River basin water quality frequency conversion sampling method and device for runoff process

Abstract

The invention discloses a drainage basin water quality frequency conversion sampling method and device oriented to a runoff process, and belongs to the field of hydrological water environment monitoring, and the method comprises the steps: obtaining historical runoff observation data and rainfall data of a to-be-monitored drainage basin and hydrological observation data at the current moment; predicting runoff forecast data through a preset runoff prediction model in combination with the hydrological observation data and the rainfall data; the runoff prediction model is obtained by coupling a long short-term memory network model and a natural resource protection service curve number model; assimilating the hydrological observation data to runoff forecast data through an ensemble Kalman filtering algorithm; determining sampling time sites of a plurality of different runoff stages in a rainfall period corresponding to the runoff forecast data by combining the assimilated runoff forecast data through a segmented sampling method; and when the water level of the to-be-monitored drainage basin reaches a preset trigger water level, sampling the to-be-monitored drainage basin according to the sampling time site. Therefore, the sampling efficiency and the sampling result accuracy in the runoff process can be improved.

Description

Technical Field

[0001] The present application relates to the field of hydrological and water environment monitoring, and in particular to a variable frequency sampling method and device for watershed water quality in a runoff process. Background Art

[0002] In the field of environmental monitoring and hydrology and water resources, accurate monitoring of water quality in the basin is of great significance. Especially during the heavy rainfall in the flood season, affected by the rainfall intensity and the previous underlying surface conditions, the water quality of the basin rivers (such as total phosphorus (TP) and total nitrogen (TN)) shows drastic dynamic changes in different stages of the runoff process (such as the rising and falling periods). Therefore, an efficient monitoring method for water quality changes in the basin is needed to accurately reflect the changes in water quality.

[0003] However, the existing technology often uses the traditional fixed-frequency sampling method or flow ratio sampling method to monitor the water quality of the basin. Among them, the traditional fixed-frequency sampling method (sampling time is fixed) cannot be flexibly adjusted according to the rapid changes in water quality during the runoff process due to the fixed sampling frequency, which often leads to a large amount of key data missing. The flow ratio sampling method limits the maximum number of samples during each precipitation period due to the uncertainty of the runoff process and the number of bottles of the automatic sampler (usually 24). It is impossible to reasonably collect water samples at different key stages of the runoff, and it is difficult to fully reflect the dynamic changes of water quality during the precipitation runoff process. Therefore, in order to improve the efficiency of water sample collection, it is necessary to accurately and timely predict the precipitation runoff process. However, since the conceptual model precipitation-runoff model requires a large amount of observation data to be determined to reduce the uncertainty of the parameters, the physical process model has limitations in the real-time flood runoff process prediction.

[0004] Therefore, when water quality changes rapidly during runoff, how to improve the efficiency of water sample collection and the accuracy of collected samples in reflecting water quality changes is a technical problem that needs to be solved at present. Summary of the invention

[0005] The present application provides a variable frequency sampling method and device for watershed water quality in the runoff process, which can solve the technical problems in the prior art that need to be solved at present, such as how to improve the efficiency of water sample collection and the accuracy of the collected samples in reflecting the changes in water quality when the water quality changes rapidly during the runoff process.

[0006] The present application provides a variable frequency sampling method for water quality in a watershed for runoff process, including: Obtain historical runoff observation data, precipitation data and current hydrological observation data of the basin to be monitored; The runoff forecast data for the entire precipitation period is predicted by combining the hydrological observation data at the current moment and the precipitation data through a preset runoff forecast model; the runoff forecast model is obtained by coupling a long short-term memory network model and a natural resource protection service curve number model; Assimilating the hydrological observation data at the current moment into the runoff forecast data through an ensemble Kalman filter algorithm; By using a segmented sampling method and combining the assimilated runoff forecast data, the sampling time and location of the runoff forecast data corresponding to several different runoff stages during the precipitation period are determined; When the water level of the monitored basin reaches a preset trigger water level, the monitored basin is sampled according to the sampling time point to obtain sampling data, and the water quality indicators and confidence intervals of the entire runoff process are calculated based on the sampling data; wherein the preset trigger water level is determined and obtained based on the historical runoff observation data.

[0007] Compared with the prior art, the embodiments of the present application have the following beneficial effects: when facing or about to face the runoff process during the precipitation period, the runoff forecast data during the entire precipitation period is predicted by calculating the long short-term memory network model with fewer parameters and the natural resource protection service curve number model, and then a small amount of actually collected water level observation data is used to assimilate the runoff forecast data according to the collected water level observation data, thereby improving the prediction accuracy of the runoff forecast data. Furthermore, when the water level of the monitored basin reaches the preset trigger water level, the dynamic changes of the water level during the entire precipitation period can be obtained according to the accurately predicted runoff forecast data, so that the sampling frequency and sampling time and location of different time periods during the precipitation period can be reasonably and dynamically set according to the accurate water level changes, avoiding invalid sampling operations, improving sampling efficiency, and at the same time making the collected samples more accurately reflect the changes in water quality during the runoff process.

[0008] Furthermore, the runoff forecast data for the entire precipitation period is predicted by combining the hydrological observation data at the current moment and the precipitation data through a preset runoff prediction model, including: The precipitation data includes historical precipitation intensity data and precipitation forecast data corresponding to the entire precipitation period; According to the historical precipitation intensity data, a unit line stream function under a preset precipitation intensity is obtained, and according to the unit line stream function under the preset precipitation intensity, a unit line stream function under any precipitation intensity is determined; Inputting the hydrological observation data and the precipitation forecast data into a preset long short-term memory network model to obtain net rainfall; The net rainfall is input into a preset natural resource protection service curve model, and combined with the unit line stream function under any precipitation intensity to predict the runoff forecast data.

[0009] Compared with the prior art, the above embodiments have the following beneficial effects: the long short-term memory network model (LSTM) is good at capturing the long-term dependency of time series, while the natural resource conservation service curve number model (NRCS-CN) has the advantages of fewer parameters and wider applicability. Combining the two solves the problems of high parameter uncertainty and high computing power requirements of traditional conceptual models, and improves computing efficiency; through the input of precipitation forecast data and historical intensity data, the entire runoff prediction model can dynamically respond to real-time precipitation changes, overcome the dependence of pure physical models on a large amount of measured data, improve the flexibility of prediction, and at the same time realize the complementarity of multi-time scale information to ensure the accuracy of prediction results.

[0010] Further, the acquiring a unit line stream function under a preset precipitation intensity according to the historical precipitation intensity data, and determining a unit line stream function under any precipitation intensity according to the unit line stream function under the preset precipitation intensity, comprises: The steps for obtaining the unit line stream function under any precipitation intensity are specifically as follows: in, Represents any precipitation intensity The unit stream function under ; Represents the preset precipitation intensity The unit stream function under ; Represents the calculation of any precipitation intensity using the unit line stream function under the preset precipitation intensity The time variable mapping function required when the unit stream function is below; and Respectively represent Peak value and peak time; and represents the power function coefficient obtained by power function fitting; and represents the power function exponent obtained by power function fitting; The catchment area of ​​the preset node for the watershed to be monitored; For in time When the precipitation intensity The cumulative runoff caused; is the time variable.

[0011] Compared with the prior art, the above embodiment has the following beneficial effects: since the actual observed data volume is limited, it is impossible to obtain the unit line stream function under any precipitation intensity by fitting historical data. Therefore, it is necessary to fit a small number of unit line stream functions under a small number of known precipitation intensities through a small amount of historical data, and then determine the unit line stream function under any precipitation intensity based on the unit line stream function under a small number of known precipitation intensities, thereby effectively solving the problem that the pure physical model relies on a large amount of measured data, and at the same time reducing the computing power required for data fitting.

[0012] Furthermore, the net rainfall is input into a preset natural resource protection service curve model, combined with the unit line stream function under any precipitation intensity, to predict the runoff forecast data, including: Calculating the curve value of the runoff forecast data corresponding to the precipitation period according to the net rainfall; According to the curve value, the potential maximum retention amount of precipitation is calculated, and the cumulative effective precipitation is calculated according to the maximum retention amount; Determine an effective precipitation intensity function with time as a variable according to the accumulated effective precipitation; The runoff forecast data is obtained through convolution integration according to the effective precipitation intensity function and the unit line stream function under the arbitrary precipitation intensity.

[0013] Compared with the prior art, the above embodiment has the following beneficial effects: by processing precipitation forecast data through LSTM, the net rainfall in the entire rainfall period can be obtained in advance. The net rainfall can be used as an intermediate variable, and the output of the subsequent NRCS-CN model can be corrected in combination with the advance predicted data, thereby alleviating the problem that the NRCS-CN model is sensitive to initial conditions, thereby accurately estimating the effective precipitation intensity that changes over time; when the accurate effective precipitation intensity function is known, the unit line stream function under any precipitation intensity can be combined to obtain accurate runoff forecast data through convolution integral.

[0014] Furthermore, the calculation formula of the runoff forecast data specifically includes: in, is the curve value; is the precipitation; is the net rainfall; is the initial interception ratio of the watershed to be monitored; Represents the cumulative effective precipitation For time variables Derivation; is the effective precipitation intensity function with time as a variable; is the accumulated precipitation in the precipitation forecast data; is the initial retention volume; is the potential maximum retention of precipitation, based on Calculate acquisition; For direct runoff forecast data; is the base flow data; For runoff forecast data; The catchment area of ​​the preset node for the watershed to be monitored; is the convolution integration process, where is the unit stream function; is the time variable.

[0015] Compared with the prior art, the above embodiment has the following beneficial effects: when calculating the final runoff forecast data, by considering direct runoff and base flow, the runoff composition can be simulated more accurately, which helps to consider more comprehensive sources of pollutants when collecting samples later, such as surface runoff and groundwater, thereby improving the accuracy of the sampling results; further, through convolution calculation, the process of precipitation and runoff conversion is dynamically simulated to improve the accuracy of the prediction results.

[0016] Furthermore, assimilating the hydrological observation data at the current moment into the runoff forecast data through an ensemble Kalman filter algorithm includes: If there is new runoff observation data in the hydrological observation data at the current moment, the state variable set at the current moment is updated according to the runoff observation data and the state variable set at the previous moment; wherein the state variable set at the initial moment is generated by using the Monte Carlo method by taking the curve value as the initial state variable; The average value of the updated state variable set is calculated, and the assimilated runoff forecast data is obtained according to the average value.

[0017] Compared with the prior art, the above embodiment has the following beneficial effects: since the data used in predicting runoff forecast data are mostly forecast data given by weather forecasts and currently observable data, due to data fluctuations and model accuracy issues, it is impossible to completely rely on the prediction results to determine the subsequent sampling frequency, and the runoff process is generally a non-steady-state process. In order to further improve the accuracy of previously acquired runoff forecast data, when in the runoff process, the previously predicted runoff forecast data for the entire rainfall period is corrected based on the real-time observed runoff observation data, thereby dynamically adjusting the subsequent sampling frequency during the runoff process to ensure the accuracy of the real-time sample collection results.

[0018] Further, the updating of the state variable set at the current moment according to the runoff observation data and the state variable set at the previous moment includes: Predicting the runoff forecast data set at the current moment according to the state variable set at the previous moment; Assimilating the runoff observation data into the runoff forecast data set at the current moment to obtain a runoff analysis value set at the current moment; The state variable set at the current moment is estimated by taking the minimum error between the runoff forecast data set at the current moment and the runoff analysis value set at the current moment as the optimization goal.

[0019] Compared with the prior art, the above embodiment has the following beneficial effects: when new runoff observation data is obtained, the state variable set is iteratively updated according to the latest runoff observation data, so that the runoff forecast data output by the NRCS-CN model in the previous sequence is gradually accurate, and at the same time, the overall runoff prediction model has the ability to adaptively update and more effectively adapt to the rapid changes in the runoff process.

[0020] Furthermore, the segmented sampling method is combined with the assimilated runoff forecast data to determine the sampling time points of the runoff forecast data corresponding to several different runoff stages during the precipitation period, including: Substituting the assimilated runoff forecast data into a preset river flow-water level relationship to obtain a water level variation function; wherein the preset river flow-water level relationship is obtained by fitting the historical runoff observation data; Calculating the first-order derivative of the water level change function to obtain the first-order derivative solution result, and dividing the time period within the precipitation period corresponding to the runoff forecast data according to the first-order derivative solution result to obtain several different runoff stages; The number of sampling points corresponding to each of the runoff stages is determined according to preset rules, and sampling time and position points corresponding to the number of sampling points are randomly set within the runoff stage.

[0021] Compared with the prior art, the above embodiment has the following beneficial effects: based on the water level change function of the assimilated runoff forecast, the first-order derivative is derived and the time period is divided according to the first-order derivative solution result. According to the divided time periods, the sampling is intensified during the steep rise / fall period of runoff and the sampling is reduced during the flat period, thereby improving the sampling efficiency; it can also ensure that samples are collected during key periods when the water quality changes drastically (such as before and after the peak), thereby avoiding the loss of important information with traditional fixed-frequency sampling; finally, the sampling start condition is set by triggering the water level, thereby avoiding invalid sampling during the low water level period.

[0022] Another embodiment of the present application also provides a variable frequency sampling device for water quality in a watershed for a runoff process, comprising: a data acquisition module, a runoff forecast data prediction module, a data assimilation module, a sampling time point confirmation module, and a sampling module; The data acquisition module is used to acquire the historical runoff observation data, precipitation data and hydrological observation data of the watershed to be monitored at the current moment; The runoff forecast data prediction module is used to predict the runoff forecast data during the entire precipitation period by combining the hydrological observation data at the current moment and the precipitation data through a preset runoff forecast model; the runoff forecast model is obtained by coupling a long short-term memory network model and a natural resource protection service curve number model; The data assimilation module is used to assimilate the hydrological observation data at the current moment into the runoff forecast data through an ensemble Kalman filter algorithm; The sampling time and location confirmation module is used to determine the sampling time and location of the runoff forecast data corresponding to several different runoff stages during the precipitation period by combining the assimilated runoff forecast data through a segmented sampling method; The sampling module is used to sample the watershed to be monitored according to the sampling time point when the water level of the watershed to be monitored reaches a preset trigger water level, so as to obtain sampling data, and calculate the water quality index and confidence interval of the entire runoff process based on the sampling data; wherein the preset trigger water level is determined and obtained based on the historical runoff observation data.

[0023] Furthermore, the runoff forecast data for the entire precipitation period is predicted by combining the hydrological observation data at the current moment and the precipitation data through a preset runoff prediction model, including: The precipitation data includes historical precipitation intensity data and precipitation forecast data corresponding to the entire precipitation period; According to the historical precipitation intensity data, a unit line stream function under a preset precipitation intensity is obtained, and according to the unit line stream function under the preset precipitation intensity, a unit line stream function under any precipitation intensity is determined; Inputting the hydrological observation data and the precipitation forecast data into a preset long short-term memory network model to obtain net rainfall; The net rainfall is input into a preset natural resource protection service curve model, and combined with the unit line stream function under any precipitation intensity to predict the runoff forecast data. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the implementation manner will be briefly introduced below. Obviously, the drawings described below are only some implementation manners of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] Figure 1 A schematic flow chart of a variable frequency sampling method for water quality in a watershed facing a runoff process provided in some embodiments of the present application; Figure 2 A schematic diagram of the structure of a watershed water quality variable frequency sampling system for runoff process provided in some embodiments of the present application; Figure 3 A schematic diagram of the structure of a long short-term memory network model provided in some embodiments of the present application; Figure 4 A schematic diagram of the results of the long short-term memory network model provided in some embodiments of the present application; Figure 5 A schematic diagram of the flow of the ensemble Kalman filter algorithm provided in some embodiments of the present application; Fig. 6A A schematic diagram of a single peak 4-segment sampling time point provided in some embodiments of the present application; Figure 6B A schematic diagram of a bimodal 6-segment sampling time point provided in some embodiments of the present application; Figure 7 A schematic diagram of the terminal structure in a watershed water quality variable frequency sampling system for runoff processes provided in some embodiments of the present application; Figure 8 A schematic diagram of a terminal control chip in a watershed water quality variable frequency sampling system for runoff processes provided in some embodiments of the present application; Fig. 9 This is a schematic diagram of the structure of a variable frequency sampling device for water quality in a watershed for a runoff process provided in some embodiments of the present application. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0028] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0029] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0030] In the description of the embodiments of the present application, the term "and / or" is only a description of the association relationship of the associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0031] In the description of the embodiments of the present application, the term "multiple" refers to more than two (including two). Similarly, "multiple groups" refers to more than two groups (including two groups), and "multiple pieces" refers to more than two pieces (including two pieces).

[0032] In the description of the embodiments of the present application, unless otherwise clearly specified and limited, technical terms such as "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present application can be understood according to the specific circumstances.

[0033] When monitoring water quality in a river basin, existing technologies often use traditional fixed-frequency sampling methods or flow-proportional sampling methods. Among them, the traditional fixed-frequency sampling method (sampling time is fixed) cannot be flexibly adjusted according to the rapid changes in water quality during the runoff process due to the fixed sampling frequency, which often leads to a large amount of key data missing. The flow-proportional sampling method limits the maximum number of samples during each precipitation period due to the uncertainty of the runoff process and the number of bottles of the automatic sampler (usually 24). It is impossible to reasonably collect water samples at different key stages of the runoff, and it is difficult to fully reflect the dynamic changes in water quality during the precipitation runoff process. Therefore, in order to improve the efficiency of water sample collection, it is necessary to accurately and timely predict the precipitation runoff process. However, since the conceptual model precipitation-runoff model requires a large amount of observation data to be determined to reduce the uncertainty of the parameters, the physical process model has limitations in the real-time flood runoff process prediction.

[0034] Please refer to Figure 1 In order to solve the technical problems in the prior art of low water sample collection efficiency and low accuracy of collected sample results when water quality changes rapidly during runoff, an embodiment of the present application provides a variable frequency sampling method for watershed water quality in a runoff process, including S101 to S105.

[0035] refer to Figure 2 , which is a variable frequency sampling system for water quality in a watershed for runoff process, equipped with the variable frequency sampling method for water quality in a watershed for runoff process in the embodiment of the present application, including a cloud, an edge and a terminal. The system adopts a cloud-edge-terminal collaborative mode; wherein, the cloud is equipped with the preset runoff prediction model and the data assimilation process described in the embodiment of the present application; the edge performs real-time dynamic planning of segmented sampling collection time and point locations based on the assimilated runoff forecast data; the terminal is used to perform variable frequency sampling according to the collection time and point locations planned in real time by the edge, perform sampling according to the sampling instructions issued by the user, or perform fixed frequency sampling, etc., and at the same time monitor and collect the hydrological observation data of the watershed to be monitored in real time.

[0036] Further, through Figure 2 It can be seen that after the sampling time point is obtained through the variable frequency sampling method for watershed water quality in the runoff process proposed in the embodiment of the present application, the terminal will collect samples according to the sampling time point only when the trigger water level is reached. At other times, such as during non-precipitation periods, if the time reaches the fixed-frequency monitoring time preset by the system (such as 8 am every Monday), the terminal automatically performs fixed-frequency water sample collection; or if a sample collection instruction is temporarily received from the cloud and transmitted through the edge, the sample collection operation is performed immediately. Each sample collected by the terminal will be stored in the terminal.

[0037] Furthermore, if Figure 1As shown, the present application includes S101 to S105 in a variable frequency sampling method for water quality in a watershed for a runoff process provided in an embodiment, which are specifically: S101: Obtain historical runoff observation data, precipitation data and current hydrological observation data of the watershed to be monitored.

[0038] Furthermore, in some embodiments of the present application, hydrological observation data include but are not limited to: information on the underlying surface of the watershed to be monitored, such as surface soil moisture; runoff observation data monitored in real time during the runoff process; and real-time data on river water level, precipitation, temperature, soil moisture, etc. of preset sections in the watershed to be monitored (such as more critical sections determined by historical data or artificially set sections).

[0039] Furthermore, in some embodiments of the present application, the historical runoff observation data is data of a historical period collected by a terminal; the precipitation data includes precipitation forecast data and historical precipitation intensity data, wherein the historical precipitation intensity data can be obtained by terminal collection or by other data platforms; the precipitation forecast data can be obtained by weather forecast models or other methods, and the data is the precipitation forecast data within the precipitation period corresponding to the runoff forecast data currently required to be predicted, including but not limited to precipitation ( ), precipitation duration (D), average precipitation intensity (ARI) and maximum precipitation intensity (MRI), average temperature (T), minimum temperature (T min ) and the maximum temperature (T max ).

[0040] S102: Predicting the runoff forecast data for the entire precipitation period through a preset runoff prediction model in combination with the hydrological observation data at the current moment and the precipitation data; the runoff prediction model is obtained by coupling a long short-term memory network model and a natural resource protection service curve number model.

[0041] Furthermore, in some embodiments of the present application, the preset runoff prediction model (Long Short-Term Memory Network Model-Curve Number Runoff Prediction Model, LSTM-CN) is a comprehensive model that couples the Long Short-Term Memory Network Model (Long Short-Term Memory, LSTM) and the Natural Resource Conservation Service Curve Number Model (Runoff Curve Number Method developed by the Soil Conservation Service of the United States Department of Agriculture, Curve Number Method, NRCS-CN).

[0042] Furthermore, in some embodiments of the present application, the runoff forecast data for the entire precipitation period is predicted by combining the hydrological observation data at the current moment and the precipitation data through a preset runoff prediction model, including: The precipitation data includes historical precipitation intensity data and precipitation forecast data corresponding to the entire precipitation period; According to the historical precipitation intensity data, a unit line stream function under a preset precipitation intensity is obtained, and according to the unit line stream function under the preset precipitation intensity, a unit line stream function under any precipitation intensity is determined; Inputting the hydrological observation data and the precipitation forecast data into a preset long short-term memory network model to obtain net rainfall; The net rainfall is input into a preset natural resource protection service curve model, and combined with the unit line stream function under any precipitation intensity to predict the runoff forecast data.

[0043] The long short-term memory network model (LSTM) is good at capturing the long-term dependencies of time series, while the natural resource conservation service curve number model (NRCS-CN) has the advantages of fewer parameters and wider applicability. The combination of the two solves the problems of high parameter uncertainty and high computing power requirements of traditional conceptual models, and improves computing efficiency. Through the input of precipitation forecast data and historical intensity data, the entire runoff prediction model can dynamically respond to real-time precipitation changes, overcome the dependence of pure physical models on a large amount of measured data, improve the flexibility of prediction, and at the same time realize the complementarity of multi-time scale information to ensure the accuracy of the prediction results.

[0044] Furthermore, in some embodiments of the present application, obtaining a unit line stream function under a preset precipitation intensity according to the historical precipitation intensity data includes: First, based on the historical precipitation intensity data of the preset section in the river channel of the monitored basin (such as the preset precipitation intensity , the unit is ), determine the above preset precipitation intensities Unit line stream function corresponding to the instantaneous unit line (IUH) .

[0045] Further, in some embodiments of the present application, determining the unit line stream function under any precipitation intensity according to the unit line stream function under the preset precipitation intensity includes: First, the runoff process S curve of the preset node (i.e., preset section) of the basin to be monitored is determined by the following formula: (1) in, Represents at time When the precipitation intensity The cumulative runoff caused by The values ​​are calculated by the above formula; Represents precipitation intensity The unit stream function corresponding to IUH is used to describe the precipitation intensity. In time The impact of time on runoff; Represents the catchment area of ​​the preset node in the watershed to be monitored.

[0046] It can be understood that the preset precipitation intensity can be calculated by formula (1): Next .

[0047] Then according to the above constructed Peak and peak time , use the power function to fit the following relationship to obtain the exponent of the power function and , and the coefficients of the power function and : (2) (3) It can be understood that any precipitation intensity can be obtained through the above formulas (2) and (3) Next and .

[0048] Finally, for any unknown IUH, According to the power exponential relationship and the principle of mass conservation, using the known reference IUH and S-curve The unit stream function for any precipitation intensity is calculated using the following formula: In the above formula, Represents the calculation of any precipitation intensity using the unit line stream function under the preset precipitation intensity The unit stream function under the time variable mapping function is required, that is, if it is necessary to use the known Sure The formula requires two time variables pass Perform mapping processing; It is calculated by formula (1), that is, .

[0049] It can be seen from the above embodiments that due to the limited volume of actual observation data, it is impossible to obtain the unit line stream function under any precipitation intensity by fitting historical data. Therefore, it is necessary to fit a small amount of unit line stream functions under a small amount of known precipitation intensities through a small amount of historical data, and then determine the unit line stream function under any precipitation intensity based on the unit line stream functions under a small amount of known precipitation intensities, thereby effectively solving the problem of pure physical models relying on a large amount of measured data, while reducing the computing power required for data fitting.

[0050] Furthermore, the LSTM model used in some embodiments of the present application is obtained by training through the following process: Table 1 pass Figure 2 The cloud automatically collects the precipitation forecast data in the basin to be monitored, then receives the hydrological observation data collected by the terminal, inputs the above data into the preset LSTM, and outputs the net rainfall. As shown in Table 1, Figure 3 Input and output data of the LSTM model shown.

[0051] Further, refer to Figure 3 , the internal input of the preset LSTM model cell unit includes the moment Input variables ,time Output variables and time Memory unit variables , the LSTM model includes input gate, forget gate, candidate gate and output gate. The calculation formulas of each part are as follows: Input Gate : Forget Gate Candidate Gate : Memory cell variables : Output Gate : Output variables : Output information : in, Represents the sigmoid activation function, also known as the logistic function; is the hyperbolic tangent function; the weight matrix , , , as well as and the bias vector , , , as well as Optimization is performed through the gradient descent algorithm and the back propagation algorithm of the error; is the point product operator; Represents the element-by-element (corresponding element) multiplication of vectors, also known as the Hadamard product.

[0052] Further, according to Figure 3 The LSTM model shown in the figure analyzes the correlation between input variables and output variables through training data. The correlation between variables is quantified by the Pearson correlation coefficient (PCC) and the maximum information coefficient (MIC), and the input variables are rearranged according to their correlation. This process is repeated by iteratively adjusting the value of n using K-fold cross-validation and random search methods. n represents the number of selected input variables (n = 1 to 8, as shown in Table 1) to determine the optimal n input variables (as shown in Table 1). Figure 4 ) corresponding to the model hyperparameters.

[0053] Both model training and output data are linearly normalized, and the prediction results are inversely normalized before output.

[0054] In the formula, is the data after forward normalization. is the original data, and are the maximum and minimum values ​​in the original data respectively.

[0055] 80% of the data is used for model training, and 20% of the data is used for validation. The adaptive moment estimation (ADAM) optimization algorithm is used to find the best hyperparameter combination, and finally obtain Figure 4 LSTM model results shown.

[0056] Furthermore, after the trained LSTM model is obtained, the hydrological observation data and the precipitation forecast data are input into a preset long short-term memory network model to obtain the net rainfall.

[0057] Furthermore, in some embodiments of the present application, the inputting of the net rainfall into a preset natural resource protection service curve numerical model, combined with the unit line stream function under any precipitation intensity, to predict the runoff forecast data includes: Calculating the curve value of the runoff forecast data corresponding to the precipitation period according to the net rainfall; According to the curve value, the potential maximum retention amount of precipitation is calculated, and the cumulative effective precipitation is calculated according to the maximum retention amount; Determine an effective precipitation intensity function with time as a variable according to the accumulated effective precipitation; The runoff forecast data is obtained through convolution integration according to the effective precipitation intensity function and the unit line stream function under the arbitrary precipitation intensity.

[0058] By processing precipitation forecast data through LSTM, the net rainfall during the entire rainfall period can be obtained in advance. The net rainfall, as an intermediate variable, can be combined with the advance predicted data to correct the output of the subsequent NRCS-CN model, alleviating the problem of the NRCS-CN model being sensitive to initial conditions, thereby accurately estimating the effective precipitation intensity that changes over time; once the accurate effective precipitation intensity function is known, the unit line stream function under any precipitation intensity can be combined to obtain accurate runoff forecast data through convolution integration.

[0059] Further, in some embodiments of the present application, the process of obtaining runoff forecast data through the NRCS-CN model is specifically as follows: First, according to the net rainfall output by the trained LSTM , and precipitation from weather forecasts , calculate the curve number of the precipitation period corresponding to the current required runoff forecast data by the following formula ( , Curve Number) value.

[0060] in, is the curve value; is the precipitation; is the net rainfall; is the initial interception ratio of the watershed to be monitored.

[0061] Afterwards, the effective precipitation intensity over time is calculated using the following formula: : in, Represents the cumulative effective precipitation For time variables Derivation; is the effective precipitation intensity function with time as a variable.

[0062] Furthermore, the above The calculation formula Calculated by the following formula: in, is the accumulated precipitation in the precipitation forecast data, which is obtained through weather forecast; is the potential maximum retention of precipitation; is the initial retention amount, and the formula Calculate and obtain, To pass Calculate and obtain.

[0063] When you get the function Then, the direct runoff forecast is calculated by convolution integral , specifically: in, is the convolution integration process, is the unit stream function, and the above-mentioned calculated value under any precipitation intensity is Sure, The catchment area of ​​the preset node in the watershed to be monitored.

[0064] Finally, the base stream data and Add together to obtain runoff forecast data, specifically: in, For direct runoff forecast data; is the base flow data, base flow Estimated using the Straight-Line Method based on historical runoff data; For runoff forecast data. The evaluation index of the above LSTM-CN model is (Nash coefficient, the result is as follows Figure 4 As shown on the right), the calculation formula is as follows: in, is the length of the time series, is the average value of the runoff observation data collected at the terminal; for Runoff forecast data output by the LSTM-CN model at this moment; for Observational runoff data at the time.

[0065] It can be seen from the above embodiments that when performing the final runoff forecast data calculation, the present application can more accurately simulate the runoff composition by considering direct runoff and base flow, which helps to consider more comprehensive sources of pollutants when collecting samples later, such as surface runoff and groundwater, thereby improving the accuracy of the sampling results; further, through convolution calculation, the process of precipitation and runoff conversion is dynamically simulated to improve the accuracy of the prediction results.

[0066] S103: Assimilating the hydrological observation data at the current moment into the runoff forecast data through an ensemble Kalman filter algorithm.

[0067] Furthermore, in some embodiments of the present application, assimilating the hydrological observation data at the current moment into the runoff forecast data through an ensemble Kalman filter algorithm includes: If there is new runoff observation data in the hydrological observation data at the current moment, the state variable set at the current moment is updated according to the runoff observation data and the state variable set at the previous moment; wherein the state variable set at the initial moment is generated by using the Monte Carlo method by taking the curve value as the initial state variable; The average value of the updated state variable set is calculated, and the assimilated runoff forecast data is obtained according to the average value.

[0068] Since the data used in predicting runoff forecast data are mostly forecast data given by weather forecasts and currently observable data, it is impossible to completely rely on the forecast results to determine the subsequent sampling frequency due to data fluctuations and model accuracy issues. The runoff process is generally a non-steady-state process. In order to further improve the accuracy of previously acquired runoff forecast data, when in the runoff process, the previously predicted runoff forecast data for the entire rainfall period is corrected based on the real-time runoff observation data, thereby dynamically adjusting the subsequent sampling frequency during the runoff process to ensure the accuracy of the real-time sample collection results.

[0069] Further, in some embodiments of the present application, the updating of the state variable set at the current moment according to the runoff observation data and the state variable set at the previous moment includes: Predicting the runoff forecast data set at the current moment according to the state variable set at the previous moment; Assimilating the runoff observation data into the runoff forecast data set at the current moment to obtain a runoff analysis value set at the current moment; The state variable set at the current moment is estimated by taking the minimum error between the runoff forecast data set at the current moment and the runoff analysis value set at the current moment as the optimization goal.

[0070] When new runoff observation data is obtained, the state variable set is iteratively updated according to the latest runoff observation data, so that the runoff forecast data output by the NRCS-CN model in the previous sequence is gradually accurate. At the same time, the overall runoff prediction model has the ability to adaptively update and more effectively adapt to the rapid changes in the runoff process.

[0071] Specifically, refer to Figure 5 The Ensemble Kalman filter (EnKF) algorithm flow diagram shown in Figure 1 shows the Ensemble Kalman filter (EnKF) algorithm flow diagram. When the LSTM-CN model output is obtained, After (next use right To simplify the representation), firstly, the current output The corresponding The value is used as the initial state variable, and further combined with the The value is added to the initial state error (which follows a normal distribution with a mean of 0 , i.e. Gaussian white noise), and randomly generated using the Monte Carlo method Collections . It can be understood as Randomly add noise to the value , and the Monte Carlo method is used to add random noise Select from the values value, forming Collections .

[0072] After obtaining the initial or updated state variables, the runoff forecast data set is calculated using the following formula, that is, each Corresponding runoff forecast data: Among them, the superscript Represents the predicted value obtained by the LSTM-CN model, that is, Corresponding runoff forecast data; superscript Represents updated data; subscript Representative A collection; Representative Collections The runoff forecast data at the moment; For the Collections Update the acquired state variable set at all times; is the LSTM-CN model operator; is the model error.

[0073] When the cloud is at this moment When receiving new runoff observation data transmitted from the terminal, the following formula is used to calculate the current time The predicted runoff forecast data is assimilated, specifically: In the formula, It is Collections Assimilated runoff observation data; It is Collections The runoff forecast data at the moment, yes The Kalman gain matrix at time t; yes Momentary runoff observation data; is the observation operator. Since the assimilated data are all runoff, it is the unit matrix; is a Gaussian white noise with an expected value of 0. It should be noted that Figure 5 Variable symbols in Represents all sets .

[0074] After assimilation is completed, it is necessary to determine the current moment The state variable set of the last moment The state variable set is updated by: The particle swarm optimization algorithm (PSO) is used to and The goal is to minimize the error between the state variables. Estimate the value and get the current time The best Value, that is .

[0075] When you get indivual Afterwards, take indivual The average value is taken as the optimal estimate and substituted into the LSTM-CN model to obtain the next moment The runoff forecast data is the runoff forecast data after assimilation. When new runoff observation data appears at a subsequent time, repeat the above steps.

[0076] S104: Determine the sampling time and location of the runoff forecast data corresponding to a number of different runoff stages during the precipitation period by combining the assimilated runoff forecast data with a segmented sampling method.

[0077] Furthermore, in some embodiments of the present application, the segmented sampling method is combined with the assimilated runoff forecast data to determine the sampling time points of the runoff forecast data corresponding to several different runoff stages during the precipitation period, including: Substituting the assimilated runoff forecast data into a preset river flow-water level relationship to obtain a water level variation function; wherein the preset river flow-water level relationship is obtained by fitting the historical runoff observation data; Calculating the first-order derivative of the water level change function to obtain the first-order derivative solution result, and dividing the time period within the precipitation period corresponding to the runoff forecast data according to the first-order derivative solution result to obtain several different runoff stages; The number of sampling points corresponding to each of the runoff stages is determined according to preset rules, and sampling time and position points corresponding to the number of sampling points are randomly set within the runoff stage.

[0078] Based on the water level change function of the assimilated runoff forecast, the first-order derivative is derived and the time period is divided according to the first-order derivative solution. According to the divided time periods, sampling is increased during the steep rise / fall period of runoff, and sampling is reduced during the flat period to improve sampling efficiency. It can also ensure that samples are collected during key periods when water quality changes drastically (such as before and after the peak) to avoid the loss of important information with traditional fixed-frequency sampling. Finally, the sampling start conditions are set by triggering the water level to avoid invalid sampling during low water level periods.

[0079] Furthermore, in some embodiments of the present application, the assimilated runoff forecast data is substituted into a preset river flow-water level relationship to obtain a water level change function, specifically: When the cloud obtains the assimilated runoff forecast data After that, the data is transmitted to the edge end, and the edge end determines the water level change function of the water level over time based on the data. The specific formula is: in, is the reference water level of the node section, and the fitting coefficient , Obtained through regression based on historical runoff observation data of preset nodes; is the assimilated runoff forecast data, that is . It is actually a data sequence in time order, so the water level at each time point in the runoff process can be determined by the above formula. The water level-time series is further fitted by a polynomial function to obtain a continuous water level change function .like Fig. 6A or Figure 6B The following are based on The water level change function obtained under different conditions The corresponding curve.

[0080] Further, in some embodiments of the present application, the first-order derivative of the water level change function is calculated to obtain the first-order derivative solution result, and the time period within the precipitation period corresponding to the runoff forecast data is divided according to the first-order derivative solution result to obtain several different runoff stages, specifically: First, determine the preset trigger water level: Specifically, based on the historical runoff observation data of the river in the basin, take the corresponding water level of the 95% quantile of the flow as the trigger water level height of the rainfall runoff event. (That is, the preset trigger water level). Use the trigger water level to determine the start time and end time , time interval represents the variable frequency sampling time interval during precipitation, such as Fig. 6A or Figure 6B The interval shown.

[0081] Once the interval is known, To take the derivative, we use the first-order derivative Reflects the rate of change of water level. Whenever the sign of the derivative changes (i.e., from positive to negative or from negative to positive), it corresponds to an inflection point or extreme point in the water level process. Assume is the number of times the sign of the first-order derivative changes, refer to Fig. 6A The single peak curve shown in the figure , according to experience, if , it is usually divided into 4 stages (rising stage, descending stage, tail stage 1, tail stage 2, such as Fig. 6A ). Figure 6B The double peak curve shown in ,like , can usually be divided into 6 stages to reflect its more complex dynamic process (main peak rising stage, main peak stage, secondary peak rising stage, secondary peak descending stage, tail stage 1, tail stage 2, such as Figure 6B As shown in Figure 1. ...

[0082] Furthermore, in some embodiments of the present application, the number of sampling points corresponding to each of the time periods is determined according to a preset rule, and the sampling time points corresponding to the number of sampling points are randomly set within the time period, specifically: Assuming that the terminal-controlled automatic water sample collection device can collect a maximum of 24 water samples, Fig. 6A The single peak 4-stage runoff has a faster rising section (time - ,in is the time when the sign of the first-order derivative changes for the first time). You can set a more intensive acquisition, such as 10 points. To the end point of the precipitation period , we can get the end point of the descending segment and the end point of the last segment 1 The number of random sampling points in the descending segment, tail segment 1, and tail segment 2 are 6, 4, and 4 respectively.

[0083] For example Figure 6B The numbers of random sampling time points of the main peak rising section, main peak section, secondary peak rising section, secondary peak falling section, tail section 1, and tail section 2 are 6, 4, 4, 4, 3, and 3 respectively. The water level in the main peak rising section changes dramatically (time - ,in is the time when the sign of the first-order derivative changes for the first time). Main peak time - ,in It is the time when the sign of the first-order derivative changes for the second time. - , It is the time when the sign of the first-order derivative changes for the third time. The end point of the descending section of the secondary peak The water level and The water level is the same, that is . Bisection time To the end point of the precipitation period , we can get the end point of the last segment 1 .

[0084] S105: When the water level of the monitored watershed reaches a preset trigger water level, sampling is performed on the monitored watershed according to the sampling time point to obtain sampling data, and the water quality index and confidence interval of the entire runoff process are calculated based on the sampling data; wherein the preset trigger water level is determined and obtained based on the historical runoff observation data.

[0085] Furthermore, in some embodiments of the present application, after the sample is collected by the terminal, the sample data is processed through the following process: For the changes in the water quality of key water samples during the entire precipitation period, as well as the average value and confidence interval, the water quality conditions at different stages (such as total nitrogen, total phosphorus and other pollution contents) are effectively captured based on the water quality information of different segmented sampling, and the average value of the entire precipitation period is estimated through the following formula of the segmented sampling method , Standard Deviation and 95% confidence interval : In the formula, Indicates the number of segments in a session (e.g. Fig. 6A The number of segments in ); is the total duration of the precipitation event (e.g. Fig. 6A In ); It is The duration of each segment; It is The mean of the segments; yes Variance of the segment mean; It is The number of samples in the stratum; It is Layer Observation values ​​of each sample, such as the content of total nitrogen, nitrate nitrogen, ammonia nitrogen, total phosphorus, soluble total phosphate, soluble orthophosphate, COD (Chemical Oxygen Demand) in water samples; is the variance of the mean during the precipitation period; the confidence interval of the event mean concentration The confidence level of the student T score is 95% (i.e., the significance level). ) of the critical value ( ) calculated.

[0086] Furthermore, in some embodiments of the present application, the specific architecture of the terminal is as follows: Figure 7 As shown in the figure, specifically: an embedded water sample automatic collection controller (Micro Controller Unit, MCU) based on the STM32 ARM processor, combined with a pumping unit, a water sample storage device and a high-frequency sensor. The system has the characteristics of automatic sampling, low energy consumption, and strong weather tolerance, and can meet the needs of variable frequency collection of river water samples during precipitation. The control unit integrates an STM32 microcontroller, which is responsible for data collection and storage, water sample device control, data reporting, and command sending and receiving to ensure the stable operation and controllability of the system. The terminal can be connected to the cloud, and can accept water sampling instructions issued by users for unconventional sampling. The terminal uses a photovoltaic and lithium battery power supply system to ensure that the system can run for a long time under unattended conditions. As Figure 8The STM32 control chip system shown is designed and equipped with communication interfaces, including 4G network module, UART (Universal Asynchronous Receiver / Transmitter) communication with the automatic water sample collection device, USB serial port communication, and supports field data download and cloud data reporting. Among them, the specific chip model that can be used by the STM32 control chip is STM32F407ZGT6; the 4G network module function is realized through pins 124 and 129 of the STM32 control chip; the edge UART communication function is realized through pins 34 and 35 of the STM32 control chip; the sensor UART to RS485 communication function is realized through pins 101 and 102 of the STM32 control chip; the water sample collection device control communication function is realized through pins 69 and 70 of the STM32 control chip; and the pin 33 of the STM32 control chip is electrically connected to the lithium battery power supply module to realize power supply.

[0087] Furthermore, in some embodiments of the present application, the STM32 embedded system controls the automatic water sample collection equipment according to the segmented collection time and position information of the precipitation runoff process, performs variable frequency water sample collection, and time-marks the water sample bottles, and stores them in a constant temperature (4°C ± 2°C) refrigerated storage in the water sample collection device. Regular low-frequency sampling (once every 1-2 weeks) is adopted during the non-precipitation period. In addition, the terminal is equipped with high-frequency sensors (sampling frequency is 15 minutes), such as water level, precipitation, air temperature, soil moisture, water temperature, pH value, conductivity EC and water turbidity. The real-time water level, precipitation, soil moisture and other observation data of the terminal are uploaded to the cloud and used as forecast data and assimilation data for the cloud model.

[0088] From the above, it can be seen that the variable frequency sampling method for watershed water quality in the runoff process provided by the present application has the following beneficial effects: when facing or about to face the runoff process during the precipitation period, the runoff forecast data during the entire precipitation period is predicted by calculating the long short-term memory network model with fewer parameters and the natural resource protection service curve number model, and then a small amount of actual collected water level observation data is used to assimilate the runoff forecast data according to the collected water level observation data, thereby improving the prediction accuracy of the runoff forecast data. Further, when the water level of the monitored basin reaches the preset trigger water level, the dynamic changes of the water level during the entire precipitation period can be obtained according to the accurately predicted runoff forecast data, so that the sampling frequency and sampling time and location of different time periods during the precipitation period can be reasonably and dynamically set according to the accurate water level changes, avoiding invalid sampling operations, improving sampling efficiency, and at the same time making the collected samples more accurately reflect the changes in water quality during the runoff process. The variable frequency sampling method for water quality in a watershed for runoff processes provided in the present application can be applied to a variable frequency sampling system for watershed water quality in a watershed for runoff processes that adopts a cloud-edge-end architecture. Through the close collaboration of the cloud-edge-end architecture, the system can collect, process and transmit hydrological, precipitation, soil moisture and other information of key sections of the watershed in real time, and use the ensemble Kalman filter algorithm to assimilate the runoff observation data of the terminal, timely correct the errors of the runoff forecast data, and improve the forecast accuracy.

[0089] like Fig. 9 As shown, based on the above-mentioned method item embodiment, the embodiment of the present application provides a corresponding device item embodiment, specifically a basin water quality variable frequency sampling device for runoff process, including: a data acquisition module 201, a runoff forecast data prediction module 202, a data assimilation module 203, a sampling time site confirmation module 204 and a sampling module 205.

[0090] Furthermore, in some embodiments of the present application, the data acquisition module 201 is used to obtain historical runoff observation data, precipitation data and hydrological observation data of the monitored basin at the current moment; the runoff forecast data prediction module 202 is used to predict the runoff forecast data for the entire precipitation period by combining the hydrological observation data and the precipitation data at the current moment through a preset runoff prediction model; the runoff prediction model is obtained by coupling a long short-term memory network model and a natural resource protection service curve number model; the data assimilation module 203 is used to assimilate the hydrological observation data at the current moment through an ensemble Kalman filter algorithm. The hydrological observation data is converted into the runoff forecast data; the sampling time point confirmation module 204 is used to determine the sampling time points of several different runoff stages during the precipitation period corresponding to the runoff forecast data by combining the assimilated runoff forecast data through a segmented sampling method; the sampling module 205 is used to sample the watershed to be monitored according to the sampling time point when the water level of the watershed to be monitored reaches a preset trigger water level to obtain sampling data, and calculate the water quality index and confidence interval of the entire runoff process according to the sampling data; wherein the preset trigger water level is determined and obtained based on the historical runoff observation data.

[0091] Furthermore, in some embodiments of the present application, the preset runoff prediction model is used to predict the runoff forecast data for the entire precipitation period in combination with the hydrological observation data at the current moment and the precipitation data, including: wherein the precipitation data includes historical precipitation intensity data and precipitation forecast data corresponding to the entire precipitation period; according to the historical precipitation intensity data, the unit line stream function under the preset precipitation intensity is obtained, and according to the unit line stream function under the preset precipitation intensity, the unit line stream function under any precipitation intensity is determined; the hydrological observation data and the precipitation forecast data are input into a preset long short-term memory network model to obtain the net rainfall; the net rainfall is input into a preset natural resource protection service curve number model, and combined with the unit line stream function under any precipitation intensity, the runoff forecast data is predicted.

[0092] Further, in some embodiments of the present application, obtaining a unit line stream function under a preset precipitation intensity according to the historical precipitation intensity data, and determining a unit line stream function under any precipitation intensity according to the unit line stream function under the preset precipitation intensity includes: The steps for obtaining the unit line stream function under any precipitation intensity are specifically as follows: in, Represents any precipitation intensity The unit stream function under ; Represents the preset precipitation intensity The unit stream function under ; Represents the calculation of any precipitation intensity using the unit line stream function under the preset precipitation intensity The time variable mapping function required when the unit stream function is below; and Respectively represent Peak value and peak time; and represents the power function coefficient obtained by power function fitting; and represents the power function exponent obtained by power function fitting; The catchment area of ​​the preset node for the watershed to be monitored; For in time When the precipitation intensity The cumulative runoff caused; is the time variable.

[0093] Furthermore, in some embodiments of the present application, the net rainfall is input into a preset natural resource protection service curve number model, combined with the unit line stream function under any precipitation intensity, to predict the runoff forecast data, including: calculating the curve value of the runoff forecast data corresponding to the precipitation period according to the net rainfall; calculating the potential maximum retention amount of precipitation according to the curve value, and calculating the cumulative effective precipitation according to the maximum retention amount; determining the effective precipitation intensity function with time as a variable according to the cumulative effective precipitation; and obtaining the runoff forecast data through convolution integral according to the effective precipitation intensity function and the unit line stream function under any precipitation intensity.

[0094] Furthermore, in some embodiments of the present application, the calculation formula of the runoff forecast data specifically includes: in, is the curve value; is the precipitation; is the net rainfall; is the initial interception ratio of the watershed to be monitored; Represents the cumulative effective precipitation For time variables Derivation; is the effective precipitation intensity function with time as a variable; is the accumulated precipitation in the precipitation forecast data; is the initial retention volume; is the potential maximum retention of precipitation, based on Calculate acquisition; For direct runoff forecast data; is the base flow data; For runoff forecast data; The catchment area of ​​the preset node for the watershed to be monitored; is the convolution integration process, where is the unit stream function.

[0095] Furthermore, in some embodiments of the present application, the assimilation of the hydrological observation data at the current moment into the runoff forecast data through the ensemble Kalman filter algorithm includes: if there is new runoff observation data in the hydrological observation data at the current moment, updating the state variable set at the current moment according to the runoff observation data and the state variable set at the previous moment; wherein the state variable set at the initial moment is generated by using the Monte Carlo method by taking the curve values ​​as the initial state variables; calculating the average value of the updated state variable set, and obtaining the assimilated runoff forecast data based on the average value.

[0096] Furthermore, in some embodiments of the present application, the updating of the state variable set at the current moment based on the runoff observation data and the state variable set at the previous moment includes: predicting the runoff forecast data set at the current moment based on the state variable set at the previous moment; assimilating the runoff observation data into the runoff forecast data set at the current moment to obtain the runoff analysis value set at the current moment; estimating the state variable set at the current moment with the minimum error between the runoff forecast data set at the current moment and the runoff analysis value set at the current moment as the optimization goal.

[0097] Furthermore, in some embodiments of the present application, the segmented sampling method is combined with the assimilated runoff forecast data to determine the sampling time and position points of several different runoff stages during the precipitation period corresponding to the runoff forecast data, including: substituting the assimilated runoff forecast data into a preset river flow and water level relationship to obtain a water level change function; wherein the preset river flow and water level relationship is obtained by fitting the historical runoff observation data; calculating the first-order derivative of the water level change function to obtain the first-order derivative solution result, and dividing the time period during the precipitation period corresponding to the runoff forecast data according to the first-order derivative solution result to obtain several different runoff stages; determining the number of sampling points corresponding to each of the runoff stages according to preset rules, and randomly setting the sampling time and position points corresponding to the number of sampling points within the runoff stage.

[0098] It can be understood that the above-mentioned device item embodiments correspond to the method item embodiments of the present application, and can implement the variable frequency sampling method for water quality in the watershed for the runoff process provided by any of the above-mentioned method item embodiments of the present application.

[0099] From the above, it can be seen that the variable frequency sampling device for water quality in a watershed for runoff process provided by the present application has the following beneficial effects: when facing or about to face the runoff process during the precipitation period, the runoff forecast data during the entire precipitation period is predicted by calculating the long short-term memory network model with fewer parameters and the natural resource protection service curve number model, and then a small amount of actually collected water level observation data is used to assimilate the runoff forecast data according to the collected water level observation data, thereby improving the prediction accuracy of the runoff forecast data. Furthermore, when the water level of the monitored watershed reaches the preset trigger water level, the dynamic change of the water level during the entire precipitation period can be obtained according to the accurately predicted runoff forecast data, so that the sampling frequency and sampling time and location of different time periods during the precipitation period can be reasonably and dynamically set according to the accurate water level change, avoiding invalid sampling operations, improving sampling efficiency, and at the same time making the collected samples more accurately reflect the changes in water quality during the runoff process. The variable frequency sampling device for water quality in a watershed for runoff processes provided in the present application can be applied to a variable frequency sampling system for watershed water quality in a watershed for runoff processes that adopts a cloud-edge-end architecture. Through the close collaboration of the cloud-edge-end architecture, the system can collect, process and transmit hydrological, precipitation, soil moisture and other information of key sections of the watershed in real time, and use the ensemble Kalman filter algorithm to assimilate the runoff observation data of the terminal, timely correct the errors of the runoff forecast data, and improve the forecast accuracy.

[0100] It should be noted that the device embodiments described above are merely illustrative, and some or all of the modules may be selected according to actual needs to achieve the purpose of the present embodiment. In addition, in the device embodiment drawings provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines. Those of ordinary skill in the art may understand and implement the present invention without creative work.

[0101] Based on the above-mentioned embodiment of the variable frequency sampling method for watershed water quality for runoff process, another embodiment of the present application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, the variable frequency sampling method for watershed water quality for runoff process of any embodiment of the present application is implemented.

[0102] Exemplarily, in this embodiment, the computer program may be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present application. The one or more module elements may be a series of computer program instruction segments capable of completing specific functions, which are used to describe the execution process of the computer program in the terminal device.

[0103] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0104] The processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc. The processor is the control center of the terminal device, and uses various interfaces and lines to connect various parts of the entire terminal device.

[0105] Based on the above method embodiments, another embodiment of the present application provides a computer-readable storage medium, including a stored computer program, wherein when the computer program is running, the device where the computer-readable storage medium is located is controlled to execute the variable frequency sampling method for water quality in the watershed for runoff process described in any one of the above method embodiments of the present application.

[0106] Wherein, the module / unit integrated in the device / terminal equipment, if implemented in the form of a software functional unit and sold or used as an independent product, can be stored in a computer-readable storage medium. Based on such an understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Wherein, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium, etc.

Claims

1. A variable frequency sampling method for water quality in a watershed for runoff process, characterized in that: include: Obtain historical runoff observation data, precipitation data and current hydrological observation data of the basin to be monitored; The runoff forecast data for the entire precipitation period is predicted by combining the hydrological observation data at the current moment and the precipitation data through a preset runoff forecast model; the runoff forecast model is obtained by coupling a long short-term memory network model and a natural resource protection service curve number model; Assimilating the hydrological observation data at the current moment into the runoff forecast data through an ensemble Kalman filter algorithm; By using a segmented sampling method and combining the assimilated runoff forecast data, the sampling time and location of the runoff forecast data corresponding to several different runoff stages during the precipitation period are determined; When the water level of the monitored basin reaches a preset trigger water level, the monitored basin is sampled according to the sampling time point to obtain sampling data, and the water quality indicators and confidence intervals of the entire runoff process are calculated based on the sampling data; wherein the preset trigger water level is determined and obtained based on the historical runoff observation data.

2. A variable frequency sampling method for water quality in a watershed for runoff process as claimed in claim 1, characterized in that: The preset runoff prediction model is used to predict the runoff forecast data during the entire precipitation period in combination with the hydrological observation data at the current moment and the precipitation data. include: The precipitation data includes historical precipitation intensity data and precipitation forecast data corresponding to the entire precipitation period; According to the historical precipitation intensity data, a unit line stream function under a preset precipitation intensity is obtained, and according to the unit line stream function under the preset precipitation intensity, a unit line stream function under any precipitation intensity is determined; Inputting the hydrological observation data and the precipitation forecast data into a preset long short-term memory network model to obtain net rainfall; The net rainfall is input into a preset natural resource protection service curve model, and combined with the unit line stream function under any precipitation intensity to predict the runoff forecast data.

3. A variable frequency sampling method for water quality in a watershed for runoff process as claimed in claim 2, characterized in that: The step of obtaining a unit line stream function under a preset precipitation intensity according to the historical precipitation intensity data, and determining a unit line stream function under any precipitation intensity according to the unit line stream function under the preset precipitation intensity includes: The steps for obtaining the unit line stream function under any precipitation intensity are specifically as follows: in, Represents any precipitation intensity The unit stream function under ; Represents the preset precipitation intensity The unit stream function under ; Represents the calculation of any precipitation intensity using the unit line stream function under the preset precipitation intensity The time variable mapping function required when the unit stream function is below; and Respectively represent Peak value and peak time; and represents the power function coefficient obtained by power function fitting; and represents the power function exponent obtained by power function fitting; The catchment area of ​​the preset node for the watershed to be monitored; For in time When the precipitation intensity The cumulative runoff caused; is the time variable.

4. A variable frequency sampling method for water quality in a watershed for runoff process as claimed in claim 2, characterized in that: The inputting of the net rainfall into a preset natural resource protection service curve model, combining the unit line stream function under any precipitation intensity, and predicting the runoff forecast data, comprises: Calculating the curve value of the runoff forecast data corresponding to the precipitation period according to the net rainfall; According to the curve value, the potential maximum retention amount of precipitation is calculated, and the cumulative effective precipitation is calculated according to the maximum retention amount; Determine an effective precipitation intensity function with time as a variable according to the accumulated effective precipitation; The runoff forecast data is obtained by convolution integration according to the effective precipitation intensity function and the unit line stream function under the arbitrary precipitation intensity.

5. A variable frequency sampling method for water quality in a watershed facing the runoff process as claimed in claim 4, characterized in that: The calculation formula of the runoff forecast data specifically includes: in, is the curve value; is the precipitation; is the net rainfall; is the initial interception ratio of the watershed to be monitored; Represents the cumulative effective precipitation For time variables Derivation; is the effective precipitation intensity function with time as a variable; is the accumulated precipitation in the precipitation forecast data; is the initial retention volume; is the potential maximum retention of precipitation, based on Calculate acquisition; For direct runoff forecast data; is the base flow data; For runoff forecast data; The catchment area of ​​the preset node for the watershed to be monitored; is the convolution integration process, where is the unit stream function.

6. A variable frequency sampling method for water quality in a watershed for runoff process as claimed in claim 4, characterized in that: The assimilation of the hydrological observation data at the current moment into the runoff forecast data by using an ensemble Kalman filter algorithm includes: If there is new runoff observation data in the hydrological observation data at the current moment, the state variable set at the current moment is updated according to the runoff observation data and the state variable set at the previous moment; wherein the state variable set at the initial moment is generated by using the Monte Carlo method by taking the curve value as the initial state variable; The average value of the updated state variable set is calculated, and the assimilated runoff forecast data is obtained according to the average value.

7. A variable frequency sampling method for water quality in a watershed for runoff process as claimed in claim 6, characterized in that: The updating of the state variable set at the current moment according to the runoff observation data and the state variable set at the previous moment comprises: Predicting the runoff forecast data set at the current moment according to the state variable set at the previous moment; Assimilating the runoff observation data into the runoff forecast data set at the current moment to obtain a runoff analysis value set at the current moment; The state variable set at the current moment is estimated by taking the minimum error between the runoff forecast data set at the current moment and the runoff analysis value set at the current moment as the optimization goal.

8. The variable frequency sampling method for water quality in a watershed facing the runoff process as claimed in claim 1 is characterized in that: The segmented sampling method is combined with the assimilated runoff forecast data to determine the sampling time points of the runoff forecast data corresponding to several different runoff stages during the precipitation period, including: Substituting the assimilated runoff forecast data into a preset river flow-water level relationship to obtain a water level variation function; wherein the preset river flow-water level relationship is obtained by fitting the historical runoff observation data; Calculating the first-order derivative of the water level change function to obtain the first-order derivative solution result, and dividing the time period within the precipitation period corresponding to the runoff forecast data according to the first-order derivative solution result to obtain several different runoff stages; The number of sampling points corresponding to each of the runoff stages is determined according to preset rules, and sampling time and position points corresponding to the number of sampling points are randomly set within the runoff stage.

9. A variable frequency sampling device for water quality in a watershed for runoff process, characterized in that: include: Data acquisition module, runoff forecast data prediction module, data assimilation module, sampling time site confirmation module and sampling module; The data acquisition module is used to acquire the historical runoff observation data, precipitation data and hydrological observation data of the watershed to be monitored at the current moment; The runoff forecast data prediction module is used to predict the runoff forecast data during the entire precipitation period by combining the hydrological observation data at the current moment and the precipitation data through a preset runoff forecast model; the runoff forecast model is obtained by coupling a long short-term memory network model and a natural resource protection service curve number model; The data assimilation module is used to assimilate the hydrological observation data at the current moment into the runoff forecast data through an ensemble Kalman filter algorithm; The sampling time and location confirmation module is used to determine the sampling time and location of the runoff forecast data corresponding to several different runoff stages during the precipitation period by combining the assimilated runoff forecast data through a segmented sampling method; The sampling module is used to sample the watershed to be monitored according to the sampling time point when the water level of the watershed to be monitored reaches a preset trigger water level, so as to obtain sampling data, and calculate the water quality index and confidence interval of the entire runoff process based on the sampling data; wherein the preset trigger water level is determined and obtained based on the historical runoff observation data.

10. A frequency conversion sampling device for water quality in a watershed for runoff process as claimed in claim 9, characterized in that: The preset runoff prediction model is used to predict the runoff forecast data during the entire precipitation period in combination with the hydrological observation data at the current moment and the precipitation data. include: The precipitation data includes historical precipitation intensity data and precipitation forecast data corresponding to the entire precipitation period; According to the historical precipitation intensity data, a unit line stream function under a preset precipitation intensity is obtained, and according to the unit line stream function under the preset precipitation intensity, a unit line stream function under any precipitation intensity is determined; Inputting the hydrological observation data and the precipitation forecast data into a preset long short-term memory network model to obtain net rainfall; The net rainfall is input into a preset natural resource protection service curve model, and combined with the unit line stream function under any precipitation intensity to predict the runoff forecast data.

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