Method, device, storage medium and electronic equipment for acquiring flood season pollutant flux

Abstract

The present application relates to a kind of methods for obtaining flood season pollutant flux, device, storage medium and electronic equipment, comprising: the data sequence of collection is preprocessed, pre-processing includes the interpolation to the data of the continuous number of data sequence marked as missing not more than missing threshold;Determine flood event of flood season based on pre-processed data sequence;For each flood event, according to the flood peak position point of the flood event, it is divided into rising section and recession section, lag index is calculated based on the data corresponding to rising section and recession section, lag type is determined according to lag index;From pre-processed data sequence, the target data of the continuous number of missing marked more than missing threshold is obtained, according to the lag type corresponding to target data and the monitoring data of corresponding flood event are reconstructed, and the reconstructed data sequence is obtained, based on reconstructed data sequence, obtain flood season total flux and the contribution ratio of the pollutant flux of each flood event to flood season total flux. The accuracy of flux acquisition can be improved.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and in particular to a method, apparatus, storage medium, and electronic device for obtaining pollutant flux during the flood season. Background Technology

[0002] River pollutant flux (load) measurement is a fundamental task in water environment management. It involves using low-frequency (usually daily, weekly, or monthly) manually sampled water quality data and employing methods such as average concentration method, proportional method (ratio estimation method), regression method (such as load estimation LOADEST), and weighted regression method such as weighted regression on time, discharge, and season (WRTDS) to extend the limited sampled water quality data into continuous flux, thereby measuring river pollutant flux.

[0003] With the development of hyperspectral-full-spectrum intelligent monitoring technology for water quality and quantity, manual sampling can be effectively replaced, enabling the acquisition frequency of water quality and flow data to reach the minute level. However, the pollutant flux calculation methods in related technologies are mainly designed for low-frequency sampling data and fail to fully utilize the advantages of high-resolution monitoring data in capturing the short-term heavy rainfall-runoff response process during the flood season. This results in low utilization efficiency of high-resolution monitoring data and low accuracy of the calculated pollutant flux. Secondly, since the flood season usually includes multiple independent flood events, the pollutant flux calculation methods lack automated methods to identify the boundaries of each flood event and perform separate calculations and summations. Finally, in actual operation, the concentration sensors of high-frequency monitoring equipment are easily affected by factors such as sudden changes in turbidity and maintenance windows, which can easily lead to periodic missing data. The simple time series interpolation method used in the calculation method does not consider the systematic difference (i.e., lag phenomenon) in the concentration-flow relationship between the rising and receding phases of the same flood event. This results in large errors in the reconstruction of water quality concentration during the missing data period and the pollutant flux calculation results based on the reconstructed water quality concentration, leading to low accuracy. Summary of the Invention

[0004] In view of this, the present invention provides a method, apparatus, storage medium and electronic device for obtaining pollutant flux during the flood season.

[0005] Specifically, the present invention is achieved through the following technical solution:

[0006] According to a first aspect of the present invention, a method for obtaining pollutant flux during the flood season is provided, the method comprising:

[0007] The collected flood season water quality and quantity monitoring data sequence is preprocessed. The preprocessing includes interpolation calculations performed on water quality and quantity monitoring data in the flood season water quality and quantity monitoring data sequence that are marked as missing for no more than a preset missing threshold, using a time-based linear interpolation method.

[0008] Based on the preprocessed flood season water quality and quantity monitoring data sequence, the flood events during the flood season and the start and end times of each flood event are determined.

[0009] For each flood event, the flood event is divided into a rising phase and a receding phase based on the location of the flood peak and the start and end times of the flood event. Based on the water quality and quantity monitoring data corresponding to the rising and receding phases, the lag index of the flood event is calculated, and the lag type of pollutant migration of the flood event is determined based on the lag index.

[0010] From the pre-processed flood season water quality and quantity monitoring data sequence, target water quality concentration data with a consecutive number of missing measurements exceeding the missing measurement threshold is obtained. Based on the lag type corresponding to the target water quality and quantity concentration data and the water quality and quantity monitoring data of the corresponding flood event, the target water quality concentration data is reconstructed.

[0011] Based on the preprocessed flood season water quality and quantity monitoring data sequence and the reconstructed target water quality concentration data, a water quality and quantity reconstructed data sequence is obtained. Based on the water quality and quantity reconstructed data sequence, the total flux during the flood season and the contribution ratio of pollutant flux of each flood event to the total flux during the flood season are obtained.

[0012] The method in this embodiment, after acquiring the water quality and quantity monitoring data sequence during the flood season, uses interpolation to complete the missing data for consecutive data with a missing number not exceeding the missing threshold. Based on the interpolated and completed data, flood events are identified, and the lag type of pollutant migration corresponding to each flood event is determined. For water quality and quantity monitoring data with a missing number exceeding the missing threshold, the missing data is reconstructed based on the lag type, the flood stage, and the corresponding flood event data. Because it considers the concentration-flow response patterns corresponding to each flood event during the flood season and the rise and fall phases of the flood events, it can obtain more accurate missing data. Therefore, the pollutant flux obtained based on the high-precision missing data has high accuracy and can be used in scenarios such as surface water environmental pollution load accounting and refined management of water pollution prevention and control, effectively improving the efficiency and accuracy of pollution source control.

[0013] According to a second aspect of the present invention, an apparatus for obtaining pollutant flux during the flood season is provided, the apparatus comprising:

[0014] The preprocessing module is used to preprocess the collected flood season water quality and quantity monitoring data sequence. The preprocessing includes interpolation operations performed using a time-based linear interpolation method on the water quality and quantity monitoring data in the flood season water quality and quantity monitoring data sequence where the number of consecutive missing data does not exceed a preset missing data threshold.

[0015] The event acquisition module is used to determine the flood events during the flood season and the start and end times of each flood event based on the flow data sequence in the preprocessed flood season water quality and quantity monitoring data sequence.

[0016] The type determination module is used to divide each flood event into a rising phase and a receding phase based on the location of the flood peak and the start and end times of the flood event. Based on the water quality and quantity monitoring data corresponding to the rising and receding phases, the module calculates the lag index of the flood event and determines the lag type of pollutant migration of the flood event based on the lag index.

[0017] The data reconstruction module is used to obtain target water quality concentration data marked as missing for more than the missing threshold from the pre-processed flood season water quality and quantity monitoring data sequence, and reconstruct the target water quality concentration data according to the lag type corresponding to the target water quality and quantity concentration data and the water quality and quantity monitoring data of the corresponding flood event.

[0018] The total flux acquisition module is used to obtain a water quality and quantity reconstructed data sequence based on the pre-processed flood season water quality and quantity monitoring data sequence and the reconstructed target water quality concentration data. Based on the water quality and quantity reconstructed data sequence, the module obtains the total flux during the flood season and the contribution ratio of pollutant flux of each flood event to the total flux during the flood season.

[0019] According to a third aspect of the present invention, a storage medium is provided having a computer program stored thereon, wherein the program, when executed by a processor, implements the steps of the method for obtaining the flux of pollutants during the flood season in any possible implementation of the first aspect.

[0020] According to a fourth aspect of the present invention, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for obtaining the pollutant flux during the flood season in any possible implementation of the first aspect. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating a method for obtaining pollutant flux during the flood season, provided by an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of a device for obtaining pollutant flux during the flood season, provided by an embodiment of the present invention.

[0025] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] When using hyperspectral-full-spectrum water quality and quantity collaborative high-frequency intelligent monitoring of water quality and quantity data, the current methods for calculating pollutant flux mainly target low-frequency sampling data, failing to fully utilize the advantages of high temporal resolution monitoring data (water quality and quantity monitoring data) in capturing the short-term heavy rainfall-runoff response process during the flood season. This results in low utilization efficiency of high temporal resolution monitoring data. Furthermore, there is a lack of automated methods for identifying the boundaries of each flood event and performing separate calculations and summations. Additionally, the simple time series interpolation method used does not consider the hydrological characteristic of a lag in the concentration-flow relationship between the rising and receding phases of the same flood event. This leads to significant errors in the reconstruction of water quality concentration data during periods of missing data and in the pollutant flux results calculated based on the reconstructed water quality concentration data.

[0028] This embodiment provides a method that can adapt to the characteristics of high temporal resolution monitoring data, automatically identify multiple flood events (flood processes), improve the reconstruction accuracy of missing water quality concentration data by utilizing hysteresis diagnosis results, and provide a method for calculating the pollutant flux during the flood season with quantifiable uncertainty. Under the condition of high temporal resolution water quality and quantity monitoring data, it can automatically identify multiple flood events during the flood season, diagnose the hysteresis characteristics of pollutant migration in each flood event, and use these hysteresis characteristics to improve the reconstruction accuracy of water quality concentration data during periods of missing water quality concentration, thereby obtaining a more accurate calculation result of the pollutant flux during the flood season with quantifiable uncertainty. Thus, it proposes a pollutant flux calculation method based on hysteresis diagnosis for high temporal resolution water quality and quantity monitoring data during the flood season, realizing automated calculation of pollutant flux (load) for the flood season, which can be used in scenarios such as surface water environmental pollution load accounting and refined management of water pollution prevention and control.

[0029] See Figure 1 This invention provides a method for obtaining pollutant flux during the flood season, which may include the following steps:

[0030] S101. Preprocess the collected flood season water quality and quantity monitoring data sequence. The preprocessing includes interpolation calculations performed on the water quality and quantity monitoring data in the flood season water quality and quantity monitoring data sequence that are marked as missing for no more than a preset missing threshold, using a time-based linear interpolation method.

[0031] In this embodiment, the water quality and quantity monitoring data sequence during the flood season includes, but is not limited to, water concentration data sequences and flow rate data sequences. As an optional embodiment, preprocessing includes outlier identification processing and short-term missing data interpolation processing for both the water concentration data sequence and the flow rate data sequence. Since flow rate data is relatively easy to obtain and less prone to missing data, this embodiment primarily reconstructs water quality and quantity monitoring data where the number of consecutive missing data points exceeds a pre-set missing data threshold.

[0032] In this embodiment, the original sequence data from high-frequency monitoring is used to construct a flood season water quality and quantity monitoring data sequence based on the flood season monitoring cycle and sampling frequency. If no water quality and quantity monitoring data is collected at the corresponding sampling time in the flood season water quality and quantity monitoring data sequence, it is marked. As an optional embodiment, outlier identification and missing data interpolation are performed on the flood season water quality and quantity monitoring data sequence. The flood season water quality and quantity monitoring data sequence includes a water quality concentration data sequence, denoted as C(t), which is the sequence of water quality concentration data observed at each discrete sampling time ti within the flood season monitoring cycle, arranged according to the chronological order of the sampling times, and a flow rate data sequence, denoted as Q(t). The processing logic for the water quality concentration data sequence and the flow rate data sequence is the same, and they are executed independently.

[0033] For outlier identification, this embodiment uses the sliding window standardized residual method for preprocessing. Considering that during flood season, water quality concentration (pollutant concentration) and flow rate can experience rapid and significant changes—for example, before and after the flood peak, water quality concentration or flow rate may change several times within tens of minutes—if a fixed judgment threshold or a global statistic calculated based on the entire monitoring period is used for outlier identification, such real, rapidly changing data may be mistakenly identified as sensor malfunction or interference noise and incorrectly removed, thus losing valuable real observation information. Therefore, as an optional embodiment, the preprocessing of the collected flood season water quality and quantity monitoring data sequence also includes:

[0034] A01, for the same sampling point within the flood season monitoring cycle, construct a water quality and quantity monitoring data sequence for the sampling point based on water quality and quantity monitoring data collected in chronological order or interpolated;

[0035] A02, traverse the water quality and quantity monitoring data sequence of the sampling points, and extract the i-th unlabeled water quality and quantity monitoring data;

[0036] A03. Based on the water quality and quantity monitoring data with a preset first value that is not marked before the i-th water quality and quantity monitoring data, the i-th water quality and quantity monitoring data, and the water quality and quantity monitoring data with a preset second value that is not marked after the i-th water quality and quantity monitoring data, obtain the median data and standard deviation of the local sliding window.

[0037] A04. Based on the i-th water quality and quantity monitoring data and the midpoint data in the local sliding window, obtain the i-th water quality and quantity error data;

[0038] A05, calculate the value of the i-th water quality and quantity error data and the standard deviation to obtain the i-th standardized residual;

[0039] A06. If the standardization residual of the i-th sample exceeds the standard deviation of the preset multiple, mark the i-th sample water quality and quantity monitoring data as missing from the water quality and quantity monitoring data sequence of the sampling point.

[0040] In this embodiment, as an optional implementation, the first preset value and the second preset value are equal, and a judgment method based on local sliding window statistics is adopted: For the target sampling point, taking water quality concentration data as an example, for each sampling time in the time series, valid observation data (unlabeled data) within 15 sampling times before and after that time (together with the sampling time itself, a total of 31 sampling times constitute a local sliding window) are taken. The median and standard deviation of the water quality concentration data within the local sliding window are calculated respectively. Then, the degree of deviation of the difference between the water quality concentration data at that sampling time and the median in the local sliding window from the standard deviation is calculated to obtain the standardized residual. Anomaly handling is performed based on the standardized residual and the standard deviation. Using the median as the central tendency estimate of the local sliding window is less sensitive to other outliers that may exist simultaneously within the local sliding window compared to the mean, and can provide a more robust judgment benchmark.

[0041] In this embodiment, each sampling point forms a water quality and quantity monitoring data sequence, and multiple sampling point water quality and quantity monitoring data sequences form the flood season water quality and quantity monitoring data. The standard deviation is the variance of each water quality and quantity monitoring data within a local sliding window.

[0042] In this embodiment, as an optional embodiment, the first value and the second value are set according to the following:

[0043] In the water quality and quantity monitoring data sequence at the sampling point, if the number of water quality and quantity monitoring data before the i-th water quality and quantity monitoring data is greater than or equal to the second value, then the first value is set to be equal to the second value. If the number of water quality and quantity monitoring data before the i-th water quality and quantity monitoring data is less than the second value, then the first value is set to be the number of water quality and quantity monitoring data before the i-th water quality and quantity monitoring data.

[0044] In this embodiment, for example, the second value is set to 15. If the number of water quality and quantity monitoring data before the i-th water quality and quantity monitoring data is 5, then the first value is 5 and the second value is 15. If the number of water quality and quantity monitoring data before the i-th water quality and quantity monitoring data is 25, then the first value is 15 and the second value is 15. Similarly, the first value is set to 15. At the end of the water quality and quantity monitoring data sequence at the sampling point, if the number of water quality and quantity monitoring data after the i-th water quality and quantity monitoring data is less than the first value, then the second value is set to the number of water quality and quantity monitoring data after the i-th water quality and quantity monitoring data.

[0045] In this embodiment, as another optional embodiment, the i-th water quality and quantity monitoring data can be deleted from the water quality and quantity monitoring data sequence of the sampling point to obtain the updated water quality and quantity monitoring data sequence of the sampling point. Based on the updated water quality and quantity monitoring data sequence of the sampling point, the step of extracting the i-th water quality and quantity monitoring data from the water quality and quantity monitoring data sequence of the sampling point can be performed.

[0046] In this embodiment, when the absolute value of the standardized residual at a certain sampling time exceeds a preset multiple (4 times in this embodiment) of the standard deviation, the observed value (water quality and quantity monitoring data value) at that sampling time is determined to be an outlier. This outlier is then removed from the water quality and quantity monitoring data sequence of the sampling point, and the water quality and quantity monitoring data value at that sampling time is marked as missing. Thus, outlier processing is based on a local sliding window. The median data (judgment benchmark) within the local sliding window is a local quantity dynamically calculated as the position of the local sliding window changes, rather than a fixed global quantity. Therefore, it can adaptively follow the fluctuation levels during floods, thereby identifying isolated, abruptly changing sampling times that significantly deviate from the background level of their local time period, without mistakenly affecting the water quality and quantity monitoring data of other rapidly changing sampling times that actually exist during the flood.

[0047] In this embodiment, as another optional embodiment, outlier identification can also employ other statistical or machine learning methods such as box plots based on interquartile range or isolated forests. This embodiment does not limit this approach.

[0048] In this embodiment, during the preprocessing of missing data interpolation, the missing data generated after removing the above-mentioned outliers, as well as the original missing data generated during the operation of the monitoring equipment due to brief power outages, communication interruptions, etc., are differentiated based on the number of consecutive missing data. If the duration of a single consecutive missing data does not exceed a preset missing data threshold, it is marked as water quality and quantity monitoring data where the number of consecutive missing data does not exceed the preset missing data threshold. For example, in this embodiment, the missing data threshold is set at 30 consecutive time points (sampling time), which is approximately 0.5 hours at a 1-minute sampling resolution of the monitoring equipment. It can be considered that within this relatively short time range, the changes in the water quality concentration and flow rate of the monitored water body are relatively limited. A time-based linear interpolation method can be used to directly fill in the missing data in the missing interval, which can meet the accuracy requirements for pollutant flux measurement and has high processing efficiency. If the continuous missing duration exceeds the preset missing threshold, the water state may undergo complex nonlinear changes within this time range. For example, the missing interval may span the stage of drastic fluctuations in water quality concentration before and after the flood peak. The simple linear interpolation method will be difficult to accurately reflect the true dynamics of the missing interval and will introduce a large error. Therefore, this step does not perform interpolation processing on such long missing periods that exceed the missing threshold, but retains the missing status and performs special missing data processing based on the subsequent hysteresis diagnosis-driven reconstruction step.

[0049] In this embodiment, as another optional embodiment, for short-term missing water quality and quantity monitoring data with a consecutive number of missing data not exceeding a preset missing threshold, interpolation can also be performed using methods such as cubic spline interpolation and Kalman filtering. The missing threshold can be adaptively determined based on the autocorrelation decay characteristics of the monitored water quality index (water quality concentration) observed during periods without missing data, rather than being a fixed value.

[0050] The key parameters and their default values ​​involved in the missing measurement processing in this embodiment are shown in Table 1 below. During implementation, they can be adjusted according to the hydrological characteristics of the watershed where the specific monitoring section is located, the rate of change of the monitored water quality indicators, and other actual conditions.

[0051]

[0052] S102. Based on the preprocessed flood season water quality and quantity monitoring data sequence, determine the flood events during the flood season and the start and end times of each flood event.

[0053] In this embodiment, based on the peak and trough detection of the flow data sequence, the flood season is divided into multiple independent flood events, and the start and end times of each flood event are determined.

[0054] In this embodiment, the flood season typically does not consist of a single flood event, but rather multiple relatively independent flood events driven by different rainfall processes, occurring sequentially or intermittently. To ensure that subsequent steps such as lag diagnosis and water quality concentration missing measurement reconstruction can be accurately performed within the clearly defined timeframe of a single flood event, rather than being treated as a general mix across multiple flood events, this step automatically identifies and segments the start and end boundaries of each independent flood event from the continuously monitored flow data sequence. Therefore, as an optional embodiment, determining flood events during the flood season based on the flow data sequence in the pre-processed flood season water quality and quantity monitoring data sequence includes:

[0055] Obtain the first data difference between the (i+1)th and the ith traffic data in the traffic data sequence;

[0056] If the difference of the first data is greater than or equal to zero, the i-th traffic data is taken as the base point; if the difference of the first data is less than zero, the (i+1)-th traffic data is taken as the base point.

[0057] Obtain the second data difference between the (i+2)th and (i+1)th traffic data in the traffic data sequence;

[0058] If the second data difference is greater than or equal to zero, continue to obtain the third data difference between the (i+3)th and (i+2)th traffic data in the traffic data sequence until the data difference of the obtained consecutive preset number is less than zero. The data point with the larger data value among the consecutive preset number of traffic data that is less than zero is taken as the peak point.

[0059] Calculate the fourth data difference between the peak point and the base point. If the fourth data difference is greater than a preset flood threshold, determine the peak point as the location of the flood peak of a flood event.

[0060] If the interval between adjacent flood peak locations exceeds a preset flood peak cycle threshold, the event corresponding to that flood peak location is confirmed as a flood event, and the base point is the start and end time of the flood event.

[0061] In this embodiment, a base point corresponding to the other side of the flood peak location can be determined using a similar method. Two base points and one flood peak location constitute a candidate flood event. If the interval between adjacent candidate flood events exceeds the flood peak period threshold, the previous candidate flood event is determined to be a flood event.

[0062] In this embodiment, firstly, based on the flow data sequence, where for multiple sampling points, the flow data sequence can be obtained by fusing multiple sampling points, or flood event analysis can be performed separately for the flow data sequence corresponding to each sampling point to detect significant local maxima points, which are then used as the peak locations of each flood event. As an optional embodiment, significant means that the height difference between the local maximum and the valley values ​​of its two adjacent base points exceeds a preset threshold (flood threshold). This can prevent some local fluctuations with small amplitude changes in the flow data sequence from being misjudged as an independent flood event. At the same time, it is required that the time interval between two adjacent flood peaks is not less than a preset number of days (2 days in this embodiment), which can improve the accuracy of flood event judgment.

[0063] In this embodiment, as another optional embodiment, determining the flood events during the flood season and the start and end times corresponding to each flood event can also be done as follows: For the preprocessed flow data sequence, starting from a certain moment, sequentially traverse (left traversal and right traversal) the number of sampling moments corresponding to a preset number of days. If the flow value corresponding to the flow data at the next sampling moment is greater than the flow value at the current sampling moment, then replace it with the flow value at the next sampling moment to form a candidate maximum flow value. If the flow value at the next sampling moment is less than the flow value at the current sampling moment, then replace it with the flow value at the next sampling moment to form a candidate minimum flow value (there are two candidate minimum flow values, located on both sides of the candidate maximum flow value). Finally, within the number of sampling moments corresponding to the preset number of days, calculate the difference between the candidate maximum flow value and the candidate minimum flow value. If the difference exceeds a preset threshold, determine that the candidate maximum flow value is the peak of the flood event, and the two candidate minimum flow values ​​are the troughs (start and end times) of the flood event. By setting the time interval between two adjacent flood peaks to be no less than a preset number of days (the flood event differentiation threshold), it is possible to avoid the erroneous segmentation and identification of multiple local small peaks in the flow fluctuations caused by the same rainfall event into multiple independent flood events. This ensures that the number of identified flood events accurately corresponds to the number of actual independent rainfall-runoff processes. After identifying the peak location (flow peak time, i.e., the flow peak threshold) of each flood event, local minimum points, i.e., valleys (base points) on the flow process line, are identified on the flow sequence. The times at which these valleys occur represent the point where the flow process has returned to a relatively stable state from the receding phase of the previous event and has not yet been affected by the next rainfall. Therefore, these valleys can serve as natural time boundaries between two adjacent flood events.

[0064] In this embodiment, for each flood event with an identified peak, the start time is taken as the nearest trough time (within a preset error range) to the left of the peak location, i.e., the base point, and the end time is taken as the nearest trough time to the right of the peak (another base point). If there is no trough value that meets the conditions to the left or right of a certain flood peak (for example, the flood peak happens to be the first or last event in the entire continuous monitoring period, and there is a lack of complete receding or rising water process data on one side), then the start or end point of the continuous monitoring period itself is used as the boundary of the corresponding side of the event. For example, assuming the flow data sequence (monitoring data) time range is January 1st to January 10th, and a flood peak occurs on January 3rd (this is the first flood in the entire 10 days), the flow to the left of the flood peak (January 1st to January 3rd) has been rising without any trough value, then the start time of the flood event is directly taken as the flow data start point on January 1st. If another flood peak occurs on January 8, and the flow on the right side of the flood peak (January 8 to January 10) is still receding, but has not yet reached the trough by the end of monitoring on January 10, then the end time of the flood event is taken as the end point of the flow data on January 10.

[0065] Table 2 is a schematic table showing the peak flow threshold (flood threshold or peak significance threshold) and flood event differentiation threshold set in this embodiment, i.e., the minimum interval between adjacent events (flood peak cycle threshold).

[0066]

[0067] In this embodiment, as another optional embodiment, the event boundary (baseline point of flood event) can also be delineated using the baseflow segmentation method, including but not limited to: digital filtering method, Lyne-Hollick filtering method, UKIH method, and joint peak / valley detection method, to identify the portion of the flow process that exceeds the baseflow level as an independent flood event.

[0068] In this embodiment, as another optional embodiment, the peak significance threshold can be determined in an adaptive manner. For example, a certain quantile (such as the 90th quantile) of the historical flood season flow sequence of the cross section can be taken as a dynamic threshold to adapt to the differences in the background level of flow at different cross sections, thereby improving the accuracy of flood event identification.

[0069] S103. For each flood event, the flood event is divided into a rising water phase and a receding water phase according to the location of the flood peak and the start and end time of the flood event. Based on the water quality and quantity monitoring data corresponding to the rising water phase and the receding water phase, the lag index of the flood event is calculated, and the lag type corresponding to the migration of pollutants in the flood event is determined according to the lag index.

[0070] In this embodiment, for each flood event, the water quality concentration data sequence and flow rate data sequence corresponding to the flood event are standardized. The flood peak time (the sampling time corresponding to the peak flow of the flood event) is used as the boundary to divide the rising water stage and the receding water stage. The lag index is calculated to determine the lag type of pollutant migration in this flood event. As an optional embodiment, the lag type includes, but is not limited to: clockwise lag, counterclockwise lag, and no significant lag.

[0071] In this embodiment, for each identified independent flood event, the lag characteristic of pollutant concentration changes relative to flow rate changes in that flood event is diagnosed. The lag characteristic refers to the hydrological phenomenon where, at the same flow rate, there is a systematic difference between the pollutant concentration observed during the rising phase and the pollutant concentration observed during the receding phase of a flood. This difference is usually related to the distance of the pollutant source from the monitoring section and the speed of its migration path. If the pollutants mainly originate from near the monitoring section and can quickly flow into the river with surface runoff (e.g., the initial flushing effect of urban non-point source pollution), then the pollutant concentration (water quality concentration) during the rising phase often reaches its peak before the flow rate and subsequently declines, exhibiting a lag characteristic within the same flow rate. When the concentration during the rising water level is higher than that during the receding water level, this situation appears as a clockwise circular trajectory on the water quality concentration-flow-Q relationship diagram, which is called clockwise lag. If the pollutants mainly come from sources far from the cross-section and need to migrate and enter through slower pathways such as interflow or groundwater (e.g., some agricultural non-point source pollution), the concentration peak often lags behind the flow peak. This manifests as the water quality concentration during the receding water level being higher than that during the rising water level at the same flow rate, corresponding to a counterclockwise circular trajectory on the CQ relationship diagram, which is called counterclockwise lag.

[0072] In this embodiment, as an optional implementation, the lag index of the flood event is calculated based on the water quality and quantity monitoring data corresponding to the rising and receding water sections, including:

[0073] A11, normalize the flow data sequence and water quality concentration data sequence corresponding to the target flood event to obtain normalized flow data sequence and normalized water quality concentration data sequence.

[0074] In this embodiment, the pre-treated water quality concentration and flow rate observations during the flood event are normalized. Specifically, the following formula is used to linearly map the respective ranges of water quality concentration and flow rate during the flood event to the interval [0, 1]:

[0075]

[0076]

[0077] In the formula, , These are the normalized flow rate data and normalized water quality concentration data at sampling time t within the target flood event, respectively.

[0078] In this embodiment, the normalization step can eliminate the differences in dimensions and numerical magnitudes between different water quality indicators, making the lag index calculated subsequently comparable between different indicators and different flood events.

[0079] A12. From the normalized flow data sequence, select a preset number of comparison reference point pairs. For each comparison reference point pair, based on the normalized flow data sequence, normalized water quality concentration data sequence, and pre-set interpolation algorithm corresponding to the flood stage to which the comparison reference point pair belongs, obtain the normalized water quality concentration data of the comparison reference point pair. The normalized water quality concentration data includes normalized water quality concentration data during flood rise and normalized water quality concentration data during flood recede.

[0080] In this embodiment, as an optional embodiment, within the range of [0.05, 0.95] in the normalized flow data sequence (discarding the intervals near the extreme values ​​where data points are relatively sparse and the interpolation results are not reliable), 20 (preset number) flow data values ​​are uniformly selected as comparison benchmark pairs (the same flow data value corresponds to a comparison benchmark point in the rising water stage and a comparison benchmark point in the receding water stage, and the two comparison benchmark points constitute a comparison benchmark pair). In the normalized water quality concentration data sequences of the rising water stage (rising water segment) and the receding water stage (receding water segment), the normalized water quality concentration data corresponding to the flow level (comparison benchmark pair) is obtained by linear interpolation.

[0081] In this embodiment, taking the flood stage to which a candidate comparison benchmark point in the comparison benchmark pair belongs as the rising water stage as an example, as an optional embodiment, based on the normalized flow data sequence, normalized water quality concentration data sequence corresponding to the flood stage to which the comparison benchmark point pair belongs, and a pre-set interpolation algorithm, the normalized water quality concentration data of the comparison benchmark point pair is obtained, including:

[0082] Sort the normalized flow data sequence corresponding to the flooded section;

[0083] From the sorted normalized flow data sequence, obtain the first reference point and the second reference point corresponding to the normalized flow data adjacent to the candidate comparison reference point;

[0084] Obtain the normalized water quality concentration data and normalized flow rate data corresponding to the first and second benchmark points, respectively, and combine them with a pre-set interpolation algorithm to obtain the normalized water quality concentration data of the candidate comparison benchmark points.

[0085] In this embodiment, the target normalized flow data of the kth comparison reference point, uniformly selected within the normalized flow data sequence [0.05, 0.95], is denoted as Qk. In the normalized flow data sequence during the rising water phase, the normalized flow data are sorted according to their magnitude to find the target normalized flow data Qk. The normalized water quality concentration data is obtained by using two immediately preceding and following normalized flow data, rather than based on the water quality and quantity data at the sampling times before and after the benchmark point or the benchmark point itself. This improves the accuracy of the obtained normalized water quality concentration data. Specifically, the normalized flow data and normalized water quality concentration data of the left neighboring point are (Qa, Ca), satisfying Qa ≤ Qk. The normalized flow rate data and normalized water quality concentration data of the right neighboring point are (Qb, Cb), which satisfy Qb ≥ Qk. .

[0086] In this embodiment, as an optional implementation, the normalized water quality concentration data and normalized flow rate data corresponding to the first and second reference points are obtained respectively. Combined with a pre-set interpolation algorithm, the normalized water quality concentration data of the candidate comparison reference points is obtained, including:

[0087] Calculate the difference between the concentration data of the second normalized water quality concentration corresponding to the second benchmark point and the concentration data of the first normalized water quality concentration corresponding to the first benchmark point;

[0088] Calculate the difference between the second normalized flow data corresponding to the second reference point and the first normalized flow data corresponding to the first reference point;

[0089] Calculate the difference between the second flow data of the comparison normalized flow data corresponding to the candidate comparison benchmark and the second flow data of the first normalized flow data corresponding to the first benchmark;

[0090] Obtain the ratio of the second flow rate data difference to the first flow rate data difference, and calculate the product of the ratio and the concentration data difference;

[0091] The sum of the product and the first normalized water quality concentration data corresponding to the first benchmark point is calculated to obtain the normalized water quality concentration data of the candidate comparison benchmark point.

[0092] In this embodiment, the normalized water quality concentration data Crise(Qk) of the candidate comparison benchmark point is obtained. The formula for calculating ) is:

[0093]

[0094] In the formula, To normalize the water quality concentration data during floods, This is the first normalized water quality concentration data. This is the second normalized water quality concentration data. This is the first normalized traffic data. This is the second normalized traffic data.

[0095] A13, calculate the difference between the normalized water quality concentration data of the rising water level and the normalized water quality concentration data of the receding water level for the comparison reference point pair, and obtain the local hysteresis value of the comparison reference point pair.

[0096] In this embodiment, the difference between the normalized water quality concentration data corresponding to the rising water section and the receding water section is the local hysteresis value at the flow level of the comparison benchmark pair:

[0097]

[0098] A14. Based on the local lag values ​​of each comparison benchmark pair, obtain the lag index of the target flood event.

[0099] In this embodiment, as an optional implementation, the arithmetic mean of the local lag values ​​obtained from all 20 comparison reference point pairs is taken to obtain a lag index that can represent the overall lag characteristics of this flood event:

[0100]

[0101] In this embodiment, as an optional embodiment, the lag type includes, but is not limited to: clockwise lag, counterclockwise lag, and no obvious lag. A lag index that is positive and exceeds a preset threshold (0.05 in this embodiment) is determined to be clockwise lag; a lag index that is negative and whose absolute value exceeds the preset threshold is determined to be counterclockwise lag; and a lag index whose absolute value does not exceed the threshold is determined to be no obvious lag phenomenon (no obvious lag) in this flood event.

[0102] In this embodiment, as an optional embodiment, the method further includes:

[0103] If the number of reference points selected from the normalized flow data sequence is less than the preset minimum confidence number, the lag type corresponding to the pollutant migration of the flood event to which the normalized flow data sequence belongs is marked as insufficient data with no obvious lag.

[0104] In this embodiment, if the number of data points (comparison benchmarks) in the normalized flow data sequence of the rising and receding water stages where both concentration and flow are valid values ​​is much smaller than the preset number (20) and less than the minimum confidence number, such as 3, then the interpolation result is insufficient to reliably reflect the concentration-flow relationship of this stage. In this case, the lag diagnosis result of this flood event is determined to be "insufficient data, treated as no obvious lag", and the confidence level of this determination is marked in the result output for reference in subsequent steps.

[0105] In this embodiment, it is important to emphasize that the hysteresis index and its determination results can not only be used to characterize the pollutant migration characteristics of the flood event itself, but also serve as a direct input basis for the subsequent concentration missing data reconstruction step: the more significant the difference between the concentration-flow relationship in the rising and receding phases of the flood event (i.e., the larger the absolute value of the hysteresis index), the more necessary it is to distinguish between the two phases during the missing data reconstruction in order to effectively improve the accuracy of the missing data reconstruction.

[0106] In this embodiment, as another optional embodiment, the number of flow quantiles, i.e. the preset number (20 in this embodiment), can be adjusted according to the duration of the flood event and the sampling density. The suggested range is 10–30. The more quantiles there are, the more precise the estimation of the lag index will be, but the requirements for data density will also be correspondingly increased.

[0107] The preset threshold, namely the lag type determination threshold (±0.05 in this embodiment), can be adjusted and determined according to the statistical distribution characteristics of the historical lag index of a specific watershed and specific water quality indicators. For example, a certain multiple of the standard deviation of the historical lag index distribution can be taken as the threshold.

[0108] S104. From the pre-processed flood season water quality and quantity monitoring data sequence, obtain the target water quality concentration data marked as missing for more than the missing threshold, and reconstruct the target water quality concentration data according to the lag type corresponding to the target water quality concentration data and the water quality and quantity monitoring data of the corresponding flood event.

[0109] In this embodiment, a concentration missing data reconstruction driven by hysteresis diagnosis is performed: for cases where there is still a long period of concentration missing data after preprocessing, based on the hysteresis type determination result, the empirical relationship established by the effective concentration-flow data points of the corresponding flood event's rising and receding water phases is used, combined with the flow data actually observed during the missing data period, to reconstruct the water quality concentration value (target water quality concentration data) during the missing data period.

[0110] In this embodiment, for the problem of long water quality concentration data gaps that exceed the preset upper limit (number of consecutive gaps) and cannot be filled by simple interpolation, based on the characteristic that there are systematic differences in the concentration-flow relationship between the rising and receding phases of the same flood event, if this difference is not distinguished during the reconstruction of the missing data, and linear interpolation is generally performed on the missing data interval in chronological order, the interpolation result is essentially a simple time-weighted average between the observed concentration values ​​at the beginning and end of the missing data, completely ignoring the drastic changes that the flow data may have undergone during the missing data period, and also ignoring the concentration-flow response law that the concentration data should follow, which varies with the rising and receding phases of the flood event. Therefore, a systematic bias related to the magnitude of the lag phenomenon will be introduced. In this embodiment, using the obtained hysteresis diagnosis results, empirical relationships between concentration and flow rate are established within the rising and receding water phases, respectively. Combined with the actual flow rate data observed during periods of data gaps (flow sensors and concentration sensors typically operate on different measurement principles and fault modes; flow rate data is often still available even when concentration sensors experience data gaps), these empirical relationships are used to "convert" the flow rate data into corresponding water quality concentration estimates, thereby obtaining a reconstruction result closer to the actual dynamics than simple time interpolation. Therefore, as an optional embodiment, based on the hysteresis type corresponding to the target water quality concentration data and the water quality and quantity monitoring data of the corresponding flood event, the target water quality concentration data is reconstructed, including:

[0111] A21. Determine the target flood event to which the target water quality concentration data belongs. Based on the normalized water quality and quantity monitoring data sequence corresponding to the rising water section of the target flood event, select sampling points for the rising water section where both water quality concentration data and flow data are not missing. Also, based on the normalized water quality and quantity monitoring data sequence corresponding to the receding water section of the target flood event, select sampling points for the receding water section where both water quality concentration data and flow data are not missing.

[0112] In this embodiment, data points (sampling points) where both water quality concentration data and flow data (flow observation values) are valid (i.e., neither is missing) are selected from the observation data (normalized water quality and quantity monitoring data sequence) of the rising and receding phases of the target flood event, respectively. The two phases are selected independently.

[0113] A22, sort the flow data corresponding to each sampling point in the rising water section and each sampling point in the receding water section respectively, take the flow data as the independent variable and the water quality concentration data corresponding to the flow data as the dependent variable, and establish the concentration-flow empirical relationship in the rising water stage and the concentration-flow empirical relationship in the receding water stage.

[0114] In this embodiment, the effective data points selected in each stage are sorted according to the magnitude of the flow rate. With flow rate as the independent variable and concentration as the dependent variable, a piecewise linear interpolation method is used (i.e., a straight line is connected between two adjacent sorted data points, and linear interpolation is performed on any flow rate value in between) to establish empirical concentration-flow rate relationships for the rising water stage and the receding water stage, respectively.

[0115] A23, obtain the missing water quality concentration data marked as missing in the normalized water quality and quantity monitoring data sequence corresponding to the target flood event, determine the flood stage to which the missing water quality concentration data belongs, and determine the water quality concentration data corresponding to the missing water quality concentration data based on the flow data corresponding to the missing water quality concentration data and the empirical formula corresponding to the flood stage. The flood stage includes the rising water stage and the receding water stage.

[0116] In this embodiment, the monitoring of water quality concentration data and flow rate data are independent of each other, while flow rate data is easier to obtain. Therefore, in the normalized flow rate data sequence marked as missing, water quality concentration data is generally missing. This embodiment reconstructs the missing water quality concentration data. As an optional embodiment, for each missing sampling time that is determined to be missing for a long period of time and for which the flow rate observation values ​​at each sampling time corresponding to the long period of missing time are still valid, for each missing sampling time, based on whether the missing sampling time is earlier or later than the time when the flow rate of the target flood event reaches its peak, it is determined whether the sampling time is in the rising or receding phase of the target flood event. If it is in the rising phase, the flow rate value (flow rate data) actually observed at the sampling time is substituted into the empirical relationship of the rising phase, and the resulting function value is used as the reconstructed water quality concentration value for that sampling time; if it is in the receding phase, the flow rate value actually observed at the sampling time is substituted into the empirical relationship of the receding phase to obtain the reconstructed water quality concentration value.

[0117] A24. Based on the acquired water quality concentration data, the water quality concentration data marked as missing in the normalized water quality and quantity monitoring data sequence of the target flood event are reconstructed and the marker is deleted.

[0118] In this embodiment, the water quality concentration values ​​at all missing sampling times are reconstructed. Specifically, the water quality concentration data values ​​corresponding to the missing data are substituted back into the corresponding sampling time positions in the original normalized water quality concentration sequence, resulting in a complete water quality and quantity monitoring data sequence that no longer contains long-term missing data and can be directly used for subsequent flux calculations. As an optional embodiment, the water quality concentration data is reconstructed using the following formula:

[0119] (t) = frise(Q(t)), t ≤ tp (Missing data occurs during the rising water stage)

[0120] (t) = ffall(Q(t)), t > tp (Missing data occurs during the receding water phase)

[0121] Where f_rise and f_fall represent the concentration-flow empirical relationship functions established based on effective data points during the rising and receding water phases, respectively, and t_p represents the moment when the flood event's flow reaches its peak.

[0122] In this embodiment, as another optional embodiment, the method further includes:

[0123] If the number of valid sampling points in the normalized water quality and quantity monitoring data sequence corresponding to the rising and receding phases of the target flood event is less than the pre-set non-discrimination threshold, the missing sampling points marked as missing in the normalized water quality and quantity monitoring data sequence corresponding to the target flood event are obtained, and the water quality concentration data of the missing sampling points are determined based on the flow data of the missing sampling points and the pre-set empirical formula for the target flood event.

[0124] In this embodiment, when the number of data points with valid concentration and flow rate values ​​during the rising or receding water phase is less than three, resulting in the piecewise linear interpolation results of this phase being insufficient to reliably reflect the true concentration-flow rate relationship, this embodiment no longer distinguishes between the rising and receding water phases. Instead, it uses all valid data points of the flood event (combining the rising and receding water phases) to establish a unified concentration-flow rate empirical relationship (empirical formula for the target flood event) for reconstruction. The reconstruction results for this period are marked as "uncertainty higher than normal" in the output results for reference by uncertainty analysis steps and users.

[0125] In this embodiment, in a verification embodiment, for a real flood event, during the rising water phase, continuous water quality concentration data of 101 sampling points (approximately 25 hours) were missing. The error of the total flux of the event calculated after reconstruction using the method of this embodiment relative to the true value (complete data calculation result) was 0.41%, while the error of the simple time linear interpolation method in the prior art that does not distinguish between the rising and receding water phases was 1.39%, improving the accuracy by more than 3 times.

[0126] In this embodiment, during the rising water phase, the concentration-flow relationship is relatively close, while during the receding water phase, the concentration-flow relationship is relatively loose and has higher dispersion. Therefore, as another optional embodiment, the flood phase to which the missing water quality concentration data belongs is determined to be the rising water phase. Based on the flow data corresponding to the missing water quality concentration data and the empirical formula corresponding to the flood phase, the water quality concentration data corresponding to the missing water quality concentration data is determined, including:

[0127] Obtain the goodness of fit of the concentration-flow empirical relationship during the flood stage;

[0128] If the goodness of fit of the obtained rise water level is greater than the preset threshold, the water quality concentration data corresponding to the missing water quality concentration data is determined based on the flow data corresponding to the missing water quality concentration data and the empirical formula corresponding to the rise water level stage.

[0129] If the goodness of fit of the obtained water level rise is not greater than the preset threshold, the water quality concentration data corresponding to the missing water quality concentration data is determined based on the water quality concentration data before and after the missing water quality concentration data and the preset interpolation algorithm.

[0130] In this embodiment, before reconstruction, the goodness of fit of the concentration-flow empirical relationship established by the effective data points of each of the rising and receding water stages can be pre-evaluated (e.g., measured by the coefficient of determination R²). Only when the goodness of fit of a certain stage exceeds a preset threshold will the missing data of that stage be reconstructed using the hysteresis-sensing reconstruction method described in this step. If the goodness of fit does not exceed the threshold, the missing data of that stage will be processed by simple time interpolation or conventional interpolation, thereby adaptively selecting between the two processing methods of "hysteresis-sensing reconstruction" and "simple interpolation" to take into account the applicable scenarios of both.

[0131] In this embodiment, as an optional embodiment, the empirical relationship between the flooding stage and the receding stage can be established not only by piecewise linear interpolation, but also by other function fitting methods such as multinomial regression, locally weighted scatterplot smoothing (LOWESS), Gaussian process regression, and kernel regression, as long as the method can establish the mapping relationship between concentration and flow rate based on the effective data points within the flooding stage (or receding stage).

[0132] In this embodiment, as an optional embodiment, after reconstructing the target water quality concentration data, the method further includes:

[0133] Based on the time interpolation method, obtain the water quality concentration interpolation data of the missing sampling points;

[0134] For each missing sampling point, the interpolated water quality concentration data of that missing sampling point is weighted and fused with the reconstructed water quality concentration data to obtain the final water quality concentration data of that missing sampling point.

[0135] In this embodiment, a modified implementation method can be used to effectively reduce the uncertainty of reconstruction results by weighting and fusing the reconstructed water quality concentration data with the water quality concentration interpolation data obtained by time interpolation during the same period. For example, the weights of the two can be dynamically adjusted according to the duration of missing data, or the weights of the two can be dynamically adjusted according to the aforementioned goodness of fit.

[0136] In this embodiment, as an optional implementation, the lag type is not limited to the three types of clockwise / counterclockwise / no obvious lag. A more refined lag mode classification method can also be adopted, such as further subdividing based on the magnitude of the lag index, and designing different reconstruction strategies based on the subdivided lag types.

[0137] S105. Based on the pre-processed flood season water quality and quantity monitoring data sequence and the reconstructed target water quality concentration data, a water quality and quantity reconstructed data sequence is obtained. Based on the water quality and quantity reconstructed data sequence, the total flux during the flood season and the contribution ratio of pollutant flux of each flood event to the total flux during the flood season are obtained.

[0138] In this embodiment, the reconstructed target water quality concentration data is written into the corresponding missing position in the flood season water quality and quantity monitoring data sequence to obtain the water quality and quantity reconstructed data sequence. Based on the water quality and quantity reconstructed data sequence, the pollutant flux during the flood season is calculated and multiple events are summed: based on the reconstructed complete concentration and flow sequence, the pollutant flux of each flood event and the total flux during the entire flood season are calculated, and the contribution ratio of the pollutant flux of each flood event to the total flux during the flood season is calculated.

[0139] In this embodiment, the basic physical relationship on which the flux calculation is based is: the mass transport rate of pollutants flowing through the monitoring section at a certain moment (i.e., instantaneous flux) is equal to the product of the pollutant concentration (water quality concentration data) and the flow rate at that moment. Here, the pollutant concentration reflects the mass of pollutants contained in a unit volume of water, the flow rate reflects the volume of water flowing through the section per unit time, and the product of the pollutant concentration and the flow rate is the mass of pollutants flowing through the section per unit time.

[0140] In this embodiment, since the commonly used unit of measurement for water quality concentration data is milligrams per liter (mg / L), while the commonly used unit of measurement for flow rate is cubic meters per second (m³ / s), the selection of volume units is inconsistent. That is, water quality concentration data is measured in liters and flow rate is measured in cubic meters. In this embodiment, the units of water quality concentration data and flow rate are converted, converting liters to cubic meters (i.e., multiplying by 1000), so that the unit of the product result is unified to milligrams per second. The unit conversion formula is as follows:

[0141]

[0142] The unit is mg / L. Flow rate, in m³ / s. Here is the unit conversion formula, with units in mg / s.

[0143] In this embodiment, after obtaining complete and continuous water quality concentration sequence data and flow rate sequence data, the total flux of each flood event is calculated. This involves integrating the instantaneous flux within the flood event over its entire duration to obtain the total flux of the flood event. As an optional embodiment, since the monitoring data (water quality concentration sequence data and flow rate sequence data) are discrete time series rather than continuous functions, this embodiment uses numerical integration to calculate the total flux of the flood event: for each sampling moment within the duration of the flood event, the instantaneous flux value at that sampling moment is calculated and multiplied by the time span corresponding to that sampling point. For example, under equal-interval sampling conditions, the time span is a fixed sampling interval, such as 60 seconds at a 1-minute resolution. As another optional embodiment, if there are non-uniform sampling intervals in the data sequence, the actual time difference from each sampling point to its next sampling point is used as the time span represented by that point. The time span is accumulated point by point for all sampling points to obtain an estimate of the total mass of pollutants during the entire duration of the flood event. The total flux of the flood event is then calculated using the following formula:

[0144]

[0145] In the formula, Let t represent the total flux of the i-th flood event, in kg, and t represent the time period corresponding to this flood event within the flood season.

[0146] In this embodiment, the accuracy of the total flux of the flood event directly benefits from the high temporal resolution monitoring data upon which the method relies: the shorter the sampling interval and the denser the sampling points, the closer the discrete summation result is to the true continuous integral value. Since the raw data collected in this embodiment is already sufficiently dense, and the missing parts are also supplemented by reconstruction using hysteresis sensing, numerical integration can be performed based on the complete measured (and reconstructed) data sequence.

[0147] In this embodiment, the total flux during the flood season is the result obtained by continuously integrating the curve of the instantaneous flux rate of pollutants over time throughout the entire flood season. This result fully includes the pollutant transport contribution at every moment during the flood season (whether during a flood event or a period of stable baseflow).

[0148] In this embodiment, as another optional embodiment, the contribution ratio of pollutant flux of each flood event to the total flux during the flood season can be further calculated, that is, the ratio of the total flux of the flood event to the total flux during the flood season. As an optional embodiment, the contribution ratio is calculated using the following formula:

[0149]

[0150] In this embodiment, based on the contribution ratio of the acquired flood events, the specific flood events that contribute most significantly to the overall pollution load during the flood season can be intuitively identified. These flood events usually correspond to high peak flows or lag diagnoses indicating significant initial scouring effects. This provides data support for subsequent targeted pollution source control, emergency response, or key monitoring deployment. Thus, the method of quantifying the contribution by flood event in this embodiment, compared to flux calculation methods that only give a general total flux for a certain period (such as the whole year or the whole flood season) without distinguishing the contribution of each flood event, can effectively improve the efficiency and accuracy of pollution source control.

[0151] In this embodiment, as an optional embodiment, the method further includes:

[0152] Using a pre-defined Monte Carlo simulation method, the confidence interval and relative uncertainty width of the total flux during the flood season are calculated.

[0153] In this embodiment, uncertainty analysis is performed on the total flux during the flood season: combining the nominal accuracy indicators of water quality and flow rate measurements collected by the monitoring equipment, the Monte Carlo simulation method is used to calculate the confidence interval and relative uncertainty width of the obtained total flux during the flood season.

[0154] The method in this embodiment addresses this deficiency by introducing uncertainty analysis, unlike existing flux measurement methods that typically only provide a single-point estimate of the flux during the flood season without specifying the degree of uncertainty inherent in that estimate. This makes it difficult for users to determine the reliability of the flux estimate and to formulate management decisions with reasonable margins. The basic idea is that the accuracy of water concentration and flow rate measurements collected by monitoring equipment is limited (usually expressed as a nominal relative measurement error, such as the equipment manual stating that the relative error of concentration measurement does not exceed a certain percentage). This means that each observed concentration and flow rate value is not the absolutely accurate true value, but rather an estimate fluctuating around an unknown true value. This uncertainty arising from the observation process propagates and accumulates in the final flood season flux result through a series of calculations involving multiplying concentration and flow rate and integrating over time, resulting in a significant uncertainty in the final flux estimate. This embodiment uses Monte Carlo simulation to numerically simulate the aforementioned error propagation process, thereby quantitatively providing the confidence interval for the flux result. Therefore, as an optional embodiment, the confidence interval and relative uncertainty width of the total flux during the flood season are calculated using a pre-set Monte Carlo simulation method, including:

[0155] A31, based on the normal distribution, converts the nominal water quality relative measurement error and flow rate relative measurement error of the monitoring equipment into the corresponding standard deviation according to the standard deviation conversion formula;

[0156] In this embodiment, the nominal relative measurement error of water quality and the relative measurement error of flow rate of the monitoring equipment are understood as the relative range of fluctuation of the measured values ​​collected by the monitoring equipment around the true value at a certain confidence level (95% in this method). Based on this, and using the statistical conversion relationship that the half-width of the 95% confidence interval in the normal distribution is approximately equal to 1.96 times the standard deviation, the nominal relative measurement error is converted into the corresponding standard deviation. The standard deviation conversion formula is as follows:

[0157] σ C = e C / 1.96 σ Q = e Q / 1.96

[0158] Where e C e Q These are the relative errors in water concentration measurement and flow rate measurement, respectively, σ. C σ Q This is the corresponding standard deviation obtained through conversion.

[0159] A32, for each Monte Carlo simulation, based on the water quality and quantity monitoring data corresponding to each sampling time in the water quality and quantity reconstruction data sequence, the sampling standard deviation is obtained based on the normal distribution, and a random disturbance ratio following a normal distribution with a mean of zero is generated based on the sampling standard deviation, and the water quality and quantity reconstruction data sequence is corrected based on the random disturbance ratio;

[0160] In this embodiment, a large number of repeated simulations (1000 times in this embodiment) are performed: In each simulation, for each time point in the processed complete concentration and flow rate sequence (water quality and quantity reconstructed data sequence), the sampling standard deviation is independently calculated according to the above conversion. Based on each sampling standard deviation, a random disturbance ratio following a normal distribution with a mean of zero is generated, and this random disturbance ratio is applied to the observed value at that time, that is, the original observed value is multiplied by the coefficient "1 plus random disturbance ratio", thereby generating a set of corrected observation sequences that may appear within the equipment measurement error range in the simulation sense. That is, the water quality and quantity reconstructed data sequence is corrected using the following formula:

[0161] C j (t) = C(t) × [1 + ε C,j (t)] ε C,j (t) ~ N(0, σ C ²)

[0162] In the formula, C(t) represents the reconstructed water quality and quantity data sequence, and ε C,j (t) represents the proportion of random disturbance.

[0163] A33, based on the corrected water quality and quantity data sequence, obtain the corrected total flux during the flood season, and construct the simulated distribution of the corrected total flux during each flood season based on the corrected total flux during each simulation;

[0164] In this embodiment, for each simulation-generated corrected concentration sequence and corrected flow sequence, the total flux for the flood season (or a specified statistical period) is recalculated using the method described in this embodiment. This process is repeated 1000 times independently to obtain 1000 corrected total fluxes (flux estimates) for the flood season. A numerical distribution is constructed based on these 1000 corrected total fluxes for the flood season. This numerical distribution characterizes the reasonable fluctuation range that the final flux result may exhibit under the limitations of equipment measurement accuracy.

[0165] A34. Based on the first and second quantiles preset in the simulated distribution, construct the lower and upper limits of the confidence interval for the total flux during the flood season.

[0166] In this embodiment, after obtaining the simulated distribution, as an optional implementation, the 2.5% quantile and 97.5% quantile of the simulated distribution are taken as the lower and upper limits of the confidence interval, respectively. The interval between the 2.5% quantile and the 97.5% quantile is the 95% confidence interval of the corrected total flux (flux estimation result) for the flood season. This means that, under the premise that the equipment measurement error meets the nominal accuracy, the actual flood season flux has a 95% probability of falling within the interval formed by the lower and upper limits.

[0167] 95%CI = [ percentile (W, 2.5%), percentile (W, 97.5%) ]

[0168] In this embodiment, as an optional embodiment, the method further includes:

[0169] Calculate the difference between the upper and lower limits of the confidence interval to obtain the width of the confidence interval;

[0170] The quotient of the confidence interval width and the flux point estimate is calculated to obtain the relative uncertainty width used to measure the accuracy of the flux measurement results.

[0171] In this embodiment, the method further divides the width of the confidence interval (upper limit minus lower limit) by the flux point estimate (total flux during the flood season) to obtain the dimensionless index of relative uncertainty width, which is used to measure the relative level of accuracy of the flux measurement results:

[0172] Relative uncertainty width = (CI) high - CI low ) / W × 100%

[0173] In this embodiment, the flux results calculated from different flood events and different water quality indicators may vary significantly in magnitude. Therefore, directly comparing the absolute width of the confidence interval is meaningless. However, after normalization, the relative uncertainty width allows for horizontal comparison between flux results from different magnitudes and different flood events: a smaller relative uncertainty width indicates a higher relative reliability of the flux result under the current equipment measurement accuracy conditions; conversely, a larger relative uncertainty width indicates a larger fluctuation range in the confidence interval relative to its point estimate, requiring more caution in application. Table 3 shows the values ​​of confidence level and number of Monte Carlo simulations in this embodiment.

[0174]

[0175] In this embodiment, the relative uncertainty width obtained above mainly refers to the measurement error of the original observation link (i.e., the nominal accuracy of the equipment). However, additional uncertainties may also be introduced in the reconstruction of missing water quality concentration data. For example, there may be a deviation between the reconstructed concentration estimate and the actual concentration value at that moment. Therefore, as another optional embodiment, the additional uncertainty of the concentration missing data reconstruction period can also be included in the Monte Carlo perturbation model. By applying a larger random perturbation to the concentration value of this period, for example, by superimposing an additional uncertainty component related to the reconstruction method on the basis of the nominal measurement error of the equipment, the final confidence interval can more comprehensively and accurately reflect the complete sources of uncertainty, including the reconstruction of missing water quality concentration data.

[0176] In this embodiment, in addition to the normal distribution, as another optional embodiment, other probability distributions such as uniform distribution and t-distribution can also be adopted according to the statistical characteristics of the measured error of the monitoring equipment.

[0177] In this embodiment, as an optional implementation, the following output can also be performed: output the estimated total flux point value during the flood season, confidence interval, pollutant flux and contribution ratio of each flood event, lag diagnosis results, etc.

[0178] The method in this embodiment can be applied to various water quality indicators such as permanganate index, ammonia nitrogen, total nitrogen, and total phosphorus, with each water quality indicator undergoing the above steps independently.

[0179] In this embodiment, taking the high temporal resolution water quality concentration data sequence and flow data sequence synchronously acquired at a certain river section as an example, the data is processed sequentially through data preprocessing, flood event identification, lag diagnosis, concentration gap reconstruction driven by lag diagnosis, flux calculation and multi-event summation, uncertainty analysis, etc., and finally outputs the pollutant flux measurement results of the section during the flood season (or any specified statistical period). The pollutant flux measurement results include, but are not limited to: total flux point estimate, confidence interval, pollutant flux and contribution ratio of each flood event, and lag diagnosis results of each flood event.

[0180] In this embodiment, clean and complete observation data can be obtained through data preprocessing. Since the lag phenomenon applies to the rising and receding phases of a single, independent flood event, the continuously monitored time series is first divided into multiple independent flood events. Then, it is determined whether a particular sampling time belongs to the rising or receding phase of the flood event. The lag index is then calculated based on the rising or receding phase of the flood event. The result of the lag diagnosis is the direct input basis for the concentration missing data reconstruction step. Therefore, the concentration missing data reconstruction is set after the lag diagnosis and before the flux calculation to ensure that the concentration sequence participating in the flux integration calculation is complete and does not contain long-term missing sequences. Uncertainty analysis is performed after all flux calculations are completed. This allows for the overall application of error propagation simulation to the final flux result obtained after all the above processing steps (including concentration missing data reconstruction), rather than just to the original observation data itself. This ensures that the final confidence interval truly reflects the uncertainty accumulated along the entire calculation chain from the original observation stage to the final flux result.

[0181] In this embodiment, each water quality indicator, such as permanganate index, ammonia nitrogen, total nitrogen, and total phosphorus, has its own independent water quality concentration sequence data. The processing of each indicator does not interfere with each other. Each indicator is executed independently once according to the above steps, and only the same flow sequence data is used as the water volume data input. This method can be applied to the flux measurement of multiple water quality indicators at the same time.

[0182] In this embodiment, as an optional implementation, the water quality concentration data and flow data in time series format can be CSV files, database records, real-time data streams, etc., outputting structured flux measurement results, including but not limited to: estimated total flux points during the flood season, confidence intervals, flux and contribution ratio of each event, and lag diagnosis results for each event, which can be used for report generation, visualization, or further invocation by water environment management decision-making systems.

[0183] The method of this embodiment is verified using pre-acquired high-frequency water quality and quantity monitoring data.

[0184] Example 1: Complete Process Example (Flood Event Identification → Flux Calculation)

[0185] Input data: Simultaneous monitoring data of flow rate and nitrate + nitrite concentration from June 1 to July 31, 2019, with a time resolution of 15 minutes, totaling 5,855 records.

[0186] Step ① (Data Preprocessing): Outlier identification and short-term missing data interpolation are performed on the original sequence to obtain the cleaned continuous concentration and flow rate sequence.

[0187] Step 2 (Flood Event Identification): Perform peak / trough detection on the flow sequence for this period (significance threshold is 15 m³ / s, minimum interval between adjacent events is 2 days), automatically identifying 3 independent flood events. The start and end times and peak flow of each event are shown in the table below.

[0188] Step 3 (Lag Diagnosis): Calculate the lag index HI for each event and determine the lag type. The results are shown in Table 4.

[0189] Step 4 (Fluorescence Calculation): Calculate the flux for each event separately. The results are shown in the table below. The total flux during the flood season is 934,577.6 kg.

[0190]

[0191] Step 5 (Uncertainty Analysis): Combining the nominal accuracy indicators of water quality relative error ≤15% and flow relative error ≤10% of the monitoring equipment, Monte Carlo simulation (M=1,000 times) is performed on the total flux during the flood season to obtain a 95% confidence interval, thereby quantifying the uncertainty of the flux measurement results.

[0192] Step 6 (Result Output): The final output includes the estimated total flux during the flood season, the 95% confidence interval, the flux and contribution percentage of each flood event, and the lag diagnosis results for each event.

[0193] Example 2: Validation of the effect of concentration missing reconstruction driven by hysteresis diagnosis

[0194] Comparison of reconstruction effects during flood season

[0195] To verify the technical effectiveness of "hysteresis diagnosis-driven concentration gap reconstruction" compared to the existing simple interpolation method, a complete flood event from June 25 to July 5, 2019 (the flow rate increased from 64.8 m³ / s to 111.3 m³ / s, and was diagnosed as counterclockwise hysteresis, i.e. source region delayed release type) was selected as the verification object. The total flux of the event (as a reference for the true value) calculated under the condition of complete data was 172,109.6 kg.

[0196] In the flooding phase of this event, 101 consecutive sampling points (approximately 25 hours) were artificially set up to result in a concentration loss. The existing simple time linear interpolation method, which does not distinguish between the flooding and receding phases, was used to reconstruct the concentration. This method was compared with the hysteresis diagnosis-driven reconstruction method described in this invention (which reconstructs the concentration based on the empirical relationship established by the effective concentration-flow data points of the flooding and receding phases of the event, according to the counterclockwise hysteresis type determined in step ③, combined with the actual flow data during the loss period). The total flux of the corresponding event was calculated, and the results are shown in Table 5 below.

[0197]

[0198] As can be seen from the table above, by using the hysteresis diagnosis-driven reconstruction method described in this invention, the relative error of flux estimation is reduced from 1.39% in the prior art to 0.41%, and the accuracy is improved by more than 3 times. This verifies the effectiveness of the technical solution of using hysteresis diagnosis results to correct flux calculation in the stage of close concentration-flow relationship.

[0199] Example 3: Analysis of Contribution Ratio in Multiple Sessions

[0200] Based on the three flood events identified in Example 1 and their flux calculation results, the contribution percentage of each event to the total flux (543,332.5 kg) during the flood season was calculated: Flood Event 1 contributed 11.2%, Flood Event 2 contributed 29.7%, and Flood Event 3 contributed 17.2%. It is evident that Flood Event 2 (peak flow 171.0 m³ / s), with the highest peak flow, contributed more than half of the pollution load in a single event, indicating that high-intensity flood events have a disproportionate contribution to the pollution load during the flood season. The method in this example can automatically identify and quantify this phenomenon, providing a decision-making basis for refined pollution prevention and control management during high-risk periods.

[0201] This embodiment has the following beneficial effects:

[0202] Through the step of "concentration missing reconstruction driven by hysteresis diagnosis", compared with the existing technology that does not distinguish between rising and receding water stages and directly performs linear interpolation based on time, this invention can reduce the relative error of flux estimation caused by concentration missing from 1.39% to 0.41% in stages where the concentration-flow relationship is relatively close (i.e., the hysteresis characteristics are significant), as verified by real high-frequency monitoring data, thus improving the accuracy by more than three times.

[0203] By using the steps of "flood event identification" and "flood season flux calculation and flood event contribution ratio calculation", the system can automatically identify the flood events that contribute the most to the pollution load during the flood season, providing a basis for decision-making in refined and targeted pollution prevention and control management.

[0204] By analyzing the uncertainty of the steps, this invention can provide a confidence interval for the flux measurement results, thus overcoming the shortcomings of existing technologies that typically only provide point estimates and lack uncertainty expression.

[0205] Based on the same inventive concept, such as Figure 2 As shown, this embodiment of the invention also provides a device for obtaining pollutant flux during the flood season, the device comprising:

[0206] The preprocessing module 201 is used to preprocess the collected flood season water quality and quantity monitoring data sequence. The preprocessing includes interpolation calculations performed using a time-based linear interpolation method on the water quality and quantity monitoring data in the flood season water quality and quantity monitoring data sequence where the number of consecutive missing data does not exceed a preset missing data threshold.

[0207] In this embodiment, as an optional embodiment, the preprocessing module 201 is further configured to:

[0208] For the same sampling point within the flood season monitoring cycle, a water quality and quantity monitoring data sequence for the sampling point is constructed based on water quality and quantity monitoring data collected in chronological order or interpolated.

[0209] Traverse the water quality and quantity monitoring data sequence of the sampling points and extract the i-th unlabeled water quality and quantity monitoring data;

[0210] Based on the water quality and quantity monitoring data with a preset first value that is not marked before the i-th water quality and quantity monitoring data, the i-th water quality and quantity monitoring data, and the water quality and quantity monitoring data with a preset second value that is not marked after the i-th water quality and quantity monitoring data, the median data and standard deviation of the local sliding window are obtained.

[0211] Based on the i-th water quality and quantity monitoring data and the median data in the local sliding window, the i-th water quality and quantity error data is obtained;

[0212] Calculate the value of the error data and standard deviation of the i-th water quality and quantity data to obtain the i-th standardized residual;

[0213] If the standardization residual of the i-th point exceeds the standard deviation of the preset multiple, the i-th water quality and quantity monitoring data is marked as missing from the water quality and quantity monitoring data sequence of the sampling point.

[0214] The event acquisition module 202 is used to determine the flood events during the flood season and the start and end times of each flood event based on the flow data sequence in the preprocessed flood season water quality and quantity monitoring data sequence.

[0215] In this embodiment, as an optional embodiment, the event acquisition module 202 is specifically used for:

[0216] Obtain the first data difference between the (i+1)th and the ith traffic data in the traffic data sequence;

[0217] If the difference of the first data is greater than or equal to zero, the i-th traffic data is taken as the base point; if the difference of the first data is less than zero, the (i+1)-th traffic data is taken as the base point.

[0218] Obtain the second data difference between the (i+2)th and (i+1)th traffic data in the traffic data sequence;

[0219] If the second data difference is greater than or equal to zero, continue to obtain the third data difference between the (i+3)th and (i+2)th traffic data in the traffic data sequence until the data difference of the obtained consecutive preset number is less than zero. The data point with the larger data value among the consecutive preset number of traffic data that is less than zero is taken as the peak point.

[0220] Calculate the fourth data difference between the peak point and the base point. If the fourth data difference is greater than a preset flood threshold, determine the peak point as the location of the flood peak of a flood event.

[0221] If the interval between adjacent flood peak locations exceeds a preset flood peak cycle threshold, the event corresponding to that flood peak location is confirmed as a flood event, and the base point is the start and end time of the flood event.

[0222] The type determination module 203 is used to divide each flood event into a rising water phase and a receding water phase based on the location of the flood peak and the start and end time of the flood event. Based on the water quality and quantity monitoring data corresponding to the rising water phase and the receding water phase, the lag index of the flood event is calculated, and the lag type of pollutant migration of the flood event is determined based on the lag index.

[0223] In this embodiment, as an optional embodiment, the type determination module 203 is specifically used for:

[0224] The flow data sequence and water quality concentration data sequence corresponding to the target flood event are normalized to obtain normalized flow data sequence and normalized water quality concentration data sequence.

[0225] From the normalized flow data sequence, a preset number of comparison reference point pairs are selected. For each comparison reference point pair, based on the normalized flow data sequence, normalized water quality concentration data sequence, and pre-set interpolation algorithm corresponding to the flood stage to which the comparison reference point pair belongs, the normalized water quality concentration data of the comparison reference point pair is obtained. The normalized water quality concentration data includes normalized water quality concentration data during flood rise and normalized water quality concentration data during flood recede.

[0226] The difference between the normalized water quality concentration data of the rising water level and the normalized water quality concentration data of the receding water level of the comparison reference point pair is calculated to obtain the local hysteresis value of the comparison reference point pair.

[0227] The lag index of the target flood event is obtained based on the local lag values ​​of each comparison benchmark pair.

[0228] In this embodiment, as an optional implementation, the flood stage to which a candidate comparison benchmark in the comparison benchmark pair belongs is the rising water stage. The step of obtaining the normalized water quality concentration data of the comparison benchmark pair based on the normalized flow data sequence, the normalized water quality concentration data sequence, and a pre-set interpolation algorithm corresponding to the flood stage to which the comparison benchmark pair belongs includes:

[0229] Sort the normalized flow data sequence corresponding to the flooded section;

[0230] From the sorted normalized flow data sequence, obtain the first reference point and the second reference point corresponding to the normalized flow data adjacent to the candidate comparison reference point;

[0231] Obtain the normalized water quality concentration data and normalized flow rate data corresponding to the first and second reference points, respectively, and combine them with a pre-set interpolation algorithm to obtain the normalized water quality concentration data of the candidate comparison reference points for rising water levels.

[0232] In this embodiment, as an optional implementation, the normalized water quality concentration data and normalized flow rate data corresponding to the first and second reference points are obtained respectively. Combined with a pre-set interpolation algorithm, the normalized water quality concentration data of the candidate comparison reference points is obtained, including:

[0233] Calculate the difference between the concentration data of the second normalized water quality concentration corresponding to the second benchmark point and the concentration data of the first normalized water quality concentration corresponding to the first benchmark point;

[0234] Calculate the difference between the second normalized flow data corresponding to the second reference point and the first normalized flow data corresponding to the first reference point;

[0235] Calculate the difference between the second flow data of the comparison normalized flow data corresponding to the candidate comparison benchmark and the second flow data of the first normalized flow data corresponding to the first benchmark;

[0236] Obtain the ratio of the second flow rate data difference to the first flow rate data difference, and calculate the product of the ratio and the concentration data difference;

[0237] The sum of the product and the first normalized water quality concentration data corresponding to the first benchmark point is calculated to obtain the normalized water quality concentration data of the candidate comparison benchmark point.

[0238] The data reconstruction module 204 is used to obtain target water quality concentration data marked as missing for more than the missing threshold from the pre-processed flood season water quality and quantity monitoring data sequence, and reconstruct the target water quality concentration data according to the lag type corresponding to the target water quality and quantity concentration data and the water quality and quantity monitoring data of the corresponding flood event.

[0239] In this embodiment, as an optional embodiment, the data reconstruction module 204 is specifically used for:

[0240] To determine the target flood event to which the target water quality concentration data belongs, based on the normalized water quality and quantity monitoring data sequence corresponding to the rising water section of the target flood event, sampling points in the rising water section where both water quality concentration data and flow data are not missing are selected; and based on the normalized water quality and quantity monitoring data sequence corresponding to the receding water section of the target flood event, sampling points in the receding water section where both water quality concentration data and flow data are not missing are selected.

[0241] The flow data corresponding to the sampling points of each rising water level and each receding water level were sorted. The flow data was used as the independent variable and the water quality concentration data corresponding to the flow data was used as the dependent variable. The concentration-flow empirical relationship of the rising water level and the concentration-flow empirical relationship of the receding water level were established.

[0242] Obtain the missing water quality concentration data marked as missing from the normalized water quality and quantity monitoring data sequence corresponding to the target flood event, determine the flood stage to which the missing water quality concentration data belongs, and determine the water quality concentration data corresponding to the missing water quality concentration data based on the flow data corresponding to the missing water quality concentration data and the empirical formula corresponding to the flood stage. The flood stage includes the rising water stage and the receding water stage.

[0243] Based on the obtained water quality concentration data, the water quality concentration data marked as missing in the normalized water quality and quantity monitoring data sequence of the target flood event are reconstructed and the marker is deleted.

[0244] The total flux acquisition module 205 is used to obtain a water quality and quantity reconstructed data sequence based on the pre-processed flood season water quality and quantity monitoring data sequence and the reconstructed target water quality concentration data. Based on the water quality and quantity reconstructed data sequence, the module obtains the total flux during the flood season and the contribution ratio of pollutant flux of each flood event to the total flux during the flood season.

[0245] Based on the same inventive concept, embodiments of the present invention also provide a storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the method for obtaining the pollutant flux during the flood season in any of the above possible implementations.

[0246] Optionally, the storage medium may be a non-transitory computer-readable storage medium, such as a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device.

[0247] Based on the same inventive concept, see [link to inventive concept] Figure 3 This invention also provides an electronic device, including a memory 101 (e.g., non-volatile memory), a processor 102, and a computer program stored on the memory 101 and executable on the processor 102. When the processor 102 executes the program, it implements the steps of the method for obtaining the pollutant flux during the flood season in any of the above possible implementations, which can be equivalent to the aforementioned device for obtaining the pollutant flux during the flood season. Of course, the processor can also be used to process other data or perform calculations. This electronic device can be a PC, server, terminal, or other similar device.

[0248] like Figure 3 As shown, the electronic device may also include: memory 103, network interface 104, and internal bus 105. In addition to these components, other hardware may also be included, which will not be described in detail here.

[0249] It should be noted that the above-mentioned device for obtaining the flux of pollutants during the flood season can be implemented by software. As a device in a logical sense, it is formed by the processor 102 of the electronic device in which it is located reading the computer program instructions stored in the non-volatile memory into the memory 103 for execution.

[0250] The embodiments of the subject matter and functional operation described in this specification can be implemented in the following ways: digital electronic circuits, tangibly embodied computer software or firmware, computer hardware including the structures disclosed in this specification and their structural equivalents, or combinations thereof. Embodiments of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions encoded on a tangible, non-transitory program carrier for execution by a data processing apparatus or for controlling the operation of a data processing apparatus. Alternatively or additionally, the program instructions may be encoded on artificially generated propagation signals, such as machine-generated electrical, optical, or electromagnetic signals, which are generated to encode information and transmit it to a suitable receiving device for execution by the data processing apparatus. The computer storage medium may be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or combinations thereof.

[0251] The processing and logic flow described in this specification can be executed by one or more programmable computers that execute one or more computer programs to perform corresponding functions by operating on input data and generating output. The processing and logic flow can also be executed by special-purpose logic circuitry—such as FPGA (Field Programmable Gate Array) or ASIC (Application-Specific Integrated Circuit), and the device can also be implemented as special-purpose logic circuitry.

[0252] Suitable computers for executing computer programs include, for example, general-purpose and / or special-purpose microprocessors, or any other type of central processing unit. Typically, the central processing unit receives instructions and data from read-only memory and / or random access memory. The basic components of a computer include a central processing unit for implementing or executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include one or more mass storage devices for storing data, such as disks, magneto-optical disks, or optical disks, or the computer will be operatively coupled to such mass storage devices to receive data from or transfer data to them, or both. However, a computer is not required to have such devices. Furthermore, a computer can be embedded in another device, such as a mobile phone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a global positioning system (GPS) receiver, or a portable storage device such as a universal serial bus (USB) flash drive, to name a few.

[0253] Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, such as semiconductor memory devices (e.g., EPROM, EEPROM, and flash memory devices), magnetic disks (e.g., internal hard disks or removable disks), magneto-optical disks, and CD-ROM and DVD-ROM disks. Processors and memory may be supplemented by or incorporated into dedicated logic circuitry.

[0254] While this specification contains numerous specific implementation details, these should not be construed as limiting the scope of any invention or the scope of the claims, but rather are primarily used to describe features of specific embodiments of a particular invention. Certain features described in the various embodiments herein may also be implemented in combination in a single embodiment. Conversely, various features described in a single embodiment may also be implemented separately in various embodiments or in any suitable sub-combination. Furthermore, while features may function in certain combinations as described above and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and a claimed combination may refer to a sub-combination or a variation thereof.

[0255] Similarly, although the operations are depicted in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order shown or sequentially, or requiring all illustrated operations to be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. Furthermore, the separation of various system modules and components in the above embodiments should not be construed as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0256] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve the desired result. Furthermore, the processes depicted in the drawings are not necessarily shown in a specific order or sequence to achieve the desired result. In some implementations, multitasking and parallel processing may be advantageous.

[0257] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0258] The above are merely specific embodiments of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for obtaining pollutant flux during the flood season, characterized in that, include: The collected flood season water quality and quantity monitoring data sequence is preprocessed. The preprocessing includes interpolation calculations performed on water quality and quantity monitoring data in the flood season water quality and quantity monitoring data sequence that are marked as missing for no more than a preset missing threshold, using a time-based linear interpolation method. Based on the preprocessed flood season water quality and quantity monitoring data sequence, the flood events during the flood season and the start and end times of each flood event are determined. For each flood event, the flood event is divided into a rising phase and a receding phase based on the location of the flood peak and the start and end times of the flood event. Based on the water quality and quantity monitoring data corresponding to the rising and receding phases, the lag index of the flood event is calculated, and the lag type of pollutant migration of the flood event is determined based on the lag index. From the pre-processed flood season water quality and quantity monitoring data sequence, target water quality concentration data with a consecutive number of missing measurements exceeding the missing measurement threshold is obtained. Based on the lag type corresponding to the target water quality and quantity concentration data and the water quality and quantity monitoring data of the corresponding flood event, the target water quality concentration data is reconstructed. Based on the preprocessed flood season water quality and quantity monitoring data sequence and the reconstructed target water quality concentration data, a water quality and quantity reconstructed data sequence is obtained. Based on the water quality and quantity reconstructed data sequence, the total flux during the flood season and the contribution ratio of pollutant flux of each flood event to the total flux during the flood season are obtained.

2. The method for obtaining pollutant flux during the flood season according to claim 1, characterized in that, The preprocessing of the collected flood season water quality and quantity monitoring data sequences also includes: For the same sampling point within the flood season monitoring cycle, a water quality and quantity monitoring data sequence for the sampling point is constructed based on water quality and quantity monitoring data collected in chronological order or interpolated. Traverse the water quality and quantity monitoring data sequence of the sampling points and extract the i-th unlabeled water quality and quantity monitoring data; Based on the water quality and quantity monitoring data with a preset first value that is not marked before the i-th water quality and quantity monitoring data, the i-th water quality and quantity monitoring data, and the water quality and quantity monitoring data with a preset second value that is not marked after the i-th water quality and quantity monitoring data, the median data and standard deviation of the local sliding window are obtained. Based on the i-th water quality and quantity monitoring data and the median data in the local sliding window, the i-th water quality and quantity error data is obtained; Calculate the value of the error data and standard deviation of the i-th water quality and quantity data to obtain the i-th standardized residual; If the standardization residual of the i-th point exceeds the standard deviation of the preset multiple, the i-th water quality and quantity monitoring data is marked as missing from the water quality and quantity monitoring data sequence of the sampling point.

3. The method for obtaining pollutant flux during the flood season according to claim 1, characterized in that, The flow data sequence in the preprocessed flood season water quality and quantity monitoring data sequence is used to determine flood events during the flood season, including: Obtain the first data difference between the (i+1)th and the ith traffic data in the traffic data sequence; If the difference of the first data is greater than or equal to zero, the i-th traffic data is taken as the base point; if the difference of the first data is less than zero, the (i+1)-th traffic data is taken as the base point. Obtain the second data difference between the (i+2)th and (i+1)th traffic data in the traffic data sequence; If the second data difference is greater than or equal to zero, continue to obtain the third data difference between the (i+3)th and (i+2)th traffic data in the traffic data sequence until the data difference of the obtained consecutive preset number is less than zero. The data point with the larger data value among the consecutive preset number of traffic data that is less than zero is taken as the peak point. Calculate the fourth data difference between the peak point and the base point. If the fourth data difference is greater than a preset flood threshold, determine the peak point as the location of the flood peak of a flood event. If the interval between adjacent flood peak locations exceeds a preset flood peak cycle threshold, the event corresponding to that flood peak location is confirmed as a flood event, and the base point is the start and end time of the flood event.

4. The method for obtaining pollutant flux during the flood season according to claim 1, characterized in that, The calculation of the lag index for the flood event based on water quality and quantity monitoring data corresponding to the rising and receding water levels includes: The flow data sequence and water quality concentration data sequence corresponding to the target flood event are normalized to obtain normalized flow data sequence and normalized water quality concentration data sequence. From the normalized flow data sequence, a preset number of comparison reference point pairs are selected. For each comparison reference point pair, based on the normalized flow data sequence, normalized water quality concentration data sequence, and pre-set interpolation algorithm corresponding to the flood stage to which the comparison reference point pair belongs, the normalized water quality concentration data of the comparison reference point pair is obtained. The normalized water quality concentration data includes normalized water quality concentration data during flood rise and normalized water quality concentration data during flood recede. The difference between the normalized water quality concentration data of the rising water level and the normalized water quality concentration data of the receding water level of the comparison reference point pair is calculated to obtain the local hysteresis value of the comparison reference point pair. The lag index of the target flood event is obtained based on the local lag values ​​of each comparison benchmark pair.

5. The method for obtaining pollutant flux during the flood season according to claim 4, characterized in that, The flood stage to which one of the candidate comparison reference points in the comparison reference point pair belongs is the rising water stage. The process of obtaining the normalized water quality concentration data of the comparison reference point pair based on the normalized flow data sequence, normalized water quality concentration data sequence, and a pre-set interpolation algorithm corresponding to the flood stage to which the comparison reference point pair belongs includes: Sort the normalized flow data sequence corresponding to the flooded section; From the sorted normalized flow data sequence, obtain the first reference point and the second reference point corresponding to the normalized flow data adjacent to the candidate comparison reference point; Obtain the normalized water quality concentration data and normalized flow rate data corresponding to the first and second benchmark points, respectively, and combine them with a pre-set interpolation algorithm to obtain the normalized water quality concentration data of the candidate comparison benchmark points.

6. The method for obtaining pollutant flux during the flood season according to claim 5, characterized in that, The step of obtaining the normalized water quality concentration data and normalized flow rate data corresponding to the first and second benchmark points, respectively, and combining them with a pre-set interpolation algorithm, to obtain the flood-induced normalized water quality concentration data of the candidate comparison benchmark points includes: Calculate the difference between the concentration data of the second normalized water quality concentration corresponding to the second benchmark point and the concentration data of the first normalized water quality concentration corresponding to the first benchmark point; Calculate the difference between the second normalized flow data corresponding to the second reference point and the first normalized flow data corresponding to the first reference point; Calculate the difference between the second flow data of the comparison normalized flow data corresponding to the candidate comparison benchmark and the second flow data of the first normalized flow data corresponding to the first benchmark; Obtain the ratio of the second flow rate data difference to the first flow rate data difference, and calculate the product of the ratio and the concentration data difference; The sum of the product and the first normalized water quality concentration data corresponding to the first benchmark point is calculated to obtain the normalized water quality concentration data of the candidate comparison benchmark point.

7. The method for obtaining pollutant flux during the flood season according to any one of claims 1 to 6, characterized in that, The process of reconstructing the target water quality concentration data based on the lag type corresponding to the target water quality concentration data and the water quality and quantity monitoring data of the corresponding flood event includes: To determine the target flood event to which the target water quality concentration data belongs, based on the normalized water quality and quantity monitoring data sequence corresponding to the rising water section of the target flood event, sampling points in the rising water section where both water quality concentration data and flow data are not missing are selected; and based on the normalized water quality and quantity monitoring data sequence corresponding to the receding water section of the target flood event, sampling points in the receding water section where both water quality concentration data and flow data are not missing are selected. The flow data corresponding to the sampling points of each rising water level and each receding water level were sorted. The flow data was used as the independent variable and the water quality concentration data corresponding to the flow data was used as the dependent variable. The concentration-flow empirical relationship of the rising water level and the concentration-flow empirical relationship of the receding water level were established. Obtain the missing water quality concentration data marked as missing from the normalized water quality and quantity monitoring data sequence corresponding to the target flood event, determine the flood stage to which the missing water quality concentration data belongs, and determine the water quality concentration data corresponding to the missing water quality concentration data based on the flow data corresponding to the missing water quality concentration data and the empirical formula corresponding to the flood stage. The flood stage includes the rising water stage and the receding water stage. Based on the obtained water quality concentration data, the water quality concentration data marked as missing in the normalized water quality and quantity monitoring data sequence of the target flood event are reconstructed and the marker is deleted.

8. A device for obtaining pollutant flux during the flood season, characterized in that, include: The preprocessing module is used to preprocess the collected flood season water quality and quantity monitoring data sequence. The preprocessing includes interpolation operations performed using a time-based linear interpolation method on the water quality and quantity monitoring data in the flood season water quality and quantity monitoring data sequence where the number of consecutive missing data does not exceed a preset missing data threshold. The event acquisition module is used to determine the flood events during the flood season and the start and end times of each flood event based on the flow data sequence in the preprocessed flood season water quality and quantity monitoring data sequence. The type determination module is used to divide each flood event into a rising phase and a receding phase based on the location of the flood peak and the start and end times of the flood event. Based on the water quality and quantity monitoring data corresponding to the rising and receding phases, the module calculates the lag index of the flood event and determines the lag type of pollutant migration of the flood event based on the lag index. The data reconstruction module is used to obtain target water quality concentration data marked as missing for more than the missing threshold from the pre-processed flood season water quality and quantity monitoring data sequence, and reconstruct the target water quality concentration data according to the lag type corresponding to the target water quality and quantity concentration data and the water quality and quantity monitoring data of the corresponding flood event. The total flux acquisition module is used to obtain a water quality and quantity reconstructed data sequence based on the pre-processed flood season water quality and quantity monitoring data sequence and the reconstructed target water quality concentration data. Based on the water quality and quantity reconstructed data sequence, the module obtains the total flux during the flood season and the contribution ratio of pollutant flux of each flood event to the total flux during the flood season.

9. A storage medium, characterized in that, The storage medium stores a program or instructions, which are executed by a processor to implement the steps of the method for obtaining the pollutant flux during the flood season as described in any one of claims 1 to 7.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the method for obtaining the pollutant flux during the flood season as described in any one of claims 1 to 7.

Images