CN105787580A - Periodic report refreshing completion time prediction method and device - Google Patents
Periodic report refreshing completion time prediction method and device Download PDFInfo
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Abstract
The invention discloses a periodic report refreshing completion time prediction method and device. According to the method and device, a linear function is introduced to analyze historical refreshing completion time, so that periodic report refreshing completion time can be accurately predicted. The method includes the following steps that: the historical refreshing completion time of a periodic report and record sequence identifiers for recording the historical refreshing completion time are obtained; a preset linear function is obtained, wherein the linear function contains variables and set parameters; the parameter values of the set parameters in the linear function are determined according to the obtained historical refreshing completion time and the record sequence identifiers for recording the historical refreshing completion time; a record sequence identifier which is corresponding to periodic report refreshing completion time required to be predicted is adopted as one variable of the linear function, and the refreshing completion time of the periodic report is predicted based on the linear function wherein the parameter values of the set parameters have been determined.
Description
Technical field
The present invention relates to field of computer technology, particularly relate to Forecasting Methodology and the device of a kind of schedule report refresh completion time.
Background technology
In reporting system, schedule report will be called according to the form of fixed cycle more new data, schedule report can start in the service data updating on backstage to form at the set time point (such as: 0: 0) of each fixed cycle (such as: every day, weekly, monthly etc.), carry out report data renewal, to generate the new report data in this cycle.The operations such as after the report data in each cycle has updated, user carries out checking according to up-to-date report data, analysis and decision.
But, owing to the data volume of the time needed for each more new data of schedule report with report data has close relation, and the data volume that each cycle needs the report data updated is different, therefore schedule report has updated the time point of report data every time is not fixing, and namely the refresh completion time of schedule report is not fixing.Therefore, user cannot know schedule report refresh completion time accurately in advance.
Summary of the invention
The present invention provides Forecasting Methodology and the device of a kind of schedule report refresh completion time, in order to the problem solving cannot know schedule report refresh completion time accurately in advance in prior art.
First aspect, the invention provides the Forecasting Methodology of a kind of schedule report refresh completion time, and the method includes:
Obtain each history refresh completion time of described schedule report and the record sequence identification of record each history refresh completion time described；
Obtaining linear function set in advance, described linear function includes variable and setup parameter；
Record sequence identification based on each history refresh completion time described in acquisition and record each history refresh completion time described, it is determined that the parameter value of the setup parameter in described linear function；
Using record sequence identification corresponding for the schedule report refresh completion time needing prediction as a variable of described linear function, based on a determination that the linear function of the parameter value of setup parameter, it was predicted that the refresh completion time of described schedule report.
In conjunction with first aspect, in the first possible implementation of first aspect, obtaining linear function set in advance is:
Y=KX+L；
Described Y and X is the variable that described linear function includes, and wherein Y represents the schedule report refresh completion time that needs are predicted, X represents the record sequence identification that schedule report refresh completion time that needs are predicted is corresponding；
Described K and L is the setup parameter that described linear function includes, and wherein K represents slope, and L represents intercept；
Based on each history refresh completion time described and record sequence identification, it is determined that the parameter value of the setup parameter in described linear function, including:
Extract the numerical value H on hour position in each described history refresh completion time and the numerical value M on minute position；
For each H-number extracted, this H-number and an Integer N preset are multiplied, and by result of product plus M, obtain the first data of correspondence；Described N be not less than 60 integer；
Based on the record sequence identification of each first data obtained and each the first data log history refresh completion time of correspondence respectively, preset algorithm is adopted to calculate the slope in described linear function and intercept.
In conjunction with the first possible implementation of first aspect, in the implementation that the second of first aspect is possible, adopting preset algorithm to calculate the slope in described linear function and before intercept, also including:
Removing the abnormal data that each first data obtained include respectively, described abnormal data refers to the data numerically undergone mutation.
In conjunction with the first possible implementation of first aspect, in the third possible implementation of first aspect, based on a determination that the linear function of the parameter value of setup parameter, it was predicted that the refresh completion time of described schedule report, including:
Using record sequence identification corresponding for the schedule report refresh completion time needing prediction as a variable of described linear function, input the linear function of the parameter value determining setup parameter, obtain the second data；
By described second data divided by described Integer N, obtain integer quotient H ' as the numerical value on hour position of the described schedule report refresh completion time needing prediction；
Described second data are deducted described H ' and the product of described Integer N, obtains M ' as the numerical value on minute position of the described schedule report refresh completion time needing prediction；
Record sequence identification according to each history refresh completion time obtained and record each history refresh completion time described, and described record sequence identification corresponding to schedule report refresh completion time needing prediction, it is determined that the year, month, day of the refresh completion time of described needs prediction；
The described year, month, day obtained, H ' and M ' are combined, as the described schedule report refresh completion time needing prediction.
In conjunction with first aspect, in the 4th kind of possible implementation of first aspect, it was predicted that after the refresh completion time of described schedule report, also include:
Present the refresh completion time doped；
When the refresh completion time that dopes and described schedule report are still in non-Flushing status described in arriving, send alarm.
Second aspect, the invention provides the prediction unit of a kind of schedule report refresh completion time, and this device includes:
First acquiring unit, the record sequence identification of each history refresh completion time and record each history refresh completion time described for obtaining described schedule report；
Second acquisition unit, is used for obtaining linear function set in advance, and described linear function includes variable and setup parameter；
Determine unit, for the record sequence identification based on each history refresh completion time described in acquisition and record each history refresh completion time described, it is determined that the parameter value of the setup parameter in described linear function；
Predicting unit, record sequence identification corresponding to schedule report refresh completion time for needing prediction is as a variable of described linear function, based on a determination that the linear function of the parameter value of setup parameter, it was predicted that the refresh completion time of described schedule report.
In conjunction with second aspect, in the first possible implementation of second aspect, the linear function set in advance that described second acquisition unit obtains is:
Y=KX+L；
Described Y and X is the variable that described linear function includes, and wherein Y represents the schedule report refresh completion time that needs are predicted, X represents the record sequence identification that schedule report refresh completion time that needs are predicted is corresponding；
Described K and L is the setup parameter that described linear function includes, and wherein K represents slope, and L represents intercept；
Described determine unit specifically for:
Extract the numerical value H on hour position in each described history refresh completion time and the numerical value M on minute position；
For each H-number extracted, this H-number and an Integer N preset are multiplied, and by result of product plus M, obtain the first data of correspondence；Described N be not less than 60 integer；
Based on the record sequence identification of each first data obtained and each the first data log history refresh completion time of correspondence respectively, preset algorithm is adopted to calculate the slope in described linear function and intercept.
In conjunction with the first possible implementation of second aspect, in the implementation that the second of second aspect is possible, adopting preset algorithm to calculate the slope in described linear function and before intercept, described determining that unit is additionally operable to:
Removing the abnormal data that each first data obtained include respectively, described abnormal data refers to the data numerically undergone mutation.
In conjunction with the first possible implementation of second aspect, in the third possible implementation of second aspect, described predicting unit specifically for:
Using record sequence identification corresponding for the schedule report refresh completion time needing prediction as a variable of described linear function, input the linear function of the parameter value determining setup parameter, obtain the second data；
By described second data divided by described Integer N, obtain integer quotient H ' as the numerical value on hour position of the described schedule report refresh completion time needing prediction；
Described second data are deducted described H ' and the product of described Integer N, obtains M ' as the numerical value on minute position of the described schedule report refresh completion time needing prediction；
Record sequence identification according to each history refresh completion time obtained and record each history refresh completion time described, and described record sequence identification corresponding to schedule report refresh completion time needing prediction, it is determined that the year, month, day of the refresh completion time of described needs prediction；
The described year, month, day obtained, H ' and M ' are combined, as the described schedule report refresh completion time needing prediction.
In conjunction with second aspect, in the 4th kind of possible implementation of second aspect, described device also includes:
Monitoring unit, for presenting the refresh completion time doped；When the refresh completion time that dopes and described schedule report are still in non-Flushing status described in arriving, send alarm.
Scheme provided by the invention, by introducing linear function, is analyzed history refresh completion time, it is possible to dope schedule report refresh completion time accurately.
Accompanying drawing explanation
The flow chart of the Forecasting Methodology of a kind of schedule report refresh completion time that Fig. 1 provides for the embodiment of the present invention；
The line diagram of one group of history refresh completion time conversion data that Fig. 2 provides for the embodiment of the present invention；
There is provided for the embodiment of the present invention one group of Fig. 3 removes the line diagram of the history refresh completion time conversion data after abnormal data；
The structure chart of the prediction unit of a kind of schedule report refresh completion time that Fig. 4 provides for the embodiment of the present invention.
Detailed description of the invention
Embodiments provide the Forecasting Methodology of a kind of schedule report refresh completion time, by introducing linear function, history refresh completion time is analyzed, it is possible to dope schedule report refresh completion time accurately.
Below in conjunction with Figure of description and each embodiment, technical solution of the present invention is illustrated.
Consulting shown in Fig. 1, embodiments provide the Forecasting Methodology of a kind of schedule report refresh completion time, the implementing procedure of the method is as follows:
Step 101: obtain each history refresh completion time of schedule report and record the record sequence identification of each history refresh completion time.
Schedule report be generally divided into diurnal periodicity form, cycle form, the moon schedule report etc..In the embodiment of the present invention, the refresh completion time of schedule report refers to schedule report and has updated the time point of report data；The record sequence identification of refresh completion time is the serial number that this refresh completion time is stored in schedule report.
Each history refresh completion time of the diurnal periodicity form obtained and the form ginseng recording sequence identification recording each history refresh completion time are shown in Table 1.
Table 1
| Record sequence identification | Refresh completion time |
| 1 | 2013/12/24 18:03 |
| 2 | 2013/12/25 7:30 |
| 3 | 2013/12/26 8:14 |
| 4 | 2013/12/27 9:40 |
| 5 | 2013/12/28 7:56 |
| 6 | 2013/12/29 9:13 |
| 7 | 2013/12/30 9:46 |
| 8 | 2013/12/31 7:53 |
| 9 | 2014/1/1 9:31 |
| 10 | 2014/1/2 6:52 |
Reporting system generally chooses the history refresh completion time of the n bar Flushing success in nearest a period of time, the value of n, it is possible to be adjusted according to the performance of the accuracy requirement of prediction and reporting system.It was verified that n is more big, it was predicted that the accuracy of the refresh completion time gone out is more high, but the consumption of systematic function is also more big.
Step 102: obtain linear function set in advance, linear function includes variable and setup parameter.
The external factor generated owing to affecting report data is relatively-stationary over a period to come, and therefore the generation time of report data keeps relative stability over a period to come.Along with the change of reporting system Batch Processing data volume, when reporting system does not change on hardware, prove it can be seen that along with the increase of refreshing frequency, the refresh completion time of schedule report is linear over a period to come in conjunction with real data.Therefore, the embodiment of the present invention adopts linear function Y=KX+L, carry out predetermined period form refresh completion time, wherein, Y and X is the variable that linear function includes, wherein Y represents the schedule report refresh completion time that needs are predicted, X represents the record sequence identification that schedule report refresh completion time that needs are predicted is corresponding；K and L is the setup parameter that linear function includes, and wherein K represents slope, and L represents intercept.
Step 103: based on each history refresh completion time obtained and the record sequence identification recording each history refresh completion time, it is determined that the parameter value of the setup parameter in linear function.
Wherein, reporting system determines that the detailed process of the parameter value of the setup parameter in linear function is: first each history refresh completion time converts to the data being easy to calculate.Year due to refresh completion time to be predicted, month, day is can to carry out speculating according to the rule of its corresponding record sequence identification and history refresh completion time, therefore linear function only focus on refresh completion time to be predicted hour and minute, thus when history refresh time is changed, only to history refresh completion time hour and minute change, particularly as follows: the numerical value H on hour position extracted in each history refresh completion time and the numerical value M on minute position, for each H-number extracted, this H-number and an Integer N preset are multiplied, and by result of product plus M, obtain the first data of correspondence, N be not less than 60 integer.Such as, when N takes 60, it is possible to this history refresh completion time of 2014/9/110:18 is converted to 618.Then, reporting system, based on the record sequence identification of each first data obtained and each the first data log history refresh completion time of correspondence respectively, adopts preset algorithm to calculate the slope in linear function and intercept.
Here the preset algorithm that reporting system adopts can be specifically method of least square, Linear regression or multiple linear regression method etc..
Preferably, preset algorithm is adopted to calculate the slope in linear function and before intercept, reporting system first can also remove the abnormal data (i.e. noise data) that each first data obtained include respectively, and abnormal data refers to the data numerically undergone mutation.
Concrete, when removing the abnormal data that each first data obtained include, each the first data can be carried out median calculating by reporting system, draw the median in each first data, then the first data differing by more than z% in each first data with this median are rejected, the value of z has had influence on the accuracy of prediction refresh completion time, can adjust accordingly according to practical situation.In addition to this it is possible to standard deviation, variance etc. for standard, remove the abnormal data that each first data include.
Step 104: using record sequence identification corresponding for the schedule report refresh completion time that needs prediction as a variable of linear function, based on a determination that the linear function of the parameter value of setup parameter, it was predicted that the refresh completion time of schedule report.
Specifically, reporting system is based on a determination that the linear function of parameter value of setup parameter, the process of the refresh completion time of predetermined period form is as follows: first corresponding for the schedule report refresh completion time needing prediction is recorded a sequence identification variable (i.e. above-mentioned X) as linear function, input determines the linear function of the parameter value of setup parameter, obtains the second data；Then by the second data divided by the Integer N remainder of definition in step 102, integer quotient H ' is obtained as the numerical value on hour position of the schedule report refresh completion time of needs prediction；The second data are deducted the product of H ' and Integer N again, obtains M ' as the numerical value on minute position of the schedule report refresh completion time of needs prediction；And according to each history refresh completion time obtained and the record sequence identification recording each history refresh completion time, and need the record sequence identification corresponding to schedule report refresh completion time of prediction, it is determined that need the year, month, day of the refresh completion time of prediction；Finally the year, month, day obtained, H ' and M ' are combined, as the schedule report refresh completion time needing prediction.
Such as, it was predicted that table 1 is designated the refresh completion time of 11, it is assumed that mark 11 substitution determines the linear function of the parameter value of setup parameter, and the second data obtained are 414.If the Integer N preset is 60, after 60, obtains integer quotient 6 by 414, deduct 60*6 by 414 after, obtain 54, namely represent schedule report refresh completion time to be predicted hour and minute position on numerical value respectively 6 and 54；Each history refresh completion time according to table 1 and record the record sequence identification of each history refresh completion time, the regularity that the year, month, day information of history refresh time changes can be obtained with the change of record sequence identification, namely table 1 is diurnal periodicity form, it is known that the value of the Year/Month/Day of record refresh completion time corresponding to sequence identification 11 is 2014/1/3；Being combined by each numerical value obtained above, the refresh completion time of measurable record sequence identification 11 correspondence is 2014/1/36:54.
In another embodiment, if the refresh completion time of prediction needs to be accurate to the second, when in step 103, each history refresh completion time is changed by reporting system, extract the numerical value S, the first data obtained=H*N*N+M*N+S on the numerical value H on hour position in each history refresh completion time, the numerical value M on minute position and second position respectively.Accordingly, after in step 104, record sequence identification input linear function corresponding for the schedule report refresh completion time needing prediction is obtained the second data by reporting system, using the second data divided by N square after the integer quotient that obtains as the numerical value H ' on hour position of the refresh completion time of prediction, second data are deducted H ' and N square product after the result that obtains, again divided by the integer quotient obtained after N as the numerical value M ' on minute position of the refresh completion time of prediction, second data are deducted H ' and N square product and the result that obtains after deducting the product of M ' and N as the numerical value S ' on the second position of the refresh completion time of prediction.
Along with the increase of schedule report refreshing frequency, circulating above-mentioned steps 101～104, it is possible to slope and intercept to linear function are optimized, predicting refresh completion time more accurately thus obtaining.
After the refresh completion time of predetermined period form, the refresh completion time doped is presented to user in front end by reporting system, and based on the refresh completion time doped, monitoring thread can be enabled reporting status is scanned, the generation state of monitoring report data.When arriving the refresh completion time and schedule report that dope still in non-Flushing status, send alarm.Based on this alarm checking, user causes that the report data of schedule report generates abnormal reason.Specifically, above-mentioned alarm, it is possible to be the alarm sending sound or light form, or according to the default Mobile Directory Number form with note, or according to the default e-mail address form with Email, issue the user with alarm.
Preferably, consider that the schedule report refresh completion time of prediction exists certain error, so reporting system does not alert at once when arriving the refresh completion time doped, but after a period of time (such as 5 minutes), again scan reporting status, if schedule report is still in non-Flushing status, just alert.
Below, it is described in detail by one group of real data Forecasting Methodology to the schedule report refresh completion time shown in Fig. 1 and effect.
As in figure 2 it is shown, for after the history refresh completion time of one group of schedule report obtained is changed, with the record sequence identification of log history refresh time for transverse axis, with the data after changing into the longitudinal axis, the line diagram of drafting.First point from Figure 2 it can be seen that there is, in these group data, the abnormal data numerically undergone mutation, in Fig. 2.
Calculate the median in the data after the conversion of this group and the deviation of the data after each conversion and this median.Result of calculation is in Table 2.
Table 2
| Record sequence identification | Refresh completion time | Translated data | Median deviation |
| 1 | 2013/12/24 18:03 | 1083 | 57.80% |
| 2 | 2013/12/25 7:30 | 450 | 1.56% |
| 3 | 2013/12/26 8:14 | 494 | 7.49% |
| 4 | 2013/12/27 9:40 | 580 | 21.21% |
| 5 | 2013/12/28 7:56 | 476 | 3.99% |
| 6 | 2013/12/29 9:13 | 553 | 17.36% |
| 7 | 2013/12/30 9:46 | 586 | 22.01% |
| 8 | 2013/12/31 7:53 | 473 | 3.38% |
| 9 | 2014/1/1 9:31 | 571 | 19.96% |
| 10 | 2014/1/2 6:52 | 412 | 10.92% |
| 11 | 2014/1/3 6:54 | 414 | 10.39% |
| 12 | 2014/1/4 7:17 | 437 | 4.58% |
| 13 | 2014/1/5 7:14 | 434 | 5.30% |
| 14 | 2014/1/6 7:40 | 460 | 0.65% |
| 15 | 2014/1/7 7:01 | 421 | 8.55% |
| 16 | 2014/1/8 10:23 | 623 | 26.65% |
| 17 | 2014/1/9 7:08 | 428 | 6.78% |
| 18 | 2014/1/10 8:09 | 489 | 6.54% |
| 19 | 2014/1/11 9:17 | 557 | 17.95% |
| 20 | 2014/1/12 9:23 | 563 | 18.83% |
| 21 | 2014/1/13 8:58 | 538 | 15.06% |
| 22 | 2014/1/14 7:16 | 436 | 4.82% |
| 23 | 2014/1/15 8:25 | 505 | 9.50% |
| 24 | 2014/1/16 7:22 | 442 | 3.39% |
| 25 | 2014/1/17 7:30 | 450 | 1.56% |
| 26 | 2014/1/18 7:28 | 448 | 2.01% |
| 27 | 2014/1/19 7:34 | 454 | 0.66% |
| 28 | 2014/1/20 9:42 | 582 | 21.48% |
| 29 | 2014/1/21 9:10 | 550 | 16.91% |
| 30 | 2014/1/22 8:05 | 485 | 5.77% |
| 31 | 2014/1/23 8:12 | 492 | 7.11% |
| 32 | 2014/1/24 9:21 | 561 | 18.54% |
| 33 | 2014/1/25 9:01 | 541 | 15.53% |
| 34 | 2014/1/26 7:57 | 477 | 4.19% |
| 35 | 2014/1/27 9:15 | 555 | 17.66% |
| 36 | 2014/1/28 9:31 | 571 | 19.96% |
| 37 | 2014/1/29 11:14 | 674 | 32.20% |
| 38 | 2014/1/30 7:47 | 467 | 2.14% |
| 39 | 2014/1/31 10:00 | 600 | 23.83% |
| 40 | 2014/2/1 7:58 | 478 | 4.39% |
| 41 | 2014/2/2 6:51 | 411 | 11.19% |
| 42 | 2014/2/3 6:55 | 415 | 10.12% |
| 43 | 2014/2/4 8:06 | 486 | 5.97% |
| 44 | 2014/2/5 7:03 | 423 | 8.04% |
| 45 | 2014/2/6 7:00 | 420 | 8.81% |
| 46 | 2014/2/7 7:05 | 425 | 7.53% |
| 47 | 2014/2/8 7:03 | 423 | 8.04% |
| 48 | 2014/2/9 7:13 | 433 | 5.54% |
| 49 | 2014/2/10 7:01 | 421 | 8.55% |
| 50 | 2014/2/11 7:06 | 426 | 7.28% |
| 51 | 2014/2/12 7:03 | 423 | 8.04% |
| 52 | 2014/2/13 9:46 | 586 | 22.01% |
| 53 | 2014/2/14 7:19 | 439 | 4.10% |
| 54 | 2014/2/15 7:16 | 436 | 4.82% |
| 55 | 2014/2/16 10:53 | 653 | 30.02% |
| 56 | 2014/2/17 7:27 | 447 | 2.24% |
| 57 | 2014/2/18 7:24 | 444 | 2.93% |
| 58 | 2014/2/19 7:30 | 450 | 1.56% |
| 59 | 2014/2/20 7:37 | 457 | 0.00% |
| 60 | 2014/2/21 7:36 | 456 | 0.22% |
| 61 | 2014/2/22 7:35 | 455 | 0.44% |
| 62 | 2014/2/23 7:56 | 476 | 3.99% |
| 63 | 2014/2/24 7:42 | 462 | 1.08% |
| 64 | 2014/2/25 7:49 | 469 | 2.56% |
| 65 | 2014/2/26 7:56 | 476 | 3.99% |
| 66 | 2014/2/27 7:52 | 472 | 3.18% |
| 67 | 2014/2/28 9:30 | 570 | 19.82% |
| 68 | 2014/3/1 8:07 | 487 | 6.16% |
| 69 | 2014/3/2 6:53 | 413 | 10.65% |
| 70 | 2014/3/3 6:59 | 419 | 9.07% |
| 71 | 2014/3/4 6:58 | 418 | 9.33% |
| 72 | 2014/3/5 7:05 | 425 | 7.53% |
| 73 | 2014/3/6 6:57 | 417 | 9.59% |
| 74 | 2014/3/7 7:03 | 423 | 8.04% |
| 75 | 2014/3/8 7:01 | 421 | 8.55% |
| 76 | 2014/3/9 7:15 | 435 | 5.06% |
| 77 | 2014/3/10 7:01 | 421 | 8.55% |
Reject in the data after the conversion of this group with abnormal data more than 10% of the deviation of median, and repaint line diagram based on the conversion data after rejecting abnormalities data, as shown in Figure 3, it is seen that, the trend of the line diagram after rejecting abnormalities data substantially meets linear relationship.
According to the data after above-mentioned rejecting abnormalities data, using method of least square, the parameter calculating linear function Y=KX+L is:
Slope K=-1.1017；Intercept L=497.1531.
To record the sample that sequence identification is 70-77, test the accuracy of above-mentioned linear function prediction refresh completion time, as shown in table 3.Visible, it was predicted that the error of refresh completion time within the scope of acceptable.
Table 3
Consulting shown in Fig. 4, embodiments provide the prediction unit of a kind of schedule report refresh completion time, for realizing the Forecasting Methodology of a kind of schedule report refresh completion time shown in Fig. 1 of the present invention, this device includes:
First acquiring unit 401, for obtaining each history refresh completion time of schedule report and recording the record sequence identification of each history refresh completion time.
Second acquisition unit 402, is used for obtaining linear function set in advance, and linear function includes variable and setup parameter.
Determine unit 403, for based on each history refresh completion time obtained and the record sequence identification recording each history refresh completion time, it is determined that the parameter value of the setup parameter in linear function.
Predicting unit 404, record sequence identification corresponding to schedule report refresh completion time for needing prediction is as a variable of linear function, based on a determination that the linear function of the parameter value of setup parameter, it was predicted that the refresh completion time of schedule report.
Concrete, the linear function set in advance that second acquisition unit 402 obtains is: Y=KX+L；Y and X is the variable that linear function includes, and wherein Y represents the schedule report refresh completion time that needs are predicted, X represents the record sequence identification that schedule report refresh completion time that needs are predicted is corresponding；K and L is the setup parameter that linear function includes, and wherein K represents slope, and L represents intercept.
Determine unit 403 specifically for: extract the numerical value H on hour position in each history refresh completion time and the numerical value M on minute position；For each H-number extracted, this H-number and an Integer N preset are multiplied, and by result of product plus M, obtain the first data of correspondence, N be not less than 60 integer；Based on the record sequence identification of each first data obtained and each the first data log history refresh completion time of correspondence respectively, preset algorithm is adopted to calculate the slope in linear function and intercept.
Alternatively, preset algorithm is being adopted to calculate the slope in linear function and before intercept, it is determined that unit 403 is additionally operable to: removing the abnormal data that each first data obtained include respectively, abnormal data refers to the data numerically undergone mutation.
Predicting unit 404 specifically for: using record sequence identification corresponding for the schedule report refresh completion time that needs prediction as a variable of linear function, input determines the linear function of the parameter value of setup parameter, obtains the second data；By the second data divided by above-mentioned Integer N, obtain integer quotient H ' as the numerical value on hour position of the schedule report refresh completion time of needs prediction；Second data are deducted H ' and the product of above-mentioned Integer N, obtains M ' as the numerical value on minute position of the schedule report refresh completion time of needs prediction；According to each history refresh completion time obtained and the record sequence identification recording each history refresh completion time, and need the record sequence identification corresponding to schedule report refresh completion time of prediction, it is determined that need the year, month, day of the refresh completion time of prediction；The year, month, day obtained, H ' and M ' are combined, as the schedule report refresh completion time needing prediction.
Described device also includes:
Monitoring unit 405, for presenting the refresh completion time doped；When arriving this refresh completion time doped and schedule report still in non-Flushing status, send alarm.
In sum, the technical scheme that the embodiment of the present invention provides, by introducing linear function, history refresh completion time is analyzed, schedule report refresh completion time accurately can be doped, make form exploitation, responsible person in advance the arrangement of production activity can be done and plan more accurately, and then improve the production efficiency of enterprise.Further, to the data genaration monitoring state on reporting system backstage, and on this basis user can be alerted, investigate the operations such as error reason based on the schedule report refresh completion time doped, improve Consumer's Experience.
Those skilled in the art are it should be appreciated that embodiments of the invention can be provided as method, system or computer program.Therefore, the present invention can adopt the form of complete hardware embodiment, complete software implementation or the embodiment in conjunction with software and hardware aspect.And, the present invention can adopt the form at one or more upper computer programs implemented of computer-usable storage medium (including but not limited to disk memory, CD-ROM, optical memory etc.) wherein including computer usable program code.
The present invention is that flow chart and/or block diagram with reference to method according to embodiments of the present invention, equipment (system) and computer program describe.It should be understood that can by the combination of the flow process in each flow process in computer program instructions flowchart and/or block diagram and/or square frame and flow chart and/or block diagram and/or square frame.These computer program instructions can be provided to produce a machine to the processor of general purpose computer, special-purpose computer, Embedded Processor or other programmable data processing device so that the instruction performed by the processor of computer or other programmable data processing device is produced for realizing the device of function specified in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame.
These computer program instructions may be alternatively stored in and can guide in the computer-readable memory that computer or other programmable data processing device work in a specific way, the instruction making to be stored in this computer-readable memory produces to include the manufacture of command device, and this command device realizes the function specified in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame.
These computer program instructions also can be loaded in computer or other programmable data processing device, make on computer or other programmable devices, to perform sequence of operations step to produce computer implemented process, thus the instruction performed on computer or other programmable devices provides for realizing the step of function specified in one flow process of flow chart or multiple flow process and/or one square frame of block diagram or multiple square frame.
Although preferred embodiments of the present invention have been described, but those skilled in the art are once know basic creative concept, then these embodiments can be made other change and amendment.So, claims are intended to be construed to include preferred embodiment and fall into all changes and the amendment of the scope of the invention.
Obviously, the embodiment of the present invention can be carried out various change and the modification scope without deviating from the embodiment of the present invention by those skilled in the art.So, if these amendments of the embodiment of the present invention and modification belong within the scope of the claims in the present invention and equivalent technologies thereof, then the present invention is also intended to comprise these change and modification.
Claims (10)
1. the Forecasting Methodology of a schedule report refresh completion time, it is characterised in that including:
Obtain each history refresh completion time of described schedule report and the record sequence identification of record each history refresh completion time described；
Obtaining linear function set in advance, described linear function includes variable and setup parameter；
Record sequence identification based on each history refresh completion time described in acquisition and record each history refresh completion time described, it is determined that the parameter value of the setup parameter in described linear function；
Using record sequence identification corresponding for the schedule report refresh completion time needing prediction as a variable of described linear function, based on a determination that the linear function of the parameter value of setup parameter, it was predicted that the refresh completion time of described schedule report.
2. the method for claim 1, it is characterised in that obtaining linear function set in advance is:
Y=KX+L；
Described Y and X is the variable that described linear function includes, and wherein Y represents the schedule report refresh completion time that needs are predicted, X represents the record sequence identification that schedule report refresh completion time that needs are predicted is corresponding；
Described K and L is the setup parameter that described linear function includes, and wherein K represents slope, and L represents intercept；
Based on each history refresh completion time described and record sequence identification, it is determined that the parameter value of the setup parameter in described linear function, including:
Extract the numerical value H on hour position in each described history refresh completion time and the numerical value M on minute position；
For each H-number extracted, this H-number and an Integer N preset are multiplied, and by result of product plus M, obtain the first data of correspondence；Described N be not less than 60 integer；
Based on the record sequence identification of each first data obtained and each the first data log history refresh completion time of correspondence respectively, preset algorithm is adopted to calculate the slope in described linear function and intercept.
3. method as claimed in claim 2, it is characterised in that adopting preset algorithm to calculate the slope in described linear function and before intercept, also including:
Removing the abnormal data that each first data obtained include respectively, described abnormal data refers to the data numerically undergone mutation.
4. method as claimed in claim 2, it is characterised in that based on a determination that the linear function of the parameter value of setup parameter, it was predicted that the refresh completion time of described schedule report, including:
Using record sequence identification corresponding for the schedule report refresh completion time needing prediction as a variable of described linear function, input the linear function of the parameter value determining setup parameter, obtain the second data；
By described second data divided by described Integer N, obtain integer quotient H ' as the numerical value on hour position of the described schedule report refresh completion time needing prediction；
Described second data are deducted described H ' and the product of described Integer N, obtains M ' as the numerical value on minute position of the described schedule report refresh completion time needing prediction；
Record sequence identification according to each history refresh completion time obtained and record each history refresh completion time described, and described record sequence identification corresponding to schedule report refresh completion time needing prediction, it is determined that the year, month, day of the refresh completion time of described needs prediction；
The described year, month, day obtained, H ' and M ' are combined, as the described schedule report refresh completion time needing prediction.
5. the method for claim 1, it is characterised in that after predicting the refresh completion time of described schedule report, also include:
Present the refresh completion time doped；
When the refresh completion time that dopes and described schedule report are still in non-Flushing status described in arriving, send alarm.
6. the prediction unit of a schedule report refresh completion time, it is characterised in that including:
First acquiring unit, the record sequence identification of each history refresh completion time and record each history refresh completion time described for obtaining described schedule report；
Second acquisition unit, is used for obtaining linear function set in advance, and described linear function includes variable and setup parameter；
Determine unit, for the record sequence identification based on each history refresh completion time described in acquisition and record each history refresh completion time described, it is determined that the parameter value of the setup parameter in described linear function；
Predicting unit, record sequence identification corresponding to schedule report refresh completion time for needing prediction is as a variable of described linear function, based on a determination that the linear function of the parameter value of setup parameter, it was predicted that the refresh completion time of described schedule report.
7. device as claimed in claim 6, it is characterised in that the linear function set in advance that described second acquisition unit obtains is:
Y=KX+L；
Described Y and X is the variable that described linear function includes, and wherein Y represents the schedule report refresh completion time that needs are predicted, X represents the record sequence identification that schedule report refresh completion time that needs are predicted is corresponding；
Described K and L is the setup parameter that described linear function includes, and wherein K represents slope, and L represents intercept；
Described determine unit specifically for:
Extract the numerical value H on hour position in each described history refresh completion time and the numerical value M on minute position；
For each H-number extracted, this H-number and an Integer N preset are multiplied, and by result of product plus M, obtain the first data of correspondence；Described N be not less than 60 integer；
Based on the record sequence identification of each first data obtained and each the first data log history refresh completion time of correspondence respectively, preset algorithm is adopted to calculate the slope in described linear function and intercept.
8. device as claimed in claim 7, it is characterised in that adopting preset algorithm to calculate the slope in described linear function and before intercept, described determines that unit is additionally operable to:
Removing the abnormal data that each first data obtained include respectively, described abnormal data refers to the data numerically undergone mutation.
9. device as claimed in claim 7, it is characterised in that described predicting unit specifically for:
Using record sequence identification corresponding for the schedule report refresh completion time needing prediction as a variable of described linear function, input the linear function of the parameter value determining setup parameter, obtain the second data；
By described second data divided by described Integer N, obtain integer quotient H ' as the numerical value on hour position of the described schedule report refresh completion time needing prediction；
Described second data are deducted described H ' and the product of described Integer N, obtains M ' as the numerical value on minute position of the described schedule report refresh completion time needing prediction；
Record sequence identification according to each history refresh completion time obtained and record each history refresh completion time described, and described record sequence identification corresponding to schedule report refresh completion time needing prediction, it is determined that the year, month, day of the refresh completion time of described needs prediction；
The described year, month, day obtained, H ' and M ' are combined, as the described schedule report refresh completion time needing prediction.
10. device as claimed in claim 6, it is characterised in that described device also includes:
Monitoring unit, for presenting the refresh completion time doped；When the refresh completion time that dopes and described schedule report are still in non-Flushing status described in arriving, send alarm.
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| CN109597734A (en) * | 2017-09-30 | 2019-04-09 | 北京国双科技有限公司 | The monitoring method and device of report operation duration |
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| CN109597734A (en) * | 2017-09-30 | 2019-04-09 | 北京国双科技有限公司 | The monitoring method and device of report operation duration |
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