A water level and flow rate intelligent alignment analysis system

Abstract

This application relates to the field of water level data measurement technology, specifically to an intelligent water level and flow rate alignment analysis system. The system includes: acquiring water level data sequences from each monitoring station for each sampling period; obtaining the water level accuracy of each monitoring station in the current sampling period based on the positional characteristics and total number of adjacent peaks in the water level data sequences; acquiring each high and low peak group in each water level data sequence; correcting the water level accuracy of each monitoring station in the current sampling period based on the regularity of the peak data and the difference in the time length of adjacent high and low peak groups; and, combined with the positional characteristics of each high and low peak group in the water level data sequences, obtaining the data usability of each monitoring station in the current sampling period, thereby selecting monitoring stations suitable for water level and flow rate alignment. This application improves the accuracy of water level and flow rate alignment analysis by selecting more accurate water level data.

Description

Technical Field

[0001] This application relates to the field of water level data measurement technology, specifically to an intelligent water level and flow rate alignment analysis system. Background Technology

[0002] The water level-discharge curve is used to describe the relationship between the water level at the basic cross section of a monitoring station and the flow rate through that cross section. How to properly handle the relationship between water level and flow rate is an important issue in the planning, design, and construction of water conservancy and hydropower projects. In particular, in recent years, the requirements for the timeliness of data compilation have become increasingly stringent, and the effective and efficient calibration of the water level-discharge curve is a powerful supporting means.

[0003] Traditional methods for determining the water level-discharge relationship curve require first determining the fitting line type based on the characteristics of the monitoring station, and then determining the fitting parameters based on multiple measured water levels and corresponding discharge data at the station cross-section, thereby determining the specific mathematical equation for the water level-discharge relationship. However, the above method often treats the measured water level value as the true water level value during calculation, but ignores the errors caused by environmental factors to the measured water level value. For example, wind and the passage of ships will cause water level fluctuations, but these fluctuations are not caused by the actual rise in water level. If the measured water level value is directly used as the true water level value, it will lead to low accuracy in subsequent water level-discharge alignment analysis. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide an intelligent water level and flow rate alignment analysis system, the specific technical solution of which is as follows:

[0005] This application proposes an intelligent water level and flow rate alignment analysis system, the system comprising:

[0006] Data acquisition module: Groups every two monitoring stations together and acquires the water level data sequence of each monitoring station for each sampling period;

[0007] Water level accuracy assessment module: Based on the difference in the positional order of adjacent peaks in the water level data sequence of each monitoring station within the current sampling period, the difference in the positional order of adjacent peaks in the water level data sequence of another monitoring station in the same group, and the difference in the positional order of adjacent peaks in the water level data sequences of other monitoring stations in each group, the same group peak interval value and inter-group peak interval value of each monitoring station within the current sampling period are obtained respectively. Combined with the proportion of the total number of peaks in the water level data sequence of each monitoring station within the current sampling period, the water level accuracy of each monitoring station in the current sampling period is obtained.

[0008] Water level accuracy correction module: acquires each high and low peak group in each water level data sequence; corrects the water level accuracy of each monitoring station in the current sampling period based on the regularity of the peak data in the water level data sequence of each monitoring station and the difference in the time length of adjacent high and low peak groups;

[0009] Water level and flow rate alignment module: Based on the difference in the average position of each high and low peak group in the water level data sequence of each monitoring station in the current sampling period and the previous sampling period, as well as the difference in the average position of each high and low peak group in the water level data sequence of each monitoring station in the current sampling period and other monitoring stations in the same group, the false water level of each monitoring station in the current sampling period is obtained. Combined with the corrected water level accuracy in the current sampling period, the data usability of each monitoring station in the current sampling period is obtained. Then, the monitoring stations that can be used for water level and flow rate alignment analysis are selected, thereby aligning the water level and flow rate.

[0010] Preferably, the specific process of obtaining the peak interval values ​​within the same group and the peak interval values ​​between groups for each monitoring station within the current sampling period is as follows:

[0011] Obtain all peaks in the water level data sequence of each monitoring station in each sampling period;

[0012] Calculate the peak interval value of the same group for the i-th monitoring station within the current sampling period: In the formula, This represents the peak interval value of the same group at the i-th monitoring station within the current sampling period. , These represent the positional order of the j-th and (j+1)-th peaks in the water level data sequence of the i-th monitoring station within the current sampling period. , , respectively, are the positional order of the j-th and j+1-th peaks in the water level data sequence of another monitoring station in the same group as the i-th monitoring station within the current sampling period, and J is the minimum value among the total number of peaks in the water level data sequence of the i-th monitoring station and the monitoring stations in the same group within the current sampling period;

[0013] Calculate the inter-group peak interval value of the i-th monitoring station within the current sampling period: In the formula, This represents the inter-group peak interval value for the i-th monitoring station within the current sampling period. , These represent the positional order of the j-th and (j+1)-th peaks in the water level data sequence of the i-th monitoring station within the current sampling period. , Let be the position of the j-th and (j+1)-th peaks in the water level data sequence of the q-th monitoring station (excluding the i-th monitoring station and its group of monitoring stations) within the current sampling period, respectively. Let Q be the total number of monitoring stations (excluding the i-th monitoring station and its group of monitoring stations), and P be the minimum value among the total number of peaks in the water level data sequences of the i-th and q-th monitoring stations within the current sampling period.

[0014] Preferably, the formula for calculating the accuracy of water levels at each monitoring station in the current sampling period is: In the formula, To determine the accuracy of the water level at the i-th monitoring station in the current sampling period. This represents the peak interval value of the same group at the i-th monitoring station within the current sampling period. This represents the inter-group peak interval value for the i-th monitoring station within the current sampling period. Let be the average fluctuation frequency of the i-th monitoring station and its group of monitoring stations; where the fluctuation frequency of each monitoring station is the ratio of the total number of peaks in the water level data sequence of each monitoring station to the total number of data within the current sampling period.

[0015] Preferably, the specific process of obtaining each high and low peak group in each water level data sequence is as follows: the peaks in each water level data sequence that are greater than or equal to a preset segmentation threshold are recorded as large peaks, and the rest are recorded as small peaks; each small peak in each water level data sequence is grouped with the single large peak that is closest to its sampling time as a group, which is called each high and low peak group of each water level data sequence.

[0016] Preferably, the specific process for correcting the accuracy of water levels at each monitoring station in the current sampling period is as follows:

[0017] Based on the regularity of peak data in the water level data sequence of each monitoring station in the current sampling period and the difference in the time length of adjacent high and low peak groups, the accuracy correction value of each monitoring station in the current sampling period is obtained.

[0018] The formula for calculating the corrected accuracy of water levels at each monitoring station during the current sampling period is as follows: In the formula, The corrected water level accuracy of the i-th monitoring station in the current sampling period. To determine the accuracy of the water level at the i-th monitoring station in the current sampling period. This is the accuracy correction value for the i-th monitoring station in the current sampling period. This is the normalization function.

[0019] Preferably, the formula for calculating the accuracy correction value of each monitoring station in the current sampling period is: In the formula, This is the accuracy correction value for the i-th monitoring station in the current sampling period. This represents the ApEn value of the peak data sequence of the i-th monitoring station in the current sampling period. , , respectively, are the time lengths of the p-th and p+1-th high and low peak groups in the water level data sequence of the i-th monitoring station in the current sampling period, where P is the total number of high and low peak groups in the water level data sequence of the i-th monitoring station; where the peak data sequence of the i-th monitoring station in the current sampling period refers to the sequence composed of all peak data in the water level data sequence of the i-th monitoring station in the current sampling period arranged according to their sampling order.

[0020] Preferably, the formula for calculating the false water level of each monitoring station in the current sampling period is: In the formula, The false alarm rate of the water level at the i-th monitoring station in the current sampling period. , These are the water level data sequences of the i-th monitoring station in the current sampling period and the previous sampling period, respectively. The average rank of the high and low peak groups, where L is the minimum total number of high and low peak groups in the water level data sequence of the i-th monitoring station in the current sampling period and the previous sampling period. , These are the water level data sequences of the i-th and q-th monitoring stations in the current sampling period. The average rank of the high and low peak groups is given by Q, which is the total number of monitoring stations excluding the i-th monitoring station and its group members. The average rank of the high and low peak groups is the average rank of the large and small peaks in the high and low peak groups.

[0021] Preferably, the formula for calculating the data availability of each monitoring station in the current sampling period is: ; The data availability of the i-th monitoring station in the current sampling period. The corrected water level accuracy of the i-th monitoring station in the current sampling period. The false reading of the water level at the i-th monitoring station in the current sampling period.

[0022] Preferably, the specific process of selecting monitoring stations that can be used for water level and flow rate alignment analysis is as follows: when the data availability of each monitoring station is greater than or equal to a preset threshold, it is determined that the water level data measured by the corresponding monitoring station can be used for water level and flow rate alignment analysis.

[0023] Preferably, the specific process of determining the water level and flow rate is as follows: obtain the flow rate data, cross-sectional area, and riverbed roughness at each monitoring station location that can be used for water level and flow rate alignment analysis; combine the water level data of each monitoring station; and use the water level and flow rate relationship alignment method to draw the water level and flow rate relationship curve.

[0024] This application has the following beneficial effects:

[0025] This application analyzes the water level change characteristics of each monitoring station in the target river area under the influence of wind or ships to construct the water level accuracy of each monitoring station, quantifying the accuracy of the water level data. By analyzing the water level data characteristics during water wave rebound and the water level change characteristics caused by sewage pipes, the water level accuracy is corrected to obtain the data usability of each monitoring station, more comprehensively and accurately reflecting the accuracy of the water level data. Furthermore, water level data from monitoring stations suitable for water level-discharge alignment analysis are selected, ensuring the accuracy of water level data in the subsequent water level-discharge relationship curve plotting process, thus improving the precision of the water level-discharge relationship curve. This solves the problem that current traditional methods of processing water level-discharge data contain errors due to environmental factors, resulting in low accuracy in subsequent determination of water level-discharge relationships. Attached Figure Description

[0026] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0027] Figure 1 A block diagram of a water level and flow rate intelligent alignment analysis system provided in one embodiment of this application;

[0028] Figure 2 The water level data distribution curves of six monitoring stations in a target river area within a single sampling period are provided as an embodiment of this application;

[0029] Figure 3 The calculation results of the water level accuracy of monitoring stations 1-6 within the current sampling period provided in one embodiment of this application;

[0030] Figure 4 A flowchart illustrating the acquisition of data availability for each monitoring station during the current sampling period, provided as an embodiment of this application;

[0031] Figure 5 A graph showing the relationship between the false water level of monitoring stations 1-6 and the corrected true water level during the current sampling period, provided as an embodiment of this application;

[0032] Figure 6 This is the result of comparing the data availability of monitoring stations 1-6 with a preset threshold during the current sampling period, as provided in one embodiment of this application. Detailed Implementation

[0033] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the intelligent water level and flow rate alignment analysis system proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, 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 one of ordinary skill in the art to which this application pertains.

[0035] The following description, in conjunction with the accompanying drawings, details the specific scheme of the intelligent water level and flow rate alignment analysis system provided in this application.

[0036] Please see Figure 1 The diagram illustrates a block diagram of an intelligent water level and flow rate alignment analysis system according to an embodiment of this application. The system includes: a data acquisition module, a water level accuracy assessment module, a water level accuracy correction module, and a water level and flow rate alignment module.

[0037] Data acquisition module: It groups every two monitoring stations together and acquires the water level data sequence of each monitoring station in each sampling period.

[0038] Multiple water level and flow monitoring stations are set up in the target river area, with two stations forming a group. The interval between stations within a group is M, and the interval between groups is N. Each monitoring station collects water level data in the target river area every time interval T, with a sampling period of 1 day. The water level data sequence of each monitoring station for each sampling period is constructed according to the order of collection time. In this embodiment, M=20m, N=500m, and T=1.5s. Implementers can adjust these settings according to actual conditions.

[0039] Water level accuracy assessment module: Based on the difference in the positional order of adjacent peaks in the water level data sequence of each monitoring station within the current sampling period, the difference in the positional order of adjacent peaks in the water level data sequence of another monitoring station in the same group, and the difference in the positional order of adjacent peaks in the water level data sequences of other monitoring stations in each group, the same group peak interval value and inter-group peak interval value of each monitoring station within the current sampling period are obtained respectively. Combined with the proportion of the total number of peaks in the water level data sequence of each monitoring station within the current sampling period, the water level accuracy of each monitoring station in the current sampling period is obtained.

[0040] Because many factors can affect water level fluctuations in river areas, water level measurements cannot always be performed under ideal conditions. When the impact is relatively weak, such as when the fluctuation range of water level measurement data is within a few centimeters, it usually has little impact on the overall water level judgment, and the data can be directly averaged. However, when the fluctuation range of water level measurement data is in the decimeter or even meter range, it is necessary to remove outlier data to ensure the accuracy of the measurement.

[0041] Many factors influence water levels, such as precipitation, wind, and waves generated by passing ships. These factors can be categorized into real and spurious influencing factors. Real influencing factors, like precipitation, cause rapid rises or falls in river levels, resulting in measured water levels that vary significantly from historical data, but are still considered normal and accurate. Spurious influencing factors, such as wind and passing ships, also cause rapid changes in water levels. The extent of their impact varies depending on wind intensity, ship size, and distance from the monitoring station. Since these factors do not actually have a substantial impact on water levels, water level data affected by spurious factors should be considered spurious and discarded.

[0042] Regarding spurious influencing factors, wind and the passage of ships create ripples or waves on the water surface, causing fluctuations in the water level measured by monitoring stations. When these waves reach the shore, the water crashes against the shore, creating ripples or waves originating from the shore—a reflection of water waves. This results in the measured water level data showing a rapid second change after an initial fluctuation. The stronger the wind and the closer the ships are to the shore, the higher the intensity and frequency of the generated ripples and waves. Since the ripples or waves generated by wind and ships have a clear direction, their intensity and propagation speed are relatively similar. Therefore, the intervals between fluctuations in water level data collected by monitoring stations within the same group are somewhat similar. However, because the influence range of ship waves is limited, the fluctuations in data collected by different groups of monitoring stations also show significant differences. Specifically, in the water level data, each location with a large fluctuation is followed by a smaller fluctuation. Furthermore, in the water level data collected by the same group of monitoring stations, the time interval between the locations of fluctuations is approximately the same, while the fluctuation amplitude and interval of the water level data collected by different groups of monitoring stations vary considerably.

[0043] For real-world influencing factors, such as precipitation, water level data can change significantly. However, slight fluctuations in the water surface are unavoidable, so the collected water level data will still exhibit some volatility. Real-world influencing factors typically cause changes in the water level across the entire river region. While the fluctuations in water level data collected from different monitoring stations may vary, the overall magnitude and rate of rise in water level data are similar. Specifically, the mean change magnitude and rate of change in water level data from different groups of monitoring stations are similar.

[0044] Based on the above analysis, the peak and trough detection algorithm obtains all peaks in the water level data sequence of each monitoring station in each sampling period. The peak and trough detection algorithm is a well-known technique, and its specific process will not be elaborated further. This embodiment uses water level monitoring data from six monitoring stations in the target river area as an example to perform data calculation and deduction. Figure 2 The diagram shows the water level data distribution curves of six monitoring stations in the target river area during a single sampling period, with the red dots representing the peaks of the water level data at each monitoring station.

[0045] In this embodiment, the peak interval value of the same group of monitoring stations within the current sampling period is denoted as... Its specific expression is: In the formula, This represents the peak interval value of the same group at the i-th monitoring station within the current sampling period. , These represent the positional order of the j-th and (j+1)-th peaks in the water level data sequence of the i-th monitoring station within the current sampling period. , , respectively, are the positional order of the j-th and j+1-th peaks in the water level data sequence of another monitoring station in the same group as the i-th monitoring station within the current sampling period, and J is the minimum value among the total number of peaks in the water level data sequence of the i-th monitoring station and the monitoring stations in the same group within the current sampling period.

[0046] The larger the value, the smaller the approximation of the peak interval between the i-th monitoring station and the monitoring stations in the same group within the current sampling period. This indicates that the i-th monitoring station in the current sampling period is less affected by false influencing factors, and the higher the authenticity of the water level data.

[0047] The peak interval values ​​of all monitoring stations in the current sampling period are calculated. In this embodiment, the calculated peak interval values ​​of monitoring stations 1 to 6 are 9.7, 5, 8.3, 7.9, 6.2 and 7.5, respectively.

[0048] In this embodiment, the inter-group peak interval value of the i-th monitoring station within the current sampling period is denoted as... Its specific expression is: In the formula, This represents the inter-group peak interval value for the i-th monitoring station within the current sampling period. , These represent the positional order of the j-th and (j+1)-th peaks in the water level data sequence of the i-th monitoring station within the current sampling period. , Let be the position of the j-th and (j+1)-th peaks in the water level data sequence of the q-th monitoring station (excluding the i-th monitoring station and its group of monitoring stations) within the current sampling period, respectively. Let Q be the total number of monitoring stations (excluding the i-th monitoring station and its group of monitoring stations), and P be the minimum value among the total number of peaks in the water level data sequences of the i-th and q-th monitoring stations within the current sampling period.

[0049] The smaller the value, the greater the approximation of the peak data interval between the i-th monitoring station and other monitoring stations in the current sampling period. This indicates that the i-th monitoring station in the current sampling period is less affected by false influencing factors, and the higher the authenticity of the water level data.

[0050] The inter-group peak interval values ​​of all monitoring stations within the current sampling period are calculated. In this embodiment, the calculated inter-group peak interval values ​​of monitoring stations 1 to 6 are 10.7, 5.4, 8.2, 8.5, 6.9, and 8.4, respectively.

[0051] As a preferred implementation, the water level accuracy of each monitoring station in the current sampling period is obtained based on the peak interval between the same group and the peak interval between groups of each monitoring station in the current sampling period, as well as the proportion of the total number of peaks in the water level data sequence of each monitoring station in the current sampling period. This accuracy is used to characterize the accuracy of the water level data of each monitoring station in the current sampling period.

[0052] In this embodiment, the accuracy of the water level at the i-th monitoring station in the current sampling period is denoted as . Its specific expression is: In the formula, To determine the accuracy of the water level at the i-th monitoring station in the current sampling period. This represents the peak interval value of the same group at the i-th monitoring station within the current sampling period. This represents the inter-group peak interval value for the i-th monitoring station within the current sampling period. Let be the average fluctuation frequency of the i-th monitoring station and its group of monitoring stations; where the fluctuation frequency of each monitoring station is the ratio of the total number of peaks in the water level data sequence of each monitoring station to the total number of data within the current sampling period.

[0053] The smaller the value, the greater the similarity of the peak intervals of the water level data collected by the monitoring stations in the same group, the smaller the similarity of the peak intervals of the water level between different groups, and the greater the fluctuation frequency of the water level data of the monitoring stations in the same group. Then, the more the characteristics of the water level data collected by the i-th monitoring station in the current sampling period match the characteristics of the water level data when the wind or ships pass by, the greater the degree of interference of the monitoring station by false influencing factors, and the lower the authenticity of the water level data.

[0054] Furthermore, the accuracy of water levels at all monitoring stations within the current sampling period is calculated, yielding the accuracy of water levels at all monitoring stations within the current sampling period. The calculation results of the accuracy of water levels at monitoring stations 1 to 6 within the current sampling period are as follows: Figure 3 As shown in the figure. The calculation results show that the water level accuracy of monitoring station 3 is the highest, while that of monitoring station 2 is the lowest.

[0055] Water level accuracy correction module: acquires each high and low peak group in each water level data sequence; and corrects the water level accuracy of each monitoring station in the current sampling period based on the regularity of the peak data in the water level data sequence of each monitoring station and the difference in the time length of adjacent high and low peak groups.

[0056] Furthermore, due to false influencing factors, ripples reaching the shore will cause water wave rebounds. These rebounding waves, due to energy loss, are usually weaker than the original arriving waves. Therefore, the continuous arrival of ripples at the shore and the constant reflections at the shore will result in each larger wave peak in the measured water level data being closely followed by a smaller wave peak. Since the ripples are caused by the same factors (i.e., wind or ships), there is a certain degree of approximation between the wave peaks. Specifically, the wave peaks in the water level data show approximately equal intervals between large and small wave peaks, and the overall wave peak values ​​exhibit a high degree of regularity. To characterize this feature, all wave peak data in the water level data sequence of a single monitoring station within a single sampling period are used as input. Using the Otsu thresholding method, the output is a wave peak data segmentation threshold, which is recorded as the preset segmentation threshold. When the wave peak data is greater than or equal to the preset segmentation threshold, it is considered a large wave peak; otherwise, it is considered a small wave peak. Within a single sampling period, the single small peak in the water level data sequence collected by a single monitoring station that is closest to its sampling time is grouped together, called the high and low peak group of that water level data sequence. Then, all the peak data in the water level data sequences of each monitoring station are arranged according to their sampling order to construct the peak data sequence of each monitoring station.

[0057] As a preferred implementation, based on the regularity of the peak data in the water level data sequence of each monitoring station in the current sampling period and the difference in the time length of adjacent high and low peak groups, the accuracy correction value of each monitoring station in the current sampling period is obtained and used to correct the accuracy of the water level of each monitoring station.

[0058] In this embodiment, the accuracy correction value of the i-th monitoring station in the current sampling period is denoted as... Its specific expression is: In the formula, This is the accuracy correction value for the i-th monitoring station in the current sampling period. This represents the ApEn value of the peak data sequence of the i-th monitoring station in the current sampling period. , Let A and B be the time lengths of the p-th and p+1-th high and low peak groups in the water level data sequence of the i-th monitoring station in the current sampling period, respectively, and let P be the total number of high and low peak groups in the water level data sequence of the i-th monitoring station. The calculation of the ApEn value is a well-known technique, and the specific process will not be elaborated further.

[0059] The formula for calculating the corrected accuracy of water levels at each monitoring station during the current sampling period is as follows: In the formula, The corrected water level accuracy of the i-th monitoring station in the current sampling period. To determine the accuracy of the water level at the i-th monitoring station in the current sampling period. This is the accuracy correction value for the i-th monitoring station in the current sampling period. This is the normalization function.

[0060] The meaning of this relationship is: the smaller the ApEn value of the peak data sequence in the water level data sequence of the i-th monitoring station, and the closer the sampling time interval between adjacent high and low peak groups, the more regular the water level peak data collected by the monitoring station is, the stronger the possibility of being affected by false influencing factors, and the lower the water level accuracy.

[0061] Furthermore, the corrected accuracy of the water level at each monitoring station in the current sampling period is calculated to obtain the corrected accuracy of the water level at all monitoring stations in the current sampling period. In this embodiment, the normalized accuracy correction values ​​for monitoring stations 1 to 6 are 0.9760, 0.9489, 0.9906, 0.9746, 0.9622, and 0.9316, respectively; the corrected accuracy of the water level at monitoring stations 1 to 6 are 0.8218, 0.5225, 0.8668, 0.8487, 0.5753, and 0.7688, respectively.

[0062] Water level and flow rate alignment module: Based on the difference in the average position of each high and low peak group in the water level data sequence of each monitoring station in the current sampling period and the previous sampling period, as well as the difference in the average position of each high and low peak group in the water level data sequence of each monitoring station in the current sampling period and other monitoring stations in the same group, the false water level of each monitoring station in the current sampling period is obtained. Combined with the corrected water level accuracy in the current sampling period, the data usability of each monitoring station in the current sampling period is obtained. Then, the monitoring stations that can be used for water level and flow rate alignment analysis are selected, thereby aligning the water level and flow rate.

[0063] Furthermore, in urban river areas, many sewage pipe outlets may be located within the river system. This discharge of sewage can raise water levels to some extent, with the impact being more pronounced in the short term due to larger volumes of sewage discharged. Since these pipes are typically situated along riverbanks, the falling water during sewage discharge creates significant ripples and waves on the river surface. These waves, upon reaching the banks, also generate reflected water waves, ultimately causing the water level data collected by monitoring stations to resemble the characteristics of false influencing factors. Calculations based solely on the water level accuracy correction module may misclassify sewage discharge as a false influencing factor, leading to misjudgments of the river's water level. Therefore, further analysis is needed.

[0064] Under normal circumstances, urban sewage discharge is governed by regulations. According to the "Administrative Measures for the Permitting of Urban Sewage Discharge into Drainage Pipelines," after obtaining a discharge permit, relevant dischargers must discharge sewage according to the discharge category, total amount, time limit, location and number of discharge outlets, and main pollutants and their concentrations specified in the discharge permit. Therefore, the discharge time of sewage pipes in river areas has a clear periodicity. For example, the peak water consumption periods for urban residents are 8-9 am, 12-1 pm, and 6-9 pm. The peak discharge periods for some domestic sewage also fall within the corresponding time periods. Therefore, the fluctuations in water level data measured by monitoring stations also exhibit a certain periodicity (i.e., the occurrence of high and low peak groups is periodic). In addition, sewage pipes may be installed at various locations in the river area, all of which will affect the local river area. That is, in the entire river area, several sets of monitoring stations may show similar periodicity in the water level data measured. For example, sewage pipes 1, 2, and 3 in the river area are located in the river area monitored by monitoring stations in groups 1, 2, and 3, respectively. Since the discharge time of sewage pipes 1 and 3 is the same (it may be because their sewage source is the same discharger, or the discharge time of the dischargers of these two pipes is the same), the periodicity of the water level data of monitoring stations in groups 1 and 3 is the same (that is, the time when the high and low peak groups appear in the water level data of the two groups of monitoring stations is close).

[0065] As a preferred implementation, the water level falsehood of each monitoring station in the current sampling period is obtained based on the difference in the average position of each high and low peak group in the water level data sequence of each monitoring station in the current sampling period and the difference in the average position of each high and low peak group in the water level data sequence of each monitoring station in the current sampling period and the other monitoring stations in the same group. This is used to characterize the degree of falsehood of the water level data of each monitoring station in the current sampling period.

[0066] In this embodiment, the false water level reading of the i-th monitoring station in the current sampling period is denoted as... Its specific expression is: In the formula, The false alarm rate of the water level at the i-th monitoring station in the current sampling period. , These are the water level data sequences of the i-th monitoring station in the current sampling period and the previous sampling period, respectively. The average rank of the high and low peak groups, where L is the minimum total number of high and low peak groups in the water level data sequence of the i-th monitoring station in the current sampling period and the previous sampling period. , These are the water level data sequences of the i-th and q-th monitoring stations in the current sampling period. The average rank of the high and low peak groups is given by Q, where Q is the total number of monitoring stations excluding the i-th monitoring station and its group members. It should be noted that the average rank of the high and low peak groups is the average of the ranks corresponding to the large and small peaks within the high and low peak groups.

[0067] The meaning of this relationship is as follows: Sewage discharge times in river areas exhibit a certain periodicity. Therefore, if a monitoring station is affected by sewage discharge, the fluctuations at that station should be similar to those in historical data, and the likelihood of high and low peaks appearing in the water level data measured by different monitoring stations should be greater. Conversely, if the data does not show these fluctuations, it indicates that the water level data at that station is not affected by sewage discharge, but may be false data. In other words, if the sampling time interval between high and low peaks in the data collected by a single monitoring station in the current sampling period and the data collected in the previous sampling period is larger, and the difference in the timing of high and low peaks between the data collected by other monitoring stations in the current sampling period is greater, then... The larger the value, the more likely the water level data is to be falsified.

[0068] The false water level values ​​of all monitoring stations within the current sampling period are calculated. In this embodiment, the calculated false water level values ​​of monitoring stations 1 to 6 are 0.0836, 0.0732, 0.0836, 0.0905, 0.0832, and 0.818, respectively.

[0069] As a preferred implementation, the data availability of each monitoring station in the current sampling period is obtained based on the false water level readings of each station and the corrected true water level readings within the current sampling period. This data is used to characterize the usability of the water level data from each monitoring station in the current sampling period for water level-discharge alignment analysis. The flowchart for obtaining the data availability of each monitoring station in the current sampling period is shown below. Figure 4 As shown.

[0070] In this embodiment, the data availability of the i-th monitoring station in the current sampling period is denoted as... Its specific expression is: ; The data availability of the i-th monitoring station in the current sampling period. The corrected water level accuracy of the i-th monitoring station in the current sampling period. The false reading of the water level at the i-th monitoring station in the current sampling period.

[0071] The meaning of this relationship is: the higher the authenticity and the lower the falsehood of the water level data collected by the i-th monitoring station, the more accurate the water level data measured by the monitoring station is, and the more it can be used in water level and flow rate alignment analysis.

[0072] At this point, the false water level readings and corrected water level readings of each monitoring station within the current sampling period can be obtained. Figure 5 This is a graph showing the relationship between the false water level readings of monitoring stations 1-6 and the corrected true water level readings during the current sampling period. From the colors of the monitoring stations in the graph, it is clear that the data availability of monitoring stations 3 and 4 is higher, while the data availability of monitoring station 2 is lower.

[0073] Furthermore, the data availability of each monitoring station within the current sampling period is calculated. Using all data availability values ​​as input, cross-validation is employed to output a data availability threshold, which is then recorded as the preset threshold. When the data availability of each monitoring station is greater than or equal to the preset threshold, it is determined that the water level data measured by the corresponding monitoring station can be used for water level-flow rate alignment analysis.

[0074] The availability of data from all monitoring stations within the current sampling period is compared with a preset threshold to obtain water level data from all monitoring stations within the current sampling period that can be used for water level-flow alignment analysis.

[0075] Figure 6 This is the result of comparing the data availability of monitoring stations 1-6 within the current sampling period with a preset threshold. In this embodiment, the preset threshold is 0.5. Figure 5It can be seen that only the data availability of monitoring station 2 is lower than the preset threshold. Therefore, among monitoring stations 1-6, except for the water level data of monitoring station 2 which is unavailable, the water level data of the other monitoring stations can be used for water level and flow rate alignment analysis.

[0076] Furthermore, flow data, cross-sectional area, and riverbed roughness at each monitoring station location that can be used for water level-flow alignment analysis are obtained. Combined with water level data from each monitoring station within the current sampling period that can be used for water level-flow alignment analysis, a water level-flow relationship curve is plotted using a water level-flow relationship alignment method, thereby improving the accuracy of the obtained water level-flow relationship curve. The water level-flow relationship alignment methods include: the single curve method, the time series method, the measured flow process line method, and the corrected water level method. Implementers can choose according to actual needs; this embodiment uses the single curve method.

[0077] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0078] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0079] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A water level and flow rate intelligent alignment analysis system, characterized in that, The system includes: Data acquisition module: Groups every two monitoring stations together and acquires the water level data sequence of each monitoring station for each sampling period; Water level accuracy assessment module: Based on the difference in the positional order of adjacent peaks in the water level data sequence of each monitoring station within the current sampling period, the difference in the positional order of adjacent peaks in the water level data sequence of another monitoring station in the same group, and the difference in the positional order of adjacent peaks in the water level data sequences of other monitoring stations in each group, the same group peak interval value and inter-group peak interval value of each monitoring station within the current sampling period are obtained respectively. Combined with the proportion of the total number of peaks in the water level data sequence of each monitoring station within the current sampling period, the water level accuracy of each monitoring station in the current sampling period is obtained. Water level accuracy correction module: acquires each high and low peak group in each water level data sequence; corrects the water level accuracy of each monitoring station in the current sampling period based on the regularity of the peak data in the water level data sequence of each monitoring station and the difference in the time length of adjacent high and low peak groups; Water level and flow rate alignment module: Based on the difference in the average position of each high and low peak group in the water level data sequence of each monitoring station in the current sampling period and the previous sampling period, as well as the difference in the average position of each high and low peak group in the water level data sequence of each monitoring station in the current sampling period and other monitoring stations in the same group, the false water level of each monitoring station in the current sampling period is obtained. Combined with the corrected water level accuracy in the current sampling period, the data availability of each monitoring station in the current sampling period is obtained. Then, the monitoring stations that can be used for water level and flow rate alignment analysis are selected, thereby aligning the water level and flow rate. The specific process for obtaining the peak interval values ​​within the same group and the peak interval values ​​between groups for each monitoring station within the current sampling period is as follows: Obtain all peaks in the water level data sequence of each monitoring station in each sampling period; Calculate the peak interval value of the same group for the i-th monitoring station within the current sampling period: In the formula, This represents the peak interval value of the same group at the i-th monitoring station within the current sampling period. , These represent the positional order of the j-th and (j+1)-th peaks in the water level data sequence of the i-th monitoring station within the current sampling period. , , respectively, are the positional order of the j-th and j+1-th peaks in the water level data sequence of another monitoring station in the same group as the i-th monitoring station within the current sampling period, and J is the minimum value among the total number of peaks in the water level data sequence of the i-th monitoring station and the monitoring stations in the same group within the current sampling period; Calculate the inter-group peak interval value of the i-th monitoring station within the current sampling period: In the formula, This represents the inter-group peak interval value for the i-th monitoring station within the current sampling period. , These represent the positional order of the j-th and (j+1)-th peaks in the water level data sequence of the i-th monitoring station within the current sampling period. , Let be the position of the j-th and (j+1)-th peaks in the water level data sequence of the q-th monitoring station (excluding the i-th monitoring station and its group of monitoring stations) within the current sampling period, respectively; Q is the total number of monitoring stations (excluding the i-th monitoring station and its group of monitoring stations); and P is the minimum value among the total number of peaks in the water level data sequences of the i-th and q-th monitoring stations within the current sampling period. The formula for calculating the accuracy of water levels at each monitoring station during the current sampling period is as follows: In the formula, To determine the accuracy of the water level at the i-th monitoring station in the current sampling period. This represents the peak interval value of the same group at the i-th monitoring station within the current sampling period. This represents the inter-group peak interval value for the i-th monitoring station within the current sampling period. Let be the average fluctuation frequency of the i-th monitoring station and its group of monitoring stations; where the fluctuation frequency of each monitoring station is the ratio of the total number of peaks in the water level data sequence of each monitoring station to the total number of data within the current sampling period.

2. The intelligent water level and flow rate alignment analysis system as described in claim 1, characterized in that, The specific process of obtaining each high and low peak group in each water level data sequence is as follows: the peaks in each water level data sequence that are greater than or equal to the preset segmentation threshold are recorded as large peaks, and the rest are recorded as small peaks; each small peak in each water level data sequence is grouped with the single large peak that is closest to its sampling time, and these are called each high and low peak group of each water level data sequence.

3. The intelligent water level and flow rate alignment analysis system as described in claim 1, characterized in that, The specific process for correcting the accuracy of water levels at each monitoring station in the current sampling period is as follows: Based on the regularity of peak data in the water level data sequence of each monitoring station in the current sampling period and the difference in the time length of adjacent high and low peak groups, the accuracy correction value of each monitoring station in the current sampling period is obtained. The formula for calculating the corrected accuracy of water levels at each monitoring station during the current sampling period is as follows: In the formula, The corrected water level accuracy of the i-th monitoring station in the current sampling period. To determine the accuracy of the water level at the i-th monitoring station in the current sampling period. This is the accuracy correction value for the i-th monitoring station in the current sampling period. This is the normalization function.

4. The intelligent water level and flow rate alignment analysis system as described in claim 3, characterized in that, The formula for calculating the accuracy correction value of each monitoring station in the current sampling period is as follows: In the formula, This is the accuracy correction value for the i-th monitoring station in the current sampling period. This represents the ApEn value of the peak data sequence of the i-th monitoring station in the current sampling period. , , respectively, are the time lengths of the p-th and p+1-th high and low peak groups in the water level data sequence of the i-th monitoring station in the current sampling period, where P is the total number of high and low peak groups in the water level data sequence of the i-th monitoring station; where the peak data sequence of the i-th monitoring station in the current sampling period refers to the sequence composed of all peak data in the water level data sequence of the i-th monitoring station in the current sampling period arranged according to their sampling order.

5. The intelligent water level and flow rate alignment analysis system as described in claim 1, characterized in that, The formula for calculating the false water level reading of each monitoring station in the current sampling period is as follows: In the formula, The false alarm rate of the water level at the i-th monitoring station in the current sampling period. , These are the water level data sequences of the i-th monitoring station in the current sampling period and the previous sampling period, respectively. The average rank of the high and low peak groups, where L is the minimum total number of high and low peak groups in the water level data sequence of the i-th monitoring station in the current sampling period and the previous sampling period. , These are the water level data sequences of the i-th and q-th monitoring stations in the current sampling period. The average rank of the high and low peak groups is given by Q, which is the total number of monitoring stations excluding the i-th monitoring station and its group members. The average rank of the high and low peak groups is the average rank of the large and small peaks in the high and low peak groups.

6. The intelligent water level and flow rate alignment analysis system as described in claim 1, characterized in that, The formula for calculating the data availability of each monitoring station in the current sampling period is as follows: ; The data availability of the i-th monitoring station in the current sampling period. The corrected water level accuracy of the i-th monitoring station in the current sampling period. The false reading of the water level at the i-th monitoring station in the current sampling period.

7. The intelligent water level and flow rate alignment analysis system as described in claim 1, characterized in that, The specific process for selecting monitoring stations that can be used for water level and flow rate alignment analysis is as follows: when the data availability of each monitoring station is greater than or equal to a preset threshold, it is determined that the water level data measured by the corresponding monitoring station can be used for water level and flow rate alignment analysis.

8. The intelligent water level and flow rate alignment analysis system as described in claim 1, characterized in that, The specific process for determining the water level and flow rate is as follows: obtain the flow rate data, cross-sectional area, and riverbed roughness at each monitoring station location that can be used for water level and flow rate alignment analysis; combine the water level data of each monitoring station; and use the water level and flow rate relationship alignment method to draw the water level and flow rate relationship curve.

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