marked as normal data.

[0067] In addition, in order to ensure the normal operation of business applications, when the data in the current time window does not meet the preset conditions and the corresponding abnormal attribution items are determined, this application will also determine the influence based on the conditions of each indicator sub-item in the current time window, and display prompt information when the data in the current time window does not meet the preset conditions.

[0068] That is to say, in order to intuitively and clearly display the reasons why the data at the current time point does not meet the preset conditions, the present application can determine the degree of influence of each indicator sub-item on the conditional judgment of the data in the current time window according to the degree of change of the data flow of each indicator sub-item in the current time window, so as to obtain the influence of each indicator sub-item on the conditional judgment in the current time window.

[0069] Then, based on the influence of the conditions of each indicator sub-item in the current time window, the indicator sub-items are sorted from high to low to obtain the indicator sub-item sequence in the current time window. Then, based on the indicator sub-item sequence in the current time window, combined with the selected data processing indicator, a prompt message can be generated when the data in the current time window does not meet the preset conditions. Furthermore, according to a certain display format, the prompt message can be displayed in the display interface, so that the corresponding indicator sub-item sequence is intuitively displayed, and the specific reasons for the data in the current time window not meeting the preset conditions in each indicator sub-item are clearly indicated.

[0070] Moreover, for the prompt information displayed when the data in the current time window does not meet the preset conditions, it can also support the playback of the historical data traffic of any indicator sub-item in the indicator sub-item sequence displayed therein within the target time period. Therefore, the present application can set a traffic playback control at the associated position of the indicator sub-item sequence in the prompt information. Then, after selecting any indicator sub-item in the indicator sub-item sequence, a traffic playback request for the indicator sub-item within the target time period can be initiated by triggering the traffic playback control. Accordingly, the present application can detect the traffic playback trigger operation of any indicator sub-item in the prompt information, and use the indicator sub-item as the target indicator sub-item. Then, in response to the traffic playback trigger operation of the target indicator sub-item in the prompt information, the data traffic change trend of the target indicator sub-item within the target time period is displayed.

[0071] That is to say, after detecting the traffic playback trigger operation of the target indicator sub-item in the prompt information, the data of the target indicator sub-item in each associated time window of the target time period can be statistically analyzed to obtain the data statistics of the target indicator sub-item in each associated time window, which is used as the data traffic of the target indicator sub-item at the historical time point corresponding to each associated time window. In this way, the data traffic change trend of the target indicator sub-item in the target time period is generated and displayed.

[0072] The technical solution provided by the embodiment of the present application first determines the data processing index and each indicator sub-item under the data processing index. Then, based on the data in each associated time window of each indicator sub-item in the target time period, it is determined whether the data in the current time window meets the preset conditions. If the data in the current time window does not meet the preset conditions, the abnormal attribution factor item corresponding to the current time window is determined from each indicator sub-item, so as to achieve accurate attribution processing of the data in each time window from each indicator sub-item of the data processing index when the preset conditions are not met, thereby improving the effectiveness of the attribution processing of the data when the preset conditions are not met.

[0073] As an optional implementation scheme in this application, considering that business applications can analyze and process the reasons for the data in any time window not meeting the preset conditions from multiple angles, for example, the reasons for the data in a certain time window not meeting the preset conditions and causing data anomalies can be analyzed from multiple angles such as the overall data flow change of the data processing index, the data flow change under each indicator sub-item of the data processing index, and the change of a certain ratio under each indicator sub-item. Moreover, when the data in the current time window does not meet the preset conditions, the influence of the conditions of each indicator sub-item in the current time window can be analyzed from multiple dimensions to ensure the accuracy of the data anomaly attribution processing.

[0074] Therefore, the present application can provide a detailed explanation of the data anomaly determination process under different data processing types and the process of determining the influence of conditional determination of each indicator sub-item in different dimensions.

[0075] Figure 3 This is a flowchart of another data processing method shown in an embodiment of the present application. The method may include the following steps:

[0076] S310, determining a data processing indicator and indicator sub-items under the data processing indicator.

[0077] S320: Determine the associated time window of the target period according to the preset data processing type.

[0078] Due to the different processing types of data anomalies, it is necessary to analyze the data flow changes of each indicator sub-item from different angles, which means that the division rules of each associated time window within the target period are different, so that the data of each associated time window can represent the data flow changes from different angles.

[0079] Therefore, according to the pre-set multiple data processing types for the division rules of the associated time windows, the present application can set a suitable sliding time window. Then, by controlling the sliding time window to dynamically slide within the target time period corresponding to each indicator sub-item, the target time period can be divided into multiple associated time windows.

[0080] In some implementations, the data processing types in the present application may include overall indicator attribution processing, indicator sub-item attribution processing, and indicator sub-item ratio attribution processing.

[0081] The overall attribution processing of the indicator can be to analyze the overall data flow changes after the statistics of each indicator sub-item to determine the attribution result when the data is abnormal due to the overall data under the entire data processing indicator not meeting the preset conditions.

[0082] Therefore, for the overall attribution of indicators, it is necessary to comprehensively analyze the overall data flow changes of each indicator sub-item within the target period, which means that it is necessary to aggregate the data of each indicator sub-item at the same time point within the target period. Figure 4aAs shown, the present application can set a first-class sliding time window for the entire data processing index, and the first-class sliding time window can contain all indicator sub-items. Then, by controlling the first-class sliding time window to slide dynamically within the target period, the target period can be divided into multiple associated time windows for all indicator sub-items of the data processing index. Then, under the overall attribution processing of the indicator, the associated time windows within the target period can be the various first-class sliding time windows after dynamic sliding of the entire data processing indicator within the target period. At this time, the current time window can be the last first-class sliding time window corresponding to the current time point in the target period.

[0083] The indicator sub-item attribution processing can be to analyze the data flow changes of each indicator sub-item separately for each indicator sub-item to determine the attribution result when the data under the single indicator sub-item does not meet the preset conditions as shown in the figure, resulting in data anomalies.

[0084] From this, we can see that for the attribution processing of indicator sub-items, it is necessary to analyze the changes in data flow of each indicator sub-item within the target period, that is, to set a second-type sliding time window for each indicator sub-item, and in order to ensure the data correlation of different indicator sub-items at the same time point, the sliding step size and window length of the second-type sliding time windows under different indicator sub-items are required to be the same.

[0085] So, if Figure 4b As shown, the present application can set a second-class sliding time window for each indicator sub-item. Then, under each indicator sub-item, by controlling the second-class sliding time window under the indicator sub-item to slide dynamically within the target time period, the target time period can be divided into multiple associated time windows under each indicator sub-item. Then, under the indicator sub-item attribution processing, the associated time window within the target time period can be each second-class sliding time window set for each indicator sub-item within the target time period and after dynamic sliding. At this time, the current time window can be the last second-class sliding time window corresponding to the current time point under each indicator sub-item within the target time period.

[0086] The indicator sub-item ratio attribution processing can be to determine the attribution result when the data is abnormal due to a specific ratio value of the data under a single indicator sub-item not meeting the preset conditions by analyzing a specific ratio value of the data statistics of each indicator sub-item within the preset historical period at the current time point.

[0087] It can be seen that for the attribution of the indicator sub-item ratio, it is necessary to analyze the data statistics of each indicator sub-item in the preset historical period (that is, the fixed time before the current time point) at the current time point, without analyzing the changes in data flow. Figure 4cAs shown, the target period in this application can be used as the preset historical period where the current time point is located, and there is no need to divide the target period into windows. Therefore, the associated time window in the target period in this application can be the entire time window where the target period is located, and the current time window and the associated time window are the same.

[0088] S330, determining whether the data in the current time window meets a preset condition based on the data of each indicator sub-item in the associated time window.

[0089] After determining each associated time window within the target period, for each indicator sub-item under the data processing indicator, the present application can first find out from the source data the large amount of data generated by each indicator sub-item during the target period due to the execution of various business operations. Then, for each indicator sub-item, by analyzing the specific generation timestamp of each data of the indicator sub-item during the target period, each business data within the target period can be divided into the corresponding associated time windows, thereby obtaining the data of each indicator sub-item in each associated time window.

[0090] Then, for each associated time window, statistics can be collected for each data in the associated time window to obtain the data statistics corresponding to the associated time window. Then, for the current time window in each associated time window, the data statistics in the current time window can be used as the data flow at the current time point. And for each other associated time window in each associated time window, the data statistics of the other associated time window can be used as the data flow at the corresponding historical time point. Furthermore, by analyzing the changes between the data flow at each historical time point and the current time point, the data flow change rate at the current time point can be determined.

[0091] Then, by determining whether the data flow rate change rate at the current time point meets the requirement of the maximum change rate threshold that can be achieved when the data flow rate changes in the above preset conditions, it is determined whether the data in the current time window meets the preset conditions.

[0092] In some possible implementations, under different preset data processing types, due to different associated time windows within the target period, the determination method for whether the data in the current time window meets the preset conditions is different. This application can illustrate the determination method for whether the data in the current time window meets the preset conditions under different data processing types through the following three situations:

[0093] In the first scenario, if the data processing type is overall attribution processing of indicators, then the associated time window within the target period is the first-class sliding time window for the entire data processing indicator within the target period, and the current time window is the last first-class sliding time window within the target period.

[0094] The method for determining whether the data in the current time window meets the preset conditions can be specifically: determining the data flow corresponding to each first-class sliding time window based on the aggregated data of each indicator sub-item in each first-class sliding time window; determining the data change rate of the current time window based on the data flow corresponding to each first-class sliding time window to determine whether the data change rate is less than the preset change rate threshold.

[0095] That is to say, since the first-class sliding time window can contain all indicator sub-items, after obtaining the data of each indicator sub-item within the target time period, for each first-class sliding time window, the data of each indicator sub-item within the same first-class sliding time window can be aggregated to obtain the aggregated data of each indicator sub-item in each first-class sliding time window.

[0096] Then, by performing statistical analysis on the aggregated data in each first-type sliding time window, the data statistics of each first-type sliding time window can be determined as the data flow corresponding to the first-type sliding time window.

[0097] Furthermore, if Figure 4a As shown, by analyzing the data flow corresponding to each first-class sliding time window, the data flow of the current time window in the first-class sliding time window and the average data flow of multiple other first-class sliding time windows can be determined. By analyzing the difference between the data flow of the current time window and the average data flow, the data flow change of the current time window relative to other first-class sliding time windows is analyzed to determine the data change rate of the current time window.

[0098] Moreover, the preset condition in the present application may be to require that the data change rate of any time window is less than a preset change rate threshold, so that the data flow of each time window can maintain a stable change and avoid a sudden increase or decrease in data.

[0099] Then, after determining the data change rate of the current time window, it is possible to determine whether the data in the current time window meets the preset conditions by comparing the data change rate of the current time window with the preset change rate threshold in the above preset conditions.

[0100] In the second scenario, if the data processing type is indicator sub-item attribution processing, then the associated time window within the target period is the second-type sliding time window set for each indicator sub-item within the target period, and the current time window is the last second-type sliding time window under each indicator sub-item within the target period.

[0101] The method for determining whether the data in the current time window meets the preset conditions can be specifically as follows: according to the partitioned data of each indicator sub-item in each second-category sliding time window under the indicator sub-item, determine the data flow corresponding to each second-category sliding time window under the indicator sub-item; for each indicator sub-item, according to the data flow corresponding to each second-category sliding time window under the indicator sub-item, determine the data change rate of the current time window under the indicator sub-item to determine whether the data change rate is less than the preset change rate threshold.

[0102] That is to say, under the indicator sub-item attribution processing, there will be a current time window under each indicator sub-item, and it is necessary to determine whether the data of the current time window under each indicator sub-item meets the preset conditions. Therefore, after obtaining the data of each indicator sub-item in the target period, the data of each indicator sub-item will be divided into different partitions.

[0103] For each indicator sub-item, all data under the indicator sub-item can be divided into each second-type sliding time window under the indicator sub-item according to the generation timestamp of each data under the indicator sub-item, so as to obtain the partition data in each second-type sliding time window under the indicator sub-item. Then, Figure 4b As shown, by performing statistical analysis on the partition data in each second-type sliding time window under the indicator sub-item, the data statistics of each second-type sliding time window under the indicator sub-item can be determined as the data flow corresponding to the second-type sliding time window. According to the above steps, the data flow corresponding to each second-type sliding time window under the indicator sub-item can be determined.

[0104] Furthermore, for each indicator sub-item, by analyzing the data flow corresponding to each second-type sliding time window under the indicator sub-item, the data flow of the current time window in each second-type sliding time window under the indicator sub-item and the average data flow of multiple other second-type sliding time windows can be determined. By analyzing the difference between the data flow of the current time window under the indicator sub-item and the average data flow, the change in the data flow of the current time window under the indicator sub-item relative to other first-type sliding time windows is analyzed, so as to determine the data change rate of the current time window under the indicator sub-item.

[0105] Moreover, the preset condition in the present application may be to require that the data change rate of any time window is less than a preset change rate threshold, so that the data flow of each time window under each indicator sub-item can maintain a stable change to avoid a sudden increase or decrease in data.

[0106] Then, after determining the data change rate of the current time window under each indicator sub-item, the data change rate of the current time window under each indicator sub-item can be compared with the preset change rate threshold in the above preset conditions to determine whether the data of the current time window under the indicator sub-item meets the preset conditions.

[0107] According to the same steps as above, it can be determined whether the data of the current time window under each indicator sub-item meets the preset conditions, so as to determine whether the data of the current time window under each indicator sub-item is abnormal.

[0108] In the third scenario, if the data processing type is indicator sub-item ratio attribution processing, the associated time window within the target period is the entire time window in which the target period is located, and the current time window is the same as the associated time window.

[0109] The method for determining whether the data in the current time window meets the preset conditions can be specifically: determining the numerator type and denominator type of the indicator sub-item ratio attribution; determining the first type of data statistics of the indicator sub-item in the current time window based on the data of each indicator sub-item under the numerator type; determining the second type of data statistics of the indicator sub-item in the current time window based on the data of each indicator sub-item under the denominator type; and determining whether the statistical ratio between the first type of data statistics and the second type of data statistics is less than the preset ratio threshold.

[0110] That is to say, under the indicator sub-item ratio attribution processing, it is necessary to analyze the specific ratio of the data of each indicator sub-item within a period of time before the current time point at a certain ratio, without defining multiple time windows, so that the target period, associated time window and current time window in this application are all the same time period. This application uniformly uses the current time window for explanation.

[0111] Then, for the attribution of the indicator sub-item ratio, we first need to determine the numerator type and denominator type corresponding to the ratio. For example, if we analyze the ratio between the amount of data successfully executed and the amount of all data requested to be executed under each indicator sub-item, we can determine that the numerator type is the data type that was successfully executed, and the denominator type is the data type that was requested to be executed.

[0112] Then, for each indicator sub-item, such as Figure 4cAs shown, according to the numerator type and denominator type of the indicator sub-item ratio attribution, the data of the indicator sub-item under the numerator type and the data of the indicator sub-item under the denominator type can be found from the source data respectively. Furthermore, for the data of each indicator sub-item under the numerator type, the data of the indicator sub-item under the numerator type in the current time window can be statistically analyzed to determine the first type of data statistics of the indicator sub-item in the current time window. Moreover, for the data of each indicator sub-item under the denominator type, the data of the indicator sub-item under the denominator type in the current time window can be statistically analyzed to determine the second type of data statistics of the indicator sub-item in the current time window.

[0113] Furthermore, by calculating the ratio between the first type of data statistics and the second type of data statistics of each indicator sub-item in the current time window, the statistical ratio of each indicator sub-item in the current time window can be determined.

[0114] Moreover, the preset condition in the present application may be to require that the actual statistical ratio of each indicator sub-item ratio is less than a preset ratio threshold, so that the data flow under each indicator sub-item can maintain a stable change to avoid a sudden increase or decrease in data.

[0115] Then, by comparing the statistical ratio of each indicator sub-item in the current time window with the preset ratio threshold in the above preset condition, it can be determined whether the data of each indicator sub-item in the current time window meets the preset condition.

[0116] S340, if the data in the current time window does not meet the preset conditions, determine the impact dimension for each preset condition, and determine the impact value of each indicator sub-item under the impact dimension of the condition determination based on the data of each indicator sub-item in the current time window to generate an indicator sub-item sequence under the impact dimension of the condition determination.

[0117] In order to ensure the accuracy of attribution processing when data in any time window does not meet the preset conditions and causes data anomalies, by analyzing the influencing factors used to determine whether the data in any time window meets the preset conditions, the present application can set multiple conditional determination influencing dimensions, such as data statistics, fluctuations, etc., to analyze from multiple dimensions the degree of influence of each indicator sub-item on the data in a certain time window that has been determined to not meet the preset conditions.

[0118] In this application, when the data in the current time window does not meet the preset conditions, this application can determine the impact value of each indicator sub-item under the conditional determination impact dimension by statistically analyzing the data of each indicator sub-item in the current time window for each conditional determination impact dimension. Then, according to the impact value of each indicator sub-item under each conditional determination impact dimension, each indicator sub-item can be sorted under each conditional determination impact dimension to obtain a sorted indicator sub-item sequence under the conditional determination impact dimension.

[0119] Therefore, within the current time window, the indicator sub-item sequence under each conditional determination impact dimension can represent the degree of influence of each indicator sub-item under the conditional determination impact dimension on the final determination that the data in the current time window does not meet the preset condition.

[0120] It is understood that if the preset data processing type is the overall attribution processing of the indicator or the attribution processing of the indicator sub-item, the conditional determination impact dimensions may include but are not limited to data statistics, fluctuations and volatility. If the preset data processing type is the attribution processing of the indicator sub-item ratio, the conditional determination impact dimensions may include but are not limited to the numerator statistics, the denominator statistics and the statistical ratio.

[0121] That is to say, under the overall attribution processing of indicators or the attribution processing of indicator sub-items, by calculating the data statistics, fluctuations and volatility of each indicator sub-item in the current time window, and sorting each indicator sub-item according to the value of this dimension in each dimension of the data statistics, fluctuations and volatility, the sequence of indicator sub-items in each dimension of the data statistics, fluctuations and volatility can be obtained.

[0122] Under the indicator sub-item ratio attribution processing, by calculating the numerator statistical value, denominator statistical value and statistical ratio of the data of each indicator sub-item in the current time window, and sorting each indicator sub-item according to the value of this dimension in each dimension of the numerator statistical value, denominator statistical value and statistical ratio, the indicator sub-item sequence in each dimension of the numerator statistical value, denominator statistical value and statistical ratio can be obtained.

[0123] S350, determining the indicator sub-item sequence under the impact dimension according to each condition, and determining the abnormal attribution factor item corresponding to the current time window.

[0124] After determining the indicator sub-item sequence under each current time window impact dimension within the current time window, it can be learned that the first few indicator sub-items in the indicator sub-item sequence under each current time window impact dimension have a great influence on the judgment result that the data in the current time window does not meet the preset conditions, which may be an important reason for the data anomaly caused by the data in the current time window not meeting the preset conditions, thereby determining the abnormal attribution factor item corresponding to the current time window from each indicator sub-item.

[0125] In some possible implementations, in order to intuitively and clearly display the reasons why the data in any time window does not meet the preset conditions, the present application will also determine the influence of the conditions of each indicator sub-item in the current time window, and display prompt information when the data in the current time window does not meet the preset conditions. The prompt information will include a sequence of indicator sub-items under each condition determination impact dimension in the current time window.

[0126] In order to ensure the accuracy of data anomaly prompts, the present application displays prompt information when the data in the current time window does not meet the preset conditions. Specifically, it can be: determine the prompt trigger of the current time window based on whether the data in the current time window and the consecutive adjacent time windows of the current time window meet the preset conditions; if the current time window has triggered the prompt, determine the influence based on the conditions of each indicator sub-item in the current time window, and display the prompt information when the data in the current time window does not meet the preset conditions.

[0127] By determining the continuous adjacent time windows of the current time window, the continuous adjacent time windows may be a plurality of continuous time windows after the current time window. Then, the data of each continuous adjacent time window is judged whether it meets the preset condition in the same way as the judgment method of whether the data of the current time window meets the preset condition.

[0128] Then, if the data in the current time window and each consecutive adjacent time window do not meet the preset conditions, it means that the data anomaly caused by the fact that the data in the current time window does not meet the preset conditions is not caused by the scenario of rapid recovery after the spike. Therefore, the influence can be determined according to the conditions of each indicator sub-item in the current time window, and the prompt information when the data in the current time window does not meet the preset conditions is displayed.

[0129] If the data in the current time window does not meet the preset conditions, but the data in each consecutive adjacent time window meets the preset conditions, it means that the data anomaly caused by the fact that the data in the current time window does not meet the preset conditions is caused by the scenario of rapid recovery after the spike, so there is no need to prompt that the data in the current time window does not meet the preset conditions, thereby improving the accuracy of the data anomaly prompt.

[0130] The technical solution provided by the embodiment of the present application first determines the data processing index and each indicator sub-item under the data processing index. Then, based on the data in each associated time window of each indicator sub-item in the target time period, it is determined whether the data in the current time window meets the preset conditions. If the data in the current time window does not meet the preset conditions, it means that the data in the current time window may be abnormal, then the abnormal attribution factor item corresponding to the current time window is determined from each indicator sub-item, so as to achieve accurate attribution processing of the data in each time window from each indicator sub-item of the data processing index when the preset conditions are not met, thereby improving the effectiveness of attribution of data when the preset conditions are not met.

[0131] Taking the live broadcast application as an example, the entire process of data anomaly attribution processing provided by this application can be exemplified: in the live broadcast application, the live broadcast room attribute can be selected as the data processing indicator, then each live broadcast room identifier in the live broadcast application, such as live broadcast room a, live broadcast room b, live broadcast room c, etc., can be an indicator sub-item under the data processing indicator. For each indicator sub-item in live broadcast room a, live broadcast room b, and live broadcast room c, as the live broadcast proceeds, interactions such as likes and comments will be generated in each live broadcast room. For each interactive operation initiated in each live broadcast room, an interactive request will be received and the interactive request will be processed once. Then, the interactive request received in each live broadcast room can be used as the data in the indicator sub-item represented by each live broadcast room. The data flow of the live broadcast room at different time points can be determined by the processing volume of the received interactive requests in each live broadcast room at different time points. When determining whether the data flow of the live broadcast application at the current time point has a sudden increase or decrease, a historical time divided forward according to a preset duration starting from the current time point can be used as the target period. Then, the preset sliding time window can be controlled to slide dynamically within the target time period, and the target time period is divided into multiple associated time windows, and the last associated time window is the current time window. By determining the generation timestamp of each data in each indicator sub-item represented by live studio a, live studio b, and live studio c, each data in each indicator sub-item represented by live studio a, live studio b, and live studio c can be divided into corresponding associated time windows, and the data of each indicator sub-item represented by live studio a, live studio b, and live studio c in each associated time window can be obtained, including the data of each indicator sub-item represented by live studio a, live studio b, and live studio c in the current time window.

[0132] Scenario 1

[0133] Under the overall attribution processing of indicators, for each associated time window, the data of each indicator sub-item represented by live broadcast room a, live broadcast room b, and live broadcast room c within the associated time window can be aggregated, and the aggregated data in each associated time window can be statistically analyzed to obtain the data flow corresponding to each associated time window, so as to determine the data change rate of the current time window.

[0134] If the data change rate of the current time window is greater than or equal to the preset change rate threshold, it means that the data of the current time window does not meet the preset conditions. Then, the data of live broadcast room a, live broadcast room b, and live broadcast room c in the current time window can be calculated to calculate the data statistics, fluctuations, and volatility of live broadcast room a, live broadcast room b, and live broadcast room c. Then, live broadcast room a, live broadcast room b, and live broadcast room c are sorted according to the data statistics, fluctuations, and volatility, and the live broadcast room identifiers with the highest sorting are used as the abnormal attribution items corresponding to the current time window.

[0135] Scenario 2

[0136] Under the attribution processing of indicator sub-items, for each associated time window, the data of each indicator sub-item represented by live broadcast room a, live broadcast room b, and live broadcast room c within the associated time window can be counted, and the data flow corresponding to live broadcast room a, live broadcast room b, and live broadcast room c in each associated time window can be obtained, so as to determine the data change rate of the current time window under each indicator sub-item represented by live broadcast room a, live broadcast room b, and live broadcast room c.

[0137] If the data change rate of the current time window under the indicator sub-item represented by a certain indicator room in live broadcast room a, live broadcast room b, and live broadcast room c is greater than or equal to the preset change rate threshold, it means that the data of the current time window under the indicator sub-item represented by the live broadcast room does not meet the preset conditions. Then, according to the data of live broadcast room a, live broadcast room b, and live broadcast room c in the current time window, the data statistics, fluctuations, and volatility corresponding to live broadcast room a, live broadcast room b, and live broadcast room c can be calculated respectively. Then, according to the data statistics, fluctuations, and volatility, live broadcast room a, live broadcast room b, and live broadcast room c are sorted respectively, and the live broadcast room identifier with the highest sorting is used as the abnormal attribution factor item corresponding to the current time window.

[0138] Scenario 3

[0139] Under the indicator sub-item ratio attribution processing, the associated time window and the current time window can be the same, both of which are the entire time window where the target period is located. The following can be described using the current time window:

[0140] If the attribution processing of the indicator sub-item ratio is the data anomaly attribution analysis under the business operation execution success rate, then the numerator type of the indicator sub-item ratio attribution can be the data type represented by the successfully executed business operation in each indicator sub-item represented by live broadcast room a, live broadcast room b, and live broadcast room c, and the denominator type can be the data type represented by any business operation that has been executed in each indicator sub-item represented by live broadcast room a, live broadcast room b, and live broadcast room c.

[0141] Through the successful business operations completed in the current time window within each indicator sub-item represented by live broadcast room a, live broadcast room b, and live broadcast room c, the first type of data statistics generated by the successful business operations executed in live broadcast room a, live broadcast room b, and live broadcast room c in the current time window can be determined respectively.

[0142] Through the business operations completed in the current time window within each indicator sub-item represented by live broadcast room a, live broadcast room b, and live broadcast room c, regardless of whether the business operation is successfully executed, the second type of data statistics generated by all business operations executed in live broadcast room a, live broadcast room b, and live broadcast room c in the current time window can be determined respectively.

[0143] Then, if the statistical ratio between the first type of data statistics and the second type of data statistics of the indicator sub-item represented by a certain indicator room in the live broadcast room a, live broadcast room b, and live broadcast room c in the current time window is greater than or equal to the preset ratio threshold, it means that the data of the current time window under the indicator sub-item represented by the live broadcast room does not meet the preset conditions. Then, according to the data of the live broadcast room a, live broadcast room b, and live broadcast room c in the current time window, the numerator statistical value, denominator statistical value, and statistical ratio corresponding to the live broadcast room a, live broadcast room b, and live broadcast room c can be calculated respectively. Then, according to the numerator statistical value, denominator statistical value, and statistical ratio, the live broadcast room a, live broadcast room b, and live broadcast room c are sorted respectively, and the live broadcast room identification with the highest sorting is used as the abnormal attribution item corresponding to the current time window.

[0144] Figure 5 FIG. 1 is a block diagram of a data processing device according to an embodiment of the present application. Figure 5 As shown, the apparatus 500 may include:

[0145] A processing index determination module 510, used to determine a data processing index and index sub-items under the data processing index;

[0146] The window data determination module 520 is used to determine whether the data of the current time window meets the preset conditions according to the data of each indicator sub-item in the associated time window of the target period, wherein the current time window is a time window corresponding to the current time point in the associated time window;

[0147] The data attribution processing module 530 is used to determine the abnormal attribution factor item corresponding to the current time window from each of the indicator sub-items if the data in the current time window does not meet the preset conditions.

[0148] In some possible implementations, the indicator sub-items under the data processing indicator are dimensional items of the data processing indicator in a single dimension or dimensional items of the data processing indicator in multiple dimensions.

[0149] In some implementations, the window data determination module 520 may include:

[0150] An associated window determination unit, used to determine an associated time window of a target period according to a preset data processing type;

[0151] The window data determination unit is used to determine whether the data in the current time window meets a preset condition based on the data of each indicator sub-item in the associated time window.

[0152] In some possible implementations, if the data processing type is indicator overall attribution processing, then the associated time window within the target period is a first-class sliding time window for the data processing indicator within the target period, and the current time window is the last first-class sliding time window within the target period.

[0153] Accordingly, the window data determination unit can be specifically used for:

[0154] Determine the data flow corresponding to each first-type sliding time window according to the aggregated data of each indicator sub-item in each first-type sliding time window;

[0155] According to the data flow corresponding to each of the first-type sliding time windows, the data change rate of the current time window is determined to determine whether the data change rate is less than a preset change rate threshold.

[0156] In some possible implementations, if the data processing type is indicator sub-item attribution processing, then the associated time window within the target period is a second-type sliding time window set for each indicator sub-item within the target period, and the current time window is the last second-type sliding time window under each indicator sub-item within the target period.

[0157] Accordingly, the window data determination unit can be specifically used for:

[0158] Determine the data flow corresponding to each second-type sliding time window under the indicator sub-item according to the partition data of each indicator sub-item in each second-type sliding time window under the indicator sub-item;

[0159] For each indicator sub-item, the data change rate of the current time window under the indicator sub-item is determined according to the data flow corresponding to each second-type sliding time window under the indicator sub-item to determine whether the data change rate is less than the preset change rate threshold.

[0160] In some possible implementations, if the data processing type is indicator sub-item ratio attribution processing, then the associated time window within the target period is the entire time window in which the target period is located, and the current time window is the same as the associated time window.

[0161] Accordingly, the window data determination unit can be specifically used for:

[0162] Determine the numerator and denominator types of the sub-item ratio attribution of the indicator;

[0163] According to the data of each indicator sub-item under the molecule type, determine the first type of data statistics of the indicator sub-item in the current time window;

[0164] Determine the second type of data statistics of each indicator sub-item in the current time window according to the data of the denominator type of each indicator sub-item;

[0165] Determine whether a statistical ratio between the first-category data statistic and the second-category data statistic is less than a preset ratio threshold.

[0166] In some implementations, the data attribution processing module 530 may be specifically used to:

[0167] For each preset conditional determination impact dimension, determine the impact value of each indicator sub-item under the conditional determination impact dimension according to the data of each indicator sub-item in the current time window, so as to generate an indicator sub-item sequence under the conditional determination impact dimension;

[0168] Determine the indicator sub-item sequence under the impact dimension according to each condition, and determine the abnormal attribution factor item corresponding to the current time window.

[0169] In some possible implementations, if the preset data processing type is indicator overall attribution processing or indicator sub-item attribution processing, then the conditional determination influencing dimensions include data statistical values, fluctuations, and volatility; if the data processing type is indicator sub-item ratio attribution processing, then the conditional determination influencing dimensions include numerator statistical values, denominator statistical values, and statistical ratios.

[0170] In some implementations, the data processing device 500 may further include:

[0171] The prompt module is used to determine the influence according to the conditions of each indicator sub-item in the current time window, and display prompt information when the data in the current time window does not meet the preset conditions.

[0172] In some possible implementations, if the preset data processing type is overall attribution processing of indicators, the prompt module may be specifically used to:

[0173] Determining a prompt trigger for the current time window according to whether data of the current time window and consecutive adjacent time windows of the current time window meet a preset condition;

[0174] If the current time window has triggered a prompt, the influence is determined according to the conditions of each indicator sub-item within the current time window, and prompt information is displayed when the data in the current time window does not meet the preset conditions.

[0175] In some implementations, the data processing device 500 may further include:

[0176] The traffic playback module is used to respond to the traffic playback trigger operation of the target indicator sub-item in the prompt information, and display the data traffic change trend of the target indicator sub-item within the target time period.

[0177] In some implementations, the data processing device 500 may further include:

[0178] The data preprocessing module is used to perform partitioning and slicing processing on the source data within the target time period according to each indicator sub-item under the data processing indicator to obtain the data of each indicator sub-item within the target time period.

[0179] In the embodiment of the present application, the data processing index and each indicator sub-item under the data processing index are first determined. Then, based on the data in each associated time window of each indicator sub-item in the target time period, it is determined whether the data in the current time window meets the preset conditions. If the data in the current time window does not meet the preset conditions, it means that the data in the current time window may be abnormal, and then the abnormal attribution factor item corresponding to the current time window is determined from each indicator sub-item, so as to achieve accurate attribution processing of the data in each time window from each indicator sub-item of the data processing index when the preset conditions are not met, thereby improving the effectiveness of the attribution processing when the data does not meet the preset conditions.

[0180] It should be understood that the device embodiment and the method embodiment may correspond to each other, and similar descriptions may refer to the method embodiment. To avoid repetition, they will not be described here. Specifically, Figure 5The device 500 shown can execute any method embodiment provided in the present application, and the aforementioned and other operations and / or functions of each module in the device 500 are respectively for implementing the corresponding processes in each method of the embodiment of the present application. For the sake of brevity, they will not be repeated here.

[0181] The above describes the device 500 of the embodiment of the present application from the perspective of the functional module in conjunction with the accompanying drawings. It should be understood that the functional module can be implemented in hardware form, can be implemented by instructions in software form, and can also be implemented by a combination of hardware and software modules. Specifically, the steps of the method embodiment in the embodiment of the present application can be completed by the hardware integrated logic circuit and / or software form instructions in the processor, and the steps of the method disclosed in the embodiment of the present application can be directly embodied as a hardware decoding processor to perform, or a combination of hardware and software modules in the decoding processor to perform. Optionally, the software module can be located in a mature storage medium in the field such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an electrically erasable programmable memory, a register, etc. The storage medium is located in a memory, and the processor reads the information in the memory, and completes the steps in the above method embodiment in conjunction with its hardware.

[0182] Figure 6 A schematic block diagram of an electronic device according to an embodiment of the present application.

[0183] like Figure 6 As shown, the electronic device 600 may include:

[0184] The memory 610 and the processor 620, the memory 610 is used to store the computer program and transmit the program code to the processor 620. In other words, the processor 620 can call and run the computer program from the memory 610 to implement the method in the embodiment of the present application.

[0185] For example, the processor 620 may be configured to execute the above method embodiments according to instructions in the computer program.

[0186] In some embodiments of the present application, the processor 620 may include but is not limited to:

[0187] General-purpose processor, digital signal processor (DSP), 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.

[0188] In some embodiments of the present application, the memory 610 includes but is not limited to:

[0189] Volatile memory and / or non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable ROM (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM) or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus random access memory (DR RAM).

[0190] In some embodiments of the present application, the computer program may be divided into one or more modules, which are stored in the memory 610 and executed by the processor 620 to complete the method provided by the present application. The one or more modules may be a series of computer program instruction segments capable of completing specific functions, and the instruction segments are used to describe the execution process of the computer program in the electronic device.

[0191] like Figure 6 As shown, the electronic device may also include:

[0192] The transceiver 630 may be connected to the processor 620 or the memory 610 .

[0193] The processor 620 may control the transceiver 630 to communicate with other devices, specifically, to send information or data to other devices, or to receive information or data sent by other devices. The transceiver 630 may include a transmitter and a receiver. The transceiver 630 may further include an antenna, and the number of antennas may be one or more.

[0194] It should be understood that the various components in the electronic device are connected via a bus system, wherein the bus system includes not only a data bus but also a power bus, a control bus and a status signal bus.

[0195] The present application also provides a computer storage medium on which a computer program is stored, and when the computer program is executed by a computer, the computer can perform the method of the above method embodiment. In other words, the present application embodiment also provides a computer program product containing instructions, and when the instructions are executed by a computer, the computer can perform the method of the above method embodiment.

[0196] When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integration. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a digital video disc (digital video disc, DVD)), or a semiconductor medium (e.g., a solid state drive (solid state disk, SSD)), etc.

[0197] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0198] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the module is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0199] The modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. For example, each functional module in each embodiment of the present application may be integrated into a processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0200] The above contents are only specific implementation methods of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data processing method, characterized in that: include: Determine data processing indicators and indicator sub-items under the data processing indicators; Determine whether the data in the current time window meets a preset condition according to the data in the associated time window of each indicator sub-item in the target period, wherein the current time window is a time window in the associated time window corresponding to the current time point; If the data in the current time window does not meet the preset conditions, the abnormal attribution factor item corresponding to the current time window is determined from each of the indicator sub-items.

2. The method according to claim 1, characterized in that: The indicator sub-items under the data processing indicator are dimensional items of the data processing indicator in a single dimension or dimensional items of the data processing indicator in multiple dimensions.

3. The method according to claim 1, characterized in that The step of determining whether the data in the current time window meets a preset condition based on the data of each indicator sub-item in the associated time window of the target period includes: Determine the associated time window of the target period according to the preset data processing type; According to the data of each indicator sub-item in the associated time window, it is determined whether the data in the current time window meets the preset condition.

4. The method according to claim 3, characterized in that If the data processing type is indicator overall attribution processing, then the associated time window in the target period is a first-class sliding time window for the data processing indicator in the target period, and the current time window is the last first-class sliding time window in the target period; Accordingly, determining whether the data in the current time window meets the preset condition according to the data of each indicator sub-item in the associated time window includes: Determine the data flow corresponding to each first-type sliding time window according to the aggregated data of each indicator sub-item in each first-type sliding time window; According to the data flow corresponding to each of the first-type sliding time windows, the data change rate of the current time window is determined to determine whether the data change rate is less than a preset change rate threshold.

5. The method according to claim 3, characterized in that: If the data processing type is indicator sub-item attribution processing, then the associated time window in the target period is a second-type sliding time window set for each indicator sub-item in the target period, and the current time window is the last second-type sliding time window under each indicator sub-item in the target period; Accordingly, determining whether the data in the current time window meets the preset condition according to the data of each indicator sub-item in the associated time window includes: Determine the data flow corresponding to each second-type sliding time window under the indicator sub-item according to the partition data of each indicator sub-item in each second-type sliding time window under the indicator sub-item; For each indicator sub-item, the data change rate of the current time window under the indicator sub-item is determined according to the data flow corresponding to each second-type sliding time window under the indicator sub-item to determine whether the data change rate is less than the preset change rate threshold.

6. The method according to claim 3, characterized in that: If the data processing type is indicator sub-item ratio attribution processing, then the associated time window within the target period is the entire time window where the target period is located, and the current time window is the same as the associated time window; Accordingly, determining whether the data in the current time window meets the preset condition according to the data of each indicator sub-item in the associated time window includes: Determine the numerator and denominator types of the sub-item ratio attribution of the indicator; According to the data of each indicator sub-item under the molecule type, determine the first type of data statistics of the indicator sub-item in the current time window; Determine the second type of data statistics of each indicator sub-item in the current time window according to the data of the denominator type of each indicator sub-item; Determine whether a statistical ratio between the first-category data statistic and the second-category data statistic is less than a preset ratio threshold.

7. The method according to claim 1, characterized in that The determining of the abnormal attribution factor item corresponding to the current time window from each of the indicator sub-items includes: For each preset conditional determination impact dimension, determine the impact value of each indicator sub-item under the conditional determination impact dimension according to the data of each indicator sub-item in the current time window, so as to generate an indicator sub-item sequence under the conditional determination impact dimension; Determine the indicator sub-item sequence under the impact dimension according to each condition, and determine the abnormal attribution factor item corresponding to the current time window.

8. The method according to claim 7, characterized in that If the preset data processing type is indicator overall attribution processing or indicator sub-item attribution processing, the conditional determination impact dimensions include data statistics, fluctuations, and volatility; If the data processing type is indicator sub-item ratio attribution processing, then the conditional judgment impact dimensions include numerator statistical values, denominator statistical values ​​and statistical ratios.

9. The method according to claim 1, characterized in that: After determining the abnormal attribution factor item corresponding to the current time window from each of the indicator sub-items, the method further includes: The influence is determined according to the conditions of each indicator sub-item within the current time window, and prompt information is displayed when the data in the current time window does not meet the preset conditions.

10. The method according to claim 9, characterized in that If the preset data processing type is indicator overall attribution processing, the influence is determined according to the conditions of each indicator sub-item in the current time window, and prompt information is displayed when the data in the current time window does not meet the preset conditions, including: Determining a prompt trigger for the current time window according to whether data of the current time window and consecutive adjacent time windows of the current time window meet a preset condition; If the current time window has triggered a prompt, the influence is determined according to the conditions of each indicator sub-item within the current time window, and prompt information is displayed when the data in the current time window does not meet the preset conditions.

11. The method according to claim 9, characterized in that The method further comprises: In response to the traffic playback triggering operation of the target indicator sub-item in the prompt information, the data traffic change trend of the target indicator sub-item within the target time period is displayed.

12. The method according to claim 1, characterized in that The method further comprises: According to each indicator sub-item under the data processing indicator, the source data within the target time period is processed in a partitioned and fragmented manner to obtain the data of each indicator sub-item within the target time period.

13. A data processing device, characterized in that: include: A processing index determination module, used to determine a data processing index and index sub-items under the data processing index; A window data determination module, used to determine whether the data in the current time window meets the preset conditions according to the data of each indicator sub-item in the associated time window of the target period, wherein the current time window is a time window corresponding to the current time point in the associated time window; The data attribution processing module is used to determine the abnormal attribution factor item corresponding to the current time window from each of the indicator sub-items if the data in the current time window does not meet the preset conditions.

14. An electronic device, characterized in that: include: A processor and a memory, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program stored in the memory to execute the data processing method according to any one of claims 1 to 12.

15. A computer-readable storage medium, characterized in that: Used to store a computer program, wherein the computer program enables a computer to execute the data processing method according to any one of claims 1 to 12.

16. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by a processor, the data processing method according to any one of claims 1 to 12 is implemented.

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