Intelligent Management and Early Warning System for Monitoring Data of Water Conservancy Projects

Abstract

The present invention relates to the technical field of water level monitoring, and particularly relates to an intelligent management and early warning system for monitoring data of water conservancy projects. The system includes: a data preprocessing module for obtaining water level data at different time periods; a distribution regularity calculation module for obtaining the distribution regularity of a time period based on the skewness coefficient and kurtosis coefficient of the water level data in that time period; a stability degree calculation module for obtaining the fluctuation smoothness of a time period based on the coefficient of variation of the water level data in the same time period of each year and the number of abnormal water level data; obtaining the stability degree by synthesizing the distribution regularity and the fluctuation smoothness; a water level normal range obtaining module for obtaining the normal range of the water level in a time period when the stability degree is greater than the stability threshold, and the normal range of the water level in a time period when the stability degree is less than or equal to the stability threshold; and an early warning module for giving an early warning to the water level according to the normal range of the water level in each time period. The present invention can improve the accuracy of water level early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of water level monitoring, and particularly relates to an intelligent management and early warning system for monitoring data of water conservancy projects. Background Art

[0002] Water conservancy projects are projects built for preventing water disasters and developing and utilizing water resources, including flood control, waterlogging control, irrigation, water supply, hydropower generation, shipping, water resource protection, soil and water conservation, as well as water-related projects in aquaculture, tourism, and improving the ecological environment. The monitoring data of water conservancy projects includes water level data, water quality data, flow data, etc. To effectively reduce the possibility of disasters or mitigate the harm of disasters, it is necessary to conduct intelligent management and early warning on the monitoring data of water conservancy projects.

[0003] The intelligent management and early warning system for monitoring data of water conservancy projects collects data such as water level and water quality in real time through monitoring devices such as sensors, and then transmits the collected data to the data storage and processing platform. Thresholds are set based on various historical monitoring data collected. When the monitoring data reaches the preset threshold, the system will automatically trigger an early warning signal and notify relevant personnel by means of text messages, emails, etc.

[0004] Among them, water level data is an important basis for flood early warning and reasonable water resource scheduling. By real-time monitoring of the change of water level, the safety and sustainability of water conservancy projects can be improved. In traditional monitoring, after collecting water level data, the current collected monitoring data is often warned by a fixed threshold set by historical water level monitoring data. However, the setting of this fixed threshold often ignores the seasonal characteristics of water level data. There may be a situation where the water level data in the dry season is abnormal compared with the same historical period but does not exceed the threshold. Therefore, according to the seasonal characteristics of water level data, box plots are used to set thresholds for each time period. However, when setting thresholds with box plots, it is easily affected by the distribution characteristics and fluctuation characteristics of the data itself, resulting in inaccurate threshold setting, and further leading to inaccurate water level early warning. Summary of the Invention

[0005] In order to solve the above technical problems, the purpose of the present invention is to provide an intelligent management and early warning system for monitoring data of water conservancy projects, and the specific technical solutions adopted are as follows:

[0006] An embodiment of the present invention provides an intelligent management and early warning system for monitoring data of water conservancy projects, and the system includes:

[0007] A data preprocessing module, configured to obtain the water level monitoring data of a preset number of years in history and perform preprocessing to obtain water level data; segment the water level data of each year to obtain water level data of different time periods;

[0008] The distribution regularity calculation module is used to process the water level data of the same period of each year using the box plot to obtain the skewness coefficient and kurtosis coefficient of the period; and obtain the distribution regularity of a period according to the skewness coefficient and kurtosis coefficient of the period;

[0009] The stability calculation module is used to obtain the fluctuation stability of the period based on the coefficient of variation of the water level data in the same period of each year and the number of abnormal water level data; and to obtain the stability of the period by using the distribution regularity and fluctuation stability of the same period of each year;

[0010] The module for obtaining the normal range of water level is used to calculate the normal range of water level in the same period of each year by using the upper quantile, lower quantile and interquartile range of water level data in the same period of each year if the stability is greater than the stability threshold; if the stability is less than or equal to the stability threshold, the module for obtaining the normal range of water level in the same period of each year is used to calculate the normal range of water level in the same period;

[0011] The early warning module is used to warn the water level according to the normal range of the water level in each time period.

[0012] Preferably, the water level data of the same period in each year are processed using a box plot to obtain the skewness coefficient and kurtosis coefficient of the period, including:

[0013] The skewness coefficient and kurtosis coefficient of the period were obtained by eliminating abnormal water level data in the water level data of the same period of each year using the box plot.

[0014] Preferably, obtaining the distribution regularity of a period according to the skewness coefficient and the kurtosis coefficient of the period includes:

[0015] The absolute value of the skewness coefficient corresponding to the same period of each year is added to the hyperparameter and inverted to obtain the inverse of the skewness of the period; the absolute value of the difference between the kurtosis coefficient corresponding to the same period of each year and the preset value is obtained and added to the hyperparameter to obtain the addition result, and the addition result is inverted to obtain the inverse of the kurtosis of the period; the water level data of the same period of each year is used to construct a data distribution curve, and the Spearman correlation coefficient between the data distribution curve and the standard normal distribution curve is obtained, and the Spearman correlation coefficient is added to the first preset value and averaged to obtain the normalized Spearman correlation coefficient of the period; the inverse of the skewness, the inverse of the kurtosis and the normalized Spearman correlation coefficient of the period are added and averaged to obtain the distribution regularity of the period.

[0016] Preferably, the fluctuation stability of the period is obtained based on the coefficient of variation of the water level data in the same period of each year and the number of abnormal water level data, including:

[0017] The inverse of the coefficient of variation of the water level data in the same period of each year is obtained and normalized to obtain the inverse of the coefficient of variation; the number of water level data in the same period of each year is taken as the numerator, and the number of abnormal water level data in the same period of each year and the sum of the hyperparameters are taken as the denominator, the two are compared and normalized to obtain the abnormal proportion; the inverse of the coefficient of variation and the abnormal proportion are added and averaged to obtain the fluctuation stability of the period.

[0018] Preferably, the distribution regularity and fluctuation stability of the same period of each year are used to obtain the stability of the period, including:

[0019] The weighted sum of the distribution regularity and fluctuation stability of the same period of each year is used to obtain the stability of the period.

[0020] Preferably, if the degree of stability is greater than the stability threshold, the upper quantile, lower quantile and interquartile range of the water level data of the same period of each year are used to calculate the normal range of the water level in the period, including:

[0021] The abnormal water level data in the water level data of the same period of each year are eliminated by using the box plot, and the upper quantile, lower quantile and interquartile range of the period are obtained by using the remaining water level data in the period; the upper quantile of the period is added to the interquartile range of the preset multiple to obtain the upper limit value; the lower quantile of the period is subtracted from the interquartile range of the preset multiple to obtain the lower limit value; the range between the lower limit value and the upper limit value is the normal range of the water level in the period.

[0022] Preferably, if the degree of stability is less than or equal to the stability threshold, the upper quantile, lower quantile, interquartile range and skewness coefficient of the water level data of the same period of each year are used to calculate the normal range of the water level in the period, including:

[0023] The upper quantile, lower quantile, interquartile range and skewness coefficient of the water level data in the same period of each year are used to calculate the upper and lower limits of the normal range of water level in that period; the range between the lower and upper limits is the normal range of water level in that period.

[0024] Preferably, the calculation formulas for the upper limit and the lower limit are:

[0025] ,

[0026] ,

[0027] in, represents the upper limit of the normal range of water level in the ith period, Indicates the lower limit of the normal range of water level in the i-th period; It represents the upper quantile of the water level data in the i-th period of each year after eliminating the abnormal water level data; It represents the lower quantile obtained after removing abnormal water level data from the water level data within the i-th time period of each year. It represents the interquartile range obtained after removing abnormal water level data from the water level data within the i-th time period of each year; e represents the natural constant. It represents the skewness coefficient of the water level data within the i-th time period of each year after removing abnormal water level data by the box plot.

[0028] Preferably, water level early warning is carried out according to the normal range of water level in each time period, including:

[0029] Determine the normal range of water level corresponding to the current moment according to the time period to which the current moment belongs. If the water level at the current moment is within the normal range of water level corresponding to the current moment, no early warning is carried out; if the water level at the current moment is not within the normal range of water level corresponding to the current moment, early warning is carried out.

[0030] The embodiments of the present invention have at least the following beneficial effects: The present invention obtains the water level monitoring data of preset years in history and performs preprocessing to obtain water level data, which can improve the quality of data and make subsequent analysis more accurate. Further, based on seasonal characteristics, the water level data of each year is divided to facilitate subsequent analysis of water level data according to seasons; then, according to the skewness coefficient and kurtosis coefficient of the same time period of each year, the distribution regularity of this time period is obtained, and based on the coefficient of variation of the water level data of the same time period of each year and the number of abnormal water level data, the fluctuation smoothness of this time period is obtained. The stability degree of this time period is obtained by synthesizing the fluctuation smoothness and distribution regularity of a time period, and it is judged whether it is accurate to set the threshold for the water level data of this time period by using the box plot. If the stability degree is greater than the stability threshold, the method of directly using the box plot to obtain the threshold is used to calculate the normal range of water level of this time period based on the upper quantile, lower quantile and interquartile range of the water level data of the same time period of each year. If the stability degree is less than or equal to the stability threshold, the normal range of water level of this time period is calculated by using the upper quantile, lower quantile, interquartile range and skewness coefficient of the water level data of the same time period of each year, which can obtain a more accurate normal range of water level and improve the accuracy of water level early warning. Description of the Drawings

[0031] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0032] Figure 1 It is a system block diagram of an intelligent management and early warning system for water conservancy project monitoring data provided by the embodiments of the present invention. Detailed Embodiments

[0033] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, details the specific implementation manner, structure, features and effects of an intelligent management and early warning system for water conservancy project monitoring data proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs.

[0035] The following specifically describes the specific solution of an intelligent management and early warning system for water conservancy project monitoring data provided by the present invention in combination with the accompanying drawings.

[0036] Embodiment:

[0037] The main application scenario of the present invention is as follows: In water conservancy projects, when monitoring and warning the water level, due to the neglect of the seasonal characteristics of water level data, the set fixed threshold often cannot accurately judge whether early warning is needed. Therefore, according to the seasonal characteristics of water level data, box plots are used to set thresholds for each time period. However, when setting thresholds using box plots, it is easily affected by the distribution characteristics and fluctuation characteristics of the data itself, resulting in inaccurate threshold setting. Therefore, it is necessary to analyze the characteristics of historical water level data, and then improve the setting of water level thresholds using box plots in order to obtain more accurate water level thresholds and improve the accuracy of water level early warning.

[0038] Please refer to Figure 1 , which shows a system block diagram of an intelligent management and early warning system for water conservancy project monitoring data provided by an embodiment of the present invention. The system includes the following modules:

[0039] A data preprocessing module, configured to obtain historical water level monitoring data for a preset number of years and perform preprocessing to obtain water level data; segment the water level data for each year to obtain water level data for different time periods.

[0040] Collect historical water level monitoring data for a preset number of years. Preferably, in the present invention, the preset number of years refers to the three years before the current time, and the implementer can adjust the preset number of years according to the actual situation. Thus, historical water level monitoring data for a preset number of years can be obtained. After obtaining the water level monitoring data, it is necessary to perform preprocessing on it. Specifically, perform missing value filling and noise reduction processing on it to obtain water level data.

[0041] When obtaining an accurate water level threshold, seasonal characteristics need to be considered. Changes in water level data are closely related to climate, especially precipitation, evaporation, etc. These climate factors often show seasonal fluctuations. For example, there may be more precipitation in spring and summer, and less precipitation in autumn and winter, which will affect the water level data. Seasonal division of water level data according to months can better reflect changes in water level data. Therefore, the present invention divides the water level data of each year into 12 time periods based on one month as a division period, and obtains water level data in different time periods for subsequent analysis.

[0042] The distribution regularity calculation module is used to process the water level data of the same period of each year using the box plot to obtain the skewness coefficient and kurtosis coefficient of the period; and obtain the distribution regularity of a period based on the skewness coefficient and kurtosis coefficient of the period.

[0043] When using box plots to set data thresholds, the data will be affected by the distribution characteristics and fluctuation characteristics of the water level data, resulting in inaccurate threshold setting results. If the water level data distribution presents a relatively standard normal distribution, then using a box plot to set the threshold for the water level data in that period will be more accurate. Therefore, it is necessary to calculate the distribution regularity of the water level data in each divided period, and use the box plot to set the water level data threshold based on the distribution regularity of the water level data in that period.

[0044] Furthermore, the water level data in the same period of each year are processed using a box plot to obtain the skewness coefficient and the kurtosis coefficient; the distribution regularity of a period is obtained based on the skewness coefficient and the kurtosis coefficient of the period.

[0045] Specifically, the water level data in the same time period of each year are processed using a box plot to obtain the skewness coefficient and the kurtosis coefficient. It should be noted that when obtaining the skewness coefficient and the kurtosis coefficient, the box plot is used to eliminate the abnormal water level data. The water level data in the same time period of each year in this application is the water level data in the same time period in three years. For example, January is the data in January of each year in three years.

[0046] Obtain the distribution regularity of a time period according to the skewness coefficient and kurtosis coefficient, including: adding the absolute value of the skewness coefficient corresponding to the same time period of each year to the hyperparameter and taking the reciprocal to obtain the skewness reciprocal of this time period; obtaining the absolute value of the difference between the kurtosis coefficient corresponding to the same time period of each year and the preset value and adding it to the hyperparameter to obtain the addition result, and taking the reciprocal of the addition result to obtain the kurtosis reciprocal of this time period; using the water level data of the same time period of each year to construct a data distribution curve, obtaining the Spearman correlation coefficient between the data distribution curve and the standard normal distribution curve, adding the Spearman correlation coefficient to the first preset value and taking the average to obtain the normalized Spearman correlation coefficient of this time period; adding the skewness reciprocal, kurtosis reciprocal and normalized Spearman correlation coefficient of this time period and taking the average to obtain the distribution regularity of this time period.

[0047] For the i-th time period of each year, the calculation model of the distribution regularity of the water level data within this time period is:

[0048] ,

[0049] where represents the distribution regularity of the i-th time period of each year, which can also be said to be the distribution regularity of the water level data within the i-th divided time period. Since the judgment of the normal distribution is greatly affected by outliers, therefore is the skewness coefficient of the water level data within the i-th time period of each year after removing the abnormal water level data by the box plot. The closer this value is to 0, that is, the larger the first term of the above formula, the closer the water level data in the i-th time period of each year is to the normal distribution, that is, the stronger the distribution regularity of the water level data within the i-th divided time period. represents the skewness reciprocal; a is a hyperparameter to prevent the denominator from being 0;

[0050] represents the kurtosis coefficient of the water level data within the i-th time period of each year after removing the outliers by the box plot. The preset value is 3. The reason for setting the preset value to 3 is that the kurtosis coefficient of the standard normal distribution is 3. Therefore, the closer the kurtosis coefficient is to 3, that is, the larger the second term of the above formula, the closer the water level data is to the normal distribution, that is, the stronger the distribution regularity of the water level data within the i-th divided time period. The skewness coefficient measures the symmetry of the data distribution, and the kurtosis coefficient measures the kurtosis of the data distribution. Both are existing technologies and will not be elaborated in this solution. is the skewness reciprocal;

[0051] represents the Spearman correlation coefficient between the data distribution curve constructed by using the data in the i-th time period of each year and the standard normal distribution curve, where takes values from -1 to 1, It is for normalization to obtain the normalized Spearman correlation coefficient. The larger this value is, the closer the distribution of water level data in the i-th time period of each year is to the normal distribution, that is, the stronger the distribution regularity of water level data in the i-th divided time period. Thus, the distribution regularity of each time period can be obtained.

[0052] The stability degree calculation module is used to obtain the fluctuation smoothness of the time period based on the coefficient of variation of water level data and the number of abnormal water level data in the same time period of each year; and obtain the stability degree of the time period by using the distribution regularity and fluctuation smoothness of the same time period of each year.

[0053] Because when using a box plot for threshold setting, it will be affected by the volatility of water level data. If the water level data fluctuates greatly, it may cause the box plot to regard normal high-volatility data values as abnormal water level data, thus affecting the threshold setting. Therefore, it is necessary to calculate the distribution regularity and fluctuation smoothness of water level data in each divided time period, comprehensively obtain the stability degree of water level data in each time period, and then conduct subsequent analysis based on the stability degree.

[0054] Furthermore, the fluctuation smoothness of the time period is obtained based on the coefficient of variation of water level data and the number of abnormal water level data in the same time period of each year. Specifically, the reciprocal of the coefficient of variation of water level data in the same time period of each year is obtained and normalized to get the reciprocal of the coefficient of variation; the number of water level data in the same time period of each year is used as the numerator, and the sum of the number of abnormal water level data and the hyperparameter in the same time period of each year is used as the denominator, and the two are compared and normalized to get the abnormal proportion; the average of the sum of the reciprocal of the coefficient of variation and the abnormal proportion is obtained to get the fluctuation smoothness of the time period.

[0055] The specific calculation model of the fluctuation smoothness is:

[0056] ,

[0057] Among them, is the fluctuation smoothness of water level data in the i-th time period of each year; is the total number of all water level data in the i-th time period (the same time period) of each year, is the number of all abnormal water level data in the i-th time period of each year, which is the abnormal water level data obtained after screening based on the box plot, represents the ratio of the total number of water level data to the number of abnormal water level data in the i-th time period of each year. The larger this value is, the smaller the number of abnormal data in the i-th time period of each year; is the coefficient of variation of water level data in the i-th time period of each year. The smaller this value is, that is, the larger the second term in the above formula, the smaller the volatility of water level data in the i-th time period of each year, that is, the stronger the fluctuation smoothness of water level data in this time period; norm( ) is the normalization function. and They represent the abnormal proportion and the inverse of the coefficient of variation respectively.

[0058] Thus, the distribution regularity and fluctuation stability of the same period of each year can be obtained, and then the stability of the period can be obtained by combining them. Specifically, the distribution regularity and fluctuation stability of the same period of each year are weighted and summed to obtain the stability of the period. The specific calculation formula is: ,

[0059] in, and are weight coefficients respectively. Since the distribution regularity of water level data in a period has a greater impact on the box plot threshold setting than the fluctuation stability, this scheme sets , , implementers can make adjustments based on specific circumstances; Indicates the stability of the water level data in the i-th period, represents the distribution regularity of water level data in the i-th period, Indicates the fluctuation stability of the water level data in the i-th period. From this, we can get the stability of each of the 12 periods.

[0060] The normal water level range acquisition module is used to calculate the normal water level range of the period using the upper quantile, lower quantile and interquartile range of the water level data of the same period of each year if the stability degree is greater than the stability threshold; if the stability degree is less than or equal to the stability threshold, the upper quantile, lower quantile, interquartile range and skewness coefficient of the water level data of the same period of each year are used to calculate the normal water level range of the period.

[0061] If the water level data within a period of time is more stable, it means that the water level data within the period of time is more accurate in screening outliers after the box plot threshold is set. At this time, the original threshold setting method can be used. If the water level data within a period of time is less stable, it means that the water level data within the period of time is less accurate in screening outliers after the original box plot threshold is set. At this time, the box plot threshold setting needs to be adaptively adjusted according to the data characteristics within the period.

[0062] Set a stability threshold. Preferably, the value of the stability threshold in the embodiment of the present invention is 0.7. The implementer can adjust it according to the actual situation. If the stability level of a time period is greater than the stability threshold, the threshold setting method will not be changed when the box plot is used to set the threshold of the water level data in the time period. It should be noted that due to the influence of abnormal values ​​that may exist in the historical water level data on the threshold setting, if the upper and lower thresholds are adjusted directly, the warning results may be inaccurate. Therefore, it is necessary to use the box plot to eliminate the abnormal values ​​before calculation.

[0063] If the degree of stability is greater than the stability threshold, the upper quantile, lower quantile and interquartile range of the water level data in the same period of each year are used to calculate the normal range of the water level in that period. Specifically, the abnormal water level data in the water level data in the same period of each year are eliminated using a box plot, and the upper quantile, lower quantile and interquartile range of that period are obtained using the remaining water level data in that period; the upper quantile of that period is added to the interquartile range of a preset multiple to obtain the upper limit value; the lower quantile of that period is subtracted from the interquartile range of a preset multiple to obtain the lower limit value; the range between the lower limit value and the upper limit value is the normal range of the water level in that period.

[0064] Among them, the specific calculation formula for the upper limit and lower limit is: and , is the upper quantile of the water level data in the i-th period of each year after removing the abnormal water level data, is the lower quantile of the water level data in the i-th period of each year after removing the abnormal water level data, It is the interquartile range of the water level data in the i-th period of each year after eliminating the abnormal water level data.

[0065] If the stability level is less than or equal to the stability threshold, the upper quantile, lower quantile, interquartile range and skewness coefficient of the water level data of the same period of each year are used to calculate the normal range of the water level in the period. Specifically, the upper quantile, lower quantile, interquartile range and skewness coefficient of the water level data of the same period of each year are used to calculate the upper limit and lower limit of the normal range of the water level in the period. The range between the lower limit and the upper limit is the normal range of the water level in the period. The calculation formula of the upper limit is:

[0066] ,

[0067] The calculation formula of the lower limit is:

[0068] ,

[0069] in, represents the upper limit of the normal range of water level in the ith period, Indicates the lower limit of the normal range of water level in the i-th period; It represents the upper quantile of the water level data in the i-th period of each year after eliminating the abnormal water level data; It represents the lower quantile of the water level data in the i-th period of each year after eliminating the abnormal water level data; represents the interquartile range of the water level data in the i-th period of each year after eliminating the abnormal water level data; e represents the natural constant; It represents the skewness coefficient of the water level data in the i-th period of each year after the abnormal water level data are eliminated through the box plot.

[0070] is an increasing function with a range of [0, 1] when x > 0; in the calculation of the upper limit value, it indicates that the water level data value in the i-th period is on the high side. For example, in the case of more rainfall during the rainy season resulting in rising water levels, when judging the current data threshold, it may be considered that the normal data value is greater than the threshold, and the normal data is classified as abnormal. Therefore, it is necessary to increase the upper limit value of the normal water level range; in the calculation of the lower limit value, the skewness coefficient when it is, it means that there are more data points smaller than the mean value of the water level data, indicating that the water level data value in the i-th period is on the low side. For example, in the case of the water level dropping during the dry season, when judging the current data threshold, it may be considered that the normal data value is less than the threshold, and the normal water level data is classified as abnormal. At this time, it is necessary to lower the lower limit value of the normal water level range.

[0071] Thus, the adaptive threshold adjustment of the water level data for each divided period is realized, and the normal water level range of the water level data for each period is obtained.

[0072] An early warning module, used to give an early warning of the water level according to the normal water level range of each period.

[0073] After obtaining the normal water level range of each period, the specific operation to judge whether the water level at the current moment is normal is as follows: determine the normal water level range corresponding to the current moment according to the period to which the current moment belongs. If the water level at the current moment is within the normal water level range corresponding to the current moment, no early warning is given; if the water level at the current moment is not within the normal water level range corresponding to the current moment, an early warning is given. In addition, the normal water level range corresponding to each month of each year can be updated in real time according to the previous historical data to ensure the accuracy of the normal water level range and the accuracy of the early warning.

[0074] To sum up, in this solution, the historical water level data is seasonally divided, the stability degree of the water level data for each time period is obtained according to the distribution characteristics and fluctuation characteristics of the historical water level data in each divided period, and then the water level threshold is adaptively set according to the box plot according to the stability degree of each time period of the historical data, and the threshold setting for each time period is obtained. Then, by judging whether the current water level data exceeds the set threshold of the current period, an early warning is given to the water level data of the current period. This method of obtaining the threshold setting of the water level data for each period according to the seasonal characteristics of the historical water level data largely avoids the inaccurate early warning of the water level data caused by a single threshold setting, and adjusts the threshold setting of the box plot according to the water level data characteristics in each period, making the threshold setting of the box plot more accurate, and further making the early warning of the water conservancy project monitoring data more accurate and effective.

[0075] It should be noted that: the above order of the embodiments of the present invention is only for description and does not represent the superiority or inferiority of the embodiments. And the above description of specific embodiments of this specification has been made. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0076] Each embodiment in this specification is described in a progressive manner. For the same or similar parts among the embodiments, reference can be made to each other, and the key point of each embodiment is to illustrate the differences from other embodiments.

[0077] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent management and early warning system for water conservancy project monitoring data, characterized in that: The system includes: The data preprocessing module is used to obtain the water level monitoring data of the preset years in history and perform preprocessing to obtain water level data; segment the water level data of each year according to the month to obtain water level data of different periods; The distribution regularity calculation module is used to process the water level data of the same period of each year using the box plot to obtain the skewness coefficient and kurtosis coefficient of the period; and obtain the distribution regularity of a period according to the skewness coefficient and kurtosis coefficient of the period; The stability calculation module is used to obtain the fluctuation stability of the period based on the coefficient of variation of the water level data in the same period of each year and the number of abnormal water level data; and to obtain the stability of the period by using the distribution regularity and fluctuation stability of the same period of each year; The module for obtaining the normal range of water level is used to calculate the normal range of water level in the same period of each year by using the upper quantile, lower quantile and interquartile range of water level data in the same period of each year if the stability is greater than the stability threshold; if the stability is less than or equal to the stability threshold, the module for obtaining the normal range of water level in the same period of each year is used to calculate the normal range of water level in the same period; The early warning module is used to warn the water level according to the normal range of the water level in each period; The step of obtaining the distribution regularity of a time period according to the skewness coefficient and the kurtosis coefficient of the time period includes: The absolute value of the skewness coefficient corresponding to the same period of each year is added to the hyperparameter and inverted to obtain the inverse of the skewness of the period; the absolute value of the difference between the kurtosis coefficient corresponding to the same period of each year and the preset value is obtained and added to the hyperparameter to obtain the addition result, and the addition result is inverted to obtain the inverse of the kurtosis of the period; the water level data of the same period of each year is used to construct a data distribution curve, and the Spearman correlation coefficient between the data distribution curve and the standard normal distribution curve is obtained, and the Spearman correlation coefficient is added to the first preset value and averaged to obtain the normalized Spearman correlation coefficient of the period; the inverse of the skewness, the inverse of the kurtosis and the normalized Spearman correlation coefficient of the period are added and averaged to obtain the distribution regularity of the period; If the stability level is less than or equal to the stability threshold, the upper quantile, lower quantile, interquartile range and skewness coefficient of the water level data of the same period of each year are used to calculate the normal range of the water level in that period, including: The upper quantile, lower quantile, interquartile range and skewness coefficient of the water level data in the same period of each year are used to calculate the upper and lower limits of the normal range of water level in that period; the range between the lower and upper limits is the normal range of water level in that period.

2. The intelligent management and early warning system for water conservancy project monitoring data according to claim 1 is characterized in that: The box plot is used to process the water level data of the same period of each year to obtain the skewness coefficient and kurtosis coefficient of the period, including: The skewness coefficient and kurtosis coefficient of the period were obtained by eliminating abnormal water level data in the water level data of the same period of each year using the box plot.

3. The intelligent management and early warning system for water conservancy project monitoring data according to claim 1 is characterized in that: The fluctuation stability of the period is obtained based on the coefficient of variation of the water level data in the same period of each year and the number of abnormal water level data, including: The inverse of the coefficient of variation of the water level data in the same period of each year is obtained and normalized to obtain the inverse of the coefficient of variation; the number of water level data in the same period of each year is taken as the numerator, and the number of abnormal water level data in the same period of each year and the sum of the hyperparameters are taken as the denominator, the two are compared and normalized to obtain the abnormal proportion; the inverse of the coefficient of variation and the abnormal proportion are added and averaged to obtain the fluctuation stability of the period.

4. The intelligent management and early warning system for water conservancy project monitoring data according to claim 1 is characterized in that: The method of using the distribution regularity and fluctuation stability of the same period of each year to obtain the stability of the period includes: The weighted sum of the distribution regularity and fluctuation stability of the same period of each year is used to obtain the stability of the period.

5. The intelligent management and early warning system for water conservancy project monitoring data according to claim 1 is characterized in that: If the stability level is greater than the stability threshold, the upper quantile, lower quantile and interquartile range of the water level data for the same period of each year are used to calculate the normal range of the water level for that period, including: The abnormal water level data in the water level data of the same period of each year are eliminated by using the box plot, and the upper quantile, lower quantile and interquartile range of the period are obtained by using the remaining water level data in the period; the upper quantile of the period is added to the interquartile range of the preset multiple to obtain the upper limit value; the lower quantile of the period is subtracted from the interquartile range of the preset multiple to obtain the lower limit value; the range between the lower limit value and the upper limit value is the normal range of the water level in the period.

6. The intelligent management and early warning system for water conservancy project monitoring data according to claim 1 is characterized in that: The calculation formulas for the upper limit and the lower limit are: ， ， in, represents the upper limit of the normal range of water level in the ith period, Indicates the lower limit of the normal range of water level in the i-th period; It represents the upper quantile of the water level data in the i-th period of each year after eliminating the abnormal water level data; It represents the lower quantile of the water level data in the i-th period of each year after eliminating the abnormal water level data; represents the interquartile range of the water level data in the i-th period of each year after eliminating the abnormal water level data; e represents the natural constant; It represents the skewness coefficient of the water level data in the i-th period of each year after the abnormal water level data are eliminated through the box plot.

7. The intelligent management and early warning system for water conservancy project monitoring data according to claim 1 is characterized in that: The water level early warning according to the normal range of water level in each period includes: The normal range of water level corresponding to the current moment is determined according to the time period to which the current moment belongs. If the water level at the current moment is within the normal range of water level corresponding to the current moment, no warning is issued. If the water level at the current moment is not within the normal range of water level corresponding to the current moment, a warning is issued.

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