CN111984658A - Report processing method and device - Google Patents
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Abstract
The invention discloses a method and a device for processing a report, wherein the method comprises the following steps: obtaining reports to be processed of a plurality of batches, and inquiring a plurality of historical processing durations of the reports to be processed of each batch; respectively inputting a plurality of historical processing durations of the to-be-processed reports of each batch into the exponential weighted moving average model, and outputting the exponential weighted moving average of the to-be-processed reports of each batch; dividing the batches of the reports to be processed into a plurality of groups according to the exponential weighted moving average value of the batches of the reports to be processed, wherein the difference value between the total predicted processing time lengths of the groups of the reports to be processed is smaller than a preset threshold value; and processing a plurality of groups of reports to be processed in parallel, and processing a plurality of batches of reports to be processed in each group in series. The invention can improve the accuracy of grouping a plurality of batch reports based on the historical processing time length and the exponential weighted moving average model, thereby improving the processing efficiency of the batch reports.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for processing a report.
Background
The method comprises the steps that a trading system of a financial institution generates a large number of trading reports and valuation reports in batches at the end of each day, the reports are downloaded to a downstream system for analysis and secondary processing, along with the fluctuation of the price of a financial market, the trading volume of different financial products fluctuates greatly in different periods, the fluctuation of the trading volume and the report processing time duration often show positive correlation, so the fluctuation of the report processing time duration can cause the fluctuation of the report processing time duration, the current commonly used scheduling tool of the reports is Tivoli Work Schedule (TWS), the average processing time duration of historical multi-batch reports is calculated through the TWS, the reports of multiple batches are manually grouped according to the average processing time duration, the average processing time duration of the historical multi-batch reports cannot reflect the fluctuation of the actual report processing time duration, so that the prediction condition has larger deviation from the actual condition, and the subsequent multi-time adjustment needs to be manually performed, the efficiency is low.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the invention provides a report processing method, which is used for improving the processing efficiency of batch reports and comprises the following steps:
obtaining reports to be processed of a plurality of batches, and inquiring a plurality of historical processing durations of the reports to be processed of each batch;
respectively inputting a plurality of historical processing durations of the to-be-processed reports of each batch into the exponential weighted moving average model, and outputting the exponential weighted moving average of the to-be-processed reports of each batch;
dividing the batches of the reports to be processed into a plurality of groups according to the exponential weighted moving average value of the batches of the reports to be processed, wherein the difference value between the total predicted processing time lengths of the groups of the reports to be processed is smaller than a preset threshold value;
and processing a plurality of groups of reports to be processed in parallel, and processing a plurality of batches of reports to be processed in each group in series.
The embodiment of the invention provides a report processing device, which is used for improving the processing efficiency of batch reports and comprises the following components:
the data acquisition module is used for acquiring the to-be-processed reports of a plurality of batches and inquiring a plurality of historical processing durations of the to-be-processed reports of each batch;
the processing time length prediction module is used for respectively inputting the historical processing time lengths of the to-be-processed reports of each batch into the exponential weighted moving average model and outputting the exponential weighted moving average of the to-be-processed reports of each batch;
the grouping module is used for grouping the to-be-processed reports of a plurality of batches into a plurality of groups according to the exponentially weighted moving average of the to-be-processed reports of the plurality of batches, wherein the difference value between the total predicted processing time lengths of the to-be-processed reports of each group is smaller than a preset threshold value;
and the report processing module is used for processing a plurality of groups of reports to be processed in parallel and processing a plurality of batches of reports to be processed in each group in series.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the report processing method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores a computer program for executing the report processing method.
The embodiment of the invention comprises the following steps: obtaining reports to be processed of a plurality of batches, and inquiring a plurality of historical processing durations of the reports to be processed of each batch; respectively inputting a plurality of historical processing durations of the to-be-processed reports of each batch into the exponential weighted moving average model, and outputting the exponential weighted moving average of the to-be-processed reports of each batch, wherein the exponential weighted moving average obtained based on the historical processing durations and the exponential weighted moving average model can better reflect the fluctuation of the batch report processing durations within a historical period of time; dividing the batches of the reports to be processed into a plurality of groups according to the exponential weighted moving average value of the batches of the reports to be processed, wherein the difference value between the total predicted processing time lengths of the groups of the reports to be processed is smaller than a preset threshold value; the report forms to be processed of multiple groups are processed in parallel, the report forms to be processed of multiple batches in each group are processed in series, accuracy of grouping of the report forms of the multiple batches can be improved based on the exponential weighted moving average, and processing efficiency of the report forms in batches is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts. In the drawings:
FIG. 1 is a schematic diagram illustrating a flow of a report processing method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a detailed flow of step 101 and step 102 in FIG. 1;
FIG. 3 is a diagram illustrating a structure of a report processing apparatus according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating another structure of a report processing apparatus according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As will be appreciated by one skilled in the art, embodiments of the present invention may be embodied as a system, apparatus, method or computer program product. Accordingly, the present disclosure may be embodied in the form of: entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or a combination of hardware and software.
In order to solve the technical problem that the average processing time obtained based on TWS calculation in the prior art cannot reflect the fluctuation of the actual report processing time, so that a predicted situation has a large deviation from an actual situation, and multiple adjustments are required manually, which results in low efficiency, an embodiment of the present invention provides a report processing method for improving the processing efficiency of batch reports, and fig. 1 is a schematic diagram of a flow of the report processing method in the embodiment of the present invention, as shown in fig. 1, the method includes:
step 101: obtaining reports to be processed of a plurality of batches, and inquiring a plurality of historical processing durations of the reports to be processed of each batch;
step 102: respectively inputting a plurality of historical processing durations of the to-be-processed reports of each batch into the exponential weighted moving average model, and outputting the exponential weighted moving average of the to-be-processed reports of each batch;
step 103: dividing the batches of the reports to be processed into a plurality of groups according to the exponential weighted moving average value of the batches of the reports to be processed, wherein the difference value between the total predicted processing time lengths of the groups of the reports to be processed is smaller than a preset threshold value;
step 104: and processing a plurality of groups of reports to be processed in parallel, and processing a plurality of batches of reports to be processed in each group in series.
As shown in fig. 1, an embodiment of the present invention is implemented by: obtaining reports to be processed of a plurality of batches, and inquiring a plurality of historical processing durations of the reports to be processed of each batch; respectively inputting a plurality of historical processing durations of the to-be-processed reports of each batch into the exponential weighted moving average model, and outputting the exponential weighted moving average of the to-be-processed reports of each batch, wherein the exponential weighted moving average obtained based on the historical processing durations and the exponential weighted moving average model can better reflect the fluctuation of the batch report processing durations within a historical period of time; dividing the batches of the reports to be processed into a plurality of groups according to the exponential weighted moving average value of the batches of the reports to be processed, wherein the difference value between the total predicted processing time lengths of the groups of the reports to be processed is smaller than a preset threshold value; the report forms to be processed of multiple groups are processed in parallel, the report forms to be processed of multiple batches in each group are processed in series, accuracy of grouping of the report forms of the multiple batches can be improved based on the exponential weighted moving average, and processing efficiency of the report forms in batches is improved.
In specific implementation, in step 101, a plurality of batches of to-be-processed reports that can be dynamically scheduled can be screened from batch reports of the financial transaction system, a plurality of historical processing durations of the to-be-processed reports of each batch can be queried from a database, configuration parameters such as the maximum allowable number of each group of reports, whether report processing fails to be re-executed, the re-execution times and the like can be predetermined, and if adjustment is needed, only configuration files need to be modified without modifying programs.
Fig. 2 is a schematic diagram of a specific process of step 101 and step 102 in fig. 1, and as shown in fig. 2, in an embodiment, the querying a plurality of historical processing durations of the to-be-processed report of each batch in step 101 may include:
step 201: for the to-be-processed report forms of each batch, inquiring the processing time length of the historical tth day of the to-be-processed report forms of the batch, wherein t represents the number of days away from the current day, and the value range of t is as follows: t is 1,2,3, … …, n;
in step 102, the step of inputting the plurality of historical processing durations of the to-be-processed report of each batch into the exponential weighted moving average model, and outputting the exponential weighted moving average of the to-be-processed report of each batch may include:
step 202: for each batch of to-be-processed reports, inputting the processing time length of the historical 1 st day of the batch of to-be-processed reports into an index weighted moving average model, and outputting the index weighted moving average value of the historical 1 st day of the batch of to-be-processed reports;
step 203: the following steps are executed in a circulating mode until t is equal to n, and the exponential weighted moving average value of the nth history of the report forms to be processed of the batch is determined;
step 204: inputting the processing time of the historical tth day of the batch of the to-be-processed reports and the exponential weighted moving average value of the historical t-1 th day of the batch of the to-be-processed reports into an exponential weighted moving average model, and outputting the exponential weighted moving average value of the historical tth day of the batch of the to-be-processed reports;
step 205: let t be t + 1;
step 206: and determining the exponential weighted moving average value of the historical nth day of the report to be processed of the batch as the exponential weighted moving average value of the report to be processed of the batch.
In one embodiment, the exponentially weighted moving average model is shown in equation (1):
EWMA(t)＝λ×y(t)+(1-λ)×EWMA(t-1)，t＝1,2,3,……,n (1)
wherein EWMA (t) is an exponentially weighted moving average of the historical th day, EWMA (t-1) is an exponentially weighted moving average of the historical th day, y (t) is the processing time length of the historical th day, n is the number of days, and lambda is an attenuation factor.
An EWMA (empirical Weighted Moving average) exponential Weighted Moving average model is a common sequence data processing mode, and the attenuation factor lambda in the EWMA model determines the capability of the EWMA model to track sudden changes of actual data, namely timeliness. Obviously, as the λ value increases, the more time-efficient the model is, the more ability to reflect the latest information. The inventor finds that the EWMA model is often applied to prediction of fluctuation rate of financial products through a large amount of research, and the fluctuation rate and the transaction amount are often in positive correlation, and the transaction amount and report processing time duration are also in positive correlation, so that timeliness and aggregation of batch report time consumption can be reflected through the EWMA model.
In specific implementation, a value of an attenuation factor λ of the EWMA model and the number n of observed values of the EWMA model may be preset in a configuration file, where the value of the attenuation factor λ is obtained based on historical data training, and when querying a plurality of historical processing durations of the to-be-processed report of each batch, the processing duration of the historical tth day of the to-be-processed report of each batch may be queried according to the number n of observed values of the EWMA model in the configuration file, where t represents a number of days from the day, and a value range of t is: t is 1,2,3, … …, n, and other time scales of intervals may be configured, and the invention is not limited thereto. Then, for each batch of reports to be processed, inputting the processing time length y (1) of the historical 1 st day of the batch of reports to be processed into the EWMA model of the formula (1), because the EWMA (0) is 0, the index weighted moving average EWMA (1) of the historical 1 st day of the batch of reports to be processed is λ × y (1), then, letting t be 2, inputting the processing time length y (2) of the historical 2 nd day of the batch of reports to be processed and the index weighted moving average EWMA (1) of the historical 1 st day of the batch of reports to be processed into the EWMA model, outputting the index weighted moving average EWMA (2) × × y (2) + (1- λ) × EWMA (1) of the historical 2 nd day of the batch of reports to be processed, letting t be +1, and circularly executing the operations until t is n, and obtaining the weighted moving average EWMA (1) of the historical 2 nd day of the batch of reports to be processed, λ × n × (1 st day of the batch of reports to be processed is λ × n × (1 st day of the batch of the report to be processed) - λ) × EWMA (n-1), and finally, determining EWMA (n) as the exponentially weighted moving average of the to-be-processed reports of the batch, and performing the above operation on the to-be-processed reports of each batch to obtain the exponentially weighted moving average of the to-be-processed reports of a plurality of batches.
The report processing time length in the previous day has a larger indicative function on the prediction of the report processing time length in the current day than the report processing time length in the previous day by one month, the most recent weight of the historical data in the current day can be given by adjusting the value of the attenuation factor, and the least weight of the historical data in the farthest day can be given by analogy in turn.
In specific implementation, the moving average value may be weighted according to indexes of multiple batches of to-be-processed reports in step 103, the maximum allowable number of each group of reports is targeted to minimize a difference between total predicted processing durations of the to-be-processed reports, the to-be-processed reports of the multiple batches are divided into N groups based on a dynamic programming algorithm, in step 104, dynamic scheduling of the batch reports may be performed according to a grouping result, the N groups of to-be-processed reports are processed in parallel, the to-be-processed reports of the multiple batches of each group are processed in series, and after processing of all reports is completed, a processing result of the batch reports is returned.
In one embodiment, the method further comprises:
after any group of to-be-processed reports is processed, if other groups have unprocessed reports, scheduling the unprocessed reports with the number less than the preset number to the corresponding groups with the processed reports.
In step 104, in order to prevent the prediction result of the report processing duration from deviating from the actual situation, when N sets of reports to be processed are processed in parallel, after any set of reports to be processed is processed, it is necessary to check whether unprocessed reports exist in other sets, if so, the unprocessed reports can be dynamically adjusted to the set corresponding to the report processing completion for processing, and during the specific adjustment, a preset number is set, and the unprocessed reports smaller than the preset number are scheduled to the set corresponding to the report processing completion.
In one embodiment, the method further comprises:
and when the to-be-processed report of any batch fails to be processed, skipping the to-be-processed report of the batch or reprocessing the to-be-processed report of the batch according to a preset configuration file.
In step 104, when the processing of the N sets of reports to be processed is interrupted or reported in error unexpectedly, the reports to be processed in the batch may be skipped or processed again according to the configuration parameters, such as whether the report processing in the configuration file in step 101 fails to be executed again, or executed again for several times.
It should be noted that while the operations of the method of the present invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.
Based on the same inventive concept, the embodiment of the present invention further provides a report processing apparatus, as in the following embodiments. Because the principle of solving the problems of the report processing device is similar to the report processing method, the implementation of the device can refer to the implementation of the method, and repeated parts are not described again. As used hereinafter, the term "unit" or "module" may be a combination of software and/or hardware that implements a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 3 is a schematic diagram of a structure of a report processing apparatus according to an embodiment of the present invention, as shown in fig. 3, in an embodiment, the apparatus includes:
the data obtaining module 01 is configured to obtain multiple batches of to-be-processed reports, and query multiple historical processing durations of the to-be-processed reports of each batch;
the processing time length prediction module 02 is used for respectively inputting the plurality of historical processing time lengths of the to-be-processed report forms of each batch into the exponential weighted moving average model and outputting the exponential weighted moving average of the to-be-processed report forms of each batch;
the grouping module 03 is configured to group the multiple batches of the to-be-processed reports into multiple groups according to the exponentially weighted moving average of the multiple batches of the to-be-processed reports, where a difference between total predicted processing durations of the multiple groups of the to-be-processed reports is smaller than a preset threshold;
the report processing module 04 is configured to process multiple sets of reports to be processed in parallel, and process multiple batches of reports to be processed in each set in series.
In one embodiment, the data obtaining module 01 is specifically configured to:
for the to-be-processed report forms of each batch, inquiring the processing time length of the historical tth day of the to-be-processed report forms of the batch, wherein t represents the number of days away from the current day, and the value range of t is as follows: t is 1,2,3, … …, n;
the processing duration prediction module 02 is specifically configured to:
for each batch of to-be-processed reports, inputting the processing time length of the historical 1 st day of the batch of to-be-processed reports into an index weighted moving average model, and outputting the index weighted moving average value of the historical 1 st day of the batch of to-be-processed reports;
the following steps are executed in a circulating mode until t is equal to n, and the exponential weighted moving average value of the nth history of the report forms to be processed of the batch is determined;
inputting the processing time of the historical tth day of the batch of the to-be-processed reports and the exponential weighted moving average value of the historical t-1 th day of the batch of the to-be-processed reports into an exponential weighted moving average model, and outputting the exponential weighted moving average value of the historical tth day of the batch of the to-be-processed reports;
let t be t + 1;
and determining the exponential weighted moving average value of the historical nth day of the report to be processed of the batch as the exponential weighted moving average value of the report to be processed of the batch.
In one embodiment, the exponentially weighted moving average model is as follows:
EWMA(t)＝λ×y(t)+(1-λ)×EWMA(t-1)，t＝1,2,3,……,n；
wherein EWMA (t) is an exponentially weighted moving average of the historical th day, EWMA (t-1) is an exponentially weighted moving average of the historical th day, y (t) is the processing time length of the historical th day, n is the number of days, and lambda is an attenuation factor.
Fig. 4 is a schematic diagram of another structure of a report processing apparatus in an embodiment of the present invention, as shown in fig. 4, in an embodiment, the apparatus further includes: an adjustment module 05 configured to:
after any group of to-be-processed reports is processed, if other groups have unprocessed reports, scheduling the unprocessed reports with the number less than the preset number to the corresponding groups with the processed reports.
As shown in fig. 4, in one embodiment, the apparatus further comprises: a failure retry module 06 to:
and when the to-be-processed report of any batch fails to be processed, skipping the to-be-processed report of the batch or reprocessing the to-be-processed report of the batch according to a preset configuration file.
The following is a specific example to facilitate an understanding of how the invention may be practiced.
Fig. 5 is a schematic diagram of an embodiment of the present invention, as shown in fig. 5, including the following steps:
the first step is as follows: the configuration module reads the configuration file to enter the memory, and the configuration parameters comprise: the method comprises the following parameters of the range of batch reports, the value of an attenuation factor lambda of an EWMA model, the number n of observed values of the EWMA model, the maximum number of each group of reports, whether report processing fails to be executed again or not, the number of times of executing again and the like.
The second step is that: the data acquisition module inquires the processing duration of the historical tth day of the report to be processed of each batch according to the range of the batch report and the number n of observed values of the EWMA model, wherein t represents the number of days away from the current day, and the value range of t is as follows: t is 1,2,3, … …, n;
the third step: the processing time length prediction module respectively inputs a plurality of historical processing time lengths of the to-be-processed reports of each batch into the EWMA model and outputs an exponential weighted moving average of the to-be-processed reports of each batch;
the fourth step: the grouping module is used for grouping the to-be-processed reports of a plurality of batches into N groups based on a dynamic programming algorithm by taking the difference between the total predicted processing time lengths of the to-be-processed reports of each group as a target to be as small as possible according to the exponential weighted moving average of the to-be-processed reports of the plurality of batches and the maximum allowable number of each group of reports;
the fifth step: the report processing module processes N groups of reports to be processed in parallel, processes a plurality of batches of reports to be processed in each group in series, checks whether other groups have reports which are not processed after any group of reports to be processed is processed, dynamically adjusts the reports which are not processed to the group corresponding to the report processing completion for processing if the reports which are not processed exist, skips the batches of reports to be processed or processes the batches of reports to be processed again according to configuration parameters such as whether report processing failure in the configuration file is re-executed and the number of times of re-execution when the reports to be processed in any batch are accidentally interrupted or error-reported, and returns the processing results of the batches of reports after all reports are processed.
The embodiment of the invention also provides computer equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor realizes the report processing method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, and the computer readable storage medium stores a computer program for executing the report processing method.
In summary, the embodiment of the present invention provides: obtaining reports to be processed of a plurality of batches, and inquiring a plurality of historical processing durations of the reports to be processed of each batch; respectively inputting a plurality of historical processing durations of the to-be-processed reports of each batch into the exponential weighted moving average model, and outputting the exponential weighted moving average of the to-be-processed reports of each batch, wherein the exponential weighted moving average obtained based on the historical processing durations and the exponential weighted moving average model can better reflect the fluctuation of the batch report processing durations within a historical period of time; dividing the batches of the reports to be processed into a plurality of groups according to the exponential weighted moving average value of the batches of the reports to be processed, wherein the difference value between the total predicted processing time lengths of the groups of the reports to be processed is smaller than a preset threshold value; the report forms to be processed of multiple groups are processed in parallel, the report forms to be processed of multiple batches in each group are processed in series, accuracy of grouping of the report forms of the multiple batches can be improved based on the exponential weighted moving average, and processing efficiency of the report forms in batches is improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and variations of the embodiment of the present invention may occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A report processing method is characterized by comprising the following steps:
obtaining reports to be processed of a plurality of batches, and inquiring a plurality of historical processing durations of the reports to be processed of each batch;
respectively inputting a plurality of historical processing durations of the to-be-processed reports of each batch into the exponential weighted moving average model, and outputting the exponential weighted moving average of the to-be-processed reports of each batch;
dividing the batches of the reports to be processed into a plurality of groups according to the exponential weighted moving average value of the batches of the reports to be processed, wherein the difference value between the total predicted processing time lengths of the groups of the reports to be processed is smaller than a preset threshold value;
and processing a plurality of groups of reports to be processed in parallel, and processing a plurality of batches of reports to be processed in each group in series.
2. The method of claim 1, wherein querying each batch for a plurality of historical processing durations for the pending reports comprises:
for the to-be-processed report forms of each batch, inquiring the processing time length of the historical tth day of the to-be-processed report forms of the batch, wherein t represents the number of days away from the current day, and the value range of t is as follows: t is 1,2,3, … …, n;
respectively inputting a plurality of historical processing durations of the to-be-processed reports of each batch into the exponential weighted moving average model, and outputting the exponential weighted moving average of the to-be-processed reports of each batch, wherein the method comprises the following steps:
for each batch of to-be-processed reports, inputting the processing time length of the historical 1 st day of the batch of to-be-processed reports into an index weighted moving average model, and outputting the index weighted moving average value of the historical 1 st day of the batch of to-be-processed reports;
the following steps are executed in a circulating mode until t is equal to n, and the exponential weighted moving average value of the nth history of the report forms to be processed of the batch is determined;
inputting the processing time of the historical tth day of the batch of the to-be-processed reports and the exponential weighted moving average value of the historical t-1 th day of the batch of the to-be-processed reports into an exponential weighted moving average model, and outputting the exponential weighted moving average value of the historical tth day of the batch of the to-be-processed reports;
let t be t + 1;
and determining the exponential weighted moving average value of the historical nth day of the report to be processed of the batch as the exponential weighted moving average value of the report to be processed of the batch.
3. The method of claim 1 or 2, wherein the exponentially weighted moving average model is as follows:
EWMA(t)＝λ×y(t)+(1-λ)×EWMA(t-1)，t＝1,2,3,……,n；
wherein EWMA (t) is an exponentially weighted moving average of the historical th day, EWMA (t-1) is an exponentially weighted moving average of the historical th day, y (t) is the processing time length of the historical th day, n is the number of days, and lambda is an attenuation factor.
4. The method of claim 1, further comprising:
after any group of to-be-processed reports is processed, if other groups have unprocessed reports, scheduling the unprocessed reports with the number less than the preset number to the corresponding groups with the processed reports.
5. The method of claim 1, further comprising:
and when the to-be-processed report of any batch fails to be processed, skipping the to-be-processed report of the batch or reprocessing the to-be-processed report of the batch according to a preset configuration file.
6. A report processing apparatus, comprising:
the data acquisition module is used for acquiring the to-be-processed reports of a plurality of batches and inquiring a plurality of historical processing durations of the to-be-processed reports of each batch;
the processing time length prediction module is used for respectively inputting the historical processing time lengths of the to-be-processed reports of each batch into the exponential weighted moving average model and outputting the exponential weighted moving average of the to-be-processed reports of each batch;
the grouping module is used for grouping the to-be-processed reports of a plurality of batches into a plurality of groups according to the exponentially weighted moving average of the to-be-processed reports of the plurality of batches, wherein the difference value between the total predicted processing time lengths of the to-be-processed reports of each group is smaller than a preset threshold value;
and the report processing module is used for processing a plurality of groups of reports to be processed in parallel and processing a plurality of batches of reports to be processed in each group in series.
7. The apparatus of claim 6, wherein the data acquisition module is specifically configured to:
for the to-be-processed report forms of each batch, inquiring the processing time length of the historical tth day of the to-be-processed report forms of the batch, wherein t represents the number of days away from the current day, and the value range of t is as follows: t is 1,2,3, … …, n;
respectively inputting a plurality of historical processing durations of the to-be-processed reports of each batch into the exponential weighted moving average model, and outputting the exponential weighted moving average of the to-be-processed reports of each batch, wherein the method comprises the following steps:
for each batch of to-be-processed reports, inputting the processing time length of the historical 1 st day of the batch of to-be-processed reports into an index weighted moving average model, and outputting the index weighted moving average value of the historical 1 st day of the batch of to-be-processed reports;
the following steps are executed in a circulating mode until t is equal to n, and the exponential weighted moving average value of the nth history of the report forms to be processed of the batch is determined;
inputting the processing time of the historical tth day of the batch of the to-be-processed reports and the exponential weighted moving average value of the historical t-1 th day of the batch of the to-be-processed reports into an exponential weighted moving average model, and outputting the exponential weighted moving average value of the historical tth day of the batch of the to-be-processed reports;
let t be t + 1;
and determining the exponential weighted moving average value of the historical nth day of the report to be processed of the batch as the exponential weighted moving average value of the report to be processed of the batch.
8. The apparatus of claim 6 or 7, wherein the exponentially weighted moving average model is as follows:
EWMA(t)＝λ×y(t)+(1-λ)×EWMA(t-1)，t＝1,2,3,……,n；
wherein EWMA (t) is an exponentially weighted moving average of the historical th day, EWMA (t-1) is an exponentially weighted moving average of the historical th day, y (t) is the processing time length of the historical th day, n is the number of days, and lambda is an attenuation factor.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 5 when executing the computer program.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program for executing the method of any one of claims 1 to 5.
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