Data processing method and apparatus, and device, storage medium and program product

Abstract

Provided in the present disclosure are a data processing method and apparatus, and a device, a storage medium and a program product. The method comprises: acquiring first indicator data corresponding to a monitoring indicator; determining a plurality of data processing algorithms, wherein the algorithm complexities of the data processing algorithms are less than or equal to a preset complexity, and the plurality of data processing algorithms are used for performing data decomposition processing on data; determining first historical indicator data corresponding to the first indicator data; and processing the first indicator data and the first historical indicator data by means of the plurality of data processing algorithms, so as to obtain trend data and / or periodic data corresponding to the monitoring indicator. Therefore, the data processing efficiency is improved.

Description

[0001] Data Processing Method, Apparatus, Device, Storage Medium, and Program Product. This disclosure claims priority to Chinese patent application number 202410180160.2, filed with the China Patent Office on February 18, 2024, and entitled "Data Processing Method, Apparatus, Device, Storage Medium, and Program Product," the entire contents of which are incorporated herein by reference. Technical Field: This disclosure relates to the field of computers, and more particularly to a data processing method, apparatus, device, storage medium, and program product. Background: Seasonal-trend decomposition (STD) is performed on periodic time series data to obtain trend data, periodicity data, and residual data corresponding to the periodic time series data. The trend data, periodicity data, and residual data facilitate further analysis of the periodic time series data. Currently, STD processing can be performed on periodic time series data using the robust seasonal and trend decomposition using loess (RobustSTL) algorithm or the single-shot time series algorithm (OneShotSTL). However, these algorithms all include an optimization process based on the L1 norm, which is highly complex and results in low data processing efficiency. SUMMARY OF THE INVENTION Various aspects of the present disclosure provide a data processing method, apparatus, device, storage medium, and program product for improving data processing efficiency. In a first aspect, embodiments of the present disclosure provide a data processing method, comprising: obtaining first indicator data corresponding to a monitoring indicator; determining multiple data processing algorithms, each having an algorithm complexity less than or equal to a preset complexity, the multiple data processing algorithms being used to perform data decomposition processing on the data; determining first historical indicator data corresponding to the first indicator data, and processing the first indicator data and the first historical indicator data using the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator.In one possible implementation, the first indicator data and the first historical indicator data are processed by the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator, including: determining a first data volume of the historical indicator data corresponding to the monitoring indicator; if the first data volume is less than or equal to, then when M indicator data corresponding to the monitoring indicator are obtained, the M indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data, and the M indicator data include the first indicator data and the first historical indicator data; if the first data volume is greater than, then the first indicator data and M-1 first historical indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data. In one possible implementation, the M indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data, including: determining at least one second indicator data among the M indicator data, where the second indicator data is the indicator data that undergoes a jump among the M indicator data; segmenting the M indicator data according to the at least one second indicator data to obtain multiple indicator data sequences; determining the trend value of each indicator data in each indicator data sequence, and determining the periodic value of each indicator data by a non-local periodic filtering algorithm; determining that the trend data includes the trend value of each indicator data among the M indicator data, and determining that the periodic data includes the periodic value of each indicator data among the M indicator data; wherein the multiple data processing algorithms include the algorithms used in the above steps.In one possible implementation, determining at least one second indicator data from the M indicator data includes: determining a plurality of third indicator data from the M indicator data, where the third indicator data is data in a middle portion of the M indicator data; determining, for any one third indicator data, a jump value of the third indicator data, where the jump value is used to indicate a difference between future indicator data and historical indicator data of the third indicator data, and a generation time of the future indicator data is after a generation time of the third indicator data; determining, based on the jump value of the third indicator data, candidate indicator data from the plurality of third indicator data, where the jump value of the candidate indicator data is a local extreme value point in the jump values ​​of the plurality of third indicator data; determining, for any one candidate indicator data, based on the M indicator data, a jump value threshold corresponding to the candidate indicator data; and determining, as the at least one second indicator data, indicator data whose absolute value of the jump value in the candidate indicator data is greater than the corresponding jump value threshold. In one possible implementation, for any indicator data sequence, determining a trend value of each indicator data in the indicator data sequence includes: if the number of indicator data included in the indicator data sequence is less than or equal to a first threshold value T, determining an average value of each indicator data in the indicator data sequence as the trend value of each indicator data in the indicator data sequence; if the number of indicator data included in the indicator data sequence is greater than the first threshold value, determining a first subsequence and a second subsequence in the indicator data sequence, and determining the trend value of each indicator data in the first subsequence based on each indicator data in the indicator data sequence, and determining the trend value of each indicator data in the second subsequence as the average value of each indicator data in the first subsequence; wherein the second subsequence includes the last T-1 indicator data in the indicator data sequence, the first subsequence includes the other indicator data in the indicator data sequence except the second subsequence, and T is an integer greater than 1. In one possible implementation, for any indicator data in any indicator data sequence, determining a periodic value of the indicator data using a non-local periodic filtering algorithm includes: determining, among the M indicator data, a plurality of first reference indicator data corresponding to the indicator data; determining a weight value of each first reference indicator data; and determining a periodic value of the indicator data based on the weight value of each first reference indicator data, each first reference indicator data, and a trend value of each first reference indicator data.In one possible implementation, processing the first indicator data and M-1 first historical indicator data using the multiple data processing algorithms to obtain the trend data and / or the periodic data includes: determining a predicted value corresponding to the first indicator data, and judging whether the first indicator data is outlier data based on the first indicator data and the predicted value; if so, determining a first number of consecutive outlier data, and determining the trend data and / or the periodic data based on the first number, the first indicator data, and the M-1 first historical indicator data; if not, determining an average value of the first indicator data and the M-1 first historical indicator data as the trend value of the first indicator data, and determining the periodic value of the first indicator data using a non-local periodic filtering algorithm, wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data. In a possible implementation, determining the trend data and / or the periodic data based on the first quantity, the first indicator data, and M-1 first historical indicator data includes: determining whether the first quantity is greater than or equal to a preset value > where £ is an integer greater than or equal to 1; if so, determining 41 second historical indicator data preceding the first indicator data among the M-1 first historical indicator data, determining a target trend value based on the first indicator data and the 41 second historical indicator data, updating the trend values ​​of the first indicator data and the 41 second historical indicator data to the target trend value, and determining the periodic value of the first indicator data using the non-local periodic filtering algorithm; if not, determining an average of the predicted value and the M-1 first historical indicator data as the trend value of the first indicator data, and determining the periodic value of the first indicator data using the non-local periodic filtering algorithm; wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data.In one possible implementation, after determining a target trend value based on the first indicator data and the least one second historical indicator data, the method further includes: updating the first indicator data and the least one second historical indicator data to the target trend value; and, for any one of the M-1 historical indicator data except the least one second historical indicator data, determining an update increment based on the target trend value and the trend value of the ▲th historical indicator data preceding the first indicator data, and updating the historical indicator data to the sum of the historical indicator data and the update increment. In another possible implementation, determining a predicted value corresponding to the first indicator data includes: determining, from multiple historical indicator data of the indicator data, multiple second reference indicator data corresponding to the first indicator data and the first trend value of the indicator data preceding the first indicator data; and determining the predicted value based on the first indicator data, the period value of each second reference indicator data, and the first trend value. In one possible implementation, the trend data includes trend values ​​of multiple historical indicator data corresponding to the monitoring indicator, and the periodic data includes period values ​​of the multiple historical indicator data. The method further includes: for any piece of historical indicator data, determining the sum of the trend value and period value of the historical indicator data as a to-be-processed value corresponding to the historical indicator data; determining the difference between the historical indicator data and the to-be-processed value as a residual value corresponding to the historical indicator data; and determining that the residual data corresponding to the monitoring indicator includes a residual value corresponding to each piece of historical indicator data. In a second aspect, an embodiment of the present disclosure provides a data processing method, comprising: obtaining first indicator data corresponding to a monitoring indicator, the first indicator data being data generated by device operation; determining multiple data processing algorithms, each having an algorithm complexity less than or equal to a preset complexity, the multiple data processing algorithms being used to perform data decomposition processing on the data; determining first historical indicator data corresponding to the first indicator data, and performing data decomposition processing on the first indicator data and the first historical indicator data using the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator.In one possible implementation, the first indicator data and the first historical indicator data are processed by the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator, including: determining a first data volume of the historical indicator data corresponding to the monitoring indicator; if the first data volume is less than or equal to, then when M indicator data corresponding to the monitoring indicator are obtained, the M indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data, and the M indicator data include the first indicator data and the first historical indicator data; if the first data volume is greater than, then the first indicator data and M-1 first historical indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data. In one possible implementation, processing the M indicator data using the multiple data processing algorithms to obtain the trend data and / or the periodic data includes: determining at least one second indicator data from the M indicator data, where the second indicator data is indicator data that undergoes a jump among the M indicator data; segmenting the M indicator data based on the at least one second indicator data to obtain multiple indicator data sequences; determining a trend value of each indicator data in each indicator data sequence, and determining a periodic value of each indicator data using a non-local periodic filtering algorithm; determining that the trend data includes the trend value of each indicator data in the M indicator data, and determining that the periodic data includes the periodic value of each indicator data in the M indicator data; wherein the multiple data processing algorithms include the algorithms used in the above steps.In one possible implementation, determining at least one second indicator data from the M indicator data includes: determining a plurality of third indicator data from the M indicator data, where the third indicator data is data in a middle portion of the M indicator data; determining, for any one third indicator data, a jump value of the third indicator data, where the jump value is used to indicate a difference between future indicator data and historical indicator data of the third indicator data, and a generation time of the future indicator data is after a generation time of the third indicator data; determining, based on the jump value of the third indicator data, candidate indicator data from the plurality of third indicator data, where the jump value of the candidate indicator data is a local extreme value point in the jump values ​​of the plurality of third indicator data; determining, for any one candidate indicator data, based on the M indicator data, a jump value threshold corresponding to the candidate indicator data; and determining, as the at least one second indicator data, indicator data whose absolute value of the jump value in the candidate indicator data is greater than the corresponding jump value threshold. In one possible implementation, for any indicator data sequence, determining a trend value of each indicator data in the indicator data sequence includes: if the number of indicator data included in the indicator data sequence is less than or equal to a first threshold value T, determining an average value of each indicator data in the indicator data sequence as the trend value of each indicator data in the indicator data sequence; if the number of indicator data included in the indicator data sequence is greater than the first threshold value, determining a first subsequence and a second subsequence in the indicator data sequence, and determining the trend value of each indicator data in the first subsequence based on each indicator data in the indicator data sequence, and determining the trend value of each indicator data in the second subsequence as the average value of each indicator data in the second subsequence; wherein the second subsequence includes the last T-1 indicator data in the indicator data sequence, the first subsequence includes other indicator data in the indicator data sequence except the second subsequence, and T is an integer greater than 1. In one possible implementation, for any indicator data in any indicator data sequence, determining a periodic value of the indicator data using a non-local periodic filtering algorithm includes: determining, among the M indicator data, a plurality of first reference indicator data corresponding to the indicator data; determining a weight value of each first reference indicator data; and determining a periodic value of the indicator data based on the weight value of each first reference indicator data, each first reference indicator data, and a trend value of each first reference indicator data.In one possible implementation, processing the first indicator data and M-1 first historical indicator data using the multiple data processing algorithms to obtain the trend data and / or the periodic data includes: determining a predicted value corresponding to the first indicator data, and judging whether the first indicator data is outlier data based on the first indicator data and the predicted value; if so, determining a first number of consecutive outlier data, and determining the trend data and / or the periodic data based on the first number, the first indicator data, and the M-1 first historical indicator data; if not, determining an average value of the first indicator data and the M-1 first historical indicator data as the trend value of the first indicator data, and determining the periodic value of the first indicator data using a non-local periodic filtering algorithm, wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data. In a possible implementation, determining the trend data and / or the periodic data based on the first quantity, the first indicator data, and M-1 first historical indicator data includes: determining whether the first quantity is greater than or equal to a preset value > where £ is an integer greater than or equal to 1; if so, determining 41 second historical indicator data preceding the first indicator data among the M-1 first historical indicator data, determining a target trend value based on the first indicator data and the 41 second historical indicator data, updating the trend values ​​of the first indicator data and the 41 second historical indicator data to the target trend value, and determining the periodic value of the first indicator data using the non-local periodic filtering algorithm; if not, determining an average of the predicted value and the M-1 first historical indicator data as the trend value of the first indicator data, and determining the periodic value of the first indicator data using the non-local periodic filtering algorithm; wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data.In one possible implementation, after determining a target trend value based on the first indicator data and the least one second historical indicator data, the method further includes: updating the first indicator data and the least one second historical indicator data to the target trend value; and, for any one of the M-1 historical indicator data except the least one second historical indicator data, determining an update increment based on the target trend value and the trend value of the ▲th historical indicator data preceding the first indicator data, and updating the historical indicator data to the sum of the historical indicator data and the update increment. In another possible implementation, determining a predicted value corresponding to the first indicator data includes: determining, from multiple historical indicator data of the indicator data, multiple second reference indicator data corresponding to the first indicator data and the first trend value of the indicator data preceding the first indicator data; and determining the predicted value based on the first indicator data, the period value of each second reference indicator data, and the first trend value. In one possible implementation, the trend data includes trend values ​​of multiple historical indicator data corresponding to the monitoring indicator, and the periodic data includes period values ​​of the multiple historical indicator data; the method further includes: for any historical indicator data, determining the sum of the trend value and the period value of the historical indicator data as the to-be-processed value corresponding to the historical indicator data; determining the difference between the historical indicator data and the to-be-processed value as the residual value corresponding to the historical indicator data; and determining that the residual data corresponding to the monitoring indicator includes the residual value corresponding to each historical indicator data. In a third aspect, an embodiment of the present disclosure provides a data processing device, comprising: an acquisition module, a determination module, and a processing module, wherein the acquisition module is used to acquire first indicator data corresponding to a monitoring indicator; the determination module is used to determine multiple data processing algorithms, where the algorithm complexity of the data processing algorithms is less than or equal to a preset complexity, and the multiple data processing algorithms are used to perform data decomposition processing on the data; the determination module is also used to determine first historical indicator data corresponding to the first indicator data; and the processing module is used to process the first indicator data and the first historical indicator data through the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator.In one possible implementation, the processing module is specifically used to determine a first data volume of the historical indicator data corresponding to the monitoring indicator; if the first data volume is less than or equal to, then when M indicator data corresponding to the monitoring indicator are obtained, the M indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data, and the M indicator data include the first indicator data and the first historical indicator data; if the first data volume is greater than the, then the first indicator data and M-1 first historical indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data. In one possible implementation, the processing module is specifically configured to: determine at least one second indicator data from the M indicator data, where the second indicator data is indicator data that undergoes a jump among the M indicator data; segment the M indicator data based on the at least one second indicator data to obtain multiple indicator data sequences; determine a trend value of each indicator data in each indicator data sequence, and determine a periodic value of each indicator data using a non-local periodic filtering algorithm; determine that the trend data includes the trend value of each indicator data in the M indicator data, and determine that the periodic data includes the periodic value of each indicator data in the M indicator data; wherein the multiple data processing algorithms include the algorithms used in the above steps. In one possible implementation, the processing module is specifically configured to: determine a plurality of third indicator data from M indicator data, where the third indicator data is data in a middle portion of the M indicator data; determine, for any one third indicator data, a jump value of the third indicator data, where the jump value is used to indicate a difference between future indicator data and historical indicator data of the third indicator data, where a generation time of the future indicator data is after a generation time of the third indicator data; determine, based on the jump value of the third indicator data, candidate indicator data from the plurality of third indicator data, where the jump value of the candidate indicator data is a local extreme value point in the jump values ​​of the plurality of third indicator data; determine, for any one candidate indicator data, based on the M indicator data, a jump value threshold corresponding to the candidate indicator data; and determine, as the at least one second indicator data, indicator data whose absolute value of the jump value in the candidate indicator data is greater than the corresponding jump value threshold.In one possible implementation, for any indicator data sequence, the processing module is specifically configured to: if the number of indicator data included in the indicator data sequence is less than or equal to a first threshold value T, determine the average value of each indicator data in the indicator data sequence as the trend value of each indicator data in the indicator data sequence; if the number of indicator data included in the indicator data sequence is greater than the first threshold value, determine a first subsequence and a second subsequence in the indicator data sequence, determine the trend value of each indicator data in the first subsequence based on each indicator data in the indicator data sequence, and determine the trend value of each indicator data in the second subsequence as the average value of each indicator data in the first subsequence; wherein the second subsequence includes the last T-1 indicator data in the indicator data sequence, the first subsequence includes the other indicator data in the indicator data sequence except the second subsequence, and T is an integer greater than 1. In one possible implementation, for any indicator data in any indicator data sequence, the processing module is specifically used to determine, among the M indicator data, multiple first reference indicator data corresponding to the indicator data; determine a weight value of each first reference indicator data; and determine a period value of the indicator data based on the weight value of each first reference indicator data, each first reference indicator data, and a trend value of each first reference indicator data. In one possible implementation, the processing module is specifically configured to determine a predicted value corresponding to the first indicator data, and determine whether the first indicator data is outlier data based on the first indicator data and the predicted value; if so, determine a first number of consecutive outlier data, and determine the trend data and / or the periodic data based on the first number, the first indicator data, and M-1 first historical indicator data; if not, determine an average value of the first indicator data and the M-1 first historical indicator data as the trend value of the first indicator data, and determine the periodic value of the first indicator data through a non-local periodic filtering algorithm, wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data.In one possible implementation, the processing module is specifically configured to determine whether the first number is greater than or equal to a preset value > where £ is an integer greater than or equal to 1; if so, determining 41 second historical indicator data preceding the first indicator data among the M-1 first historical indicator data, determining a target trend value based on the first indicator data and the 41 second historical indicator data, updating the trend values ​​of the first indicator data and the 41 second historical indicator data to the target trend value, and determining a periodic value of the first indicator data using the non-local periodic filtering algorithm; if not, determining an average value of the predicted value and the M-1 first historical indicator data as the trend value of the first indicator data, and determining a periodic value of the first indicator data using the non-local periodic filtering algorithm; wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data. In one possible implementation, the data processing device may further include an updating module, wherein the updating module is configured to update the first indicator data and the least one second historical indicator data to the target trend value; for any other historical indicator data among the M-1 historical indicator data except the least one second historical indicator data, the determining module is further configured to determine an update increment based on the target trend value and the trend value of the ▲th historical indicator data preceding the first indicator data; and the updating module is further configured to update the historical indicator data to the sum of the historical indicator data and the update increment. In one possible implementation, the processing module is specifically configured to determine, from the plurality of historical indicator data, a plurality of second reference indicator data corresponding to the first indicator data and a first trend value of the indicator data preceding the first indicator data; and determine the predicted value based on the first indicator data, the period value of each second reference indicator data, and the first trend value. In one possible implementation, the trend data includes trend values ​​of multiple historical indicator data corresponding to the monitoring indicator, and the periodic data includes period values ​​of the multiple historical indicator data; the determination module is further used to, for any historical indicator data, determine the sum of the trend value and the period value of the historical indicator data as the to-be-processed value corresponding to the historical indicator data; determine the difference between the historical indicator data and the to-be-processed value as the residual value corresponding to the historical indicator data; and determine that the residual data corresponding to the monitoring indicator includes the residual value corresponding to each historical indicator data.In a fourth aspect, an embodiment of the present disclosure provides a data processing device, comprising: an acquisition module, a determination module, and a processing module, wherein the acquisition module is used to acquire first indicator data corresponding to a monitoring indicator, where the first indicator data is data generated by the operation of the device; the determination module is used to determine multiple data processing algorithms, where the algorithm complexity of the data processing algorithms is less than or equal to a preset complexity, and the multiple data processing algorithms are used to perform data decomposition processing on the data; the determination module is also used to determine first historical indicator data corresponding to the first indicator data; and the processing module is used to process the first indicator data and the first historical indicator data through the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator. In one possible implementation, the processing module is specifically used to determine a first data volume of the historical indicator data corresponding to the monitoring indicator; if the first data volume is less than or equal to, then when M indicator data corresponding to the monitoring indicator are obtained, the M indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data, and the M indicator data include the first indicator data and the first historical indicator data; if the first data volume is greater than the, then the first indicator data and M-1 first historical indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data. In one possible implementation, the processing module is specifically configured to: determine at least one second indicator data from the M indicator data, where the second indicator data is indicator data that undergoes a jump among the M indicator data; segment the M indicator data based on the at least one second indicator data to obtain multiple indicator data sequences; determine a trend value of each indicator data in each indicator data sequence, and determine a periodic value of each indicator data using a non-local periodic filtering algorithm; determine that the trend data includes the trend value of each indicator data in the M indicator data, and determine that the periodic data includes the periodic value of each indicator data in the M indicator data; wherein the multiple data processing algorithms include the algorithms used in the above steps.In one possible implementation, the processing module is specifically configured to: determine a plurality of third indicator data from M indicator data, where the third indicator data is data in a middle portion of the M indicator data; determine, for any one third indicator data, a jump value of the third indicator data, where the jump value is used to indicate a difference between future indicator data and historical indicator data of the third indicator data, where a generation time of the future indicator data is after a generation time of the third indicator data; determine, based on the jump value of the third indicator data, candidate indicator data from the plurality of third indicator data, where the jump value of the candidate indicator data is a local extreme value point in the jump values ​​of the plurality of third indicator data; determine, for any one candidate indicator data, based on the M indicator data, a jump value threshold corresponding to the candidate indicator data; and determine, as the at least one second indicator data, indicator data whose absolute value of the jump value in the candidate indicator data is greater than the corresponding jump value threshold. In one possible implementation, for any indicator data sequence, the processing module is specifically configured to: if the number of indicator data included in the indicator data sequence is less than or equal to a first threshold value T, determine the average value of each indicator data in the indicator data sequence as the trend value of each indicator data in the indicator data sequence; if the number of indicator data included in the indicator data sequence is greater than the first threshold value, determine a first subsequence and a second subsequence in the indicator data sequence, determine the trend value of each indicator data in the first subsequence based on each indicator data in the indicator data sequence, and determine the trend value of each indicator data in the second subsequence as the average value of each indicator data in the first subsequence; wherein the second subsequence includes the last T-1 indicator data in the indicator data sequence, the first subsequence includes the other indicator data in the indicator data sequence except the second subsequence, and T is an integer greater than 1. In one possible implementation, for any indicator data in any indicator data sequence, the processing module is specifically used to determine, among the M indicator data, multiple first reference indicator data corresponding to the indicator data; determine a weight value of each first reference indicator data; and determine a period value of the indicator data based on the weight value of each first reference indicator data, each first reference indicator data, and a trend value of each first reference indicator data.In one possible implementation, the processing module is specifically configured to determine a predicted value corresponding to the first indicator data, and determine whether the first indicator data is outlier data based on the first indicator data and the predicted value; if so, determine a first number of consecutive outlier data, and determine the trend data and / or the periodic data based on the first number, the first indicator data, and M-1 first historical indicator data; if not, determine an average value of the first indicator data and the M-1 first historical indicator data as the trend value of the first indicator data, and determine the periodic value of the first indicator data through a non-local periodic filtering algorithm, wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data. In one possible implementation, the processing module is specifically configured to determine whether the first number is greater than or equal to a preset value > where £ is an integer greater than or equal to 1; if so, determining 41 second historical indicator data preceding the first indicator data among the M-1 first historical indicator data, determining a target trend value based on the first indicator data and the 41 second historical indicator data, updating the trend values ​​of the first indicator data and the 41 second historical indicator data to the target trend value, and determining a periodic value of the first indicator data using the non-local periodic filtering algorithm; if not, determining an average value of the predicted value and the M-1 first historical indicator data as the trend value of the first indicator data, and determining a periodic value of the first indicator data using the non-local periodic filtering algorithm; wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data. In one possible implementation, the data processing device may further include an update module, wherein the update module is used to update the first indicator data and the at least one second historical indicator data to the target trend value; for any other historical indicator data among the M-1 historical indicator data except the at least one second historical indicator data, the determination module is further used to determine an update increment based on the target trend value and the trend value of the ▲th historical indicator data before the first indicator data; and the update module is further used to update the historical indicator data to the sum of the historical indicator data and the update increment.In one possible implementation, the processing module is specifically configured to, from multiple historical indicator data of the indicator data, determine a first trend value of multiple second reference indicator data corresponding to the first indicator data, and the indicator data preceding the first indicator data; and determine the predicted value based on the first indicator data, the period value of each second reference indicator data, and the first trend value. In another possible implementation, the trend data includes the trend values ​​of multiple historical indicator data corresponding to the monitoring indicator, and the periodic data includes the period value of the multiple historical indicator data. The determination module is further configured to, for any historical indicator data, determine the sum of the trend value and the period value of the historical indicator data as the to-be-processed value corresponding to the historical indicator data; determine the difference between the historical indicator data and the to-be-processed value as the residual value corresponding to the historical indicator data; and determine that the residual data corresponding to the monitoring indicator includes the residual value corresponding to each historical indicator data. In a fifth aspect, embodiments of the present disclosure provide a data processing device comprising: a memory and a processor; the memory storing computer-executable instructions; and the processor executing the computer-executable instructions stored in the memory, causing the data processing to perform the method described in either the first or second aspect. In a sixth aspect, embodiments of the present disclosure provide a computer-readable storage medium storing computer-executable instructions. When executed by a processor, the computer-executable instructions are used to implement the method described in either the first or second aspect. In a seventh aspect, embodiments of the present disclosure provide a computer program product, including a computer program. When executed by a processor, the computer program implements the method described in either the first or second aspect. Embodiments of the present disclosure provide a data processing method, apparatus, device, storage medium, and program product. The data processing device can obtain first indicator data corresponding to a monitoring indicator and determine multiple data processing algorithms. The data processing device can process the first indicator data and first historical indicator data using the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator. In the above method, the multiple data processing algorithms have low complexity, thereby reducing the complexity of decomposing the indicator data (including the first indicator data and the first historical indicator data) into trend data and / or periodic data corresponding to the monitoring indicator, thereby improving data processing efficiency. BRIEF DESCRIPTION OF THE DRAWINGS The drawings described herein are used to provide a further understanding of the present disclosure and constitute a part of the present disclosure. The illustrative embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation on the present disclosure.In the accompanying drawings: Figure 1 is a schematic diagram of a scenario provided by an exemplary embodiment of the present disclosure; Figure 2 is a flowchart of a data processing method provided by an exemplary embodiment of the present disclosure; Figure 3 is a schematic diagram of the amount of indicator data in a sliding window provided by an exemplary embodiment of the present disclosure; Figure 4 is a flowchart of another data processing method provided by an exemplary embodiment of the present disclosure; Figure 5 is a schematic diagram of an indicator data sequence provided by an exemplary embodiment of the present disclosure; Figure 6 is a schematic diagram of a neighborhood provided by an exemplary embodiment of the present disclosure; Figure 7 is a schematic diagram of the structure of a data processing device provided by an exemplary embodiment of the present disclosure; Figure 8 is a schematic diagram of the structure of another data processing device provided by an exemplary embodiment of the present disclosure; Figure 9 is a schematic diagram of the structure of yet another data processing device provided by an exemplary embodiment of the present disclosure; Figure 10 is a schematic diagram of the structure of yet another data processing device provided by an exemplary embodiment of the present disclosure; and Figure 11 is a schematic diagram of the structure of a data processing device provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties. The collection, use, and processing of the relevant data must comply with relevant laws, regulations, and standards, and corresponding operation portals are provided for users to choose to authorize or reject. To further clarify the objectives, technical solutions, and advantages of this disclosure, the following will provide a clear and complete description of the technical solutions of this disclosure in conjunction with specific embodiments and corresponding figures. Obviously, the described embodiments represent only a portion of the embodiments of this disclosure, and are not exhaustive. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of this disclosure without inventive effort are within the scope of protection of this disclosure. To facilitate understanding, the technical terms used in the embodiments of this disclosure are first explained. Elastic Compute Service (ECS): A secure, reliable, and elastically scalable cloud computing service.

[0002] STD refers to seasonal-trend decomposition, a problem that decomposes a time series into a trend term (i.e., trend data), a seasonal term (i.e., cyclical data), and a residual term (i.e., residual data). This problem generally assumes that the trend term changes slowly, the seasonal term is cyclical, and the residual term is white noise. Optimization based on the L1 norm: The L1 norm is the sum of the absolute values ​​of each element in a vector. Optimization problems with the L1 norm as the optimization objective generally have no closed-form solution and are computationally complex. To facilitate understanding, the application scenarios involved in the embodiments of the present disclosure are described below with reference to Figure 1. Figure 1 is a schematic diagram of a scenario provided by an exemplary embodiment of the present disclosure. Referring to Figure 1, STD processing of periodic time series data can yield trend data, cyclical data, and residual data. Currently, STD processing of periodic time series data can be performed using algorithms such as the Robust STL algorithm or the ZeroShot STL algorithm. Both the RobustST L algorithm and the OneShotST L algorithm include an optimization step based on the L1 norm. The inventors have discovered that the L1 norm-based optimization step is highly complex. When performing STD processing on periodic time series data using the RobustST L algorithm or the OneShotST L algorithm, the L1 norm-based optimization step consumes a significant amount of time, resulting in low data processing efficiency. In view of this, an embodiment of the present disclosure provides a data processing method in which data can be decomposed using multiple simple algorithms to obtain corresponding trend data and / or periodic data, eliminating the need for an L1 norm-based optimization process and improving data processing efficiency. The technical solutions presented in this disclosure are described in detail below through specific embodiments. It should be noted that the following embodiments may exist independently or in combination with each other, and identical or similar content will not be repeated in different embodiments. Figure 2 is a flow chart of a data processing method provided by an exemplary embodiment of the present disclosure. Referring to Figure 2, the method may include:

[0003] S201. Obtain first indicator data corresponding to a monitoring indicator. The execution entity of this embodiment may be a data processing device or a data processing apparatus provided within the data processing device. The data processing apparatus may be implemented via software or a combination of software and hardware. For ease of understanding, the following description uses the data processing device as an example. The monitoring indicator may be an operating parameter or a service parameter of the monitored device. For example, the device may be a server or a computer, and the monitoring indicator may be central processing unit (CPU) utilization or memory utilization. The indicator data corresponding to the monitoring indicator may be a parameter value corresponding to a parameter of the monitored device (including operating parameters and service parameters). For example, if the monitoring indicator is CPU utilization, the indicator parameter corresponding to the monitoring indicator may be 45%. In other words, the indicator data corresponding to the monitoring indicator may be data generated by device operation. The first indicator data corresponding to the monitoring indicator may be the maximum indicator data at the time of acquisition. In other words, the first indicator data corresponding to the monitoring indicator may be the most recently acquired data generated by device operation. Optionally, the first indicator data may be operating data or service data generated in a cloud computing scenario. For example, the first indicator data may be ECS instance operation data or ECS instance business data obtained by monitoring the operation of the ECS instance. The data processing device may monitor the monitoring indicator to obtain the first indicator data; alternatively, the data processing device may receive the first indicator data sent by another device. For example, the other device may be a monitoring device. For example, to analyze the operational stability of the ECS instance, the data processing device may monitor the operation of the ECS instance and obtain data generated by the operation of the ECS instance; alternatively, the data processing device may receive data generated by the operation of the ECS instance from the monitoring device.

[0004] S202. Determine multiple data processing algorithms. The data processing algorithms may be algorithms that perform STD processing on the first indicator data. For example, the data processing algorithms may be algorithms that determine whether the indicator data has a jump, an algorithm that calculates a trend value corresponding to the indicator data, and an algorithm that calculates a period value corresponding to the indicator data. In this embodiment, the algorithm complexity of the data processing algorithms is less than or equal to a preset complexity. The preset complexity may be the complexity of an optimization process based on the L1 norm. In other words, the multiple data processing algorithms may be algorithms with a complexity less than that of an optimization process based on the L1 norm. In this embodiment, the storage space of the data processing device may store multiple data processing algorithms. After obtaining the first indicator data, the data processing device may determine multiple data processing algorithms to facilitate STD processing of the first indicator data.

[0005] S203. Determine first historical indicator data corresponding to the first indicator data. The first historical indicator data may be indicator data acquired at a time later than the time the first indicator data was acquired. In other words, the first historical indicator data may be indicator data corresponding to a monitoring indicator acquired before the first indicator data was acquired. In this embodiment, the data processing device may acquire and store indicator data corresponding to the monitoring indicator. During specific implementation, the data processing device may determine indicator data corresponding to the monitoring indicator acquired at a time later than the time the first indicator data was acquired as the first historical indicator data corresponding to the first indicator data.

[0006] S204. Process the first indicator data and the first historical indicator data using multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator. In this embodiment, the first indicator data and the first historical indicator data may be subjected to STD processing using multiple data processing algorithms. For example, the first indicator data and the first historical indicator data may be processed using an algorithm that calculates a trend value corresponding to the indicator data to obtain trend data corresponding to the monitoring indicator; and the first indicator data and the first historical indicator data may be processed using an algorithm that calculates a period value corresponding to the indicator data to obtain periodic data corresponding to the monitoring indicator. The trend data corresponding to the monitoring indicator may include the trend value of the first indicator data obtained by performing STD processing on the first indicator data and the first historical indicator data, as well as the trend value of the first historical indicator data. The periodic data corresponding to the monitoring indicator may include the period value of the first indicator data obtained by performing STD processing on the first indicator data and the first historical indicator data, as well as the period value of the first historical indicator data. Processing the first indicator data and the first historical indicator data using a less complex data processing algorithm reduces data processing complexity and conserves data processing resources. In this embodiment, a data processing device can obtain the first indicator data corresponding to a monitoring indicator and determine multiple data processing algorithms. The multiple data processing algorithms can then be used to process the first indicator data and the first historical indicator data to obtain trend data and / or periodic data corresponding to the monitoring indicator. In the above method, the multiple data processing algorithms have relatively low complexity, which reduces the complexity of decomposing the indicator data (including the first indicator data and the first historical indicator data) into trend data and / or periodic data corresponding to the monitoring indicator, thereby improving data processing efficiency. In actual applications, indicator data corresponding to the monitoring indicator can be generated in real time or periodically. During an initial period, when the amount of indicator data corresponding to the monitoring indicator is relatively small, the indicator data can be accumulated until the number of indicators reaches a preset value M, at which point the M indicator data can be processed. This process can be referred to as an initialization process. After the initialization process, each time new indicator data is generated, the new indicator data can be processed in real time. This process can be referred to as an online update process. It should be noted that the preset value M can be the number of indicator data included in a sliding window of fixed length. In this embodiment, a sliding window of fixed length can be maintained, and the sliding window can include multiple historical indicator data. During specific implementation, all historical indicator data in the sliding window can be processed each time to obtain trend data and / or periodic data corresponding to the monitoring indicator.Optionally, M = (K + 1)T, where T can be the number of indicator data included in one period of the periodic time series, K can be the number of historical monitoring periods, and K can be an integer greater than or equal to 1. It should be understood that M can be an integer multiple of T. Below, M is described in conjunction with FIG3 , taking K equal to 2 as an example. FIG3 is a schematic diagram of the number of indicator data in a sliding window provided by an exemplary embodiment of the present disclosure. Referring to FIG3 , the sliding window can include M indicator data. The M indicator data can be indicator data collected within three monitoring periods, where T indicator data can be collected within each monitoring period. In other words, the M indicator data can be periodic. For any historical indicator data, the sum of the trend value and the period value of the historical indicator data is determined as the pending value corresponding to the historical indicator data; the difference between the historical indicator data and the pending value is determined as the residual value corresponding to the historical indicator data; and the residual data corresponding to the monitoring indicator is determined to include the residual value corresponding to each historical indicator data. The data processing method provided by an embodiment of the present disclosure is described in detail below, based on the embodiment shown in FIG. 2 above and in conjunction with FIG. 4 . FIG. 4 is a flow chart illustrating another data processing method provided by an exemplary embodiment of the present disclosure. Referring to FIG. 4 , the method may include:

[0007] 5401. Obtain first indicator data corresponding to the monitoring indicator.

[0008] 5402. Determine multiple data processing algorithms.

[0009] S403: Determine first historical indicator data corresponding to the first indicator data. It should be noted that the specific implementation of S401-S403 can be found in S201-S203 and will not be repeated here.

[0010] S404: Determine a first data volume of historical indicator data corresponding to the monitoring indicator. If the first data volume is less than or equal to the first data volume, execute S405; if the first data volume is greater than the first data volume, execute S409. It should be noted that S405-S408 correspond to the aforementioned initialization process, and S409-S412 correspond to the aforementioned online update process.

[0011] S405. When M indicator data corresponding to the monitoring indicator are obtained, at least one second indicator data is determined from the M indicator data. The second indicator data is indicator data that undergoes a jump in the M indicator data. Wherein, may be the jump variable of the i-th indicator data among the M indicator data; may be the i-th indicator data among the M indicator data;

[0012] T can be the number of indicator data collected within a single monitoring cycle. It should be noted that the jump value for each piece of third indicator data can be determined using Formula 1 above, resulting in jump values ​​for multiple pieces of third indicator data. In this embodiment, the jump value for the candidate indicator data can be a local extreme value point among the jump values ​​for the multiple pieces of third indicator data. In other words, the third indicator data corresponding to a local extreme value point among the jump values ​​for the multiple pieces of third indicator data can be the candidate indicator data. Specifically, the jump value for the candidate indicator data can be a local maximum value point or a local minimum value point among the jump values ​​for the multiple pieces of third indicator data. For any local maximum value point, the local maximum value point can be greater than the jump values ​​for the two adjacent pieces of third indicator data; for any local minimum value point, the local minimum value point can be less than the jump values ​​for the two adjacent pieces of third indicator data. In this embodiment, for any piece of candidate indicator data, if the jump value for the candidate indicator data satisfies Formula 2 below, the candidate indicator data can be determined as the second indicator data.

[0013] \di\ > nx max{std(yj, ... y i+T-1 ), std(y i-T , (Formula 2) Wherein, can be the parameter of N-Sigma detection. Optionally, the value can be 6. That is, for any jump value of the selected indicator data, the jump value threshold of the force can be 71 ×

[0014] The algorithm for determining the jump value of the third indicator data in S405 and the algorithm for determining the second indicator data may both be data processing algorithms.

[0015] S406. Based on at least one second indicator data, segment the M indicator data to obtain multiple indicator data sequences. In this embodiment, the M indicator data may be indicator data collected at M consecutive moments. The M indicator data may include at least one second indicator data. The at least one second indicator data may segment the M consecutive indicator data into multiple indicator data sequences. Multiple indicator data sequences are described below with reference to FIG5 . FIG5 is a schematic diagram of an indicator data sequence provided by an exemplary embodiment of the present disclosure. As shown in FIG5 , the M indicator data may include two second indicator data. The two second indicator data may be second indicator data 1 and second indicator data 2. The second indicator data 1 and the second indicator data 2 may segment the M indicator data to obtain indicator data sequence 1, indicator data sequence 2, and indicator data sequence 3.

[0016] S407. Determine the trend value of each indicator data in each indicator data sequence. In this embodiment, for any indicator data sequence, if the number of indicator data included in the indicator data sequence varies, the method for determining the trend value of each indicator data in the indicator data sequence also varies. The method for determining the trend value of each indicator data in the indicator data sequence includes at least the following two cases: Case 1: For any indicator data sequence, if the number of indicator data included in the indicator data sequence is less than or equal to a first threshold T, determine the average value of each indicator data in the indicator data sequence as the trend value of each indicator data in the indicator data sequence. T may be the number of indicator data collected within a single monitoring cycle. In this case, the number of indicator data included in the indicator data sequence is less than or equal to the number of indicator data collected within a single monitoring cycle. The data processing device may determine the average value of each indicator data in the indicator data sequence as the trend value of each indicator data in the indicator data sequence. For example, assume that indicator data sequence 2 includes m indicator data, where m is an integer greater than 1 and less than T. The trend value of each indicator data in indicator data sequence 2 can be the average value of the m indicator data included in indicator data sequence 2. Case 2: For any indicator data sequence, if the number of indicator data included in the indicator data sequence is greater than a first threshold, a first subsequence and a second subsequence are determined in the indicator data sequence. Based on each indicator data in the indicator sequence, the trend value of each indicator data in the first subsequence is determined, and the trend value of each indicator data in the second subsequence is determined as the average value of each indicator data in the second subsequence. The second subsequence includes the last T-1 indicator data in the indicator data sequence, the first subsequence includes the remaining indicator data in the indicator data sequence excluding the second subsequence, and T is an integer greater than 1. Specifically, for any indicator data in the first subsequence, the trend value of the indicator data y can be recorded as y, and T can be determined as T = ym + ym+1 + ym+1 + ym+1. The indicator data sequence with an acquisition time less than or equal to p-T+1 can form a first subsequence. The indicator data sequence with an acquisition time greater than p-T+1 can form a second subsequence. That is, the trend value of each indicator data in the M indicator data included in the sliding window during the initialization process can be determined in the manner of S407. The trend data can include the trend value of each indicator data in the M indicator data.

[0017] The algorithm for determining the trend value of each indicator data in each indicator data sequence in S407 may be a data processing algorithm.

[0018] S408. Determine the periodic value of each indicator data by using a non-local periodic filtering algorithm. It should be noted that the method for determining the periodic value of any indicator data in any indicator data sequence is consistent. Below, taking any indicator data in any indicator data sequence as an example, the method for determining the periodic value of the indicator data is described. In this embodiment, multiple first reference indicator data corresponding to the indicator data can be determined in M ​​indicator data; the weight value of each first reference indicator data is determined; and the periodic value of the indicator data is determined based on the weight value of each first reference indicator data, each first reference indicator data, and the trend value of each first reference indicator data. Assume that the indicator data is; Yt, where * can be the acquisition time of the reference. In this embodiment, K neighborhoods can be determined in the K historical monitoring periods corresponding to %, wherein one neighborhood can be determined in each historical monitoring period. The center of the neighborhood can be It should be noted that the method for determining the periodic value of the indicator data may be a non-local periodic filtering algorithm, and the non-local periodic filtering algorithm may be a data processing algorithm. In this embodiment, the periodic value of each indicator data in the M indicator data may be determined in accordance with S408. The periodic data may include the periodic value of each indicator data in the M indicator data. Below, taking K equal to 2 as an example, in conjunction with FIG6 , the K neighborhoods corresponding to % are described. FIG6 is a neighborhood schematic diagram provided by an exemplary embodiment of the present disclosure. As shown in FIG6 , assuming that the acquisition time of % is t, the center of the neighborhood in the first historical monitoring period corresponding to % may be t-T, and the center of the neighborhood in the second historical monitoring period corresponding to % may be t-2T. a The width of each neighborhood can be H. The starting time of the neighborhood in the first historical monitoring period can be t - T - H. The ending time of the neighborhood in the first historical monitoring period can be t - T + H. The starting time of the neighborhood in the second historical monitoring period can be t - 2T - H. The ending time of the neighborhood in the second historical monitoring period can be t - 2T + Ho.

[0019] S409. Determine the predicted value corresponding to the first indicator data. In this embodiment, multiple second reference indicator data corresponding to the first indicator data and the first trend value of the indicator data preceding the first indicator data can be determined from multiple historical indicator data of the indicator data. The predicted value is determined based on the first indicator data, the period value of each second reference indicator data, and the first trend value. Assume that the first indicator data is %1, where n can be the time when %1 was acquired. Then, the multiple second reference indicator data corresponding to the first indicator data %1 can be %1, where i 6 (1^ (1 = (i | (t , = t — kT, i = t' + / i))^ k = 1,2 ... , h = 0,1 ... , H The algorithm for determining the predicted value corresponding to the first indicator data may be a data processing algorithm.

[0020] S410: Determine whether the first indicator data is outlier data based on the first indicator data and the predicted value. If so, execute S411; if not, execute S412. In this embodiment, M consecutive first historical indicator data may be determined as a set if the first indicator data satisfies:

[0021] \yti - yti\ > « X std(Y) Wherein, 71 can be a parameter for N-Sigma detection. Optionally, the hole can be 6. Then it can be determined that the first indicator data is outlier data. The above algorithm for determining whether the first indicator data is outlier data can be a data processing algorithm. In this embodiment, if the first indicator data is an outlier, the indicator data with the earliest acquisition time in the set Y can be removed, and the predicted value corresponding to the first indicator data is added to the set to obtain an updated set body. If the first indicator data is not an outlier, the indicator data with the earliest acquisition time in the set Y can be removed, and the first indicator data is added to the set to obtain an updated set body.

[0022] S411: Determine a first number of continuous outlier data, and determine trend data and / or periodic data based on the first number, the first indicator data, and M-1 first historical indicator data. In this embodiment, it can be determined whether the first number is greater than or equal to a preset number (i.e., an integer greater than or equal to 1). If so, determine L-1 second historical indicator data preceding the first indicator data from the M-1 first historical indicator data, determine a target trend value based on the first indicator data and L-1 second historical indicator data, update the trend values ​​of the first indicator data and the M-1 second historical indicator data to the target trend value, and determine the periodic value of the first indicator data using a non-local periodic filtering algorithm. If not, determine the trend value of the first indicator data using the average of the predicted value and the M-1 first historical indicator data, and determine the periodic value of the first indicator data using a non-local periodic filtering algorithm. The trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data. Specifically, if the first quantity is greater than or equal to q: The target trend value may be: wherein, f = ZZ Vj fT j=tl-L+l - yti) 2 ) 252 If the first number is less than q: The trend value of the first indicator data may be the average value of the predicted value and M-1 first historical indicator data. That is, the trend value of the first indicator data may be, The set may include the first indicator data and M-1 first historical indicator data. It should be noted that the method for determining the periodic value of the first indicator data can be found in S411 and will not be further described here. In this embodiment, the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data. The method for determining the trend value of each first historical indicator data and the periodic value of each first historical indicator data can be found in the method for determining the trend value of the first indicator data and the periodic index of the first indicator data, or in S405-S408. The aforementioned algorithms for determining the periodic index of the first indicator data and the algorithm for determining the trend value of the first indicator data can both be data processing algorithms. In this embodiment, the data processing device can obtain first indicator data corresponding to the monitoring indicator and determine multiple data processing algorithms; can determine first historical indicator data corresponding to the first indicator data; can determine a first data volume of the historical indicator data corresponding to the monitoring indicator; if the first data is less than or equal to, when M indicator data corresponding to the monitoring indicator are obtained, at least one second indicator data can be determined from the M indicator data; can segment the M indicator data based on the at least one second indicator data to obtain multiple indicator data sequences; can determine a trend value of each indicator data in each indicator data sequence; and can determine a period value of each indicator data using a non-local periodic filtering algorithm. If the first number is less than the predicted value corresponding to the first indicator data, the first indicator data and the predicted value can be used to determine whether the first indicator data is outlier data. If the first indicator data is outlier data, the first number of consecutive outlier data can be determined, and trend data and / or periodic data can be determined based on the first number, the first indicator data, and M-1 first historical indicator data. If the first indicator data is not outlier data, the average of the first indicator data and the M-1 first historical indicator data can be determined as the trend value of the first indicator data, and the period value of the first indicator data can be determined using a non-local periodic filtering algorithm. In the above method, the complexity of the multiple data processing algorithms is relatively low, which reduces the complexity of decomposing the indicator data (including the first indicator data and the first historical indicator data) into trend data and / or periodic data corresponding to the monitoring indicator, thereby improving data processing efficiency. Figure 7 is a schematic structural diagram of a data processing device provided by an exemplary embodiment of the present disclosure.Referring to FIG. 7 , the data processing device 10 includes an acquisition module 11, a determination module 12, and a processing module 13. The acquisition module 11 is configured to acquire first indicator data corresponding to a monitoring indicator; the determination module 12 is configured to determine multiple data processing algorithms, each having an algorithm complexity less than or equal to a preset complexity, and the multiple data processing algorithms are configured to perform data decomposition processing on the data; the determination module 12 is further configured to determine first historical indicator data corresponding to the first indicator data; and the processing module 13 is configured to process the first indicator data and the first historical indicator data using the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator. The data processing device provided in the embodiments of the present disclosure can implement the technical solutions shown in the above-described method embodiments. The implementation principles and beneficial effects thereof are similar and are not further described here. In one possible implementation, the processing module 13 is specifically used to determine a first data volume of the historical indicator data corresponding to the monitoring indicator; if the first data volume is less than or equal to, then when M indicator data corresponding to the monitoring indicator are obtained, the M indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data, and the M indicator data include the first indicator data and the first historical indicator data; if the first data volume is greater than the, then the first indicator data and M-1 first historical indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data. In one possible implementation, the processing module 13 is specifically configured to: determine at least one second indicator data from the M indicator data, where the second indicator data is indicator data that undergoes a jump among the M indicator data; segment the M indicator data according to the at least one second indicator data to obtain multiple indicator data sequences; determine a trend value of each indicator data in each indicator data sequence, and determine a periodic value of each indicator data using a non-local periodic filtering algorithm; determine that the trend data includes the trend value of each indicator data in the M indicator data, and determine that the periodic data includes the periodic value of each indicator data in the M indicator data; wherein the multiple data processing algorithms include the algorithms used in the above steps.In one possible implementation, the processing module 13 is specifically configured to: determine a plurality of third indicator data from the M indicator data, where the third indicator data is data in a middle portion of the M indicator data; determine, for any one piece of third indicator data, a jump value of the third indicator data, where the jump value indicates a difference between future indicator data and historical indicator data of the third indicator data, where a generation time of the future indicator data is after a generation time of the third indicator data; determine, based on the jump value of the third indicator data, candidate indicator data from the plurality of third indicator data, where the jump value of the candidate indicator data is a local extreme value point in the jump values ​​of the plurality of third indicator data; determine, for any one piece of candidate indicator data, a jump value threshold corresponding to the candidate indicator data based on the M indicator data; and determine, as the at least one second indicator data, indicator data whose absolute value of the jump value is greater than the corresponding jump value threshold in the candidate indicator data. In one possible implementation, for any indicator data sequence, the processing module 13 is specifically configured to: if the number of indicator data included in the indicator data sequence is less than or equal to a first threshold value T, determine the average value of each indicator data in the indicator data sequence as the trend value of each indicator data in the indicator data sequence; if the number of indicator data included in the indicator data sequence is greater than the first threshold value, determine a first subsequence and a second subsequence in the indicator data sequence, determine the trend value of each indicator data in the first subsequence based on each indicator data in the indicator data sequence, and determine the trend value of each indicator data in the second subsequence as the average value of each indicator data in the first subsequence; wherein the second subsequence includes the last T-1 indicator data in the indicator data sequence, the first subsequence includes the other indicator data in the indicator data sequence except the second subsequence, and T is an integer greater than 1. In one possible implementation, for any indicator data in any indicator data sequence, the processing module 13 is specifically used to determine, among the M indicator data, multiple first reference indicator data corresponding to the indicator data; determine a weight value of each first reference indicator data; and determine a period value of the indicator data based on the weight value of each first reference indicator data, each first reference indicator data, and a trend value of each first reference indicator data.In one possible implementation, the processing module 13 is specifically used to determine the predicted value corresponding to the first indicator data, and judge whether the first indicator data is outlier data based on the first indicator data and the predicted value; if so, determine a first number of consecutive outlier data, and determine the trend data and / or the periodic data based on the first number, the first indicator data, and M-1 first historical indicator data; if not, determine the average value of the first indicator data and the M-1 first historical indicator data as the trend value of the first indicator data, and determine the periodic value of the first indicator data through a non-local periodic filtering algorithm, wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data. In one possible implementation, the processing module 13 is specifically configured to determine whether the first number is greater than or equal to a preset value > £ is an integer greater than or equal to 1; if so, determining 41 second historical indicator data before the first indicator data among the M-1 first historical indicator data, determining a target trend value based on the first indicator data and the 41 second historical indicator data, updating the trend values ​​of the first indicator data and the 41 second historical indicator data to the target trend value, and determining a period value of the first indicator data through the non-local periodic filtering algorithm; if not, determining an average value of the predicted value and the M-1 first historical indicator data as the trend value of the first indicator data, and determining a period value of the first indicator data through the non-local periodic filtering algorithm; wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the period value of the first indicator data and the period value of each first historical indicator data. The data processing device provided in the embodiment of the present disclosure can execute the technical solution shown in the above method embodiment. Its implementation principles and beneficial effects are similar and will not be described in detail here. FIG8 is a structural diagram of another data processing device provided in the exemplary embodiment of the present disclosure.Based on FIG. 7 , the data processing device 10 may further include an updating module 14, wherein the updating module 14 is configured to update the first indicator data and the 41 second historical indicator data to the target trend value. For any historical indicator data among the M-1 historical indicator data except the M-1 second historical indicator data, the determining module 12 is further configured to determine an update increment based on the target trend value and the trend value of the 1st historical indicator data before the first indicator data. The updating module 14 is further configured to update the historical indicator data to the sum of the historical indicator data and the update increment. In one possible implementation, the processing module 13 is specifically configured to determine, from the plurality of historical indicator data, a plurality of second reference indicator data corresponding to the first indicator data and a first trend value of the indicator data preceding the first indicator data; and determine the predicted value based on the first indicator data, the period value of each second reference indicator data, and the first trend value. In one possible implementation, the trend data includes trend values ​​for multiple historical indicator data corresponding to the monitoring indicator, and the periodic data includes period values ​​for the multiple historical indicator data. The determination module 12 is further configured to, for any piece of historical indicator data, determine the sum of the trend value and period value of the historical indicator data as the to-be-processed value corresponding to the historical indicator data; determine the difference between the historical indicator data and the to-be-processed value as the residual value corresponding to the historical indicator data; and determine that the residual data corresponding to the monitoring indicator includes the residual value corresponding to each piece of historical indicator data. The data processing device provided in the embodiments of the present disclosure can implement the technical solutions shown in the above-mentioned method embodiments. The implementation principles and beneficial effects thereof are similar and are not further described here. Figure 9 is a schematic structural diagram of another data processing device provided in an exemplary embodiment of the present disclosure.Referring to Figure 9 , the data processing device 20 includes an acquisition module 21, a determination module 22, and a processing module 23. The acquisition module 21 is configured to acquire first indicator data corresponding to a monitoring indicator, where the first indicator data is data generated by device operation. The determination module 22 is configured to determine multiple data processing algorithms, each having an algorithm complexity less than or equal to a preset complexity, and the multiple data processing algorithms are configured to perform data decomposition processing on the data. The determination module 22 is further configured to determine first historical indicator data corresponding to the first indicator data. The processing module 23 is configured to process the first indicator data and the first historical indicator data using the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator. The data processing device provided in the embodiments of the present disclosure can implement the technical solutions shown in the above-described method embodiments. The implementation principles and beneficial effects thereof are similar and are not further described here. In one possible implementation, the processing module 23 is specifically used to determine a first data volume of the historical indicator data corresponding to the monitoring indicator; if the first data volume is less than or equal to, then when M indicator data corresponding to the monitoring indicator are obtained, the M indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data, and the M indicator data include the first indicator data and the first historical indicator data; if the first data volume is greater than the, then the first indicator data and M-1 first historical indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data. In one possible implementation, the processing module 23 is specifically configured to: determine at least one second indicator data from the M indicator data, where the second indicator data is indicator data that undergoes a jump among the M indicator data; segment the M indicator data according to the at least one second indicator data to obtain multiple indicator data sequences; determine a trend value of each indicator data in each indicator data sequence, and determine a periodic value of each indicator data using a non-local periodic filtering algorithm; determine that the trend data includes the trend value of each indicator data in the M indicator data, and determine that the periodic data includes the periodic value of each indicator data in the M indicator data; wherein the multiple data processing algorithms include the algorithms used in the above steps.In one possible implementation, the processing module 23 is specifically configured to: determine a plurality of third indicator data from the M indicator data, where the third indicator data is data in a middle portion of the M indicator data; determine, for any one piece of third indicator data, a jump value of the third indicator data, where the jump value is used to indicate a difference between future indicator data and historical indicator data of the third indicator data, where a generation time of the future indicator data is after a generation time of the third indicator data; determine, based on the jump value of the third indicator data, candidate indicator data from the plurality of third indicator data, where the jump value of the candidate indicator data is a local extreme value point in the jump values ​​of the plurality of third indicator data; determine, for any one piece of candidate indicator data, a jump value threshold corresponding to the candidate indicator data based on the M indicator data; and determine, as the at least one second indicator data, indicator data whose absolute value of the jump value is greater than the corresponding jump value threshold in the candidate indicator data. In one possible implementation, for any indicator data sequence, the processing module 23 is specifically configured to: if the number of indicator data included in the indicator data sequence is less than or equal to a first threshold value T, determine the average value of each indicator data in the indicator data sequence as the trend value of each indicator data in the indicator data sequence; if the number of indicator data included in the indicator data sequence is greater than the first threshold value, determine a first subsequence and a second subsequence in the indicator data sequence, determine the trend value of each indicator data in the first subsequence based on each indicator data in the indicator data sequence, and determine the trend value of each indicator data in the second subsequence as the average value of each indicator data in the first subsequence; wherein the second subsequence includes the last T-1 indicator data in the indicator data sequence, the first subsequence includes the other indicator data in the indicator data sequence except the second subsequence, and T is an integer greater than 1. In one possible implementation, for any indicator data in any indicator data sequence, the processing module 23 is specifically used to determine, among the M indicator data, multiple first reference indicator data corresponding to the indicator data; determine a weight value of each first reference indicator data; and determine a period value of the indicator data based on the weight value of each first reference indicator data, each first reference indicator data, and a trend value of each first reference indicator data.In one possible implementation, the processing module 23 is specifically configured to determine a predicted value corresponding to the first indicator data, and determine whether the first indicator data is outlier data based on the first indicator data and the predicted value; if so, determine a first number of consecutive outlier data, and determine the trend data and / or the periodic data based on the first number, the first indicator data, and M-1 first historical indicator data; if not, determine an average value of the first indicator data and the M-1 first historical indicator data as the trend value of the first indicator data, and determine the periodic value of the first indicator data through a non-local periodic filtering algorithm, wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data. In one possible implementation, the processing module 23 is specifically configured to determine whether the first number is greater than or equal to a preset value > £ is an integer greater than or equal to 1; if so, determining 41 second historical indicator data before the first indicator data among the M-1 first historical indicator data, determining a target trend value based on the first indicator data and the 41 second historical indicator data, updating the trend values ​​of the first indicator data and the 41 second historical indicator data to the target trend value, and determining a period value of the first indicator data through the non-local periodic filtering algorithm; if not, determining an average value of the predicted value and the M1 first historical indicator data as the trend value of the first indicator data, and determining a period value of the first indicator data through the non-local periodic filtering algorithm; wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the period value of the first indicator data and the period value of each first historical indicator data. The data processing device provided in the embodiment of the present disclosure can implement the technical solutions shown in the above method embodiments. Its implementation principles and beneficial effects are similar and will not be described in detail here. FIG10 is a schematic diagram of the structure of another data processing device provided in the exemplary embodiment of the present disclosure.Based on FIG9 , the data processing device 20 may further include an updating module 24, wherein the updating module 24 is configured to update the first indicator data and the 41 second historical indicator data to the target trend value. For any historical indicator data among the M-1 historical indicator data except the M-1 second historical indicator data, the determining module 22 is further configured to determine an update increment based on the target trend value and the trend value of the 1st historical indicator data before the first indicator data. The updating module 24 is further configured to update the historical indicator data to the sum of the historical indicator data and the update increment. In one possible implementation, the processing module 23 is specifically configured to determine, from the plurality of historical indicator data, a plurality of second reference indicator data corresponding to the first indicator data and a first trend value of the indicator data preceding the first indicator data; and determine the predicted value based on the first indicator data, the period value of each second reference indicator data, and the first trend value. In one possible implementation, the trend data includes trend values ​​for multiple historical indicator data corresponding to the monitoring indicator, and the periodic data includes period values ​​for the multiple historical indicator data. The determination module 22 is further configured to, for any piece of historical indicator data, determine the sum of the trend value and period value of the historical indicator data as the to-be-processed value corresponding to the historical indicator data; determine the difference between the historical indicator data and the to-be-processed value as the residual value corresponding to the historical indicator data; and determine that the residual data corresponding to the monitoring indicator includes the residual value corresponding to each piece of historical indicator data. The data processing device provided in the embodiments of the present disclosure can implement the technical solutions shown in the above-mentioned method embodiments. The implementation principles and beneficial effects are similar and will not be further described here. Figure 11 is a schematic diagram of the structure of a data processing device provided in an exemplary embodiment of the present disclosure. Referring to Figure 11, the data processing device 30 may include a processor 31 and a memory 32. For example, the processor 31 and the memory 32 are interconnected via a bus 33. The memory 32 stores computer-executable instructions; the processor 31 executes the computer-executable instructions stored in the memory 32, causing the processor 31 to perform the method described in the above method embodiment. The data processing device shown in FIG11 may be a server, a computer, or other electronic device. The data processing device provided in the embodiments of the present disclosure can implement the technical solutions described in the above method embodiments. The implementation principles and beneficial effects thereof are similar and will not be further described here.Accordingly, embodiments of the present disclosure provide a computer-readable storage medium storing computer-executable instructions. When executed by a processor, the computer-executable instructions are used to implement the methods described in the above method embodiments. Accordingly, embodiments of the present disclosure may also provide a computer program product, including a computer program. When executed by a processor, the computer program may implement the methods described in the above method embodiments. Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, may be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, such that the instructions, when executed by the processor of the computer or other programmable data processing device, produce means for implementing the functions specified in one or more flows in the flowchart and / or one or more blocks in the block diagram. These computer program instructions can also be stored in a computer-readable memory capable of directing the computer or other programmable data processing device to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means, which implement the functions specified in one or more flows in the flowchart and / or one or more blocks in the block diagram. These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, such that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more flows in the flowchart and / or one or more blocks in the block diagram. In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.Memory may include non-permanent storage in computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media. Computer-readable media, including both permanent and non-permanent, removable and non-removable media, can be implemented using any method or technology for information storage. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change RAM (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carrier waves. It should also be noted that the terms "comprise," "include," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, product, or apparatus comprising a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, product, or apparatus. Without further limitation, an element specified by the phrase "comprising a..." does not preclude the presence of additional identical elements in the process, method, product, or apparatus comprising the recited element. The foregoing description is merely an example of the present disclosure and is not intended to limit the present disclosure. Persons skilled in the art will readily appreciate that various modifications and variations of the present disclosure are possible. Any modifications, equivalent substitutions, improvements, and the like made within the spirit and principles of the present disclosure are intended to be encompassed by the claims of the present disclosure.

Claims

Claims 1. A data processing method, wherein: include: Obtaining first indicator data corresponding to the monitoring indicator; determining a plurality of data processing algorithms, wherein the algorithm complexity of the data processing algorithms is less than or equal to a preset complexity, and the plurality of data processing algorithms are used to perform data decomposition processing on the data; First historical indicator data corresponding to the first indicator data is determined, and the first indicator data and the first historical indicator data are processed by the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator.

2. A data processing method, wherein: include: Obtaining first indicator data corresponding to the monitoring indicator, where the first indicator data is data generated by the operation of the device; determining a plurality of data processing algorithms, wherein the algorithm complexity of the data processing algorithms is less than or equal to a preset complexity, and the plurality of data processing algorithms are used to perform data decomposition processing on the data; First historical indicator data corresponding to the first indicator data is determined, and data decomposition processing is performed on the first indicator data and the first historical indicator data using the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator.

3. The method according to claim 1 or 2, wherein: The first indicator data and the first historical indicator data are processed by the multiple data processing algorithms to obtain trend data and / or periodic data corresponding to the monitoring indicator, including: determining a first data volume of the historical indicator data corresponding to the monitoring indicator; if the first data volume is less than or equal to, then when M indicator data corresponding to the monitoring indicator are obtained, the M indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data, and the M indicator data include the first indicator data and the first historical indicator data; if the first data volume is greater than, then the first indicator data and M-1 first historical indicator data are processed by the multiple data processing algorithms to obtain the trend data and / or the periodic data.

4. The method according to claim 3, wherein: Processing the M indicator data using the multiple data processing algorithms to obtain the trend data and / or the periodic data includes: determining at least one second indicator data among the M indicator data, where the second indicator data is indicator data that undergoes a jump among the M indicator data; segmenting the M indicator data based on the at least one second indicator data to obtain multiple indicator data sequences; determining a trend value of each indicator data in each indicator data sequence, and determining a periodic value of each indicator data using a non-local periodic filtering algorithm; determining that the trend data includes the trend value of each indicator data among the M indicator data, and determining that the periodic data includes the periodic value of each indicator data among the M indicator data; wherein the multiple data processing algorithms include the algorithms used in the above steps.

5. The method according to claim 4, wherein: Determining at least one second indicator data from the M indicator data includes: determining a plurality of third indicator data from the M indicator data, where the third indicator data is data in a middle portion of the M indicator data; determining, for any one of the third indicator data, a jump value of the third indicator data, where the jump value is used to indicate a difference between future indicator data and historical indicator data of the third indicator data, where a generation time of the future indicator data is after a generation time of the third indicator data; determining, based on the jump value of the third indicator data, candidate indicator data from the plurality of third indicator data, where the jump value of the candidate indicator data is a local extreme value point in the jump values ​​of the plurality of third indicator data; and determining, based on the M indicator data, a jump value threshold corresponding to the candidate indicator data. 24 Indicator data whose absolute value of the jump amount is greater than the corresponding jump amount threshold in the candidate indicator data is determined as the at least one second indicator data.

6. The method according to claim 4 or 5, wherein: For any indicator data series; Determining the trend value of each indicator data in the indicator data sequence includes: if the number of indicator data included in the indicator data sequence is less than or equal to a first threshold value T, determining the average value of each indicator data in the indicator data sequence as the trend value of each indicator data in the indicator data sequence; if the number of indicator data included in the indicator data sequence is greater than the first threshold value, determining a first subsequence and a second subsequence in the indicator data sequence, and determining the trend value of each indicator data in the first subsequence based on each indicator data in the indicator data sequence, and determining the trend value of each indicator data in the second subsequence as the average value of each indicator data in the second subsequence; wherein the second subsequence includes the last T-1 indicator data in the indicator data sequence, the first subsequence includes other indicator data in the indicator data sequence except the second subsequence, and T is an integer greater than 1.

7. The method according to claim 4 or 5, wherein: For any indicator data in any indicator data sequence; determining the periodic value of the indicator data through a non-local periodic filtering algorithm, including: determining multiple first reference indicator data corresponding to the indicator data among the M indicator data; determining a weight value of each first reference indicator data; and determining the periodic value of the indicator data based on the weight value of each first reference indicator data, each first reference indicator data, and a trend value of each first reference indicator data.

8. The method according to any one of claims 2 to 6, wherein: Processing the first indicator data and M-1 first historical indicator data using the multiple data processing algorithms to obtain the trend data and / or the periodic data includes: determining a predicted value corresponding to the first indicator data, and judging whether the first indicator data is outlier data based on the first indicator data and the predicted value; if so, determining a first number of consecutive outlier data, and determining the trend data and / or the periodic data based on the first number, the first indicator data, and one first historical indicator data; if not, determining an average value of the first indicator data and the M-1 first historical indicator data as the trend value of the first indicator data, and determining the periodic value of the first indicator data using a non-local periodic filtering algorithm, wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data.

9. The method according to claim 8, wherein: Determining the trend data and / or the periodic data based on the first quantity, the first indicator data, and M-1 first historical indicator data includes: determining whether the first quantity is greater than or equal to a preset £, where £ is an integer greater than or equal to 1; if so, determining 41 second historical indicator data preceding the first indicator data among the M-1 first historical indicator data, determining a target trend value based on the first indicator data and the 41 second historical indicator data, updating the trend values ​​of the first indicator data and the M-1 second historical indicator data to the target trend value, and determining the periodic value of the first indicator data through a non-local periodic filtering algorithm; if not, determining the average value of the predicted value and the M-1 first historical indicator data as the trend value of the first indicator data, and determining the periodic value of the first indicator data through the non-local periodic filtering algorithm; wherein the trend data includes the trend value of the first indicator data and the trend value of each first historical indicator data, and the periodic data includes the periodic value of the first indicator data and the periodic value of each first historical indicator data.

10. The method according to claim 9, wherein: After determining the target trend value based on the first indicator data and the less than one second historical indicator data, the method further includes: updating the first indicator data and the less than one second historical indicator data to the target trend value; and, for any other historical indicator data in the M-1 historical indicator data except the more than one second historical indicator data, updating the target trend value based on the target trend value and the trend of the ▲th historical indicator data before the first indicator data. value, determine an update increment, and update the historical indicator data to the sum of the historical indicator data and the update increment.

11. The method according to any one of claims 8 to 10, wherein: Determining the predicted value corresponding to the first indicator data includes: determining, among multiple historical indicator data of the indicator data, multiple second reference indicator data corresponding to the first indicator data, and a first trend value of indicator data previous to the first indicator data; and determining the predicted value based on the first indicator data, the periodic value of each second reference indicator data, and the first trend value.

12. The method according to any one of claims 1 to 11, wherein: The trend data includes trend values ​​of multiple historical indicator data corresponding to the monitoring indicator, and the periodic data includes period values ​​of the multiple historical indicator data; the method also includes: for any historical indicator data, determining the sum of the trend value and the period value of the historical indicator data as the to-be-processed value corresponding to the historical indicator data; determining the difference between the historical indicator data and the to-be-processed value as the residual value corresponding to the historical indicator data; and determining that the residual data corresponding to the monitoring indicator includes the residual value corresponding to each historical indicator data.

13. A data processing device, wherein: include: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the data processing device to perform the method according to any one of claims 1 to 12.

14. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer-executable instructions, and when the processor executes the computer-executable instructions, the method according to any one of claims 1 to 12 is implemented.

15. A computer program product, comprising a computer program, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 12 is implemented.

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