CN111581855A - Sponge urban flow data online monitoring and processing method - Google Patents
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Abstract
The invention discloses an online monitoring and processing method of sponge urban flow data, which comprises the following steps: a data range judging step, namely setting a data cleaning range according to the reasonable range of the field measurement environment parameters and the monitoring values; a runoff coefficient judging step, namely acquiring online monitoring flow data of a preset time period and rainfall data of the time period, calculating theoretical maximum runoff total amount and online monitoring total flow, and when the theoretical maximum runoff total amount and the online monitoring total flow meet preset judging conditions, regarding the online monitoring flow data of the time period as abnormal data; and a correlation coefficient judgment step, namely obtaining an improved-Spanish rank correlation algorithm according to the Spanish rank correlation algorithm and the linear cross correlation algorithm, automatically acquiring rainfall-runoff peak staggering time and peak staggering correlation by utilizing the improved-Spanish rank correlation algorithm, and further judging whether the online monitoring flow data is abnormal or not. The invention can ensure the accuracy and the authenticity of the runoff monitoring data.
Description
Technical Field
The invention relates to a hydrology and water conservancy data processing method, in particular to an online monitoring and processing method for sponge urban flow data.
Background
In the prior art, the on-line monitoring of the flow of the sponge city comprises the flow monitoring of sponge facilities, sponge projects, drainage pipe networks of sponge construction areas and river inlets, and the monitoring data has important significance for evaluating the sponge construction effect and analyzing the runoff characteristics of the areas. The on-line monitoring data of the runoff of the sponge city is the minute-level monitoring data obtained by a flowmeter, and abnormal data and other conditions can be generated due to environmental interference, instrument noise and limitation of a measuring method in the measuring process. If the sponge is directly used without cleaning, effective data and abnormal data are mixed, so that the evaluation of sponge construction effect deviates from the actual condition, and even the subsequent data accumulation and application are influenced. The existing cleaning method is simple, please refer to fig. 1, the extreme value (maximum value or minimum value) is removed by setting a wide threshold, the method cannot clean abnormal data within a reasonable value range, for example, please refer to fig. 2, when the data does not conform to the natural rainfall law, a large amount of error data is caused, thereby interfering with data application and analysis mining.
Disclosure of Invention
The technical problem to be solved by the invention is to provide an online monitoring and processing method for sponge city flow data, which can overcome the defect that the traditional method can only carry out isolated value judgment and modification, carry out layer-by-layer data cleaning by utilizing the data range, the runoff coefficient and the rainfall-runoff correlation, and combine the natural law and statistical analysis to further ensure the accuracy and authenticity of runoff monitoring data.
In order to solve the technical problems, the invention adopts the following technical scheme.
A sponge city flow data online monitoring and processing method comprises the following steps: a data range judging step, which is used for setting a data cleaning range according to the reasonable range of the field measurement environment parameters and the monitoring values; a runoff coefficient judging step, which is used for acquiring online monitoring flow data of a preset time period and rainfall data of the time period, calculating theoretical maximum runoff total amount and online monitoring total flow, and when the theoretical maximum runoff total amount and the online monitoring total flow meet preset judging conditions, regarding the online monitoring flow data of the time period as abnormal data; and a correlation coefficient judgment step, namely obtaining an improved-Spanish rank correlation algorithm according to the Spanish rank correlation algorithm and the linear cross correlation algorithm, automatically acquiring rainfall-runoff peak staggering time and peak staggering correlation by using the improved-Spanish rank correlation algorithm, and further judging whether the online monitoring flow data is abnormal or not.
Preferably, in the data range determining step, the data cleansing range includes: the liquid level in the rainwater pipe is lower than the diameter of the rainwater pipe; the runoff flow rate of the rain pipe is less than 3 m/s.
Preferably, in the runoff coefficient determining step, each online flow monitoring device collects surface runoff in a catchment area, according to a total amount calculation principle, a product of a total rainfall amount and the catchment area in a preset time period is a theoretical maximum runoff total amount of the catchment area in the time period, a rainwater pipe network is set to collect a% to B% of the runoff total amount, when the flowmeter operates normally, the monitored flow falls in the a% to B% interval, and if the flow falls outside the a% to B% interval, an abnormal condition is determined to exist.
Preferably, in the runoff coefficient determining step, when there is an error in the monitoring instrument or the sponge emission reduction facility reduces the runoff, if the local rainfall is lower than the lower threshold
Surface runoff can not be generated, the credibility range is expanded to A '% to B'%, and proper abnormal data screening conditions are determined according to repeated tests.
In the sponge city flow data online monitoring processing method disclosed by the invention, firstly, the runoff coefficient is utilized to carry out online monitoring flow data cleaning, and the rainfall and the runoff have natural rules and are presented in the runoff coefficient mode, so that the runoff coefficient is utilized to carry out data discrimination, and the runoff data is ensured to be in a reasonable range under any rainfall condition. And then, cleaning on-line monitoring flow data by using an improved-spearman rank correlation algorithm, judging the conformity degree of the trends among a plurality of number arrays in statistical analysis of the correlation algorithm, wherein the correlation algorithm is a method for judging the conformity degree of the trends among a plurality of number arrays, and because the peak staggering phenomenon exists among rainfall-runoff number arrays and the traditional correlation algorithm cannot meet the requirements, the invention provides the improved-spearman rank correlation algorithm, and the algorithm is applied to judge the flow data so as to ensure that the runoff data conforms to the rainfall trend. Compared with the prior art, the method utilizes the physical significance and the natural law of the data on the basis of the original extreme value data processing, combines an innovative statistical analysis means, judges the effectiveness of the data by utilizing the relation between rainfall and runoff, has high credibility on the basis of a physical principle, has accurate data judgment capability for massive online monitoring data, and provides a reliable data basis for the application of subsequent data.
Drawings
FIG. 1 is a flow chart of a conventional method for processing on-line monitoring traffic data;
FIG. 2 is a chart of error flow data for a rainfall event;
FIG. 3 is a flow chart of the sponge city flow data on-line monitoring processing method of the invention;
FIG. 4 is a flow data chart for low correlation, anomalous data;
FIG. 5 is a graph of flow data for high correlation, normal data;
FIG. 6 is a data chart of online monitoring data and rainfall at a corresponding time interval in an example of application of the present invention;
FIG. 7 is a graph of online monitored flow data after cleaning in an example application.
Detailed Description
The invention is described in more detail below with reference to the figures and examples.
The invention discloses an online monitoring and processing method for sponge city flow data, please refer to fig. 3, which comprises the following steps:
a data range judging step, which is used for setting a data cleaning range according to the reasonable range of the field measurement environment parameters and the monitoring values; wherein, the reasonable range of the monitoring value can be summarized according to the previous data;
a runoff coefficient judging step, which is used for acquiring online monitoring flow data of a preset time period and rainfall data of the time period, calculating theoretical maximum runoff total amount and online monitoring total flow, and when the theoretical maximum runoff total amount and the online monitoring total flow meet preset judging conditions, regarding the online monitoring flow data of the time period as abnormal data;
and a correlation coefficient judgment step, namely obtaining an improved-Spanish rank correlation algorithm according to the Spanish rank correlation algorithm and the linear cross correlation algorithm, automatically acquiring rainfall-runoff peak staggering time and peak staggering correlation by using the improved-Spanish rank correlation algorithm, and further judging whether the online monitoring flow data is abnormal or not.
In the method, firstly, the runoff coefficient is utilized to carry out online monitoring flow data cleaning, and the rainfall and the runoff are presented in a runoff coefficient mode due to the natural law, so that the runoff coefficient is utilized to carry out data discrimination, and the runoff data is ensured to be in a reasonable range under any rainfall condition. And then, cleaning on-line monitoring flow data by using an improved-spearman rank correlation algorithm, judging the conformity degree of the trends among a plurality of number arrays in statistical analysis of the correlation algorithm, wherein the correlation algorithm is a method for judging the conformity degree of the trends among a plurality of number arrays, and because the peak staggering phenomenon exists among rainfall-runoff number arrays and the traditional correlation algorithm cannot meet the requirements, the invention provides the improved-spearman rank correlation algorithm, and the algorithm is applied to judge the flow data so as to ensure that the runoff data conforms to the rainfall trend. Compared with the prior art, the method utilizes the physical significance and the natural law of the data on the basis of the original extreme value data processing, combines an innovative statistical analysis means, judges the effectiveness of the data by utilizing the relation between rainfall and runoff, has high credibility on the basis of a physical principle, has accurate data judgment capability for massive online monitoring data, and provides a reliable data basis for the application of subsequent data.
Further, in the data range determining step, the data cleansing range includes:
the liquid level in the rainwater pipe is lower than the diameter of the rainwater pipe;
the runoff flow rate of the rain pipe is less than 3 m/s.
As a preferable mode, in the runoff coefficient determining step, each online flow monitoring device collects surface runoff in a catchment area, according to a total amount calculation principle, a product of a total rainfall amount and the catchment area in a preset time period is a theoretical maximum runoff total amount of the catchment area in the time period, a% to B% of the total runoff amount collected by a rainwater pipe network is set, and when the flowmeter normally operates, the monitored flow falls into an interval from the a% to the B%, wherein a denotes a runoff coefficient lower limit, and B denotes a runoff coefficient upper limit. If the flow rate falls outside the A% to B% interval, an abnormal condition is deemed to exist.
Further, in the runoff coefficient determination step, when an error exists in the monitoring instrument or the runoff is reduced by the sponge emission reduction facility, if the local rainfall is lower than the lower threshold
Surface runoff can not be generated, the credibility range is expanded to A '% to B'%, and proper abnormal data screening conditions are determined according to repeated tests.
Based on the above conditions, the runoff coefficient determining step in this embodiment specifically includes:
step S10, extracting online monitoring flow data within n hours and rainfall data of corresponding time period;
step S11, calculating the theoretical maximum runoff total amount in n hours:
Vn＝Shn；
wherein the content of the first and second substances,
Vn: theoretical maximum runoff total (m) in n hours3)；
S: the catchment area (m) of the device2)；
hn: total rainfall (m) over n hours;
step S12, calculating the total online monitoring flow rate in n hours, and obtaining the flow rate q from the flow meter, and if q is equal to dQ, then:
wherein Q isn: total flow (m) measured by a flowmeter over n hours3)
Step S13, performing comparison determination under the following conditions:
in any rainfall case, Qn＞B′％×Vn；
When the rainfall is greater than
When is, Qn＜A′％×Vn；
And when the online monitoring data meet any judgment condition, determining that the online monitoring data in the period are abnormal data.
In practical application, in a certain period of time, the rainfall curve and the online monitoring flow curve are in accordance with the trend, that is, the correlation degree is high, please refer to fig. 5; on the contrary, when the correlation degree is lower than the threshold ξ, the two trends are proved to be inconsistent, and abnormal data exist, as shown in fig. 4. Because of the peak-shifting phenomenon, the conventional correlation calculation method is not suitable for calculating the rainfall-runoff correlation, and for this reason, in this embodiment, the correlation coefficient determination step includes:
step S20, dividing the on-line monitoring flow data and the rainfall data of the corresponding time into a calculation unit every Y minutes, wherein Y is more than X, and X is the maximum rainfall-runoff peak staggering duration obtained through historical data statistics;
step S21, the improved spearman rank correlation algorithm, calculating a correlation of the traffic data and the rainfall data in each calculation unit, wherein the traffic data q is q { (q) }j，j∈[0，y]H, rainfall data h ═ hl，l∈[0，y]The calculation result is the sequence R, R ═ RkAnd k is 0, 1, 2,.., X }, then:
wherein:
Rk: the value of the kth component of the cross-correlation computation result;
di: the rank difference between the rainfall and the flow;
y: number of monitor data points obtained in Y minutes;
x: number of monitoring data points obtained in X minutes;
the largest R among R is foundkAnd obtaining the rainfall-runoff peak offset correlation degree in the calculation unit.
Step S22, performing comparison determination:
the degree of correlation is lower than xi, wherein xi is a preset lower limit threshold value which is determined through repeated experiments and calculation;
the rainfall or runoff is not 0;
when the on-line monitoring flow data simultaneously meets the two judgment conditions, the on-line monitoring flow data in the calculation unit is determined as abnormal data.
In order to better describe the technical scheme of the invention, the invention provides the following embodiments according to practical tests:
example one
Referring to fig. 6, fig. 6 shows the online monitoring data of the total outlet flow of the rainwater pipe network in a region of shenzhen from 26 th month to 28 th month in 2019 and rainfall in the corresponding time period. The catchment area of the area is about 166000 square meters, and the average runoff coefficient of the area is 0.68. FIG. 6 shows three segments of abnormal data screened by the present invention, which are marked as (I), (II). Wherein:
marking that the correlation coefficient is too low, and the typical flow rate does not accord with the rainfall trend. According to observation, a single-peak rainfall process exists in the time period, the flow rate shows two peak values and the time is advanced, the general trends are contrary, and the abnormal data belong to;
the label (II) simultaneously comprises the conditions of too low correlation coefficient and too high runoff coefficient. According to observation, the total rainfall in the period is about 0.8mm, the rainfall is extremely low, the total runoff quantity of online monitoring exceeds 2967 cubic meters, the calculated runoff coefficient is about 22.34 and far exceeds the reasonable upper limit, and therefore the runoff coefficient is judged to be too high, and meanwhile, the runoff starting time is greatly earlier than the rainfall starting time, and the trend is inconsistent, so that the correlation coefficient is too low. In summary, the time period belongs to the abnormal data.
And thirdly, marking that the runoff coefficient is too high, belonging to typical abnormal extreme value data, wherein the rainfall in the time period is 16.7mm, the medium rain level is achieved, the online monitoring peak flow is 5959 cubic meters per hour, the total runoff is 2509 cubic meters, and the calculated runoff coefficient is about 1.01. The runoff coefficient exceeds 1 and does not accord with the natural law, so that the runoff coefficient is judged to pass the draft and belongs to abnormal data.
Referring to fig. 7, fig. 7 shows the cleaned online flow monitoring data, which has no obvious abnormality after cleaning and improved usability, and lays a good foundation for subsequent data applications.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents or improvements made within the technical scope of the present invention should be included in the scope of the present invention.
Claims (6)
1. A sponge city flow data online monitoring processing method is characterized by comprising the following steps:
a data range judging step, which is used for setting a data cleaning range according to the reasonable range of the field measurement environment parameters and the monitoring values;
a runoff coefficient judging step, which is used for acquiring online monitoring flow data of a preset time period and rainfall data of the time period, calculating theoretical maximum runoff total amount and online monitoring total flow, and when the theoretical maximum runoff total amount and the online monitoring total flow meet preset judging conditions, regarding the online monitoring flow data of the time period as abnormal data;
and a correlation coefficient judgment step, namely obtaining an improved-Spanish rank correlation algorithm according to the Spanish rank correlation algorithm and the linear cross correlation algorithm, automatically acquiring rainfall-runoff peak staggering time and peak staggering correlation by using the improved-Spanish rank correlation algorithm, and further judging whether the online monitoring flow data is abnormal or not.
2. The sponge city flow data online monitoring processing method according to claim 1, wherein in the data range determining step, the data cleaning range includes:
the liquid level in the rainwater pipe is lower than the diameter of the rainwater pipe;
the runoff flow rate of the rain pipe is less than 3 m/s.
3. The sponge city flow data online monitoring processing method as claimed in claim 1, wherein in the runoff coefficient determining step, each online flow monitoring device collects surface runoff in a catchment area, according to a total amount calculation principle, a product of a total rainfall amount and the catchment area in a preset time period is a theoretical maximum runoff total amount of the catchment area in the time period, a% to B% of the collected runoff total amount of a rainwater pipe network is set, wherein a is a runoff coefficient lower limit, and B is a runoff coefficient upper limit, when the flowmeter operates normally, the monitored flow falls in the interval from a% to B%, and if the flow falls outside the interval from a% to B%, an abnormal condition is determined to exist.
4. The sponge city flow data on-line monitor of claim 3The measuring and processing method is characterized in that in the runoff coefficient judging step, when an error exists in a monitoring instrument or the runoff quantity is reduced by a sponge emission reduction facility, if the local rainfall is lower than a lower limit threshold value
Surface runoff can not be generated, the credibility range is expanded to A '% to B'%, and proper abnormal data screening conditions are determined according to repeated tests.
5. The sponge city flow data online monitoring processing method according to claim 4, wherein the runoff coefficient determining step comprises:
step S10, extracting online monitoring flow data within n hours and rainfall data of corresponding time period;
step S11, calculating the theoretical maximum runoff total amount in n hours:
Vn＝Shn；
wherein the content of the first and second substances,
Vn: the theoretical maximum runoff total in n hours;
s: the catchment area of the device;
hn: total rainfall in n hours;
step S12, calculating the total online monitoring flow rate in n hours, and obtaining the flow rate q from the flow meter, and if q is equal to dQ, then:
wherein Q isn: total flow measured by flowmeter within n hours
Step S13, performing comparison determination under the following conditions:
in any rainfall case, Qn＞B′％×Vn；
When the rainfall is greater than
When is, Qn＜A′％×Vn；
And when the online monitoring data meet any judgment condition, determining that the online monitoring data in the period are abnormal data.
6. The sponge city flow data online monitoring processing method according to claim 1, wherein the correlation coefficient determining step includes:
step S20, dividing the on-line monitoring flow data and the rainfall data of the corresponding time into a calculation unit every Y minutes, wherein Y is more than X, and X is the maximum rainfall-runoff peak staggering duration obtained through historical data statistics;
step S21, the improved spearman rank correlation algorithm, calculating a correlation between the traffic data and the rainfall data in each calculation unit, wherein the traffic data q ═ { q ═ qj，j∈[0，y]H, rainfall data h ═ hl，l∈[0，y]The calculation result is the sequence R, R ═ RkAnd k is 0, 1, 2,.., X }, then:
wherein:
Rk: the value of the kth component of the cross-correlation computation result;
di: the rank difference between the rainfall and the flow;
y: number of monitor data points obtained in Y minutes;
x: number of monitoring data points obtained in X minutes;
the largest R among R is foundkAnd obtaining the rainfall-runoff peak offset correlation degree in the calculation unit.
Step S22, performing comparison determination:
the degree of correlation is lower than xi, wherein xi is a preset lower limit threshold value which is determined through repeated experiments and calculation;
the rainfall or runoff is not 0;
when the on-line monitoring flow data simultaneously meets the two judgment conditions, the on-line monitoring flow data in the calculation unit is determined as abnormal data.
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Citations (3)
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|---|---|---|---|---|
| US20050071139A1 (en) * | 2003-09-29 | 2005-03-31 | Patwardhan Avinash S. | Method and system for water flow analysis |
| CN106706033A (en) * | 2016-11-24 | 2017-05-24 | 北京无线电计量测试研究所 | Sponge city performance monitoring system and method |
| CN111199345A (en) * | 2019-12-27 | 2020-05-26 | 河北建筑工程学院 | Measuring and calculating method for design rainfall of sponge city and terminal equipment |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050071139A1 (en) * | 2003-09-29 | 2005-03-31 | Patwardhan Avinash S. | Method and system for water flow analysis |
| CN106706033A (en) * | 2016-11-24 | 2017-05-24 | 北京无线电计量测试研究所 | Sponge city performance monitoring system and method |
| CN111199345A (en) * | 2019-12-27 | 2020-05-26 | 河北建筑工程学院 | Measuring and calculating method for design rainfall of sponge city and terminal equipment |
Non-Patent Citations (1)
| Title |
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| 杨婷婷;李志一;赵冬泉;: "基于物联网大数据的城市降雨径流控制率分析" * |
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