CN116305726A - Method for comprehensively evaluating rating effect of drainage model - Google Patents
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Abstract
The invention discloses a method for comprehensively evaluating the rating effect of a drainage model, which comprises the following steps: step 1, obtaining simulation data output by a drainage model and corresponding historical monitoring data, and performing time sequence matching to obtain a calibration time period; step 2, calculating rating parameters, which specifically comprise rainfall grade calculation, nash coefficient calculation, maximum peak value difference calculation and maximum peak distance calculation; step 3, multi-parameter weighted single-field scoring; step 4, comprehensive score calculation; and 5, evaluating the rating effect according to the comprehensive score. The invention can automatically complete the rating result analysis of different rain sewage drainage models in a short time, and can generate the optimal Nash coefficient, single-field score, comprehensive score and rating effect of the drainage model under different rainfall grades.
Description
Technical Field
The invention relates to the technical field of computer aided design, in particular to a method for comprehensively evaluating the rating effect of a drainage model.
Background
With the rapid popularization of digital transformation in the field of urban drainage, a drainage model is also gaining more and more attention as a core calculation engine in the digital center of a drainage system.
Moreover, the latest revised industry mandatory standard outdoor drainage design standard (GB 50014-2021) is also clear: when the catchment area is larger than 2 square kilometers, the designed rainwater flow is determined by adopting a mathematical model method.
In the prior art, after the drainage model is constructed, calibration work is required to be carried out on the drainage model in order to ensure the accuracy of the drainage model.
Whereas the calibration work of the drainage model involves a large number of parameters, mainly including: and (3) selecting a rated time period, counting rainfall in the rated time period, simulating the matching degree of data and actual measurement data processes by a model, simulating data peaks, and matching degree of peak occurrence time and actual measurement data.
Among the many parameters affecting the rating effect, the parameter affecting the most is the choice of rating time period. In general, the simulation time of the drainage model is 1-2 days less, and 1 year longer, and in such a long simulation time, 1-3 typical time periods (generally, one time period is 2-6 hours) need to be selected to perform calibration work of the model.
In addition, the rainfall intensity in the different rated time periods is selected to have a great influence on the rated effect of the drainage model, and the rating of the model is generally required in the industry to consider the rating of working conditions of different rainfall levels (such as light rain, medium rain and heavy rain).
At present, for the calibration of a drainage model, a period of time is selected manually to calculate a simple Nash coefficient to evaluate the matching degree of the simulated flow/liquid level and the corresponding actually measured flow/liquid level process, and the method has the following defects:
1. because the engineering is calibrated according to a time period selected by the personal engineering experience, the engineering cannot guarantee that a piece of data with the best calibration effect can be accurately extracted.
2. Because the model calibration judging process only adopts a judging condition of Nash coefficient to carry out the process matching of the model simulation numerical value and the measured data, the influence of the fitting degree of the maximum peak value and the maximum peak distance on the calibration effect of the drainage model is not comprehensively considered.
3. Because the calibration process does not consider the influence of model calibration results corresponding to rainfall levels (such as light rain, medium rain and heavy rain), the consistency of model calibration effects under different rainfall levels can not be well distinguished.
Therefore, how to process the long-duration drainage model simulation result and the history actual measurement data in batches, a plurality of time periods with the best rate setting effect are selected according to the set conditions, and comprehensively evaluating the rate setting effect of the drainage model becomes a technical problem which needs to be solved by the technicians in the field.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the present invention provides a method for comprehensively evaluating the rating effect of a drainage model, which aims to not only improve the rating efficiency of the model, but also enable engineers to have a comprehensive understanding on the rating quality condition of the whole drainage model, and unify the evaluation standard of the rating quality of the drainage model: the consistency of model calibration under different rainfall levels is considered, and the influence of human factors on the calibration effect of the drainage model is reduced.
In order to achieve the above purpose, the invention discloses a method for comprehensively evaluating the rating effect of a drainage model, which comprises the following steps:
step 4, comprehensive score calculation;
and 5, evaluating the rating effect according to the comprehensive score.
Preferably, the drainage model outputs analog data for segments, and the step 1 specifically includes:
step 1.1, using a Sorted () function, and using a Key word Key as a time node to perform ascending sort on all the read analog data;
step 1.2, comparing the head-to-tail time nodes of the simulation data of which the sorting is finished with the corresponding historical monitoring data, and judging whether the head-to-tail time nodes are consistent with the corresponding historical monitoring data or not;
if the two times are consistent, the two times are matched in a one-to-one correspondence manner;
if the index positions of the head-to-tail time nodes are inconsistent, determining the index positions of the inconsistent head-to-tail time nodes in the corresponding historical monitoring data;
then cutting and deleting redundant time sequence data through the index position;
if the inconsistent head-to-tail time nodes cannot find the index position in the corresponding historical monitoring data, the index position is indicated to have no time period which can be matched with the head-to-tail time nodes, and check data are returned.
Preferably, the step 2 adopts a window sliding calculation mode to calculate the rating parameter; wherein the reasonable sliding time step is 5 minutes to 30 minutes and the nominal time period is 4 hours to 48 hours.
More preferably, the rainfall grade calculation is specifically as follows:
firstly, setting parameter rotation to ensure that the data step length participating in calculation is kept consistent;
and then, according to the simulation data and the history monitoring data which are matched in the time sequence, calculating window sliding times in combination with the rated time periods, and the maximum rainfall intensity in each rated time period, and then, matching with corresponding specifications to obtain the rainfall grade.
More preferably, the specific calculation formula of the nash coefficient calculation is as follows:
wherein n is the number of the sequence process data; y is s Is an analog value; y is o Is the actual measurement value; y is o Average number of measured values;
firstly, setting parameter rotation to ensure that the data step length participating in calculation is kept consistent;
then, according to the simulation data and the history monitoring data which are matched with the time sequence, calculating window sliding times;
setting the circulation times equal to the times of sliding the window, and calculating the Nash coefficients of all data in the window once each time the window slides;
if the Nash coefficient is an exponent with a ratio and the denominator is 0, then NSEC is set to a value of-999, denoted None.
More preferably, the maximum peak difference calculation is specifically as follows:
judging the wave crest of the continuous data by adopting a curve slope mode;
if the slope at the data node of the continuous data is 0, it is a peak or a trough. In the actual data, it is difficult to directly calculate the position where the slope of the acquisition curve is 0;
or judging by adopting a curve change form mode;
judging that the curve before the data node of the continuous data is in an ascending state and the curve after the data node is in a descending state, wherein the data at the time point is peak data;
after all wave crest positions and wave crest values of the whole curve are obtained, the simulation data and the historical monitoring data are in one-to-one correspondence by utilizing the wave crest positions, absolute values of wave crest differences are calculated, and the maximum value of the wave crest differences is recorded as the maximum peak value difference.
More preferably, the calculation of the maximum peak distance is specifically as follows:
on the basis of the maximum peak difference calculation, after all peak positions and peak values of the whole curve are obtained, time position information of the peaks is synchronously recorded, the time span absolute values of the peaks corresponding to the simulation data and the historical monitoring data are calculated by circularly traversing all corresponding peaks, and finally the maximum peak distance is recorded.
More preferably, the step 3 specifically comprises the following steps:
step 3.1, carrying out normalization processing on the maximum peak difference and the maximum peak distance to a unified dimension; the unified dimension is [0,1];
step 3.2, adopting a maximum and minimum value normalization method to respectively find a maximum peak value and a minimum peak value of the maximum peak value difference list, then subtracting the current peak value from the maximum peak value according to a maximum and minimum value normalization formula, and then dividing the current peak value by the maximum peak value to obtain a normalization result of the maximum peak value difference;
the calculation mode of normalization processing of the maximum peak distance is consistent with the calculation mode of the maximum peak value;
the weighted single field scores are calculated using the following formula:
Score i ＝w 1 ·NSEC i +w 2 ·(1-V i )+w 3 ·(1-D i )；
wherein Score i Scoring for a single field; w (w) 1 Is Nash coefficient weight; NSEC (NSEC) i Nash coefficients for a single field; w (w) 2 Is the maximum peak difference weight; v (V) i The maximum peak value difference after normalization; w (w) 3 Is the maximum peak distance weight; d is the normalized maximum peak distance difference.
More preferably, the step 4 specifically includes the following steps:
and (3) comprehensively scoring different rainfall grades according to the single-field scores obtained in the step (3), wherein the specific formula is as follows:
Score final ＝k 1 ·Score rain with small size +k 2 ·Score Middle rain +k 3 ·Score Heavy rain ；
Wherein Score final Rating a composite score for the drainage model; k (k) 1 The weight is the weight of the working condition of the rain; score Rain with small size The optimal single-field score under the working condition of light rain is obtained; k (k) 2 The weight is the weight of the medium rain working condition; score Middle rain The optimal single-field score under the medium rain working condition is obtained; k (k) 3 The weight is given to heavy rain working conditions; score Heavy rain Is the best single-field score under heavy rain working conditions.
Preferably, the step 5 specifically includes the following steps:
the evaluation effect is rated according to the comprehensive score as follows:
the comprehensive score is less than 60, and the evaluation effect is poor;
the comprehensive score is more than or equal to 60 and less than 80, and the evaluation effect is good;
the integrated score is 80 or more and 100 or less, and the evaluation effect is excellent.
The invention has the beneficial effects that:
the invention can automatically complete the rating result analysis of different rain sewage drainage models in a short time, and can generate the optimal Nash coefficient, single-field score, comprehensive score and rating effect of the drainage model under different rainfall grades.
The invention can display the time period with the best rating effect under the working conditions of light rain, medium rain and heavy rain, nash coefficient, single-field score, comprehensive score and rating effect in the form of images and charts, and is convenient for engineers to evaluate the rating effect of the drainage model based on visual data.
The conception, specific structure, and technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, features, and effects of the present invention.
Drawings
Fig. 1 shows a flow chart of an embodiment of the invention.
Fig. 2 shows a light rain situation presentation in which the rating effect is good in an embodiment of the invention.
Fig. 3 shows a medium rain situation display with good evaluation rating effect in an embodiment of the invention.
Fig. 4 shows a display of a heavy rain condition with good rating in an embodiment of the invention.
Fig. 5 shows a small rain situation presentation in which the rating effect is excellent in an embodiment of the present invention.
Fig. 6 shows a display of a medium rain condition with excellent rating effect in an embodiment of the present invention.
Fig. 7 shows a display of a heavy rain condition in which the rating effect is excellent in an embodiment of the present invention.
Detailed Description
Examples
As shown in fig. 1, the method for comprehensively evaluating the rating effect of the drainage model comprises the following steps:
step 4, comprehensive score calculation;
and 5, evaluating the rating effect according to the comprehensive score.
The principle of the invention is as follows:
according to the method, according to the length of a designated time period, after the simulation result of a long-duration drainage model and corresponding historical monitoring data are extracted section by section, the rainfall grade, nash coefficient, maximum peak value difference and maximum peak distance difference in each period are respectively calculated.
And dividing the data into a plurality of rainfall levels (such as light rain, medium rain and heavy rain) according to rainfall intensity, calculating the optimal rating time period according to the weight in each rainfall level, obtaining the highest single-field score of each rainfall level, averaging the on-site scores with the highest different rainfall levels to obtain the comprehensive score rated by the drainage model, and finally evaluating the rating effect of the drainage model through the comprehensive score.
The invention can quickly evaluate the comprehensive calibration effect of the drainage model, so that the engineer can integrally know the accuracy of the drainage model, and the judgment error caused by manually selecting the calibration time period can be avoided.
In some embodiments, the drainage model outputs analog data for the segment, step 1 is specifically as follows:
step 1.1, using a Sorted () function, and using a Key word Key as a time node to perform ascending order sequencing on all the read analog data;
step 1.2, comparing the head-to-tail time nodes of each section of simulation data which has been subjected to sorting with corresponding historical monitoring data, and judging whether the head-to-tail time nodes are consistent with the corresponding historical monitoring data;
if the two times are consistent, the two times are matched in a one-to-one correspondence manner;
if the first time node and the second time node are inconsistent, determining index positions of the inconsistent first time node and the inconsistent second time node in corresponding historical monitoring data;
then cutting and deleting redundant time sequence data through the index position;
if the inconsistent head and tail time nodes cannot find the index position in the corresponding historical monitoring data, the index position is indicated to have no time period which can be matched with the time period, and the check data is returned.
In practical application, the comparison and calibration are generally carried out by outputting analog data in a segmented way, so that the time of actually measured data and the time of analog data are required to be correspondingly matched; and because of the limitation of the received analog data, the obtained actual measurement data has a certain defect, and the data can be used after being rewashed.
And redundant time series data are cut and deleted through the position index, so that the data quantity is reduced as much as possible, and the subsequent operation pressure is lightened.
In some embodiments, step 2 performs calculation of the rating parameter by means of window sliding calculation; wherein the reasonable sliding time step is 5 minutes-30 minutes and the nominal time period is 4 hours-48 hours.
In practical application, the calculation of the rating parameter is performed in a window sliding calculation mode, so that the rating to the result of each time period can be ensured.
In some embodiments, the rainfall level calculation is specifically as follows:
firstly, setting a parameter, wherein the parameter is usually set to be 5 minutes as a unit, so that the data step length participating in calculation is ensured to be consistent;
in practical applications, the longer the desired simulation time, the longer the processing time. According to the actual engineering project test, aiming at the simulation result of an entire year, 5 minutes step by step, 105120 data are obtained in total, if the calculation is carried out by adopting a rated time period of 4 hours, the required processing time is 30 seconds.
And then, according to the simulation data and the historical monitoring data which are matched in the completed time sequence, calculating window sliding times in combination with the rated time periods, and obtaining the rainfall level by matching the maximum rainfall intensity in each rated time period with the unit millimeter/hour with the corresponding standard.
And (3) carrying out the evaluation of light rain, medium rain and heavy rain according to the rainfall evaluation standard of Shanghai city, wherein the evaluation standard is shown in the following table:
| rainfall grade | Grading standard (millimeter/hour) |
| Rain with small size | Less than 2.5mm |
| Middle rain | 2.6-8.0mm |
| Heavy rain | 8.1-15.9mm |
In some embodiments, the specific calculation formula for the nash coefficient calculation is as follows:
wherein n is a sequenceThe number of process data; y is s Is an analog value; y is o Is the actual measurement value; y is o Average number of measured values;
firstly, setting a parameter, wherein the parameter is usually set to be 5 minutes as a unit, so that the data step length participating in calculation is ensured to be consistent;
then, calculating window sliding times according to the simulation data and the historical monitoring data matched with the completed time sequence;
setting the circulation times equal to the times of sliding the window, and calculating Nash coefficients of all data in the window once each time the window slides;
if the Nash coefficient is an index with a ratio and the denominator is 0, then NSEC is set to a value of-999, denoted as None.
In practical applications, a Nash (NSEC) coefficient is generally used to describe the degree of coincidence between a simulation process and an actual measurement process.
In some embodiments, the maximum peak difference calculation is specifically as follows:
judging the wave crest of the continuous data by adopting a curve slope mode;
if the slope at the data node of the continuous data is 0, it is a peak or trough here. In the actual data, it is difficult to directly calculate the position where the slope of the acquisition curve is 0;
or judging by adopting a curve change form mode;
judging that the curve before the data node of continuous data is in an ascending state, namely the slope is greater than 0, and the curve after the data node is in a descending state, wherein the slope is less than 0, and the data at the time point is peak data;
after all wave crest positions and wave crest values of the whole curve are obtained, the wave crest positions are utilized to correspond the simulation data and the historical monitoring data one by one, the absolute value of the wave crest difference is calculated, and the maximum value of the wave crest difference is recorded as the maximum peak value difference.
In the calibration of the model result, the simulation result in a period of time is good or bad, and is greatly matched with the actual measurement peak value data or not in the period of time. The larger peak of the measured data represents the occurrence of the emergency, and the accurate simulation prediction of the emergency is more important than the prediction of the conventional event when the model is simulated. The smaller peak-to-peak value difference indicates that the higher the simulation prediction accuracy of the model to the emergency, the better the model rating result, and otherwise, the worse the model rating result.
In some embodiments, the calculation of the maximum peak distance is specifically as follows:
on the basis of the maximum peak difference calculation, after all peak positions and peak values of the whole curve are obtained, time position information of the peaks is synchronously recorded, the time span absolute values of the peaks corresponding to the analog data and the historical monitoring data are calculated by circularly traversing all corresponding peaks, usually, 5 minutes are taken as a unit, and finally, the maximum peak distance is recorded.
In practical application, the maximum peak distance represents the time span between two peaks in the corresponding peaks of the measured data and the simulation result data. In the model calibration, the peak represents an emergency, the peak-to-peak distance represents the simulation reaction capacity of the model to the emergency, and the smaller the peak-to-peak distance represents the model can rapidly predict the emergency, the better the calibration result of the model. Otherwise, the simulation can not timely respond to the emergency, and the calibration result of the model is worse.
In certain embodiments, step 3 is specifically as follows:
step 3.1, carrying out normalization processing on the maximum peak value difference and the maximum peak distance to obtain unified dimension; the unified dimension is [0,1];
step 3.2, a maximum peak value and a minimum peak value of a maximum peak value difference list are respectively searched by adopting a maximum and minimum value normalization method, then the current peak value is subtracted by utilizing the maximum peak value according to a maximum and minimum value normalization formula, and then the normalization result of the maximum peak value difference is obtained by dividing the maximum peak value by the minimum peak value;
the calculation mode of normalization processing is consistent with that of the maximum peak value;
the weighted single field scores are calculated using the following formula:
Score i ＝w 1 ·NSEC i +w 2 ·(1-V i )+w 3 ·(1-D i )；
wherein Score i Scoring for a single field; w (w) 1 Is Nash coefficient weight; NSEC (NSEC) i Nash coefficients for a single field; w (w) 2 Is the maximum peak difference weight; v (V) i The maximum peak value difference after normalization; w (w) 3 Is the maximum peak distance weight; d is the normalized maximum peak distance difference.
In practical application, the judgment of the quality of the model rating result cannot be simply carried out by means of a certain parameter, and the situation in each middle is comprehensively considered, so that the result is evaluated by means of each parameter.
For the NSEC coefficient, the larger the NSEC, the better the simulation result, and conversely the worse the result, the cumulative error ratio between the analog value and the measured value over the rated time span of the reaction is subtracted by 1.
The larger the maximum peak difference is, the larger the maximum peak distance is, the worse the response of the model to the actual situation is, and the worse the result of the model simulation is. Under actual operation conditions, the emergency event occupation ratio is relatively small.
And for the model, the result under the normal operation condition can be better simulated, and the simulation result of partial emergency can be simulated to be a better rating result.
For NSEC, maximum peak difference and maximum peak distance, the three are not in the same dimension, and the weight calculation of the comprehensive score cannot be directly carried out. Since NSEC is a ratio index, the effective result lies in the range of 0, 1.
Therefore, the maximum peak difference and the maximum peak distance are required to be normalized to a unified dimension, and the unified dimension is selected from the [0,1] range.
In certain embodiments, step 4 is specifically as follows:
and (3) comprehensively scoring different rainfall grades according to the single-field scores obtained in the step (3), wherein the specific formula is as follows:
Score final ＝k 1 ·Score rain with small size +k 2 ·Score Middle rain +k 3 ·Score Heavy rain ；
Wherein Score final Rating a composite score for the drainage model; k (k) 1 The weight is the weight of the working condition of the rain; score Rain with small size The optimal single-field score under the working condition of light rain is obtained; k (k) 2 The weight is the weight of the medium rain working condition; score Middle rain The optimal single-field score under the medium rain working condition is obtained; k (k) 3 The weight is given to heavy rain working conditions; score Heavy rain Is the best single-field score under heavy rain working conditions.
In practical application, in order to comprehensively consider the influence of different rainfall conditions on the rating of the drainage model, the rating results of the drainage model under different rainfall levels need to be comprehensively considered, so that the optimal single-field scores under different rainfall levels need to be extracted, and the comprehensive scores of the rating of the drainage model are obtained through weighted average.
In the embodiment, the grades of light rain, medium rain and heavy rain are all 1/3.
In certain embodiments, step 5 is specifically as follows:
the evaluation effect is rated according to the comprehensive score as follows:
the comprehensive score is less than 60, and the evaluation effect is poor;
the comprehensive score is more than or equal to 60 and less than 80, and the evaluation effect is good;
the overall score is 80 or more and 100 or less, and the evaluation effect is excellent.
Finally, the drainage model calibration results shown in fig. 2 to 7 are obtained.
The foregoing describes in detail preferred embodiments of the present invention. It should be understood that numerous modifications and variations can be made in accordance with the concepts of the invention by one of ordinary skill in the art without undue burden. Therefore, all technical solutions which can be obtained by logic analysis, reasoning or limited experiments based on the prior art by the person skilled in the art according to the inventive concept shall be within the scope of protection defined by the claims.
Claims (10)
1. A method for comprehensively evaluating the rating effect of a drainage model; the method is characterized by comprising the following steps of:
step 1, obtaining simulation data output by a drainage model and corresponding historical monitoring data, and performing time sequence matching to obtain a calibration time period;
step 2, calculating rating parameters, which specifically comprise rainfall grade calculation, nash coefficient calculation, maximum peak value difference calculation and maximum peak distance calculation;
step 3, multi-parameter weighted single-field scoring;
step 4, comprehensive score calculation;
and 5, evaluating the rating effect according to the comprehensive score.
2. The method for comprehensively evaluating the rating effect of a drainage model according to claim 1, wherein the drainage model outputs simulation data for segments, and the step 1 is specifically as follows:
step 1.1, using a Sorted () function, and using a Key word Key as a time node to perform ascending sort on all the read analog data;
step 1.2, comparing the head-to-tail time nodes of the simulation data of which the sorting is finished with the corresponding historical monitoring data, and judging whether the head-to-tail time nodes are consistent with the corresponding historical monitoring data or not;
if the two times are consistent, the two times are matched in a one-to-one correspondence manner;
if the index positions of the head-to-tail time nodes are inconsistent, determining the index positions of the inconsistent head-to-tail time nodes in the corresponding historical monitoring data;
then cutting and deleting redundant time sequence data through the index position;
if the inconsistent head-to-tail time nodes cannot find the index position in the corresponding historical monitoring data, the index position is indicated to have no time period which can be matched with the head-to-tail time nodes, and check data are returned.
3. The method for comprehensively evaluating the rating effect of the drainage model according to claim 1, wherein the step 2 is characterized in that a window sliding calculation mode is adopted for calculating rating parameters; wherein the reasonable sliding time step is 5 minutes-30 minutes and the nominal time period is 4 hours-48 hours.
4. The method for comprehensively evaluating the rating effect of a drainage model according to claim 3, wherein the rainfall level calculation is specifically as follows:
firstly, setting parameter rotation to ensure that the data step length participating in calculation is kept consistent;
and then, according to the simulation data and the history monitoring data which are matched in the time sequence, calculating window sliding times in combination with the rated time periods, and the maximum rainfall intensity in each rated time period, and then, matching with corresponding specifications to obtain the rainfall grade.
5. The method for comprehensively evaluating the rating effect of a drainage model according to claim 3, wherein a specific calculation formula of the nash coefficient calculation is as follows:
wherein n is the number of the sequence process data; y is s Is an analog value; y is o Is the actual measurement value;
average number of measured values;
firstly, setting parameter rotation to ensure that the data step length participating in calculation is kept consistent;
then, according to the simulation data and the history monitoring data which are matched with the time sequence, calculating window sliding times;
setting the circulation times equal to the times of sliding the window, and calculating the Nash coefficients of all data in the window once each time the window slides;
if the Nash coefficient is an exponent with a ratio and the denominator is 0, then NSEC is set to a value of-999, denoted None.
6. The method for comprehensively evaluating the rating effect of a drainage model according to claim 3, wherein the maximum peak difference calculation is specifically as follows:
judging the wave crest of the continuous data by adopting a curve slope mode;
if the slope at the data node of the continuous data is 0, it is a peak or a trough. In the actual data, it is difficult to directly calculate the position where the slope of the acquisition curve is 0;
or judging by adopting a curve change form mode;
judging that the curve before the data node of the continuous data is in an ascending state and the curve after the data node is in a descending state, wherein the data at the time point is peak data;
after all wave crest positions and wave crest values of the whole curve are obtained, the simulation data and the historical monitoring data are in one-to-one correspondence by utilizing the wave crest positions, absolute values of wave crest differences are calculated, and the maximum value of the wave crest differences is recorded as the maximum peak value difference.
7. The method for comprehensively evaluating the rating effect of a drainage model according to claim 6, wherein the calculation of the maximum peak distance is specifically as follows:
on the basis of the maximum peak difference calculation, after all peak positions and peak values of the whole curve are obtained, time position information of the peaks is synchronously recorded, the time span absolute values of the peaks corresponding to the simulation data and the historical monitoring data are calculated by circularly traversing all corresponding peaks, and finally the maximum peak distance is recorded.
8. The method for comprehensively evaluating the rating effect of a drainage model according to claim 1, wherein the step 3 is specifically as follows:
step 3.1, carrying out normalization processing on the maximum peak difference and the maximum peak distance to a unified dimension; the unified dimension is [0,1];
step 3.2, adopting a maximum and minimum value normalization method to respectively find a maximum peak value and a minimum peak value of the maximum peak value difference list, then subtracting the current peak value from the maximum peak value according to a maximum and minimum value normalization formula, and then dividing the current peak value by the maximum peak value to obtain a normalization result of the maximum peak value difference;
the calculation mode of normalization processing of the maximum peak distance is consistent with the calculation mode of the maximum peak value;
the weighted single field scores are calculated using the following formula:
Score i ＝w 1 ·NSEC i +w 2 ·(1-V i )+w 3 ·(1-D i )；
wherein Score i Scoring for a single field; w (w) 1 Is Nash coefficient weight; NSEC (NSEC) i Nash coefficients for a single field; w (w) 2 Is the maximum peak difference weight; v (V) i The maximum peak value difference after normalization; w (w) 3 Is the maximum peak distance weight; d is the normalized maximum peak distance difference.
9. The method for comprehensively evaluating the rating effect of a drainage model according to claim 8, wherein the step 4 is specifically as follows:
and (3) comprehensively scoring different rainfall grades according to the single-field scores obtained in the step (3), wherein the specific formula is as follows:
Score final ＝k 1 ·Score rain with small size +k 2 ·Score Middle rain +k 3 ·Score Heavy rain ；
Wherein Score final Rating a composite score for the drainage model; k (k) 1 The weight is the weight of the working condition of the rain; score Rain with small size The optimal single-field score under the working condition of light rain is obtained; k (k) 2 The weight is the weight of the medium rain working condition; score Middle rain The optimal single-field score under the medium rain working condition is obtained; k (k) 3 The weight is given to heavy rain working conditions; score Heavy rain Is the best single-field score under heavy rain working conditions.
10. The method for comprehensively evaluating the rating effect of a drainage model according to claim 1, wherein the step 5 is specifically as follows:
the evaluation effect is rated according to the comprehensive score as follows:
the comprehensive score is less than 60, and the evaluation effect is poor;
the comprehensive score is more than or equal to 60 and less than 80, and the evaluation effect is good;
the integrated score is 80 or more and 100 or less, and the evaluation effect is excellent.
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